Monitoring and Evaluating Nonpoint Source Watershed Projects May 2016


Monitoring and Evaluating Nonpoint Source
Watershed Projects
May 2016
Developed under Contract to U.S. Environmental Protection Agency by Tetra Tech, Inc.
GS Contract #GS-10F-0268K
Order # EP-G135-00168
Authors:  Dressing, S.A., D.W. Meals, J.B. Harcum, J. Spooner1, J.B. Stribling, R.P. Richards2,
        C.J. Millard, S.A. Lanberg, and J.G. O'Donnell
        1 North Carolina State University, Raleigh, NC
        2Heidelberg University, Tiffin, OH.
                               I
                  United States Environmental Protection Agency
                               Office of Water
                        Nonpoint Source Control Branch
                            Washington, DC 20460
                             EPA841-R-16-010
                                 May 2016
This document is available at: https://www.epa.gov/polluted-runoff-nonpoint-source-
pollution/monitoring-and-evaluating-nonpoint-source-watershed

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Monitoring and Evaluating Nonpoint Source Watershed Projects Foreword
The diffuse nature of nonpoint sources and the variety of pollutants generated by them create a challenge
for their effective control requiring a systematic approach based on assessment, planning,
implementation, and evaluation. Monitoring is an important component in all four of these activities.
While substantial progress has been made since 1972 in the protection and enhancement of water quality,
much work is still needed to identify nonpoint source management strategies that are both effective and
economically achievable under a wide range of conditions. Lack of adequate information on best
management approaches is the major obstacle in developing effective watershed management strategies.
We are relearning previous lessons because we have failed to institutionalize previous lessons learned
from intensive monitoring efforts from 1970 to the present. This version of the nonpoint source
monitoring guide (guide) incorporates the monitoring lessons learned from the Rural Clean Water
Program (RCWP), the Clean Water Act Section 319 National Nonpoint Source Monitoring Program
(NNPSMP), and other efforts to provide a state-of-the-reference for monitoring nonpoint source projects.
Monitoring plays an important role in addressing the need to evaluate our watershed management efforts
and document the lessons learned so we can use them as a foundation for future management efforts.

This guide is written primarily for those who develop and implement monitoring plans for watershed
management projects, but it can also be used by those who wish to evaluate the technical merits of
monitoring proposals they might sponsor. It is an update to the 1997 Monitoring Guidance for
Determining the Effectiveness of Nonpoint Source Controls (EPA 841-B-96-004) and includes many
references to that document.

The style and technical level of this guidance are intended to make it accessible to both beginners and
experts alike. Numerous real-world examples from RCWP and NNPSMP projects are provided to give
the reader a true sense of the challenges faced by those who have monitored waters impacted by nonpoint
sources. Included in the guidance document are many references to other related resource materials for
those seeking additional or more detailed information.

This guidance begins with an overview of the extent and types of nonpoint source problems reported by
the States and Tribes. The overview is intended to provide perspective and set the stage for the chapters
that follow. Subsequent chapters describe the basic steps involved in designing a nonpoint source
monitoring plan, including sections and chapters devoted to biological, photopoint, and land use
monitoring. A chapter that focuses on ways to address the many unique challenges associated with
nonpoint source monitoring is also included. The chapter on data analysis describes and illustrates
techniques ranging from exploratory data analysis to advanced statistical approaches for assessing the
effectiveness of both individual best management practices and watershed projects. Pollutant load
estimation methods are also described in detail. A chapter on quality assurance and quality control is then
followed by a chapter addressing monitoring costs.

Good monitoring design begins with a clear monitoring objective and an understanding of the water
quality problem or concern addressed. Because problems and objectives vary, there is no single approach
that can be applied to nonpoint source monitoring efforts. It is hoped this guidance provides a foundation
that allows practitioners to design monitoring programs that meet their unique needs.

Readers are encouraged to consult the many resources listed in this document. In addition to these resources,
readers are urged to contact monitoring and quality assurance experts in academia and at the local, State,
Tribal, and federal levels for assistance in developing monitoring plans and analyzing the collected data.

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Monitoring and Evaluating Nonpoint Source Watershed Projects Acknowledgements
Ackno wledg ments
This document has been reviewed by EPA and approved for publication. It was developed by Tetra Tech
Inc. under the direction of Mr. Thomas Davenport and Mr. Paul Thomas of EPA Region 5.

The authors gratefully acknowledge the helpful technical reviews provided by Dr. Brian Fontenot of EPA
Region 6, Dr. Marty Kelly of Atkins North America, and Mr. John McCoy of the Columbia Association
in Maryland. In addition, the authors thank the many individuals who have contributed to the knowledge
base on nonpoint source monitoring and data analysis over the past quarter century or more. The
references contained in this document only begin to recognize the contributions of others.

Inspiration for this document was provided long before the 1997 version for which this serves as an
upgrade. Mr. James W. Meek, former Chief of the Nonpoint Source Control Branch at EPA Headquarters,
was particularly inspirational in his support for developing and documenting improved methods to
demonstrate the effectiveness of nonpoint source control measures and programs. The late Dr. Frank J.
Humenik, Professor in the Department of Biological and Agricultural Engineering at North Carolina State
University, was instrumental in the promotion of long-term monitoring projects to evaluate the
effectiveness of approaches to solve water quality problems at the watershed level. Finally, Mr. Thomas
Davenport of EPA Region 5 has been the driving force behind EPA's continued involvement in nonpoint
source watershed projects that began in earnest with the Model Implementation Program, Nationwide
Urban Runoff Program, and Rural Clean Water Program. Mr. Davenport has led the effort to document
the effectiveness of nonpoint source pollution control efforts through sound scientific approaches, and he
has been the major proponent of developing this upgraded nonpoint source monitoring guidance.

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Monitoring and Evaluating Nonpoint Source Watershed Projects                                       Contents
    1.1   Definition of aNonpoint Source	
    1.2   Extent of Nonpoint Source Problems in the United States ..
    1.3   Major Categories of Nonpoint Source Pollution	
        1.3.1  Agriculture	
        1.3.2  Urban Sources	
        1.3.3  Removal of Streamside Vegetation..
        1.3.4  Hydromodification	
1   Overview of the Nonpoint Source Problem	1-1
                                                                                                -1
                                                                                                -2
                                                                                                -7
                                                                                                -7
                                                                                                -8
                                                                                                -9
                                                                                                -9
        1.3.5  Mining	1-10
        1.3.6  Forestry	1-11
        1.3.7  Construction	1-12
        1.3.8  Marinas	1-13
    1.4   Solving the Problem	1-14
    1.5   References	1-15
2   Nonpoint Source Monitoring Objectives and Basic Designs	2-1
    2.1   Monitoring Objectives	2-1
    2.2   Fundamentals of Good Monitoring	2-4
        2.2.1  Understand the System	2-4
           2.2.1.1   Causes and Sources	2-5
           2.2.1.2   Pollutant Transport	2-5
           2.2.1.3   Seasonality	2-6
           2.2.1.4   Water Resource Considerations	2-7
               2.2.1.4.1  Rivers and Streams	2-7
               2.2.1.4.2  Lakes, Reservoirs, and Ponds	2-9
               2.2.1.4.3  Wetlands	2-12
               2.2.1.4.4  Estuaries	2-14
               2.2.1.4.5  Nearshore Waters	2-16
               2.2.1.4.6  Ground Water	2-17
           2.2.1.5   Climate	2-21
           2.2.1.6   Soils, Geology and Topography	2-22
        2.2.2  Monitor Source Activities	2-22
        2.2.3  Critical Details	2-23
           2.2.3.1   Logistics	2-23
           2.2.3.2   Quality Assurance/Quality Control and the Quality Assurance Project Plan
                    (QAPP)	2-24
           2.2.3.3   Data Management and Record-keeping	2-25
           2.2.3.4   Roles and Responsibilities	2-25
           2.2.3.5   Review of Monitoring Proposals	2-26
        2.2.4  Feedback	2-27
        2.2.5  Limitations of Monitoring	2-27
    2.3   Monitoring Scale Selection	2-28

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Monitoring and Evaluating Nonpoint Source Watershed Projects                                        Contents

        2.3.1  General Considerations	2-28
        2.3.2  Options	2-29
           2.3.2.1   Statewide or regional	2-29
           2.3.2.2   Watershed	2-29
           2.3.2.3   BMP or practice	2-31
               2.3.2.3.1  Plot	2-32
               2.3.2.3.2  Field	2-32
           2.3.2.4   Summary	2-33
    2.4   Monitoring Design Selection	2-33
        2.4.1  General Considerations	2-33
        2.4.2  Design Options	2-33
           2.4.2.1   Reconnaissance or Synoptic	2-34
           2.4.2.2   Plot	2-35
           2.4.2.3   Paired	2-36
           2.4.2.4   Single Watershed Before/After	2-38
           2.4.2.5   Single-Station Long-Term Trend	2-39
           2.4.2.6   Above/Below	2-39
           2.4.2.7   Side-by-Side Before/After	2-41
           2.4.2.8   Multiple	2-41
           2.4.2.9   Input/Output	2-41
           2.4.2.10 Summary	2-43
    2.5   References	2-44
3   Monitoring Plan Details	3-1
    3.1   Variable Selection	3-1
        3.1.1  General Considerations	3-1
        3.1.2  Selection Factors	3-2
           3.1.2.1   Program Objectives	3-2
           3.1.2.2   WaterbodyUse	3-2
           3.1.2.3   Waterbody Use Impairment	3-3
           3.1.2.4   Type of Water Resource Sampled	3-3
           3.1.2.5   Pollutant Sources	3-3
           3.1.2.6   Response to Treatment	3-4
           3.1.2.7   Difficulty or Cost of Analysis	3-6
           3.1.2.8   Method Comparability	3-7
           3.1.2.9   Logistical Constraints	3-8
           3.1.2.10 Need for Covariates	3-9
           3.1.2.11 Set Priorities	3-9
        3.1.3  Physical and Chemical Water Quality Data	3-10
           3.1.3.1   Measuring Surface Water Flow	3-10
           3.1.3.2   Commonly Measured Physical and Chemical Water Quality Constituents	3-22
           3.1.3.3   Surrogates	3-26
        3.1.4  Biological Data	3-27
        3.1.5  Weather Data	3-30
        3.1.6  Watershed Characterization	3-31

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Monitoring and Evaluating Nonpoint Source Watershed Projects                                        Contents


           3.1.6.1   Topographic Data	3-31
           3.1.6.2   Soil Characteristics	3-32
           3.1.6.3   Land Use/Land Cover	3-32
    3.2   Sample Type Selection	3-33
       3.2.1  General Considerations	3-33
       3.2.2  Types	3-35
           3.2.2.1   Grab	3-35
           3.2.2.2   Composite	3-35
           3.2.2.3   Integrated	3-36
           3.2.2.4   Continuous	3-37
    3.3   Station Location	3-38
       3.3.1  Macro-scale	3-38
       3.3.2  Micro-scale	3-41
           3.3.2.1   General Considerations	3-42
           3.3.2.2   Locations for Flow Measurement	3-42
           3.3.2.3   Locations for Biological Monitoring	3-43
    3.4   Sampling Frequency and Duration	3-43
       3.4.1  General Considerations	3-43
           3.4.1.1   Estimating the  Mean	3-44
           3.4.1.2   Detecting a Step or Linear Trend	3-45
       3.4.2  Minimum Detectable Change (MDC) Analysis	3-47
           3.4.2.1   Definition and  Overview	3-47
           3.4.2.2   Steps to Calculate the MDC	3-48
               3.4.2.2.1  Step 1. Define the Monitoring Goal and Choose the Appropriate
                         Statistical Trend Test Approach	3-48
               3.4.2.2.2  Step 2. Exploratory Data Analyses	3-48
               3.4.2.2.3  Step 3. Data Transformations	3-48
               3.4.2.2.4  Step 4. Test for Autocorrelation	3-49
               3.4.2.2.5  Step 5. Calculate the Estimated Standard Error	3-49
               3.4.2.2.6  Step 6. Calculate the MDC	3-50
               3.4.2.2.7  Step 7. Express MDC as a Percent Change	3-50
           3.4.2.3   Examples	3-50
           3.4.2.4   Factors Affecting the  Magnitude of the MDC	3-52
       3.4.3  Sampling Duration	3-56
    3.5   Monitoring Station Construction  and Operation	3-56
       3.5.1  Grab Sampling	3-57
       3.5.2  Perennial Streams and Rivers	3-58
       3.5.3  Edge of Field	3-60
       3.5.4  Structures/BMPs	3-61
       3.5.5  Meteorology	3-67
    3.6   Sample Collection and Analysis Methods	3-69
       3.6.1  General Considerations	3-69
           3.6.1.1   Documentation and Records	3-69
           3.6.1.2   Preparation for Sampling	3-69

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Monitoring and Evaluating Nonpoint Source Watershed Projects                                       Contents

           3.6.1.3  Cleaning	3-69
           3.6.1.4  Safety	3-70
    3.6.2 Field Procedures	3-70
           3.6.2.1  Field Measurements	3-70
           3.6.2.2  Grab Sampling	3-71
           3.6.2.3  Passive Sampling	3-73
           3.6.2.4  Autosampling	3-77
           3.6.2.5  Benthic Macroinvertebrate Sampling	3-78
           3.6.2.6  Fish Sampling	3-80
           3.6.2.7  Aquatic Plant Sampling	3-82
           3.6.2.8  Bacteria/Pathogen Sampling	3-82
           3.6.2.9  Habitat Sampling	3-83
           3.6.2.10 Specialized Sampling	3-83
       3.6.3   From Field to Laboratory	3-84
           3.6.3.1  Sample Processing	3-84
           3.6.3.2  Sample Preservation and Transport	3-84
           3.6.3.3  Sample Custody	3-89
           3.6.3.4  Performance Audits	3-90
       3.6.4   Laboratory Considerations	3-90
    3.7   Land Use and Land Treatment Monitoring	3-91
       3.7.1   General Considerations	3-91
       3.7.2   Basic Methods	3-92
           3.7.2.1  Direct Observation	3-92
           3.7.2.2  Log Books	3-93
           3.7.2.3  Interviews	3-93
           3.7.2.4  Agency reporting	3-94
           3.7.2.5  Remote Sensing	3-95
       3.7.3   Temporal and Spatial Scale	3-97
       3.7.4   Monitoring Variables	3-98
       3.7.5   Sampling Frequency	3-98
       3.7.6   Challenges	3-99
    3.8   Special considerations for pollutant load estimation	3-101
       3.8.1   Sample Type and Sampling Equipment	3-102
       3.8.2   Sampling Frequency and Timing	3-102
       3.8.3   Planning and Cost Considerations	3-104
    3.9   Data Management	3-105
       3.9.1   General considerations	3-105
       3.9.2   Data acquisition	3-105
       3.9.3   Datastorage	3-106
    3.10  Data Reporting and Presentation	3-107
       3.10.1 General considerations	3-107
       3.10.2 Communicating with Stakeholders	3-107
       3.10.3 Final reports	3-107
    3.11  References	3-108
                                                IV

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Monitoring and Evaluating Nonpoint Source Watershed Projects                                        Contents


4   Biological Monitoring of Aquatic Communities	4-1
    4.1   Overview	4-1
    4.2   Background	4-6
        4.2.1  Types of Biological Monitoring	4-7
           4.2.1.1   Benthic Macroinvertebrates	4-7
           4.2.1.2   Fish	4-7
               4.2.1.2.1  Length, Weight, and Age Measurements	4-8
               4.2.1.2.2  Fish External Anomalies	4-9
           4.2.1.3   Periphyton	4-9
        4.2.2  Linkages to Habitat	4-10
        4.2.3  Limitations of Biological Assessments	4-11
        4.2.4  Reference Sites and Conditions	4-12
    4.3   Biomonitoring Program Design	4-14
    4.4   Biological Assessment Protocols	4-30
        4.4.1  Field Sampling	4-30
           4.4.1.1   Benthic macroinvertebrates	4-31
           4.4.1.2   Fish	4-32
           4.4.1.3   Periphyton	4-32
           4.4.1.4   Quality control measures	4-33
        4.4.2  Sample  processing/laboratory analysis	4-33
           4.4.2.1   Benthic macroinvertebrates	4-33
               4.4.2.1.1  Sorting and subsampling	4-34
               4.4.2.1.2  Taxonomic identification	4-35
           4.4.2.2   Fish (field taxonomic identification)	4-36
           4.4.2.3   Periphyton	4-36
           4.4.2.4   Quality control measures/data quality documentation	4-36
        4.4.3  Data reduction/indicator calculation	4-37
           4.4.3.1   Multimetric indexes	4-37
               4.4.3.1.1  Metric and index calculations	4-44
               4.4.3.1.2  Quality control measure	4-45
           4.4.3.2   Predictive models (observed/expected [O/E])	4-45
           4.4.3.3   Quantitative decision analysis systems (biological condition gradient
                    [BCG])	4-46
        4.4.4  Index scoring and site assessment	4-47
        4.4.5  Reporting assessment results at multiple spatial scales	4-48
           4.4.5.1   Watershed or area-wide	4-48
           4.4.5.2   Stream- or site-specific	4-50
           4.4.5.3   Relative to specific sources	4-50
    4.5   References	4-53
5   Photo-Point Monitoring	5-1
    5.1   Introduction	5-1
    5.2   Procedure	5-1
        5.2.1  Setting Objectives	5-2
        5.2.2  Selecting Methods	5-3

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Monitoring and Evaluating Nonpoint Source Watershed Projects                                       Contents

           5.2.2.1   Qualitative Monitoring	5-5
           5.2.2.2   Quantitative Monitoring	5-5
               5.2.2.2.1   Photo Grid Analysis	5-6
               5.2.2.2.2   Transect Photo Sampling	5-7
               5.2.2.2.3   Digital Image Analysis	5-8
       5.2.3  Selecting Areas to Monitor	5-9
       5.2.4  Identifying Photo Points	5-9
       5.2.5  Establishing Camera Points	5-11
       5.2.6  Marking and Identifying Photo and Camera Points	5-12
       5.2.7  Identifying a Witness Site	5-12
       5.2.8  Recording Important Site Information	5-12
       5.2.9  Determining Timing and Frequency of Photographs	5-13
       5.2.10 Creating a Field Book	5-14
       5.2.11 Defining Data Analysis Plans	5-14
       5.2.12 Establishing a Data Management System	5-14
       5.2.13 Taking and Documenting Photographs	5-15
    5.3   Equipment Needs	5-17
    5.4   Applications of Photo-Point Monitoring	5-19
       5.4.1  Comparison Photos	5-19
       5.4.2  Repeat Photography	5-19
    5.5   Advantages, Limitations, and Opportunities	5-21
       5.5.1  Advantages	5-21
       5.5.2  Limitations	5-22
       5.5.3  Opportunities	5-22
    5.6   References	5-25
6   Monitoring Challenges and Opportunities	6-1
    6.1   Monitoring Pitfalls	6-1
       6.1.1  Design Flaws	6-1
       6.1.2  Procedural Problems	6-2
    6.2   Lag Time Issues in Watershed Projects	6-4
       6.2.1  Project Management Components	6-5
           6.2.1.1   Time Required for an Installed or Adopted Practice to Produce an Effect	6-5
           6.2.1.2   Time Required for the Effect to be Delivered to the Water Resource	6-6
           6.2.1.3   Time Required for the Waterbody to Respond to the Effect	6-7
       6.2.2  Effects Measurement Components of Lag Time	6-8
           6.2.2.1   The Magnitude of Lag Time	6-8
       6.2.3  How to Deal with Lag Time	6-9
           6.2.3.1   Recognize Lag Time and Adjust Expectations	6-9
               6.2.3.
               6.2.3.

               6.2.3.
               6.2.3.
               6.2.3.
.1   Characterize the Watershed	6-10
.2   Consider Lag Time Issues in Selection, Siting, and Monitoring of
    Best Management Practices	6-10
.3   Monitor Small Watersheds Close to Sources	6-11
.4   Select Indicators Carefully	6-11
.5   Design Monitoring Programs to Detect Change Effectively	6-12
                                                VI

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Monitoring and Evaluating Nonpoint Source Watershed Projects                                       Contents

    6.3   Integrating Monitoring and Modeling	6-12
       6.3.1   The Roles of Monitoring and Modeling	6-12
           6.3.1.1   Monitoring	6-12
           6.3.1.2   Modeling	6-13
       6.3.2   Using Monitoring and Modeling Together	6-14
    6.4   Supporting BMP and Other Databases	6-17
       6.4.1   General Considerations	6-17
       6.4.2   International Urban Stormwater BMP Database	6-18
    6.5   References	6-18
7   Data Analysis	7-1
    7.1   Introduction	7-1
    7.2   Overview of Statistical Methods	7-1
       7.2.1   Exploratory Data Analysis and Data Transformations	7-2
       7.2.2   Dealing with Censored Data	7-5
       7.2.3   Data Analysis for Water Quality Problem Assessment	7-6
       7.2.4   Project Planning Data Analysis	7-7
       7.2.5   BMP and Project Effectiveness Data Analysis	7-7
       7.2.6   Practice Datasets	7-9
    7.3   Exploratory Data Analysis (EDA) and Data Adjustment	7-10
       7.3.1   Steps in Data Exploration	7-12
       7.3.2   Describe Key Variable Characteristics	7-13
           7.3.2.1   Central Tendency	7-13
           7.3.2.2   Variability	7-13
           7.3.2.3   Skewness	7-14
           7.3.2.4   Data Distribution	7-15
           7.3.2.5   Transformations to Handle Non-normal Data with Parametric Statistical
                    Tests	7-17
       7.3.3   Examination for Extreme, Outlier, Missing, or Anomalous Values	7-17
           7.3.3.1   Extremes and Outliers	7-17
           7.3.3.2   Anomalous Values	7-19
           7.3.3.3   Missing Data	7-20
       7.3.4   Examination for Frequencies	7-20
       7.3.5   Examination for Seasonality or Other Cycles	7-21
       7.3.6   Autocorrelation	7-22
           7.3.6.1   Methods to Handle Autocorrelation	7-28
           7.3.6.2   Methods to Handle Autocorrelation Caused by Seasonality	7-29
       7.3.7   Examination of Two or More Locations or Time Periods	7-30
       7.3.8   Examine Relationships between Variables	7-31
       7.3.9   Next Steps	7-33
    7.4   Dealing with Censored Data	7-33
       7.4.1   Types of Censoring	7-33
       7.4.2   Methods for Handling Censored Data	7-34
           7.4.2.1   Past Methods	7-34
           7.4.2.2   Using Probability Distribution Theory to Estimate the Summary Statistics	7-35
                                                VII

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Monitoring and Evaluating Nonpoint Source Watershed Projects                                        Contents

           7.4.2.3  Hypothesis Testing with Censored Data	7-35
    7.5   Data Analysis for Problem Assessment	7-36
       7.5.1 Problem Assessment - Important Considerations	7-36
       7.5.2 Data Analysis Approaches	7-38
           7.5.2.1  Summarize Existing Conditions	7-38
           7.5.2.2  Assess Compliance with Water Quality Standards	7-39
           7.5.2.3  Identify Major Pollutant Sources	7-41
           7.5.2.4  Define Critical Areas	7-42
           7.5.2.5  Additional Approaches	7-44
    7.6   Data Analysis for Project Planning	7-47
       7.6.1 Estimation and Hypothesis Testing	7-47
       7.6.2 Determine Pollutant Reductions Needed	7-48
       7.6.3 Estimate Land Treatment Needs	7-55
       7.6.4 Estimate Minimum Detectable Change	7-55
       7.6.5 Locate Monitoring Stations	7-56
    7.7   Data Analysis for Assessing Individual BMP Effectiveness	7-56
       7.7.1 Analysis of Plot Study Data	7-57
       7.7.2 Analysis of BMP Input/Output Data	7-60
       7.7.3 Analysis of BMP Above/Below Data	7-63
       7.7.4 Analysis of BMP Paired-Watershed Data	7-65
    7.8   Data Analysis for Assessing Project Effectiveness	7-65
       7.8.1 Recommended Watershed Monitoring Designs	7-65
       7.8.2 Recommended Statistical Approaches	7-66
           7.8.2.1  Paired Watershed	7-66
               7.8.2.1.1   Analysis of Covariance (ANCOVA) Procedure - Paired-Watershed
                         Analysis	7-66
               7.8.2.1.2   Multivariate ANCOVA-Paired Watershed with Explanatory Variables	7-73
               7.8.2.1.3   Multiple Paired Watersheds	7-74
               7.8.2.1.4   Multiple Time Periods within a Paired-Watershed Study	7-75
               7.8.2.1.5   Other Statistical Approaches for Paired-Watershed Analyses	7-76
           7.8.2.2  Above/Below -  Before/After	7-77
               7.8.2.2.1   Comparing Means and Differences between Means	7-77
               7.8.2.2.2   ANCOVA	7-78
           7.8.2.3  Nested Watershed	7-82
           7.8.2.4  Single Watershed Trend Station	7-82
               7.8.2.4.1   Monotonic Trends	7-85
               7.8.2.4.2   Step Trends	7-90
           7.8.2.5  Multiple Watersheds	7-90
       7.8.3 Linking Water Quality Trends to Land Treatment	7-91
    7.9   Load Estimation	7-93
       7.9.1 General Considerations	7-94
           7.9.1.1  Definitions	7-94
           7.9.1.2  Issues of Variability	7-95
           7.9.1.3  Practical Load Estimation	7-96
           7.9.1.4  Planning for Load Estimation	7-99
                                                VIII

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Monitoring and Evaluating Nonpoint Source Watershed Projects                                        Contents

       7.9.2  Approaches to Load Estimation	7-100
           7.9.2.1  Numeric Integration	7-100
           7.9.2.2  Regression	7-100
           7.9.2.3  Ratio Estimators	7-104
           7.9.2.4  Comparison of Load Estimation Approaches	7-104
       7.9.3  Load Duration Curves	7-105
       7.9.4  Assessing Load Reductions	7-108
    7.10  Statistical Software	7-108
    7.11  References	7-109
8   Quality Assurance and Quality Control	8-1
    8.1   Introduction	8-1
       8.1.1  Definitions of Quality Assurance and Quality Control	8-2
           8.1.1.1  Quality assurance:	8-2
           8.1.1.2  Quality control:	8-2
       8.1.2  Importance of QA/QC Programs	8-3
       8.1.3  EPA Quality Policy	8-3
    8.2   Data Quality Objectives	8-4
       8.2.1  The Data Quality Objectives Process	8-5
           8.2.1.1  (1) State the problem	8-6
           8.2.1.2  (2) Identify the goal of the monitoring program	8-6
           8.2.1.3  (3) Identify information inputs	8-6
           8.2.1.4  (4) Define the boundaries of the study	8-6
           8.2.1.5  (5) Develop the analytic approach	8-7
           8.2.1.6  (6) Specify performance or acceptance criteria	8-7
           8.2.1.7  (7) Develop the plan for obtaining data	8-8
       8.2.2  Data Quality Objectives and the QA/QC Program	8-9
    8.3   Elements of A Quality Assurance Project Plan	8-9
    8.4   Field Operations	8-12
       8.4.1  Field Design	8-12
       8.4.2  Sampling Site Selection	8-12
       8.4.3  Sampling Equipment	8-13
       8.4.4  Sample Collection	8-13
       8.4.5  Sample Handling and Transport	8-14
       8.4.6  Safety and Training	8-14
    8.5   Laboratory Operations	8-15
       8.5.1  General Laboratory QA/QC	8-15
       8.5.2  Instrumentation and Materials for Laboratory Operations	8-16
       8.5.3  Analytical Methods	8-16
       8.5.4  Method Validation	8-16
       8.5.5  Training and Safety	8-16
       8.5.6  Procedural Checks and Audits	8-17
    8.6   Data and Reports	8-17
       8.6.1  Generation of New Data	8-17
                                                 IX

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       8.6.2  Use of Historical Data	8-18
       8.6.3  Documentation, Record Keeping, and Data Management	8-18
       8.6.4  Report Preparation	8-19
    8.7   Geospatial Data	8-20
       8.7.1  Performance Criteria for a Geospatial Data Project	8-20
       8.7.2  Spatial Data Quality Indicators for Geospatial Data	8-21
    8.8   References	8-22
9   Monitoring Costs	9-1
    9.1   Introduction	9-1
    9.2   Monitoring Cost Items and Categories	9-1
       9.3   Cost Estimation Examples	9-3
       9.3.1  Cost Estimates for a Diverse Range of Monitoring Options	9-4
           9.3.1.1  Discussion	9-4
       9.3.2  Cost Estimates for Watershed-Scale Evaluation of Agricultural BMP
               Implementation	9-6
           9.3.2.1  Discussion	9-6
       9.3.3  Cost Estimates for Five-Year Trend and Above/Below Monitoring	9-8
           9.3.3.1  Discussion	9-9
       9.3.4  Major Conclusions from Cost Estimation Scenarios	9-12
    9.4   Using Minimum Detectable Change to Guide Monitoring Decisions	9-13
    9.5   References	9-15
    Appendix 9-1. Overview of Cost Estimation Spreadsheets	9-16
    Appendix 9-2. Cost Estimates for a Diverse Range of Monitoring Options	9-22
    Appendix 9-3. Cost Estimates for Watershed-Scale Evaluation of Agricultural BMP
         Implementation	9-29
    Appendix 9-4. Cost Estimates for Five-Year Trend and Above/Below Monitoring	9-39

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Monitoring and Evaluating Nonpoint Source Watershed Projects Contents
Figure 2-1. Vertical sediment concentration and flow velocity distribution in atypical stream cross
section (after Brakensiek et al. 1979) 2-8
Figure 2-2. Schematic diagram of stream vertical showing position of sediment load terms (after
Brakensiek et al. 1979) 2-9
Figure 2-3. Map of water quality monitoring stations in Lake Champlain lake regions (Lake
Champlain Basin Program) 2-10
Figure 2-4. Thermally stratified lake in mid-summer (USEPA 1990). Curved solid line is water
temperature. Open circles are DO in an unproductive (oligotrophic) lake and solid
circles are DO in aproductive (eutrophic) lake 2-12
Figure 2-5. Wetlands and waterways of the Inland Bays watershed (DE CIB n.d.) 2-13
Figure 2-6. Mixing of salt water and fresh water in an estuary (after CBP 1995) 2-14
Figure 2-7. Salinity in the fall and spring in the Chesapeake Bay (CBF n.d.) 2-16
Figure 2-8. Basic aquifer types 2-18
Figure 2-9. NO3 concentration versus depth to water table (after Rich 2001) 2-20
Figure 2-10. Determining ground water flow patterns (Winter et al 1998) 2-20
Figure 2-11. Reconnaissance sampling design 2-35
Figure 2-12. Paired sampling design 2-37
Figure 2-13. Above/below sampling design 2-40
Figure 2-14. Input/output sampling design 2-42
Figure 2-15. Multiple input pathways for rain garden 2-42
Figure 3-1. Cross-sectional area and water velocity for streams and pipes 3-11
Figure 3-2. Traditional crest-stage gage 3-12
Figure 3-3. 120° V-notch weir, Englesby Brook, Burlington, VT 3-14
Figure 3-4. Field application of small Parshall flume 3-14
Figure 3-5. Palmer-Bowlus flume 3-15
Figure 3-6. 2-foot (0.6 m) H-flume in place for edge-of-field monitoring, East Montpelier, VT 3-15
Figure 3-7. Measuring stream discharge (USGS) 3-16
Figure 3 -8. Delineation of stream-width segments for discharge measurement 3-17
Figure 3-9. Measuring the cross-section profile of a stream channel 3-17
Figure 3-10. Measuring discharge from bridge using an ADCP (acoustic Doppler current profiler)
unit (USGS 2007) 3-18
Figure 3-11. Measuring discharge from a bridge using a current meter and crane (USGS n.d.) 3-18
Figure 3-12. Staff gauge in stream 3-19
Figure 3-13. Example of a stage-discharge rating for a stream 3-20
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Figure 3-14. Stilling well design schematic (Wahl et al. 1995) 3-21
Figure 3-15. Traditional clock-drive chart recorder at a stilling well 3-21
Figure 3-16. Simplified version of the nitrogen cycle 3-25
Figure 3-17. Possible sampling locations for a synoptic survey 3-39
Figure 3-18. Potential lake monitoring locations 3-40
Figure 3-19. Possible groundwater monitoring locations (after USDA-NRCS 2003) 3-41
Figure 3-20. Schematic of sampling frequency as a function of system type (after USDA-NRCS
2003) 3-44
Figure 3-21. MDC versus frequency and years of monitoring. Assumes p=0.1 for 26x/yr and 0.3
for 52x/yr, CV=0.7, and 95% confidence level 3-54
Figure 3-22. MDC versus confidence level. Assumes p=0.1 for 26x/yr and 0.3 for 52x/yr, 7 years
of monitoring, and CV=0.7 3-54
Figure 3-23. MDC versus coefficient of variation. CV calculated using unadjusted std. dev.
Assumes p=0.1 for 26x/yr and 0.3 for 52x/yr, 7 years of monitoring, and 95%
confidence level 3-55
Figure 3-24. MDC versus coefficient of autocorrelation (p). Assumes 7 years of monitoring,
52x/yr, CV=0.7, and 95% confidence level. MDC = 13% if no autocorrelation is
assumed 3-55
Figure 3-25. Monitoring station with submersible transducer in stream (Freeman et al. 2004) 3-59
Figure 3-26. Drawing and field installation of depth-integrated sample arm for automatic samplers
(photo by R.T. Bannerman, Wisconsin DNR) 3-59
Figure 3-27. Edge-of-field monitoring stations, a, b, Wisconsin Discovery and Pioneer Farms
(Stuntebeck et al. 2008); c, d, Vermont (Meals et al. 201 la) 3-62
Figure 3-28. Examples of passive runoff samplers that can be used for edge-of-field or BMP
studies (A-Graczyk et al. 2000, B-Waschbusch et al. 1999, C-Brakensiek et al. 1979,
and D-Parker and Busch 2013; photo D by P. Parker, University of Wisconsin-
Platteville) 3-63
Figure 3 -29. Flow measurement and water quality sampling in stormwater pipes 3-64
Figure 3-30. Examples of first-flush runoff samplers (A-Nalgene 2007, B-Barrett 2005, C-GKY
2014) 3-65
Figure 3-31. Passive sampling setup for lawn runoff (after Waschbusch et al. 1999) 3-66
Figure 3-32. Examples of automatic samplers with capabilities for variable sampling frequencies
(Hach®2013a, Teledyne Isco 2013a) 3-66
Figure 3-33. Precipitation gage placement relative to obstructions 3-68
Figure 3-34. Photograph of a meteorological monitoring station (Meals etal. 201 la) 3-68
Figure 3-35. Measuring dissolved oxygen, specific conductance, pH, and water temperature using
a hand-held probe 3-71
Figure 3-36. Examples of isokinetic depth-integrating samplers (Wilde etal. 2014) 3-72
Figure 3-37. Depth-specific samplers for lake sampling (Wilde etal. 2014) 3-73
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Figure 3-38. Examples of passive samplers, a, Passive runoff sampler/flow splitter, University of
Georgia, Tifton, GA (photo by D.W. Meals); b, Multi-slot divisor (after Brakensiek et
al. 1979); c, Water and sediment sampler (Dressing et al. 1987, photo by S.A. Dressing) 3-74
Figure 3-39. Single-stage passive sampler (diagram: Wilde et al. 2014, photo by D.W. Meals) 3-75
Figure 3-40. Lysimeters before and after installation (photos by R. Traver, Villanova University) 3-76
Figure 3-41. Examples of portable and refrigerated autosamplers (Hach® 2013b, Teledyne Isco
2013b) 3-77
Figure 3-42. Preparing to take samples in a low-gradient stream 3-79
Figure 3-43. Using a D-frame net to sample a gravel bottom stream for benthic macroinvertebrates ....3-79
Figure 3-44. Sampling devices for biological and habitat variables, a, D-frame net; b, Surber
sampler (Rickly 2016); c, Ponar dredge (Rickly 2016); d, Hester-Dendy artificial
substrate (Rickly 2016); e, Rock basket artificial substrate (Ben Meadows 2016) 3-80
Figure 3-45. Backpack electrofishing (USEPA) 3-81
Figure 3-46. Field processing offish sample: taxonomic identification and data recording 3-81
Figure 3-47. Plankton nets (NOAA 2014) 3-82
Figure 3-48. Churn and cone splitters (FISP 2014) 3-84
Figure 3-49. Examples of agricultural activity data recording forms 3-93
Figure 3-50. Weekly (top panel) and monthly and quarterly (bottom panel, solid and dashed lines,
respectively) load time series superimposed on idealized daily load time series (adapted
from Richards 1998) 3-103
Figure 4 1. Using a D-frame net to sample woody snag habitat for stream benthic
macroinvertebrates 4-8
Figure 4 2. Approach to establishing reference conditions (after Gibson et al. 1996) 4-13
Figure 4 3. Removing a benthic macroinvertebrate sample from a sieve bucket and placing the
sample material in a 1-liter container with approximately 95% ethanol preservative 4-31
Figure 4 4. Labelling benthic macroinvertebrate sample containers and recording field data 4-32
Figure 4 5. Examining, washing, and removing large components of sample material prior to
putting in sample container 4-34
Figure 4 6. Percent degradation of subwatersheds as measured by biological monitoring and
assessment, Lake Allatoona/Upper Etowah River watershed (Millard et al. 2011) 4-49
Figure 4 7. Distribution of stream biological assessments in the Lake Allatoona/Upper Etowah
River watershed, using a benthic MMI developed by the Georgia Environmental
Protection Division (Millard etal. 2011) 4-51
Figure 4 8. More detailed examination of the Yellow Creek subwatershed, Lake Allatoona/Upper
Etowah River watershed, Georgia, reveals a sample location, rated biologically as
"poor," is on a stream flowing through a poultry production operation (Millard et al.
2011) 4-52
Figure 5-1. Comparison photos 5-4
Figure 5-2. Illustration of a photo identification card and a meter board 5-6
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Figure 5-3. Photo illustrating photo points (A and B) and camera points (1 and 2). Photos of A and
         B are taken from cameras located at 1 and 2	5-10
Figure 5-4. Photo identification card	5-16
Figure 5-5. Various potential applications of photo-point monitoring	5-21
Figure 6-1. Detection of violation of sampling protocol (R.P. Richards, Heidelberg University,
         Tiffin, OH)	6-3
Figure 6-2. Effects of changing (1) a defective probe and (2) a laboratory method detection limit
         (Meals 2001)	6-4
Figure 6-3. Lag time conceptual model	6-5
Figure 7-1. Right-skewed distribution	7-14
Figure 7-2. Quantile plot or cumulative frequency plot of E. coli data, Berry Brook, 1996
         (Meals 2001)	7-16
Figure 7-3. Boxplot of weekly TP concentration, Samsonville Brook, 1995 (Meals 2001)	7-16
Figure 7-4. Time plot of weekly TP concentration, Godin Brook, 1999 (Meals 2001)	7-19
Figure 7-5. Time plot of TKN  data from three stream stations, 1995-1996 (Meals 2001)	7-19
Figure 7-6. Percent composition of the orders of macroinvertebrates, Godin Brook, 2000
         (Meals 2001)	7-21
Figure 7-7. Time series plot of weekly E. coli counts, Godin Brook, 1995-1999 (Meals 2001). Red
         line indicates Vermont WQS of 77 E.coli/100 ml	7-21
Figure 7-8. Lag-one plot of streamflow observations, Samsonville Brook,  1994 (Meals 2001)	7-23
Figure 7-9. A) Time series plot, B) autocorrelation function (ACF) graph, and C) partial
         autocorrelation function (PACF) graph of Log(lO) weekly flow from the Corsica River
         National Nonpoint Source Monitoring Program Project generated by Minitab. The steps
         are: Stat > Time Series > Autocorrelation (or Partial Autocorrelation).  Identify the time
         series variable and enter number of lags. Select options for storing ACF, PACF,
         t statistics, and Ljung-Box Q statistics as desired. Press ok	7-25
Figure 7-10. Autocorrelation Function (ACF) graph of weekly flow from the Corsica River
         National Nonpoint Source Monitoring Program Project generated by JMP. The steps
         are: Click "Analyze" tab, select "Modeling" followed by "Time Series." Select Y time
         series (LFLOW)  and X time series (Date)	7-26
Figure 7-11. A) Time series plot, B) autocorrelation function (ACF) graph, and C) partial
         autocorrelation function (PACF) graph of data with zero autocorrelation (i.e.,
         independent data with respect to time)	7-27
Figure 7-12. Boxplots of TSS concentration for three stream stations, 1998 (Meals 2001)	7-30
Figure 7-13. Scatterplot of weekly TP export from control and treatment watersheds, calibration
         period (Meals 2001)	7-31
Figure 7-14. Scatterplot of E. coli vs. streamflow, Godin Brook, 1995-1998, all data combined
         (Meals 2001)	7-32
Figure 7-15. Scatterplot of E. coli vs. streamflow, Godin Brook, 1995-1998, where solid circles =
         winter, open circles = summer (Meals 2001)	7-32
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Figure 7-16. Boxplots of conductivity at three Vermont monitoring stations, October 1999 -
September 2000 (Meals 2001) 7-39

Figure 7-17. Example time series plot of observed aluminum concentrations compared to water
quality criteria 7-40

Figure 7-18. Cumulative frequency plot of three years of E. coli data from a Vermont stream
(adapted from Meals 2001). Red lines represent frequency of observations at or below
the VT WQS of 77 cfu/100 ml and the frequency of observations at or below the EPA
criterion of 23 5 cfu/100 ml 7-41

Figure 7-19. Map of synoptic sampling results from 41 stations in the Corsica River Watershed
(Maryland) for NO2+NO3-N concentration (Primrose 2003). Pink and red shaded
subwatersheds represent drainage areas contributing high (3-5 mg/L) and excessive
(>5 mg/L) NO2+NO3-N concentrations, respectively 7-43
Figure 7-20. Correspondence analysis biplot of Grande Ronde fish data (Drake, 1999) 7-45
Figure 7-21. Regression of output versus input load (data from Erickson et al. 201 Ob) 7-61
Figure 7-22. Effluent probability plot for input/output monitoring of a wet detention pond 7-63
Figure 7-23. Conceptualized regression plots for paired-watershed data. The red line indicates the
comparison of the treatment watershed from the calibration vs. treatment periods
evaluated at the LSMEANS value of 2.5 (the mean of all sampled values in the control
watershed over the entire sampling duration (both treatment and calibration period) 7-69
Figure 7-24. Example of intersecting regression lines (Meals 2001) 7-75
Figure 7-25. Basic data preparation and analysis procedure for above/below-before/after study in
Pennsylvania (Galeone et al. 2006) 7-80
Figure 7-26. Linking stream nitrate concentration to land cover (Schilling and Spooner 2006) 7-93
Figure 7-27. Imaginary plot of pollutant flux over time at a monitoring station (Richards 1998) 7-94

Figure 7-28. Plot of suspended solids loads for the Sandusky River, water year 1985 (Richards
1998). Top, daily TSS samples; Middle, weekly samples; Bottom, monthly samples.
Weekly and monthly sample values were drawn from actual daily sample data series.
Flux is on y-axis, time is onx-axis, and area under curve is load estimate 7-96
Figure 7-29. Weekly (red line in top panel), monthly (red line), and quarterly (black line in bottom
panel) suspended solids load time series superimposed on a daily load time series
(Richards 1998). Log of flux is on y-axis, time is on x-axis, and area under curve is load
estimate 7-97
Figure 7-30. Flow-concentration regressions from the Maumee River, Ohio (Richards 1998).
Top panel, regression relationship between log of total suspended solids concentration
and log of flow for the 1991 water year dataset; Bottom panel, plot of same data divided
into two groups based on time of year. Within each season, the regression model is
stronger, has lower error, and provides a more accurate load estimate 7-102
Figure 7-31. Flow duration curve for the Sevier River near Gunnison, UT, covering the period
January 1977 through September 2002 7-107
Figure 7-32. Load duration curve for the Sevier River near Gunnison, UT, January 1977 through
September 2002. Blue line represents allowable total P load calculated as the product of
each observed flow duration interval and the target total P concentration of 0.05 mg/L.
Yellow points represent observed total P loads at the same flow duration intervals 7-107
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Figure 9-1. Breakout of costs for diverse range of monitoring options	9-5
Figure 9-2. Cost estimates for watershed-scale assessment of agricultural BMP projects	9-7
Figure 9-3. Comparison of labor cost category percentage overtime	9-8
Figure 9-4. Box plots summarizing cost estimates for five-year monitoring efforts	9-10
Figure 9-5. Box plots summarizing five-year labor costs	9-11
Figure 9A1-1.  Pie chart from simplified spreadsheet	9-20
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Table 1 1. National causes of impairment (excerpted from USEPA 2016) 1-4
Table 1 2. National probable sources contributing to impairments (excerpted from USEPA 2016) 1-5
Table 1 3. National cumulative TMDLs by pollutant (excerpted from USEPA 2016) 1-5
Table 2-1. Complementary program and monitoring objectives 2-3
Table 2-2. Selected NPS pollutants and watershed source activities to monitor 2-23
Table 2-3. Monitoring scale as afunction of objective 2-33
Table 2-4. Monitoring design as a function of objective 2-44
Table 3-1. Monitoring variable groups by direct relationship to selected designated water use
(adapted from USDA-NRCS 2003) 3-4
Table 3-2. Monitoring variables by selected water resource types (adapted from USDA-NRCS
2003) 3-5
Table 3-3. Monitoring variable groups by selected nonpoint source activities (adapted from
USDA-NRCS 2003) 3-6
Table 3-4. Representative laboratory analytical costs for selected water quality variables. Costs
will vary by region and by laboratory (Dressing 2014) 3-7
Table 3-5. EPA-recommended preservation conditions and hold times for selected water quality
variables (40 CFR 136.3 and NEMI2006) 3-8
Table 3-6. Selected physical and chemical water quality variables commonly measured in NPS
watershed monitoring programs 3-23
Table 3-7. Selected biological water quality variables commonly measured in NPS watershed
monitoring programs 3-28
Table 3-8. Anderson Level II land use and land cover classification system for use with remote
sensor data (Anderson et al. 1976) 3-33
Table 3-9. Sample type as a function of monitoring objective (adapted from USDA-NRCS 2003) 3-34
Table 3-10. Number of total samples per indicated sample frequency and number of years 3-46
Table 3-11. Values of V(Xi- X )2 for biweekly sampling for selected monitoring durations,
assuming Xi is measured as a 'Date' or daily variable 3-47
Table 3-12. Required containers, preservation techniques, and holding times 3-85
Table 3-13. Relationship of water quality and land use/land treatment variables 3-98
Table 4 1. General strengths and limitations of biological monitoring and assessment approaches 4-12
Table 4 2. Comparison of probability-based and targeted monitoring designs 4-15
Table 4 3. Waterbody stratification hierarchy 4-29
Table 4 5. Metrics and associated scoring formulas for four site classes from an example
monitoring and assessment program 4-44
Table 4 6. Degradation thresholds to which MMI score are compared for determination of status 4-47
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Table 6 1. Examples of lag times reported in response to environmental impact or treatment 6-9
Table 7-1. Exploratory data analysis methods (see discussion, section 7.3) 7-2
Table 7-2. Methods for adjusting data for subsequent analysis (see discussion, section 7.3) 7-4
Table 7-3. Methods to deal with censored data (see discussion, section 7.4) 7-5
Table 7-4. Data analysis methods for problem assessment (see discussion, section 7.5) 7-6
Table 7-5. Data analysis methods for project planning (see discussion, section 7.6) 7-7
Table 7-6. Data analysis methods for assessing BMP or watershed project effectiveness (see
discussion, sections 7.7 and 7.8) 7-8
Table 7-7. Practice datasets 7-10
Table 7-8. Classification of tests for monotonic (nonparametric) or linear (parametric) trend
(adapted from Helsel and Hirsch 2002) 7-86
Table 7-9. Sampling of available statistics software packages 7-108
Table 8 1. Common QA/QC activities 8-2
Table 8 2. Elements required in an EPA Quality Assurance Project Plan. (USEPA, 2001b) 8-10
Table 8 3. Continuum of Geospatial Projects with Differing Intended Uses 8-20
Table 9 1. Costs grouped by type of item or activity 9-2
Table 9 2. Costs grouped by project phase or element 9-2
Table 9 3. Summary of scenario costs for diverse range of monitoring options 9-5
Table 9 4. Labor costs assumed for watershed-scale evaluation scenarios 9-6
Table 9 5. Cost reductions due to lowering of labor and equipment costs 9-12
Table 9 6. Illustration of costs and MDC in response to changes in sampling program in Tillamook
Bay, Oregon (Spooner et al. 1987) 9-14
Table 9A1-1. Sample type and variable set options for simplified spreadsheet 9-17
Table 9A1 2. Grab sampling variable sets 9-17
Table 9A1 3. Load monitoring variable sets 9-18
Table 9A1-4. Biological monitoring variable sets 9-19
Table 9A1-5. Sondes monitoring variable sets 9-19
Table 9A1-6. Tabular output from simplified spreadsheet 9-20
Table 9A1-7. Annual costs from simplified spreadsheet 9-21
Table 9A2-1. Labor costs assumed for scenarios 9-23
Table 9A2-2. Total costs for eight diverse scenarios 9-28
Table 9A3 -1. Driving and labor assumptions for discharge observations as stand-alone trips 9-31
Table 9A3-2. Sampling distances and times within watersheds 9-31
Table 9A3-3. Field work costs for site selection 9-32
Table 9A3-4. Major equipment and materials costs for stations measuring continuous discharge 9-33
Table 9A3-5. Site establishment costs for sites designed for load estimation 9-33
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Table 9A3-6. Site demolition and restoration costs	9-34
Table 9A3 -7. Driving and labor assumptions for land use/treatment tracking	9-35
Table 9A3-8. Labor assumptions for data analysis and reporting	9-35
Table 9A4-1. Factors used in creating cost estimation scenarios	9-39
Table 9A4-2. Watershed characterization costs as function of design and watershed size	9-40
Table 9A4-3. Variability of costs for data analysis and reporting	9-40
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                                                            Acronym List
AA



ac



ac/ft



ACF



ADCP



AFDM



Ag



Al



ANCOVA



ANOVA



APA



ARIMA



As



ATTAINS



Au



BCG



BEACH



BioK



BMP



BOD



BOD5



CADDIS



CAFO



CCA



C-CAP



Cd



CEAP
atomic absorption



acre



acre-foot



autocorrelation function



acoustic Doppler current profiler



ash-free dry mass



silver



aluminum



analysis of covariance



analysis of variance



acid/alkaline phosphatase activity



autoregressive integrated moving average



arsenic



assessment TMDL tracking & implementation system



gold



biological condition gradient



beaches environmental assessment, closure and health



biological/habitat with kick net



best management practice



biochemical oxygen demand



5-day biochemical oxygen demand



causal analysis/diagnosis decision information system



concentrated animal feeding operation



canonical correlation analysis and canonical correspondence analysis



coastal change analysis program



cadmium



conservation effects assessment project
                                              XXI

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                                                             Acronym List
cfs



CI



Cl1



cm



cms



Co



COD



Cu



CV



CWA



DBI



DD



DEM



d.f.



DIA



DL



DO



DQO



EDA



EDI



EMC



EMMA



EPA



EPT



EWI



Fe



FSA



ft



ft3/s



CIS
cubic feet per second



confidence interval



chloride



centimeter



cubic meters per second



cobalt



chemical oxygen demand



copper



coefficient of variation



Clean Water Act



diatom bioassessment index



detectable difference



digital elevation model



degree of freedom



digital image analysis



detection limit



dissolved oxygen



data quality objective



exploratory data analysis



equal discharge interval



event mean concentration



environmental monitoring and measurement advisor



U.S. Environmental Protection Agency



Ephemeroptera-Plecoptera-Trichoptera



equal width interval



iron



Farm Service Agency



feet



cubic feet per second



geographic information system
                                               XXII

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                                                             Acronym List
GPS
H2SO4
ha
HBI
Hg
HNO3
IBI
ICP
in
IQR
IR
IWL
kg
KS
L
Li
LA
LIA
LID
LiDAR
LOWESS
LS-means
LSD
LULC
m
m3/s
MA
MAI
MBI
MDC
global positioning system
sulfuric acid
hectare
Hilsenhoff Biotic Index
mercury
nitric acid
Index of Biological Integrity
inductively coupled plasma
inch
interquartile range
integrated reporting
Izaak Walton League
kilogram
Kolmogorov-Smirnov
liter
lithium
load allocation
line-intersect analysis
low impact development
light detection and ranging
locally weighted scatterplot smoothing
least square means
least significant difference
land use/land cover
meter
cubic meters per second
moving average
macroinvertebrate aggregated index
macroinvertebrate biotic index
minimum detectable change
                                               XXIII

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Monitoring and Evaluating Nonpoint Source Watershed Projects
                                                            Acronym List
MDNR



mg



mi



ml



MLE



mm



MMI



Mn



MOS



MQO



mRPD



N



NAWQA



NELAC



NEMI



NGO



NH3-N



Ni



NLCD



NNPSMP



NO3



NPDES



NFS



NRCS



NRI



NRSA



NSC



NSL



NWQI



O/E
Maryland Department of Natural Resources



milligram



mile



milliliter



maximum likelihood estimation



millimeter



multimetric index



manganese



margin of safety



measurement quality objective



median relative percent difference



nitrogen



national water-quality assessment program



national environmental laboratory accreditation conference



national environmental methods index



non-governmental organization



ammoniacal nitrogen



nickel



national land cover dataset



national nonpoint source monitoring program



nitrate nitrogen



national pollution discharge elimination system



nonpoint source



Natural Resources Conservation Service



national resources inventory



national rivers  and streams assessment



nutrient and sediment grab samples



nutrient and sediment loads



national water quality initiative



observed/expected
                                              XXIV

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Monitoring and Evaluating Nonpoint Source Watershed Projects
                                                            Acronym List
P
PACF
Pb
PCA
PDTG
PGDER
PIBI
POCIS
PPCC
PROC AUTOREG

QAP
QAPP
QHEI
QL
QMP
RCB
RCWP
ROS
RPD
RUSLE
SA
SAP
SAS
Sb
sec
SIMPLE
SNT
SO42
SOP
phosphorus
partial autocorrelation function
lead
principal component analysis
percent dominant taxa (generic level)
Prince George's County Department of Environmental Resources
potential index of biological integrity
polar organic chemical integrative samplers
probability plot correlation coefficient
SAS procedure to estimate and forecast linear regression models for time series
data
quality-assurance plan (USGS)
quality assurance project plan
qualitative habitat evaluation index
quantitation limit
quality management plan
randomized complete block
rural clean water program
regression on order statistics
relative percent difference
revised universal soil loss  equation
subjective analysis
sampling and analysis plan
SAS Institute, Inc.
antimony
suspended sediment concentration
spatially integrated models for phosphorus loading and erosion
sondes for nutrients and turbidity
sulfate
standard operating procedure
                                              XXV

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Monitoring and Evaluating Nonpoint Source Watershed Projects
                                                             Acronym List
SPARROW


SRP

SSC

STEPL

STORET


SWAT

SWM

SWP

TDS

TIGER

TIR

TKN

TMDL

TNTC

TP

TSS

USDA

USGS

VIF

W/D

WLA

WQS

WRTDS

WWTP

Zn
spatially referenced regressions on watershed attributes watershed modeling
technique

soluble reactive phosphorus

suspended sediment concentration

spreadsheet tool for estimating pollutant load

EPA's storage and retrieval database for water quality, biological, and physical
data

soil and water assessment tool

statewide monitoring network

stormwater detention/retention pond

total dissolved solids

topologically integrated geographic encoding and referencing

thermal  infrared

total Kjeldahl nitrogen

total maximum daily load

too numerous to count

total phosphorus

total suspended  solids

U.S. Department of Agriculture

U.S. Geological Survey

variance inflation factor

width-to-depth ratio

waste load allocation

water quality standards

weighted regressions on time, discharge, and season

wastewater treatment plant

zinc
                                              XXVI

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Monitoring and Evaluating Nonpoint Source Watershed Projects Chapter 1
1 Overview of the Nonpoint Source Problem
By S.A. Dressing, D.W. Meals, J.B. Harcum, and J. Spooner

1.1 Definition of a Nonpoint Source
Nonpoint sources of water pollution are both diffuse in nature and difficult to define. Nonpoint source
(NFS) pollution can generally be defined as the pollution of waters caused by rainfall or snowmelt
moving over and through the ground. As water moves over or through soil, it picks up and carries away
natural contaminants and pollutants associated with human activity, finally depositing the contaminants
into lakes, rivers, wetlands, coastal waters, and ground waters. Habitat alteration, such as the removal of
riparian vegetation, and hydrologic modification, such as damming a river or installing bridge supports
across the mouth of a bay, can cause adverse effects on the biological and physical integrity of surface
waters and are also treated as nonpoint sources of pollution. Atmospheric deposition, the wet and dry
deposition of airborne pollutants onto the land and into waterbodies, is also considered to be nonpoint
source pollution. At the federal level, the term nonpoint source is defined to mean any source of water
pollution that does not meet the legal definition of point source in Section 502(14) of the Clean Water Act
(CWA):

The term "point source" means any discernible, confined and discrete conveyance, including but not
limited to any pipe, ditch, channel, tunnel, conduit, well, discrete fissure, container, rolling stock,
concentrated animal feeding operation, or vessel or other floating craft, from which pollutants are or
may be discharged. This term does not include agricultural storm water discharges and return flows
from irrigated agriculture.

The distinction between nonpoint sources and diffuse point sources is sometimes unclear. Although
diffuse runoff is usually treated as nonpoint source pollution, runoff that enters and is discharged from
conveyances, as described above, is treated as a point source discharge and is subject to the federal permit
requirements under Section 402 of the Clean Water Act.

Stormwater can be classified as a point or nonpoint source of pollution. Stormwater is classified as a point
source when it is regulated through the National Pollution Discharge Elimination System (NPDES)
Stormwater Program. An NPDES Stormwater permit is required for medium and large municipal separate
storm sewer systems (MS4s) of incorporated areas and counties with populations of more than 100,000,
certain industrial activities, and construction activities disturbing five ac or more. An NPDES permit is
also required for small MS4s in "urbanized areas" and small construction activities disturbing between
one and five acres (ac) of land. The NPDES permitting authority may also require operators of small
MS4s not in urbanized areas and small construction activities disturbing less than one ac to obtain an
NPDES permit based on the potential for contribution to a violation of a water quality standard. Detailed
information on the NPDES Storm Water Program is available at http://www.epa.gov/npdes/npdes-
stormwater-program. If Stormwater originates from a location that does not fall within the NPDES permit
requirements, it is considered to be nonpoint source pollution (USEPA 2005). Concentrated animal
feeding operations (CAFOs) are also classified as point sources and regulated under the NPDES program
(USEPA 2012b). Despite differing regulatory requirements, monitoring issues and concepts encountered
for permitted Stormwater and CAFOs are similar to those of nonpoint sources.
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Monitoring and Evaluating Nonpoint Source Watershed Projects                                      Chapter 1


1.2  Extent of Nonpoint Source Problems in the United States
During the last three decades, significant achievements have been made nationally in the protection and
enhancement of water quality. Much of this progress, however, has resulted from controlling point
sources of pollution. Pollutant loads from nonpoint sources continue to present problems for achieving
water quality goals and maintaining designated uses in many parts of the United States. Nonpoint sources
are generally considered the number one cause of water quality problems reported by states, tribes, and
territories.

Categories of nonpoint source pollution affecting waterbodies include  agriculture, atmospheric
deposition, channelization, construction, contaminated sediment, contaminated ground water, flow
regulation, forest harvesting (silviculture), ground water loading, highway maintenance/runoff,
hydrologic and habitat modification, in-place contamination, land development, land disposal, marinas,
onsite disposal systems, recreational activities, removal of riparian vegetation, resource extraction,
shoreline modification, streambank destabilization, and unspecified or other nonpoint source pollution.

Nonpoint sources can generate both conventional pollutants (e.g., nutrients, sediment) and toxic pollutants
(e.g., pesticides, petroleum products). Even though nonpoint sources can contribute many of the same
kinds of pollutants as point sources, these pollutants are usually generated in different timeframes,
volumes, combinations, and concentrations.

Pollutants from nonpoint sources are mobilized primarily during  rainstorms or snowmelt. Consequently,
waterborne NPS pollution is generated episodically, in contrast to the more continuous discharges of
point sources of pollution. However, the adverse impacts of NPS pollution downstream from its source,
or on downgradient waterbodies, can be continuous under some circumstances. For example, sediment-
laden runoff that is not completely flushed out of a surface water prior to a storm can combine with storm
runoff to create a continuous adverse impact; toxic pollutants carried in runoff and deposited in sediment
can exert a continuous adverse impact long after a rainstorm; physical  alterations to a stream course
caused by runoff can have a permanent and continuous effect on the watercourse; and the chemical and
physical changes caused by NPS pollution can have a continuous adverse impact on resident biota.

Nutrient pollution (i.e., nitrogen [N] and phosphorus [P]) is often associated with NPS and has received
increasing attention as algal blooms and resulting hypoxic or "dead" zones caused by the decay of algae
have negatively affected waterbodies around the country (NOAA 2012).  Various other pollutants
contributed by NPS include sediment, pathogens, salts, toxic substances, petroleum products, and
pesticides. Each of these pollutants, as well as habitat alteration and hydrologic modification, can have
adverse effects on aquatic systems and, in some cases, on human health.
   «   Waste from livestock, wildlife, and pets contain bacteria that contaminate swimming, drinking, and
      shellfishing waters, as well as oxygen-demanding substances that deplete dissolved oxygen (DO)
      levels  in aquatic systems. Suspended sediment generated by construction, overgrazing, logging, and
      other activities in riparian areas, along with particles carried in runoff from cropland, highways, and
      bridges, reduces sunlight to aquatic plants,  smothers fish spawning areas, and clogs filter feeders
      and fish gills.
   •   Salts from irrigation water become concentrated at the soil surface through evapotranspiration and
      are carried off in return flow from surface irrigation. Road  salts from deicing accumulate along the
      edges  of roads and are often carried via storm sewer systems to surface waters. Salts cause the soil
      structure to break down, decrease water infiltration, and decrease the productivity of cropland. Salts
      can also be toxic to plants at high concentrations.
                                               1-2

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Monitoring and Evaluating Nonpoint Source Watershed Projects Chapter 1
• Some pesticides are persistent in aquatic systems and biomagnify in animal tissue (primarily fish
tissue) as they are passed up through the food chain. Biomagnification has detrimental
physiological effects in animals and negative human health impacts. Herbicides can be toxic to
aquatic plants and therefore remove a food source for many aquatic animals. Herbicides can also
kill off the protective cover that aquatic vegetation offers to many organisms.
* Finally, the trampling of stream bottoms by livestock and equipment; stream bank erosion caused
by grazing, logging, and construction; conversion of natural habitats to agricultural, urban, and
other land uses; flow regulation; and activities in riparian areas (e.g., tree removal, buffer removal)
can reduce the available habitat for aquatic species, increase erosion, increase water temperature via
reduced shading, and create flow regimes that are detrimental to aquatic life.

Every two years, states and territories are required to submit a 305(b) report that describes the status of all
assessed waters and a 303(d) report that lists the impaired waters, the causes of impairment and the status
of their restoration. In 2001, the U.S. Environmental Protection Agency (EPA) issued guidance to the
states encouraging submitting one electronic, integrated water monitoring and assessment report. This
report is currently expected to include the 305(b), 303(d), and 314 (Clean Lakes Program) assessments
(Keehner 2011). Currently there are no plans to release a new National Water Quality Inventory Report to
Congress. The last Report to Congress was released in 2009 and provided a synopsis of 2004 data.
Information on Integrated Reporting, including the guidance issued by EPA, is available at
www.epa.gov/tmdl/integrated-reporting-guidance. The Assessment TMDL Tracking & Implementation
System (ATTAINS) provides the most current 305(b) and 303(d) information available for all 50 states
and territories. ATTAINS summarizes state-reported data for the nation, individual states, individual
waters and the 10 EPA regions.

A national summary of assessment data submitted by the states from 2004 through 2014 (with over 80
percent for the period 2010-2014) documents the extent of the nonpoint source problem (USEPA 2016).
The share of waters assessed by the states in these reports was 32 percent of river miles (mi); 45 percent
of lake, reservoir, and pond acreage; 40 percent of bay and estuary square mileage; 14 percent of coastal
shoreline mi; 3 percent of ocean and near coastal water square mileage; 1 percent of wetlands acreage; 85
percent of Great Lakes shoreline; and 88 percent of Great Lakes open water square mileage. For these
assessed waters, Table 1-1 shows national totals for causes of impairments or threats to impairment that
are often associated with nonpoint sources. A wide range of causes frequently associated with nonpoint
sources are at the top of the list for rivers and streams, including pathogens, sediment, nutrients, organic
enrichment/oxygen depletion, temperature, metals, habitat and flow alterations, and turbidity. Nutrients,
organic enrichment/oxygen depletion, turbidity, metals, and sediment are also leading causes of
impairments and threats to lakes, while pathogens and organic enrichment/ depleted oxygen are among
the top causes of problems identified in bays and estuaries and coastal shoreline. Organic enrichment/
depleted oxygen is the largest cause of impairment to wetlands, with metals, pathogens, and nutrients also
among the leading causes of impairment. Pesticides were found to be a significant cause of problems in
Great Lakes open waters and along the Great Lakes shoreline, while organic enrichment/ depleted oxygen
was the largest cause of impairment to ocean and near coastal waters.
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Monitoring and Evaluating Nonpoint Source Watershed Projects
Chapter 1
             Table 1-1. National causes of impairment (excerpted from USEPA 2016)
Cause of Impairment
Group
Algal Growth
Ammonia
Flow Alteration(s)
Habitat Alterations
Metals (other than Mercury)
Nutrients
Oil and Grease
Organic Enrichment/Oxygen
Depletion
Pathogens
Pesticides
Sediment
Temperature
Turbidity
Size of Assessed Waters with Listed Causes of Impairment
in
TO
£
X
-a
c
TO
§?
£1
6,013
11,673
42,694
67,242
89,069
117,412
3,014
99,578
178,219
19,565
145,289
93,513
47,854
Lakes, Reservoirs,
and Ponds (Acres)
908,513
214,501
190,228
319,965
1,304,587
3,586,616
44,285
1,697,788
549,515
494,613
788,465
240,684
1,341,862
Z
•n
TO
.3 "3T
in -------
Monitoring and Evaluating Nonpoint Source Watershed Projects
Chapter 1
  Table 1-2. National probable sources contributing to impairments (excerpted from USEPA 2016)











Probable Source Group
Agriculture
Construction
Habitat Alterations (Not Directly
Related to Hydromodification)
Hydromodification
Recreational Boating And
Marinas
Resource Extraction
Silviculture (Forestry)
Unspecified NPS
Urban-Related
Runoff/Stormwater
Size of Assessed Waters with Probable Sources of Impairments
in
TO

0 JT
£ <£
—
^* O"
m J2-
3,056
1
2,231
1,717
789

320
0
3,363
3,086

r—
•^
S.
o
j=
CO

TO . — .
•55 8
TO —


Jj
^^
>«^
(/>

c
TO
"S
1
201,786
1,000
33
6,762
72,320

32,112

1,324
54
_c
IE
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CO
(/)
0>
J£

—1 — ,
4.J (/)
s =
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620
18
90
231




6
99
"3T
-------
Monitoring and Evaluating Nonpoint Source Watershed Projects                                      Chapter 1
23 percent is in fair condition, and 55 percent is in poor condition (no data for 1 percent). Of the four
chemical stressors assessed in this study (total P [TP], total N [TN], salinity, and acidification), it was
concluded that P and N are by far the most widespread. It was found that 40 percent of the nation's river
and stream length has high1 levels of P and 28 percent has high levels of N. Poor biological condition (for
macroinvertebrates) was found to be 50 percent more likely in rivers and streams with high levels of P
and 40 percent more likely in rivers and streams with high levels of N. Four indicators of physical habitat
condition (excess streambed sediments, riparian vegetative cover, riparian disturbance, and in-stream fish
habitat) were also assessed for the study. Results indicated that poor riparian vegetative cover and high
levels of riparian disturbance are the most widespread physical stressors, reported in 24 percent and
20 percent of the nation's river and stream length, respectively. Excess levels of streambed sediments,
however, were reported in 15 percent of river and stream length and were found to have a greater impact
on biological condition. The study concluded that poor biological condition is 60 percent more likely in
rivers and streams with excessive levels of streambed sediments. While this study was not designed to
identify the sources of stressors, other research has shown that nonpoint sources are often contributors to
both the chemical and physical stressors described here. The draft report was released for comment on
March 25, 2013, and is currently undergoing final revision.

EPA also performed a National Wetland Condition Assessment (NWCA) to determine the ecological
integrity of wetlands at regional and national scales through a statistical survey approach. Field data were
collected in 2011 and a draft report was released for public comment through January 6, 2016 (USEPA
2015c). Draft findings indicate that nationally, 48% of the wetland area is in good condition, 20% is in
fair condition and the remaining 32% of the area is in poor condition. The  study also assessed a number of
physical, chemical, and biological indicators of stress that reflect potential negative impact to wetland
condition. These indicators were assigned to "low," "moderate," or "high" stressor levels depending on
criteria established for each indicator. Of the six physical indicators, vegetation removal and hardening
(e.g., pavement, soil compaction) stressors were assessed as high for 27% of wetland area nationally,
while the ditching stressor was high for 23% of wetland area. Both of the chemical indicators (a heavy
metal index and soil P concentration) were low for the majority of wetland area nationally, but at variable
levels across the four aggregated ecoregions created for the study. A Nonnative Plant Stressor Indicator
developed for NWCA was used to assess the level of biological stress in wetlands. Nationally, 61% of
wetland area had low stressor levels for nonnative plants, but results varied across aggregated ecoregions.

Still, other reports indicate the pervasive nature of NPS pollution and the need to document and solve the
many problems it causes. For example:
  •   Based on the sampling of over  1,000 lakes across the country in 2007, it was determined that poor
      lake physical habitat is the biggest problem affecting biological condition, followed by high
      nutrient levels (USEPA 2009).  This statistical survey found that lakes with excess nutrients (i.e., a
      "poor" stressor condition) are two-and-a-half times more likely to have poor biological health2.
  •   EPA's 2012 National Coastal Condition Report noted that U.S. coastal areas are facing significant
      population pressures and associated higher volumes of urban nonpoint source runoff with
      53 percent of the U.S. population living in coastal areas that comprise only 17 percent of the total
      conterminous U.S. land area (USEPA 2012a). This report rated the U.S. coasts as "fair" on a scale
1 Thresholds for high, medium and low values were set on a regional basis relative to the least-disturbed reference
sites for each of the nine NRSA ecoregions.)
2 This likelihood is expressed relative to the likelihood of Poor response condition in lakes that have Not-Poor
stressor condition (USEPA 2010).
                                                1-6
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Monitoring and Evaluating Nonpoint Source Watershed Projects                                      Chapter 1
      of good, fair, or poor. Dissolved inorganic P levels, one of the five components of the water quality
      index, was also rated "fair."
  «  Nonpoint sources, particularly from the agricultural areas north of the confluence of the Ohio and
      Mississippi Rivers, contribute most of the N and P loads to the Gulf of Mexico (Goolsby et al.
      1999). The nitrate load to the Gulf approximately tripled from 1970 to 2000, with the greatest
      sources believed to be basins in southern Minnesota, Iowa, Illinois, Indiana, and Ohio that drain
      agricultural land (Goolsby et al. 2001).
  *  In 2015, the Gulf of Mexico hypoxic zone measured 6,474 square miles (4.14 million ac), larger
      than the state of Hawaii (USEPA 2015f). The greatest source of pollution causing the hypoxic zone
      in the Gulf of Mexico is nonpoint source runoff from agriculture. It has been estimated that corn
      and soybean cultivation contributes 52 percent of the N delivered to the Gulf from the Mississippi
      River Basin, with other cropland, manure on pasture and range land, and forest contributing 14, 5,
      and 4 percent, respectively (Alexander et al. 2008). It was also estimated that animal manure on
      pasture and rangeland, corn and soybeans, other cropland, and forest contribute 37, 25, 18, and 8
      percent of the P, respectively.


1.3  Major Categories of Nonpoint Source  Pollution

1.3.1  Agriculture
The 2012 Census of Agriculture reported that there are 2,109,303 farms covering 914,527,657 acres (ac)
in the U.S.  (USDA-NASS 2014). Approximately 1.5 million farms grew crops on 390 million ac, and
there were about 415 million ac of permanent pasture and range on nearly 1.2 million farms. Woodland
covered 77 million acres, while other agricultural features (e.g., farmsteads, buildings, livestock facilities,
ponds, and roads) accounted for 32 million ac of farmland. Animal agriculture included nearly 90 million
cattle and calves on approximately 900 thousand farms, 66 million hogs and pigs on 63 thousand farms,
and 1.5 billion broilers on 42 thousand farms.

The primary agricultural nonpoint source pollutants are inorganic and organic nutrients (N and P),
sediment, organic matter and pathogens from animal waste, salts, and agricultural chemicals. Agriculture
and agricultural activities can also have direct impacts on aquatic habitat. N and P are applied to
agricultural land in several different forms and come from various sources, including commercial
fertilizer, manure from animal production facilities, municipal and industrial treatment plant sludge and/or
effluent applied to agricultural lands, legumes and crop residues, irrigation water, and atmospheric
deposition.

Land disturbance and clearing for agricultural operations can increase sediment loadings in runoff and
surface waters. In addition, increased instream flows resulting from this land clearing can also contribute
to accelerated stream bank erosion. Sediment loss and runoff are especially high if it rains or if high
winds occur while the soil is being disturbed or soon afterward.

Animal waste includes the fecal and urinary wastes of livestock and poultry; process water; and the feed,
bedding, litter, and soil from confined animal  facilities. Runoff water and process wastewater from
confined animal facilities can contain oxygen-demanding substances; N, P, and other nutrients; organic
solids; salts; bacteria, viruses, and other microorganisms; and sediment.

Large amounts of salt can be added to agricultural soils by irrigation water that has a natural base load of
dissolved mineral salts, regardless of whether the water is supplied by ground water or surface water
                                               1-7
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Monitoring and Evaluating Nonpoint Source Watershed Projects                                      Chapter 1
sources. Irrigation water is consumed by plants and lost to the atmosphere by evaporation, and the salts in
the water remain on and become concentrated in the soil.  Salt accumulation leads to soil dispersion, soil
compaction, and possible toxicity to plants and soil fauna. Salt can also be carried from fields in irrigation
return flows.

Agricultural chemicals—including pesticides, herbicides, fungicides, and their degradation products—can
enter ground and surface waters in solution, in emulsion, or bound to soil colloids. Some types of
agricultural chemicals are resistant to degradation and can persist and accumulate in aquatic ecosystems.
Application to agricultural fields is a major source of pesticide contamination of surface water and ground
water. Other sources are atmospheric deposition; drift during application; misuse; and spills, leaks, and
discharges associated with pesticide storage, handling, and disposal.

Riparian vegetation and its pollutant buffering capacity are lost when crops are planted too close to
surface waters. Livestock grazing can cause loss of cover vegetation on pasturelands, resulting in erosion,
loss of plant diversity on pasturelands, and adverse impacts on stream courses and surface waters. Cattle
with access to streams can directly deliver fecal contamination to waterbodies, trample riparian vegetation
and disturb stream bank soils, leading to bank erosion. In  addition, grazing can alter riparian vegetation
species composition.

1.3.2  Urban Sources
The most common pollutants coming from stormwater sources include sediment, pathogens, nutrients,
and metals (USEPA 2015b). Other pollutants in runoff from urban areas include oil, grease and toxic
chemicals from motor vehicles; pesticides and nutrients from lawns and gardens; viruses, bacteria and
nutrients from pet waste and failing septic systems; road salts; heavy metals from roof shingles, motor
vehicles and other sources; and thermal  pollution from impervious surfaces such as streets and rooftops
(USEPA 2015e). Research has indicated that the unit area contribution of pesticides to watersheds by
urbanized areas (e.g. golf courses and home lawn  care) may be greater than that from agriculture (Steele
etal. 2010).

Urbanization converts large portions of vegetated  land to unvegetated, impervious land, thus changing the
extent to which the land can absorb and filter rainfall and  runoff before it enters waterbodies. The amount
of impervious surface in urban areas—such as rooftops, roads, parking lots, and sidewalks—can range
from 35 percent or lower in lightly urbanized areas to nearly 100 percent in heavily urbanized areas.
These changes to the landscape increase pollutant loadings, stormwater runoff volumes, and peak flow
rates in urban streams. Pollutants carried in urban  runoff often reach surface waters without treatment.

The impacts of urbanization on local hydrology can be particularly acute. Urban streams are frequently
flashy, meaning that discharge rates increase rapidly in response to storms, followed by a quick return to
normal after the storm passes. A study in the Piedmont of western Georgia, for example, showed that high
flow pulses and elevated peak discharges were more frequent in urban watersheds than any other land
cover, and baseflow inputs in urban streams were  lower than other watersheds (Schoonover et al. 2006).
Streams in urbanized areas are also often characterized by accelerated bank erosion, channel widening,
and sedimentation (Roy et al. 2010), with much of this due to the destructive energy of large volumes  of
rapidly moving stormwater runoff. The  frequency of flooding is also increased in many cases, particularly
during spring snowmelt and rain-on-snow events (Buttle and Xu 1988, Pitt and McLean 1992). The
combination of pollutants and hydrologic impacts in urban settings tends to produce biotic assemblages of
low diversity dominated by tolerant and nonnative species (Roy et al. 2010). Wide-ranging research
relating  impervious  cover to stream quality has been incorporated within the Impervious Cover Model
                                                1-8
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Monitoring and Evaluating Nonpoint Source Watershed Projects                                      Chapter 1
(ICM), a watershed planning model that predicts that most stream quality indicators decline when
watershed impervious cover exceeds  10 percent, with severe degradation expected beyond 25 percent
impervious cover (CWP 2003). Urbanization can change in-stream processing of nutrients and other
elements through the combined impacts of changes to stream hydrology, sediment texture, organic matter
levels, and stream flora and fauna (Steele et al. 2010).

1.3.3 Removal of Streamside Vegetation
Riparian zones are transitional areas,  containing elements of both aquatic and terrestrial habitats (Knutson
and Naef 1997). Riparian habitat performs many functions, including (Knutson and Naef 1997, USDOI
1991):
  •   providing shade to cool stream  waters;
  «   stabilizing stream banks and controlling erosion and sedimentation;
  «   rebuilding floodplains; and
  •   contributing leaves, twigs, and  insects to streams, thereby providing basic food and nutrients that
      support fish and aquatic wildlife.

Fish also benefit from large trees that fall into streams creating pools, riffles, backwater, small dams, and
off-channel habitat. In addition, riparian areas filter sediments and pollutants from runoff and moderate
stream volumes by reducing peak flows and slowly releasing water to maintain base flows.

Losses of riparian or streamside vegetation are attributed to conversion to farmland, drainage for
agriculture, forest harvesting, channelization, damming, creating impoundments, irrigation diversions,
ground water pumping, and overgrazing (Brinson et al. 1981). Riparian vegetation is also lost due to
urbanization (MSD 2012, Ozawa and Yeakley 2007).

Removal of riparian vegetation cuts off the natural supply of nutrients and energy to biological
communities in low-order streams (USEPA 1991). Terrestrial and aquatic habitat available for shelter,
forage, and reproduction is destroyed, and canopy removal results in increased stream temperatures and
greater temperature fluctuations. Streambank stability is reduced and erosion and sedimentation are
increased when the rooting systems of riparian vegetation are destroyed or removed (Brinson et al. 1981).
In addition, stream flow buffering is reduced, flooding may increase, and in-stream sedimentation and
pollutant loads may increase, all of which can cause severe stress to aquatic plant and animal
communities.

1.3.4 Hydromodification
Hydromodification is the alteration of the hydrologic characteristics of coastal and non-coastal waters,
which in turn could cause degradation of water resources (USEPA 2007). It includes channelization or
channel modification and flow alteration. Channel modification is river and stream channel engineering
undertaken for the purpose of flood control, navigation, drainage improvement, or reduction of channel
migration potential (Brookes 1990). Examples of channel modification include straightening, widening,
deepening, or relocating existing stream channels;  excavation of borrow pits, canals, underwater mining,
and other practices that change the depth, width, or location of waterways or embayments in coastal areas;
and clearing or snagging operations. Channel modification typically results in more uniform channel cross
sections, steeper stream gradients, and reduced average pool depths.
                                               1-9
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Monitoring and Evaluating Nonpoint Source Watershed Projects                                      Chapter 1
Flow alteration describes a category of hydromodification activities that results in either an increase or a
decrease in the usual supply of fresh water to a stream, river, wetland, lake, or estuary. Flow alterations
include diversions, withdrawals, and impoundments. In rivers and streams, flow alteration can also result
from transportation embankments, tide gates, sluice gates, weirs, and the installation of undersized
culverts. Levees and dikes are also flow alteration structures.

Channel modification can deprive wetlands and estuarine shorelines of enriching sediment; change the
ability of natural systems to absorb hydraulic energy and filter pollutants from surface waters; increase
transport of suspended sediment to coastal and near-coastal waters during  high-flow events; increase
instream water temperature; and accelerate the discharge of pollutants (Sherwood et al.  1990).
Channelization can also increase the risk of flooding by causing higher flows during storm events
(USEPA 2007). Hydromodification often diminishes the suitability of instream and riparian habitat for
fish and wildlife through reduced flushing, lowered DO levels, saltwater intrusion, interruption of the life
cycles of aquatic organisms, and loss of streamside vegetation. Dams, for example, can change water
temperatures and impact fish spawning (USEPA 2007).

1.3.5  Mining
Much of the environmental damage caused by mining occurred prior to passage of the Surface Mining
Control and Reclamation Act (SMCRA) of 1977, when standards for environmental protection during
mining operations and the means for reclaiming abandoned mines were generally lacking (Demchak et al.
2004). For example, past practices used to mine silver (Ag) and gold (Au) from low-grade ore generated
large volumes of waste material (spoil) that were dumped at the heads of drainages, potentially serving as
sources of sediment to streams as they weathered overtime (Sidle and Sharma 1996). Mercury (Hg) was
used to separate Au and Ag from ore in the past and is contained in waste piles from the amalgamation
process (Oak Ridge National Laboratory 1993). Numerous pollutants are released from coal and ore
mining. Acid drainage from coal mining contains sulfates, acidity, heavy metals, ferric hydroxide, and silt
(USEPA/USDOI 1995, Stewart and Skousen 2003). The heavy metals released from mining activities
include Ag, arsenic (As), copper (Cu), cadmium (Cd), Hg, lead (Pb), antimony (Sb), and zinc (Zn)
(Horowitz etal. 1993).

While modern-day mining practices are much improved, there remains a need to address the
environmental impacts of past mining practices in many locations. For example, two Section 319
National Nonpoint Source Monitoring Program (NNPSMP) projects were  designed to monitor the effects
of restoration activities on water quality in areas impacted by past mining activities. In Pennsylvania,
monitoring was carried out to determine the effectiveness of remediation efforts designed to counter the
impact of abandoned anthracite mines on the aquatic ecosystem and designated beneficial uses of Swatara
Creek (Cravotta et al. 2010). Impairments were caused both by acid mine drainage and losses of surface
water to the abandoned underground mines. In Michigan's Keweenaw Peninsula efforts are underway to
address problems caused by fine-grained stamp sands from  historic copper mining operations (Rathbun
2007). These sands erode into streams and wetlands and degrade fish and macroinvertebrate communities
by smothering aquatic habitat features and leaching copper  into the water column.

While remediation efforts often result in water quality improvements, solutions are sometimes more
complicated than initially envisioned. For example, acid mine drainage resulting from Cu mining in the
Ducktown Mining District of Tennessee introduced significant amounts of toxic trace metals into
tributaries of the Ocoee River (Lee et al. 2008). Downstream neutralization of acidic water resulted in the
precipitation of iron hydroxides and the sorption of trace metals to the suspended particulates which were
then transported downstream to a lake where they settled on the lake bottom. This sediment layer contains
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elevated levels of Fe, Al, Mn, and trace metals such as Cu, Zn, Pb, Ni, and Co. Study results have shown
that even a modest decrease in pH of the sediment pore water from 6.4 to 5.9 caused significant release of
trace metals to the environment, creating a risk of ingestion by bottom-dwelling aquatic species.

1.3.6  Forestry
Forestry operations can degrade water quality in several ways, with sediment, organic debris, nutrients,
and silvicultural chemicals the major pollutants of concern (Binkley et al. 1999, Michael 2003, Ryan and
Grant 1991). Construction of forest roads and yarding areas, as well as log dragging during harvesting,
can accelerate erosion and sediment deposition in streams, thus harming instream habitats (Ryan and
Grant 1991, USEPA 2015a). Road construction and road use are the primary sources  of NFS pollution on
forested lands, contributing up to 90 percent of the total sediment from forestry operations (USEPA
2015a). Removal of overstory riparian shade can increase stream water temperatures  (USEPA 2015a).
Harvesting operations can leave slash and other organic debris to accumulate in waterbodies, resulting in
depleted dissolved oxygen (DO) and altered instream habitats. Fertilizer applications  can increase nutrient
levels and accelerate eutrophication, whereas pesticide applications can lead to adverse wildlife and
habitat impacts (Brown 1985). Herbicides can be applied with reduced or shorter-term environmental
impact, however, in situations where macroinvertebrate recolonization is rapid and herbicide
concentrations are low and short-lived because of acidic soil and water conditions (Michael 2003).

A review of forest fertilization studies around the world concluded that, in general, peak stream
concentrations of nitrate-N increase  after forest fertilization, with a few studies reporting concentrations
as high as 10-25 milligrams (mg) nitrate (NOs)-N/L (lithium) (Binkley et al. 1999). In addition, the
highest reported annual average NOs-N concentration found was 4 mg N/L. The higher nitrate
concentrations were related to repeated fertilization, use of ammonium nitrate instead of urea, and
fertilization of N-saturated hardwood forests. It was found that phosphate fertilization could create peak
concentrations exceeding 1 mg P/L,  but annual averages remain  below 0.25 mg P/L. A study of the
effects of fertilizer addition to an artificially drained North Carolina pine plantation resulted in the
flushing out of all excess nutrients by three major rain events within 47 days of application (Beltran et al.
2010). Researchers considered this to be a worst-case scenario, however, noting that N concentrations did
not exceed EPA's drinking water standard of 10 mg N/L and loading rates returned to pretreatment or
lower levels as soon as 90 days after fertilization. Still, the results point out the importance of timing of
fertilizer applications to reduce potential losses.

The use of forest lands for application of biosolids and animal wastes has received increased attention in
the literature, reflecting concerns that such applications could increase nutrient loadings from these lands.
For example, a study designed to evaluate the potential for using loblolly pine stands  for poultry litter
application in the South indicated that moderate  application rates (~20 kilograms [kg] N/ hectare (ha),
-92 kg P/ha) can increase tree growth with minimal impacts to water quality (Friend et al. 2006). Higher
application rates (800 kg N/ha, 370 kg P/ha), however, resulted in soil water nitrate levels exceeding
10 mg N/L and P buildup in soils. A study examining surface runoff of N and P in a small, forested
watershed in Washington yielded no evidence of direct runoff of N or P from biosolids into surface
waters (Grey and Henry 2002). This study illustrated the importance of best management practices
(BMPs) as N-based application rates were used and a 20-meter (m) buffer was established around the
creek and all ephemeral drainages. Only 40 percent of the watershed received nutrient applications
(700 kg N/ha,  500 kg P/ha) and the acidic soils were expected to reduce P mobility. Before biosolids
application, however, there was no relationship between discharge and nitrate-N concentration, but within
nine months of application discharge and  nitrate-N concentrations were positively correlated, indicating
the potential for impacts to water quality with continued biosolids applications.
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1.3.7  Construction
Stormwater runoff from construction activities can have a significant impact on water quality (USEPA
2015g). As Stormwater flows over a construction site, it can pick up pollutants like sediment, debris, and
chemicals and transport these to a nearby storm sewer system or directly to a river, lake, or coastal water.
Although construction activities are generally temporary at any given location, polluted runoff from
construction sites can harm or kill fish and other wildlife. Sedimentation can destroy aquatic habitat, high
volumes of runoff can cause stream bank erosion, and debris can clog waterways.

Potential pollutants associated with construction activities include sediment, suspended solids, nutrients,
chemicals, petroleum products, fuel, fertilizers, pesticides, and pH modifying contaminants (e.g., bulk
cement) (WA DOE 2014). The variety of pollutants present and the severity of their effects depend on the
nature of the construction activity, the physical characteristics of the construction site, and the proximity
of surface waters to the construction area.

Soil loss rates from construction sites can be 1,000 times the average of natural soil erosion rates and
20 times that from agricultural lands (Keener et al. 2007).  Even with control measures, waters discharged
from disturbed lands often contain higher than desired concentrations of suspended solids, particularly the
finer particles (Przepiora, et al. 1998). Ehrhart et al. (2002) investigated the effects of sedimentation basin
discharges on receiving streams at three construction sites, reporting that stream sediment concentrations
increased significantly with high levels persisting for at least 100 m below the basin discharge. A two-
year study of runoff from three residential construction sites in Wisconsin showed that pollutant loads
(suspended solids and nutrients) from these sites are variable and site dependent (Daniel et al. 1979).
Compared to an adjacent watershed in dairy agriculture, however, the annual yield of suspended solids
from the construction sites was considerably higher (19.2 vs. < 1 metric ton/ha). Similar differences in
total nutrient yields were also observed between the construction and agricultural sites.

The 10-year Jordan Creek (CT) NNPSMP project compared Stormwater runoff from three urban
watersheds using a paired-watershed design (Clausen  2007). The watersheds were: a developed watershed
serving as the control, a watershed  being developed using traditional practices and subdivision
requirements, and a watershed developed using a BMP approach (e.g., alternative driveway pavement
treatments). The volume of Stormwater runoff from the BMP watershed decreased (-97%) during the
construction period compared to the control watershed while Stormwater runoff from the traditional
watershed increased compared to the control watershed. The concentrations of total suspended solids
(TSS), NO3-N, NH3-N, total Kjeldahl nitrogen  (TKN), and TP increased during construction in the BMP
watershed, with peaks associated with turfgrass development. Because of the decreased Stormwater runoff
volume, however, exports from the BMP watershed generally did not change during the construction
period, except for TSS and TP which increased and Zn which decreased. In the traditional watershed,
concentrations either did not change or, for TKN and TP, declined during construction. Because of the
increased Stormwater runoff volume, however, exports from the traditional site increased for all variables
during construction despite the observation that the erosion and sediment controls used during
construction appeared to work.

Chemical pollutants, such as paints, acids for cleaning masonry surfaces, cleaning solvents, asphalt
products, soil additives used for stabilization, pollutants in wash water from concrete mixers, and
concrete-curing compounds, can also be  carried in runoff from construction sites. When eroded sediment
is transported to nearby surface waters, it can carry with it fertilizers, pesticides, fuels, and other
contaminants and substances that readily attach to soil particles (Keener et al. 2007). Pollutants attached
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to sediment from construction sites can become desorbed quickly and transported in their soluble form
which is often more reactive and bioavailable to organisms (Faucette et al. 2009).

Petroleum products used during construction include fuels and lubricants for vehicles, power tools, and
general equipment maintenance. Asphalt paving also can be harmful because it releases various oils for a
considerable time period after application. Solid waste on construction sites includes trees and shrubs
removed during land clearing and structure installation, wood and paper from packaging and building
materials, scrap metal, sanitary wastes, rubber, plastic, glass, and masonry and asphalt products.

1.3.8 Marinas
Because marinas are located right at the water's edge, there is a high potential for marina waters to
become contaminated with pollutants generated from the various activities that occur there, such as boat
cleaning, fueling operations, and marine head discharge, or from the entry of stormwater runoff from
parking lots and hull maintenance and repair areas into marina basins (USEPA 2015d). Chemicals used to
maintain and repair boats, such as solvents, oils, paints, and cleansers, may spill into the water, or make
their way into waterbodies via runoff (NOAA 2013). Spilling fuel (gasoline or oil) at marinas or
discharging uncombusted fuels from engines also contribute to NFS pollution (McCoy and Johnson
2010). In addition, poorly maintained sanitary waste systems aboard boats or poorly maintained pump-out
stations at marinas can significantly increase bacteria and nutrient levels in the water.

Studies have shown that boats can be a source of fecal coliform bacteria in  estuaries with high boat
densities and poor flushing (Fisher et al. 1987, Gaines and Solow 1990, Milliken and Lee 1990, NCDEM
1990, Sawyer and Golding 1990, Seabloom et al. 1989). Fecal coliform levels in marinas and mooring
fields become most elevated during periods of high boat occupancy and usage, such as holiday weekends.
In addition, DO levels in marina basins can be lowered by inadequate water circulation and the
decomposition of organic materials from sources such as sewage and fish waste.

Both the construction and design of marina or port construction can negatively affect the ecology of an
area; effects include loss of habitat and alterations to local hydrodynamics.  Protective measures like
bulkheads, breakwaters and jetties are built near marinas to prevent damage to boats and shoreline
structures, but these structures can have unintended water quality impacts. Both the attenuation of waves
by in-water structures and the creation of waves by the increased boat traffic in marinas and ports affect
shoreline processes, often result in increased turbidity, resuspension of sediment-bound pollutants, and
increased shoreline erosion (USFWS 1982).

Metals and metal-containing compounds are contained in fuel additives, antifouling paints, ballast, and
other marina structures. Arsenic is used in paint pigments, pesticides, and wood preservatives. Zn anodes
are used to deter corrosion of metal hulls and engine parts (McCoy  and Johnson 2010). Cu and tin (Sn)
are used as biocides in antifoulant paints (McCoy and Johnson 2010). Other metals (Fe, chrome, etc.) are
used in the construction of marinas and boats. These metals are released to  marina waters through
spillage, incomplete fuel combustion, wear on boat hulls and marina structures, and boat bilge discharges
(McCoy and Johnson 2010, NCDEM 1990). Elevated levels of Cu, Zn, Cd, Cr, Pb, Sn, and PCBs have
been found in oysters, other bivalves, and algae in some marinas (CARWQCB 1989, Marcus and Stokes
1985, McMahon 1989, NCDEM 1990, Nixon et al. 1973, SCDHEC 1987, Wendt et al. 1990, Young et al.
1979).
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1.4  Solving the Problem

A wide range of federal, state, and local efforts with varying objectives, methods, and resources have
been employed over the past few decades to address NFS problems at the local to national levels. A
program central to many of these efforts is EPA's NFS program authorized under Section 319 of the
CWA. Under this program, states, territories and tribes receive grant money that supports a wide variety
of activities including technical assistance, financial assistance, education, training, technology transfer,
demonstration projects and monitoring to assess the success of specific NFS implementation projects.
Federal funds allocated under Section 319(h) of the CWA are distributed based on a state-by-state
allocation formula to implement approved nonpoint source management programs. Section 319 funding
grew from its initial funding level of $37 million in FY1990 to $238.5 million in FY2003, dropping back
to $164.5 million in FY2012. Additional information on Section 319, including success stories,  is
available at EPA's website.

While the Section 319 program is a very important part of efforts to solve the NFS problem, there are
numerous other programs and activities that are carried out in conjunction with or separate from Section
319 to address various aspects of the problem. Information about state programs can be found at EPA's
NPS program website. Other examples include:
  •   The new Urban Waters Federal Partnership was designed to reconnect urban communities with
      their waterways by improving coordination among federal agencies and collaborating with
      community-led revitalization efforts to improve our nation's water systems and promote their
      economic, environmental and social benefits (USEPA 2015h). Stormwater runoff is one of several
      sources of pollution in urban settings creating public and environmental health hazards such as
      lowered drinking water quality and water bodies that are unsafe for swimming.
  •   The U.S. Department of Agriculture (USDA) Natural Resources Conservation Service's (NRCS)
      Environmental Quality Incentives Program (EQIP) is a voluntary program that provides financial
      and technical assistance to agricultural producers through contracts up to a maximum term of
      10 years in length. These contracts provide financial assistance to help plan and implement
      conservation practices that address natural resource concerns and for opportunities  to improve soil,
      water, plant, animal, air and related resources on agricultural land and non-industrial private
      forestland. In addition, a purpose of EQIP is to help producers meet federal, state, tribal and local
      environmental regulations.
  •   Under the USDA's National Water Quality Initiative, the NRCS works with farmers and ranchers
      in small watersheds throughout the nation to improve water quality where this is a  critical concern.
      In 2013, NRCS will provide nearly $35 million in financial assistance to help farmers and ranchers
      implement conservation systems to reduce N, P, sediment and pathogen contributions from
      agricultural land. This is the second year of the initiative; NRCS provided $34 million in 2012.
  •   Efforts that help define the problem also support NPS programs. For example, in 2011, numeric
      nutrient water quality standards were established for lakes and flowing waters in Florida to address
      harmful  algal blooms caused by excess nutrients from fertilizer, stormwater, and wastewater runoff
      (FLDEP2015).
  •   Hundreds of local projects across the nation are addressing various NPS problems.  For example,
      alum (aluminum sulfate) treatments and upland nutrient management practices have been employed
      in the Grand Lake St. Marys watershed in western Ohio to address hypereutrophic  conditions
      caused by high inflows of P (Tetra Tech 2013).
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Beltran, B.J., D.M. Amatya, M. Youssef, M. Jones, T.J. Callahan, R.W. Skaggs, and J.E. Nettles. 2010.
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Binkley, D., H. Burnhamb, and H.L. Allen. 1999. Water quality impacts of forest fertilization with
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Brinson, M.M., B.L. Swift, R.C. Plantico, and J.S. Barclay.  1981. Riparian Ecosystems: Their Ecology
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Brookes, A. 1990. Restoration and enhancement of engineered river channels: some European
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Brown, R.G. 1985. Effects of an urban wetland on sediment and nutrient loads in runoff. Wetlands
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Buttle, J.M., and F. Xu. 1988. Snowmelt runoff in suburban environments. Nordic Hydrology 19:19-40.

CARWQCB (California Regional Water Quality Control Board).  1989. Nonpoint Source Experience in
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Clausen, J.C. 2007. Jordan Cove Watershed Project Final Report. University of Connecticut, College of
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Cravotta III, C.A., R.A. Brightbill, and M.J. Langland. 2010. Abandoned mine drainage in the Swatara
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Daniel, T.C., P.E. McGuire, D. Stoffel, and B. Miller.  1979. Sediment and nutrient yield from residential
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Ehrhart, B.J., R.D. Shannon, and A.R. Jarrett. 2002. Effects of construction site sedimentation basins on
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Faucette, L.B., F.A. Cardoso-Gendreau, E. Codling, A.M. Sadeghi, Y.A. Pachepsky, and D.R. Shelton.
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Gaines, A.G., Jr., and A.R. Solow.  1990. The Distribution of Fecal Coliform Bacteria in Surface Waters
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Goolsby, D.A., W.A. Battaglin, B.T. Aulenbach, and RP. Hooper. 2001. Nitrogen input to the Gulf of
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Grey, M. and C. Henry. 2002. Phosphorus and nitrogen runoff from a forested watershed fertilized with
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Horowitz, A.J., K.A. Elrick, J.A.  Robbins, and RB. Cook. 1993. The Effect of Mining and Related
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Keener, H.M., B. Faucette, and M.H. Klingman. 2007. Flow-through rates and evaluation of solids
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Knutson, K.L. and V.L. Naef 1997. Management Recommendations for Washington's Priority Habitats:
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Lee, G., J.M. Bigham, and D.J. Williams. 2008. Metal release from bottom sediments of Ocoee Lake No.
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Marcus, J.M., and T.P. Stokes. 1985. Polynuclear aromatic hydrocarbons in oyster tissue and around three
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Przepiora, A., D. Hesterberg, J.E. Parsons, J.W. Gilliam, O.K. Cassel, and W. Faircloth. 1998. Field
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Sherwood, C.R., D.A. Jay, R.B. Harvey, P. Hamilton, and C.A. Simenstad.  1990. Historical changes in
       the Columbia River Estuary. Progress in  Oceanography 25:299-352.

Sidle, R.C. and A.Sharma 1996. Stream channel changes associated with mining and grazing in the Great
       Basin. Journal of Environmental Quality  25:1111-1121.

Steele, M.K., W.H. McDowell, and J.A. Aitkenhead-Peterson. 2010.  Chemistry of urban, suburban, and
       rural surface waters. In Urban Ecosystem Ecology, Agronomy Monographs 55, ed. J. Aitkenhead-
       Peterson and A. Voider. American Society of Agronomy, Crop Science Society of America, Soil
       Science Society of America, Madison, WI.

Stewart, J. and J. Skousen 2003. Water quality changes in a polluted  stream over a twenty-five-year
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TetraTech. 2013. Preliminary Assessment of Effectiveness of the 2012 Alum Application—Grand Lake
       St. Marys. Tetra Tech, Inc., Fairfax, VA. Accessed March 24, 2016.
       http://www.lakeimprovement.com/sites/default/files/GLSM%20Alum%20Report%2002202013
       .pdf.
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       Census Of Agriculture - United States Summary and State Data. Geographic Area Series, Volume
       1, Part 51, AC-12-A-51. U.S. Department of Agriculture, National Agricultural Statistics Service,
       Washington, DC. Accessed January 8, 2016. http://www.agcensus.usda.gov/Publications/20121.

USDOI (U.S. Department of the Interior). 1991. Riparian-Wetland Initiative for the I990's.
       BLM/WO/GI-91/001+4340. U.S. Department of the Interior, Bureau of Land Management,
       Washington, DC.

USEPA (U.S. Environmental Protection Agency). 1991. Characteristics of Successful Riparian
       Restoration Projects in the Pacific Northwest. EPA 910/9-91-033. U.S. Environmental Protection
       Agency, Region 10, Water Division, Nonpoint Sources Section, Seattle, WA.

USEPA (U.S. Environmental Protection Agency). 2005. National Management Measures to Control
       Nonpoint Source Pollution from Urban Areas. EPA-841-B-05-004. U.S. Environmental
       Protection Agency, Office of Water, Washington, DC. Accessed January 8, 2016.
       http://www.epa.gov/polluted-runoff-nonpoint-source-pollution/urban-runoff-national-
       management-measures.

USEPA (U.S. Environmental Protection Agency). 2007. National Management Measures to Control
       Nonpoint Source Pollution from Hydromodification. EPA  841-B-07-002. U.S. Environmental
       Protection Agency, Office of Water, Washington, DC. Accessed January 8, 2016.
       http: //www. epa.gov/polluted-runoff-nonpoint-source -pollution/hydromodification-and-habitat-
       alteration-national .

USEPA (U.S. Environmental Protection Agency). 2009. National Lakes Assessment: a Collaborative
       Survey of the Nation's Lakes. EPA-841-R-09-001. U.S. Environmental Protection Agency, Office
       of Water and Office of Research and Development, Washington, DC. Accessed January 11, 2016.
       http://www.epa.gov/national-aquatic-resource-surveys/national-lakes-assessment-2007-results.

USEPA (U.S. Environmental Protection Agency). 2010. National Lakes Assessment Technical Appendix:
       Data Analysis Approach. EPA-841-R-09-001a.  U.S. Environmental Protection Agency, Office of
       Water and Office of Research and Development, Washington, DC. Accessed January 11, 2016.
       http://www.epa.gov/national-aquatic-resource-surveys/national-lakes-assessment-2007-results.

USEPA (U.S. Environmental Protection Agency). 2012a. National Coastal Condition Report IV. EPA-
       842-R-10-003. U.S. Environmental Protection Agency, Office of Research and Development and
       Office of Water, Washington, DC. Accessed January 11, 2016.
       http://water.epa.gOv/tvpe/oceb/assessmonitor/nccr/upload/0 NCCR 4 Report  508 bookmarks.pdf

USEPA (U.S. Environmental Protection Agency). 2012b. NPDES Permit Writers' Manual for
       Concentrated Animal Feeding Operations. EPA 833-F-12-001. U.S. Environmental Protection
       Agency, Office of Water, Office of Wastewater Management, Washington, DC. Accessed
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                                              1-19
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       January 11, 2016. http://www.epa.gov/polluted-runoff-nonpoint-source-pollution/nonpoint-
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       http://ofmpub.epa.gov/waterslO/attains nation  cy.control.
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       Progress Report. Statement of Mutual Intent Strategic Plan for the Restoration and Protection of
       Streams and Watersheds Polluted by Acid Mine Drainage from Abandoned Coal Mines. U.S.
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       Surface Mining, Appalachian Regional Coordinating Center, Pittsburgh, PA.

USFWS (U.S. Fish and Wildlife Service). 1982. Mitigation and Enhancement Techniques for the Upper
       Mississippi River System and Other Large River Systems. Resource Publication 149.
       U.S. Department of the Interior, U.S. Fish and Wildlife Service, Washington, DC.

WA DOE (Washington State Department of Ecology). 2014. 2072 Stormwater Management Manual for
       Western Washington as Amended in December 2014. Publication No.  14-10-055. Washington
       State Department of Ecology, Water Quality Program. Accessed April 21, 2016.
       https ://fortress .wa. gov/ecy/publications/Summary Pages/1410055 .html.

Wendt, P.H., R.F. van Dolah, M.Y. Bobo, and J.J. Manzi. 1990. Effects of Marina Proximity  on Certain
       Aspects of the Biology of Oysters and Other Benthic Macrofauna in a  South Carolina Estuary.
       Technical Report No. 74. South Carolina Marine Resources Center, Charleston, SC.

Young, D.R., G.V. Alexander, and D. McDermott-Ehrlich. 1979. Vessel-related contamination of
       southern California harbors by copper and other metals. Marine Pollution Bulletin 10:50-56.
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Monitoring and Evaluating Nonpoint Source Watershed Projects                                    Chapter 2
2   Nonpoint Source Monitoring Objectives and

     Basic Designs

     By S.A. Dressing, D.W. Meals, J.B. Harcum, and J. Spooner

Water quality monitoring is performed to support a wide range of programs. National-level monitoring
with continuous water-quality monitors is performed by the USGS, for example, to assess the quality of
the Nation's surface waters (Wagner et al. 2006). Studies of large basins such as the Mississippi River
Basin and Great Lakes Basin are designed to assess the general condition of waterbodies, track the health
of fisheries, identify the causes and sources of designated beneficial use support impairments, and aid in
the design of programs and projects to solve identified problems. EPA, states, and tribes conduct a series
of surveys of the nation's aquatic resources that can also be used to track changes in condition over time.
Each year the survey focuses on a different aquatic resource (i.e., rivers and streams, lakes, wetlands, or
coastal waters) to yield unbiased estimates of the condition of the whole water resource being studied.1
Monitoring  of smaller watersheds is done for a number of purposes, including assessing local water
quality problems, developing watershed plans to address current and prevent future problems, and
educating the public about the water environment. Monitoring of individual practices or BMPs is
typically carried out to determine the effectiveness of the particular practices, provide data for the
development or validation of watershed modeling tools, document efforts to address watershed-scale
problems, and inform stakeholders.

2.1   Monitoring Objectives

Monitoring  is an information gathering exercise that is intended to generate data that serve management
decision-making needs (USEPA 2003a). The formulation of clear monitoring objectives is an essential
first  step in  developing an efficient and effective monitoring plan. Monitoring supports a range of water
quality management objectives including establishing water quality standards, determining water quality
status and trends, identifying impaired waters, identifying causes and sources of water quality problems,
and evaluating program effectiveness. Specific objectives appropriate for NPS monitoring plans include:
  •  Identify water quality problems, use impairments, causes, and pollutant sources.
  •  Develop TMDLS and load or wasteload allocations.
  •  Analyze trends.
  •  Assess the effectiveness of BMPs or watershed projects.
  •  Assess permit compliance.
  •  Validate or calibrate models.
  •  Conduct research.
1 http://water.epa.gov/tvpe/watersheds/monitoring/aquaticsurvev index.cfm
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Monitoring and Evaluating Nonpoint Source Watershed Projects
                                     Chapter 2
All monitoring programs should be designed to answer questions. The design process begins with
identifying the problem and setting objectives that pose the questions. Then an experimental design
appropriate to those objectives is selected and decisions related to sample type, sampling locations, which
variables to monitor, and how to collect and analyze the samples that need to be made. Because the
purpose of NFS monitoring is often to evaluate practice effectiveness and programs taking place on the
land, land use and land management monitoring is an integral part of the overall effort. Finally,
management and analysis of data gathered through the monitoring program must also be incorporated.
                                                   Overview of Steps in Monitoring Design
                                                    1.
                                                    2.
                                                    3.
                                                    4.
                                                    5.
                                                    6.
                                                    7.
                                                    8.
                                                    9.
     Identify problem(s)
     Form objectives
     Design experiment
     Select scale
     Select variables
     Choose sample type
     Locate stations
     Determine sampling frequency
     Design stations
  10. Define collection/analysis methods
  11. Define land use monitoring
  12. Design data management
(USDA-NRCS 2003)
The specific steps essential in designing a
monitoring program to meet objectives are
discussed in detail in this chapter and chapter 3.

EPA and states need comprehensive water quality
monitoring and assessment information to help set
levels of protection in water quality standards.
This information will also help to identify
emerging problem areas or areas that need
additional regulatory and non-regulatory actions to
support water quality management decisions such
as TMDLs, NPDES permit limits, enforcement,
and NFS management (USEPA 2003a). Statewide
monitoring to assess the degree to which
designated beneficial uses (e.g., drinking,
swimming, aquatic life) are supported is an
essential component of efforts to achieve CWA
goals. For example, CWA §106(e)(l) and 40 CFR
Part 35.168(a) provide that EPA award Section
106 funds to a state only if the state has provided for or is carrying out the establishment and operation of
appropriate devices, methods,  systems and procedures necessary to monitor and to compile and analyze
data on the quality of its navigable  waters. States must also update the data in the Section 305(b) report.

In accordance with EPA water quality standards regulations, states designate uses for each waterbody or
waterbody segment and establish criteria (e.g., dissolved oxygen [DO] levels, temperature, metals
concentrations) that must be met through their water quality standards programs. Monitoring of the
criteria parameters is then performed to assess whether the criteria are being met (USEPA 2003b).
Biological monitoring of aquatic systems has been increasingly used to assess aquatic life use support.

Because of the magnitude of the task, states generally monitor portions of the state on a rotating basis
(USEPA 2011). Ohio, for example, has a 15-year plan for monitoring all 8-digit FfUCs in the state (OEPA
2013). Each year Ohio EPA collects data from streams and rivers in five to seven different areas of the
state. A total of 400 to 450 sampling sites are examined, and each site is visited more than once per year.
During these studies, Ohio EPA technicians collect chemical samples, examine and count fish and aquatic
insects, and take measurements of the  stream. There are three major objectives for the studies:
  •   Determine how the stream is doing compared to goals assigned in the Ohio Water Quality
      Standards;
  •   Determine if the goals assigned to the river or stream are appropriate and attainable; and
  •   Determine if the stream's condition has changed since the last time the  stream was monitored.
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Monitoring and Evaluating Nonpoint Source Watershed Projects
Chapter 2
The findings of each biological and water quality study can be used in Ohio EPA regulatory actions. The
results are incorporated into Water Quality Permit Support Documents, State Water Quality Management
Plans, and the Ohio Nonpoint Source Assessment. This information also provides the basis for the
Integrated Water Quality Monitoring and Assessment Report - a biennial statewide report on the
condition of Ohio's waters and the list of impaired and threatened waters required by sections 303(d) and
305(b) of the Clean Water Act.

The growing linkage between the national NPS and TMDL programs has resulted in a greater emphasis
on estimating pollutant loads (USEPA 2003b). Specific considerations and recommendations for
monitoring to estimate pollutant loads are presented in section 3.8. Development of load-duration curves
has become an important step in developing TMDLs for many watersheds, and this topic is addressed in
detail in section 8.9.4.

Well-formulated monitoring objectives drive the rest of the monitoring study design and are critical to a
successful monitoring project (USDA-NRCS 2003). It is also important that monitoring objectives  are
directly linked to overall program or project objectives that depend on the monitoring data. Table 2-1
illustrates this important linkage between program and monitoring objectives.

                  Table 2-1. Complementary program and monitoring objectives
Program Objective
Reduce annual P loading to lake by at least 15 percent in 5
years with nutrient management
Reduce E. coli load to stream to meet water quality standards
in 3 years
Complementary Monitoring Objective
Measure changes in annual P loading to lake and link to
management actions
Measure changes in compliance with water quality standard
for E. coli
A good example to illustrate the development of program goals, supporting monitoring goals, and specific
monitoring designs is the USGS National Water-Quality Assessment Program (NAWQA). NAWQA was
designed to assess the status and trends in the nation's ground- and surface-water resources and to link the
status and trends with an understanding of the natural and human factors that affect water quality (Gilliom
et al., 1995). The study design balanced the unique assessment requirements of individual hydrologic
systems with a nationally consistent design structure that incorporated a multi-scale, interdisciplinary
approach. The Occurrence and Distribution Assessment, the most important component of the first
intensive study phase in each of the NAWQA study units, was intended to characterize, in a nationally
consistent manner, the broad-scale geographic and seasonal distributions of water-quality conditions in
relation to major contaminant sources and background conditions. General goals for study-units were:
  •  Identify the occurrence of water quality conditions that are significant issues.
  •  Characterize the broad-scale geographic and seasonal distribution of a wide range of water quality
      conditions in relation  to natural factors and human activities.
  •  Evaluate study-unit priorities and required study approaches for effectively assessing long-term
      trends and changes.
  •  Evaluate geographical and seasonal distribution in greater detail and in relation to the sources,
      transport, fate and effects of contaminants for water quality conditions of greatest importance.
      Determine the priorities and design for follow-up case studies.
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Monitoring and Evaluating Nonpoint Source Watershed Projects                                      Chapter 2
USGS developed a study design to address each of the above goals for stream monitoring, employing
three interrelated components: water-column, bed sediment and tissue, and ecological studies. Ecological
studies incorporate three strategies:
  •   Fixed-Site Reach Assessments provide nationally consistent ecological information at water
      column sites as part of an integrated physical, chemical, and biological assessment of water quality.
  •   Intensive Ecological Assessments assess spatial and temporal variability associated with biological
      communities and habitat characteristics.
  •   Ecological Synoptic Studies provide improved spatial resolution of selected ecological
      characteristics in relation to land uses, contaminant sources, and habitat conditions.

While the NAWQA program is an exceptionally large monitoring program in both scope and scale, the
approach of linking monitoring to program goals and developing clear and specific monitoring goals and
objectives to drive monitoring program design is applicable to most if not all monitoring efforts.

2.2  Fundamentals of Good  Monitoring
Water quality monitoring is a complex and demanding enterprise. Conducted well, monitoring can
provide fundamental information about the water resource and its impairments. Monitoring data can allow
managers to document changes through time, show response to NFS pollution reduction practices and
programs, and confirm achievement of management objectives. Conducted poorly, monitoring can fail to
meet objectives, create confusion, leave critical questions unanswered and waste time and money. It is
essential that monitoring be designed to meet project and program objectives efficiently. The purpose of
this section is to present key elements of good monitoring design and execution.

2.2.1 Understand the  System
When little is known about a watershed, monitoring may be used to assess the problem. For this purpose,
monitoring requires a fairly general approach, e.g., reconnaissance or synoptic sampling (see section
2.4.2.1). When designing a monitoring program to assess a response to nonpoint source control programs,
a thorough understanding  of the system (e.g., a farm, an urban catchment, or a rural watershed) is
required. An early step in watershed planning and management is characterization of the watershed,
which includes topography, geology, climate, soils, hydrology, biota, land use, infrastructure, and cultural
resources (USEPA 2008b). This is essential information  about the system and will assist in the design of
the monitoring system. The first of EPA's Nine Key Elements of watershed restoration plans requires
identification of the causes of impairment and the sources of pollutants that will need to be controlled to
achieve the goals of the watershed plan (USEPA 2008b). Basic questions that  should be addressed when
characterizing a watershed include:
  •   What are the critical water quality impairments or  threats?
  •   What are the key pollutants involved?
  •   What are the sources of these pollutants?
  •   How are pollutants transported through the watershed?
  •   What are the most important drivers of pollutant generation and delivery?
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Monitoring and Evaluating Nonpoint Source Watershed Projects                                      Chapter 2


2.2.1.1  Causes and Sources
Decisions on where, when, and how often to sample, what to measure, and other elements of monitoring
design depend on knowledge of the watershed being monitored. The source of pollutants is an obvious
issue. For example, suspended sediment exported from a watershed may arise from upland erosion from
agricultural fields, streambank and streambed erosion, or a combination of both. Knowledge of the source
of suspended sediment measured at the watershed outlet is essential to designing a system to monitor
watershed response to implementation of erosion control measures (as well as in selection of the
appropriate control measures). In Minnesota, for example, Sekely et al. (2002) estimated that streambank
sources accounted for 31 percent to 44 percent of the suspended sediment load at the mouth of the Blue
Earth River. Allmendinger et al.  (2007) reported that upland sediment production and sediment from
enlargement of stream channels were approximately equal sources of sediment yield in an urbanizing
watershed in Maryland. Unusually high mean NOs-N  concentrations (about 5 mg/1 in the major
tributaries) in watersheds that drain into the western and central basins of Lake Erie are thought to reflect
the extensive use of tile drainage systems in the region (Baker 1988).

Indicator bacteria like fecal coliform or E. coll  commonly cause impairment of recreation and shellfish
harvesting in U.S. waters. These organisms can arise from numerous sources in a watershed, including
wildlife, livestock, pets, and human wastes. It is essential to know the source(s) of these organisms both
to apply the right control measures and to monitor response to control programs. For example, in the Oak
Creek Canyon (AZ) NNPSMP project seasonal increases in fecal indicator bacteria were initially believed
to originate primarily from poor  sanitation practices by recreational users and a lack of adequate restroom
facilities (Donald et al. 1998). Bacteriological water quality failed to improve after sanitation BMPs  were
installed, however, and analysis of fecal coliform levels in sediments versus those in the water column led
to the conclusion that resuspension of contaminated sediments by recreational users and major storm
events was the major cause of water quality violations. While sediment was identified as the major
reservoir of bacteria, this analysis fell short of identifying the primary source(s) of the bacteria found in
the sediment. DNA fingerprinting was used to determine the relative contributions of human, livestock,
and wildlife  sources, resulting in the finding that wildlife contributed a greater share  of fecal pollution
than humans. Still, at the conclusion of the NNPSMP  project questions remained regarding the major
sources of fecal contamination in the watershed (Donald et al. 1998, Spooner et al. 2011). A subsequent
study by Poff and Tecle (2002) suggested that domesticated and wild animals, residential homes (septic
systems), and business establishments were probably greater sources of E. coli than recreational visitors.
Monitoring and BMP implementation have continued in the Oak Creek watershed, with establishment of
a TMDL for E. coll in 2010 and completion of a watershed implementation plan in 2012 (OCWC 2012).
Implementation efforts are now focused on education and outreach as the top priority, followed by septic
systems, stormwater, recreation,  and agriculture (OCWC 2012). Continued uncertainty regarding sources
of fecal pollution is reflected by plan recommendations for additional monitoring of Oak Creek sediment
to identify E. coll sediment reservoir hot spots and locate up-gradient sources of E. coll.

2.2.1.2  Pollutant Transport
It is absolutely critical to understand the mode of pollutant transport through the watershed from source to
receiving water before setting up a monitoring system. Particulate pollutants, such as sediment and
attached substances, generally move in surface waters. Monitoring for sediment or particulate phosphorus
is generally best focused on surface runoff and streamflow. Dissolved pollutants, like nitrate-nitrogen
(NOs-N), are transported in both surface and ground waters.  The relative importance of these distinct
pathways needs to be understood to decide where and when to sample. For example in the Chesapeake
Bay watershed, it is estimated that 40 percent of the annual N load to the Bay is delivered by groundwater
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Monitoring and Evaluating Nonpoint Source Watershed Projects                                      Chapter 2


(STAC 2005). As much as 80 percent of annual export of NOs-N, sulfate (SCV2), and chloride (Cl"1) from
small Iowa watersheds occurred in baseflow (Schilling 2002).

In many cases, additional details regarding pollutant pathways must be understood to fine tune monitoring
plans. For example, decisions on whether to focus on high-flow events (e.g., for particulate pollutants
delivered episodically in surface runoff or storm flows) or base flows (e.g., for dissolved pollutants that
tend to be delivered continuously via ground water) require an understanding of how pollutants move
through the system. The Warner Creek (MD) NNPSMP discovered that base flow contributed 76 percent
of total streamflow and that subsurface flow, including a substantial portion from outside the surface
watershed boundaries, was the major pathway for transport of NOs-N to the stream (Shirmohammadi et
al. 1997, Shirmohammadi and Montas 2004). This complex hydrology contributed to the failure of an
above/below monitoring design for this project.

The timing of sampling during storm events can also be informed by knowledge of pollutant pathways.
For example, analysis of long-term data collected in the Lake Erie basin showed that peak concentrations
of particulate and soluble pollutants occurred during different parts of the storm hydrograph (Baker et al.
1985, Baker 1988). TP and sediment concentrations reached their peak early in the runoff event before
peak discharge, and decreased faster than the discharge decreased. The TP concentration did not decline
as rapidly as the sediment concentration due to the presence of soluble P and an increasing ratio of
particulate P to  sediment as the sediment concentration decreased. The atrazine concentration pattern
followed the hydrograph very closely, indicating movement off the fields with surface runoff water.
Nitrate (NOs) increased during the falling limb of the hydrograph due to tile drainage and interflow.

2.2.1.3  Seasonality
Seasonal patterns are often critical factors in monitoring design because NPS pollution is highly weather-
driven. In  northern regions, snowmelt and spring rains are often the dominant hydrologic feature of the
annual cycle and a majority of the annual pollutant load may be delivered in a few weeks. A  seven-year
study on corn-cropped watersheds in southwestern Iowa, for example, showed that most of the average
annual total N and P losses occurred during the fertilizer application, seedbed preparation, and crop
establishment period from April through June (Alberts et al. 1978). February accounted for 23 percent of
the total P load  in a two-year study in the Clear Lake watershed in Iowa, indicating that the snowmelt
period is a time of significant P loss from fields (Klatt et al. 2003). In Wisconsin, Stuntebeck et al. (2008)
stated that it was critical that agricultural NPS monitoring take place year-round to fully characterize
sediment and nutrient losses throughout the year rather than just during the growing season.

For herbicides such as atrazine, losses from agricultural fields in humid areas are highly episodic, with the
majority of annual losses occurring in transient storm events soon after herbicide application. In a
comparative study of agricultural watersheds in different climatic regions, Domagalski et al.  (2007) found
that stormwater runoff after application was the primary determinant of pesticide loads in humid
environments. A significant portion of the load of some pesticide degradation products, however, can be
transported under base-flow conditions in humid environments. In such cases, a monitoring effort would
need to reliably monitor short, intense and unpredictable events during specific seasons, depending on
both seasonal and agronomic factors. Sampling of base flow would be needed to track degradation
products. The same comparative study, however, found that irrigation practices and timing of chemical
use greatly affected pesticide transport in the semiarid basins, suggesting a monitoring effort that would
need to be focused on irrigation events in these regions.
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Monitoring and Evaluating Nonpoint Source Watershed Projects                                      Chapter 2


The importance of characterizing seasonality depends on the specific program and monitoring objectives.
In cases where available water quality data are not sufficient to assess seasonality in a specific watershed,
it may be necessary to perform seasonal synoptic surveys (see section 2.4.2.1), collect year-round samples
initially, or rely on watershed modeling to better define seasonality and facilitate fine-tuning of the
monitoring design.

2.2.1.4  Water Resource Considerations
Each type of water resource—rivers and streams; lakes, reservoirs, and ponds; wetlands; estuaries; coastal
shoreline waters; and ground water—possesses unique hydrologic and biological features that must be
considered in the design of a monitoring program. All water resource types exhibit both temporal (long-
and short-term) and spatial (small- and large-scale) variability. For example, suspended sediment
concentrations vary with depth and location in reservoirs; salinity concentrations in estuaries vary
vertically and horizontally, as well as temporally as they are affected by relatively light fresh water
flowing over heavier salt water; and ground water quality varies with soil and aquifer type and geozone.
Placement of monitoring stations and the timing and duration of sampling are affected by consideration of
these and other sources of variability.

2.2.1.4.1 Rivers and Streams
Streams can be classified at various levels of detail using a number of criteria (e.g., Montgomery and
Buffington 1997, Rosgen and Silvey 1996), but streams can also be lumped for monitoring considerations
into two major groups - intermittent and perennial - based simply on general flow characteristics.
Clearly, water quality sampling cannot be conducted in intermittent streams when they do not have flow;
however, the ability to measure and sample intermittent flows when they do occur is often critical and
usually challenging. Year-to-year variations in precipitation can have major impacts on flow duration and
frequency, pollutant loads, and water quality in intermittent streams. Flow in perennial streams and rivers
is also affected by seasonal rainfall and snowfall patterns, reservoir discharge management, and irrigation
management. Pollutant loads and concentrations, in turn, are affected by these patterns as the highest
concentrations of suspended sediment and nutrients often occur during spring runoff, winter thaws, or
intense rainstorms.

Because good flow measurement is essential to estimating pollutant loads, it is also important to
understand spatial flow patterns in the monitored stream or river. Water velocities vary both horizontally
(e.g., outside vs. inside  meander bends) and vertically with depth (USDA-NRCS 2003, Meals  and
Dressing 2008). In addition, the complexity of currents at obstructions and points of constriction (e.g.,
bridges) makes them poor monitoring sites (Meals and Dressing 2008). Rudimentary stream classification
can be very helpful in predicting a river's behavior from its appearance, which, in turn, can be  useful in
identifying locations for fixed sampling stations. Flow measurement is discussed in greater detail in
section 3.1.3.1.

Flow patterns often play a significant role in determining the variability of water quality, both within a
stream cross-section and throughout a stream reach. Figure 2-1 illustrates the relationship between
pollutant concentrations and the vertical variability of stream velocity (Brakensiek et al. 1979). The
effects of tributary flows must be considered when designing a stream or river monitoring program. Such
flows can add pollutant loads, dilute pollutant loads, and create horizontal gradients. In some cases
mixing below tributary junctions  might be incomplete, with tributary flow primarily following one bank
or forming spatially and temporally persistent plumes or bands (Sommer et al. 2008). If a representative
sample of a river is required, it is important to select a sampling point where the flow is uniform and well-
mixed, without sharp flow variations or distinct tributary inflow plumes. If more detail is required,
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Monitoring and Evaluating Nonpoint Source Watershed Projects
                                                                 Chapter 2
segmentation of a stream into fairly homogeneous segments prior to monitoring might be necessary, with
one to several monitoring stations located in each segment (Coffey et al. 1993). When dividing a stream
into homogeneous segments, both land use and drainage area should be considered because both affect
the quantity and quality of flows.

    Water Surface
f
 v
P
      Channel
                  Average Spatial
                  Concentration
         Spatial
     Concentration
Bed
            Concentration
              Flow
            Velocity
        	  Velocity

Figure 2-1. Vertical sediment concentration and flow velocity distribution
in a typical stream cross section (after Brakensiek et al. 1979)

Vertical variability is particularly important during runoff events and in slow-moving streams because
suspended solids, dissolved oxygen (DO), and algal productivity can vary substantially with depth (Figure
2-2) (Brakensiek et al. 1979). Levels of contaminants in bed sediment also vary horizontally and
vertically, as deposition and scouring are strongly influenced by water velocity.

Biological communities in stream systems vary with a number of factors including landscape position,
type of substrate, light, water temperature, current velocity, and amount and type of aquatic and riparian
vegetation. Monitoring of aquatic communities is discussed in chapter 4.
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Monitoring and Evaluating Nonpoint Source Watershed Projects
                                                         Chapter 2
          By Particle
              Size
          Transport Rates
 By Method
of Transport
By Sampling
 Capability
                     Saltation
                     Load \
                               Suspended
                                 Load
                     Measured
                       Load
                                                         Unmeasured
                                                         Load
                        Total
                        Load
Figure 2-2. Schematic diagram of stream vertical showing position of sediment load terms
(after Brakensiek et al. 1979)

2.2.1.4.2 Lakes, Reservoirs, and Ponds
Lakes are defined here as natural standing or slow-moving bodies of water. Reservoirs are considered to
be human-made lakes typically created by impounding a river or stream. Ponds can be either natural or
human-made, and are generally much smaller and shallower than lakes. The following discussion focuses
primarily on lakes and reservoirs with lake referring to both types of water bodies.

Lakes are more than simple bowls of water. The physical, chemical, and biological characteristics of lakes
vary horizontally, vertically, seasonally, and throughout the day. In addition, reservoirs can exhibit
characteristics of both rivers and lakes, with the upstream section more river-like and downstream areas
near the dam more lake-like. The balance between river and lake characteristics can vary widely among
reservoirs with some more river-lake throughout. This variability must be understood and considered
when designing a lake monitoring program.

Hydrology and geomorphology are strong determinants of the physical, chemical, and biological
characteristics of lakes (Wetzel 1975). Lakes can be classified based on how water enters and exits the
lake: seepage lakes, spring lakes, groundwater drained lakes, drainage lakes, and impoundments (WAL
2009). Knowledge of the primary sources of water and the presence or absence of inlets and outlets is
essential to determining options for an effective monitoring plan.

Lake shape has major implications for monitoring design. Lakes and ponds with simple, rounded shapes
may tend to be well-mixed at most times and might require only a single sampling station to provide an
accurate representation of water quality. Lakes with complex interconnected basins or with dendritic
shapes like reservoirs tend to exhibit significant spatial variability as mixing is inhibited; such lakes may
require numerous sampling stations to represent more uneven water quality characteristics (USEPA
                                              2-9
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Monitoring and Evaluating Nonpoint Source Watershed Projects
                                         Chapter 2
1990). In Lake Champlain (VT-NY-Quebec) for example, the lake's complex geometry of bays, islands,
and bathymetry generally divide the lake into five distinct regions for monitoring (Figure 2-3).
         N
        A
       10
                         QUEBEC
                        South Lake
                        NY
           H Miles
                                                  Mssisquoi Bay
 :  Long Term Water Quality Monitoring
•  Emerging Contaminants Monitoring
•  Mercury Monitoring
                                               vr
Figure 2-3. Map of water quality monitoring stations in Lake Champlain lake regions
(Lake Champlain Basin Program)
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Monitoring and Evaluating Nonpoint Source Watershed Projects                                       Chapter 2
Tributary inflows and effluent discharge points also contribute to horizontal variations in lake water
quality. Localized inputs of large water and/or pollutant loads - e.g., suspended sediment from a large
tributary river basin draining agricultural land or a nutrient load from a WWTP - can influence localized
water quality, especially in a confined bay. Locations of such discharges are key factors in placing
monitoring stations - either to deliberately sample them to represent important localized impairments or
distinct components of total lake inputs, or to deliberately avoid them as unrepresentative of the broad
lake, depending on program objectives.

Vertical variability in lakes can affect water quality and consequently monitoring design choices.
Uniformly shallow lakes such as Grand Lake St. Marys in Ohio (GLWWA 2009) tend to be well-mixed
vertically and have extensive photic zones, yielding a fairly homogeneous water column that can be
effectively sampled at a single depth. Deeper lakes tend to stratify seasonally because of the temperature-
density properties of water (Figure 2-4). Vertical stratification in lakes and reservoirs depends largely on
depth, temperature, and seasonality, all of which should be included as covariates when monitoring lakes.
When stratification is strong, the upper waters (epilimnion) may exhibit water quality characteristics
(e.g., warm temperatures, high DO, low dissolved P) very different from those of the lower waters
(hypolimnion) (e.g., cold temperatures, low DO, high dissolved P) because the two layers do not mix
readily for long periods of time. This stratification breaks down in many lakes during fall and spring,
when the water column mixes due to wind (turnover) and water quality is more uniform vertically.
Depending on study objectives (e.g., monitoring algae populations in the epilimnion or measuring oxygen
depletion in the hypolimnion), monitoring at different points  with depth during periods of peak
stratification may be appropriate. Alternatively, sampling during the periods when the water column is
completely mixed (e.g., at spring or fall turnover) may yield information on the general character of the
lake for that year. Some mass-balance lake P models, for example, use P concentration at spring turnover
to represent the overall nutrient status of the lake.

Vertical variability is also important in lake biological monitoring. Chlorophyll levels and phytoplankton
populations are naturally concentrated in the upper waters where sunlight can penetrate (the photic zone).
However, zooplankton are mobile and show diurnal vertical migrations, moving up in the water column at
night to feed and down during the day to avoid predators (Lampert  1989, Stich and Lampert 1981, Zaret
and Suffern 1976).

Lake currents (primarily wind and inflow-driven) influence the dispersal of pollutants in a lake. In a
reservoir, pollutant concentrations may exhibit a longitudinal gradient as circulation is dominated by inflow
from the main tributary and outflow at the dam. Conditions in  small embayments can be very different from
conditions in open water. These conditions are due to circulation patterns caused by prevailing winds if
currents tend to retain pollutants in the bay and inhibit mixing  with the main lake waters.

Finally, sediment/water interactions exert strong controls on some pollutant dynamics in lakes.
Concentrations of pollutants like P or toxic compounds that are strongly adsorbed to sediment particles
can vary  across the lakebed as sediments delivered from large tributary river basins settle around tributary
mouths or are moved by currents into deeper lake regions. These dynamics may lead to hot-spots of high
sediment pollutant levels that could be important for biological monitoring. In some cases, bottom
sediments store pollutants like P for long periods as particles settle out over time or even sorb P from the
water. In other cases, (especially where bottom waters are low in oxygen), P and other pollutants can be
released from lake sediments to add to the lake pollutant load. Consequently, sediment remediation is
sometimes part of efforts to reduce in-lake P concentrations,  e.g., dredging (GLWWA 2009) or alum
treatment (Welch and Cook 1999).
                                               2-11
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Monitoring and Evaluating Nonpoint Source Watershed Projects
Chapter 2
                EPILIMNIONOR MIXED LAYER-WARM LIGHT)WATER

                           METALIMNION
                                                                   ttfti-:*.:
                                                               /4: xMCte
                                                          x^p^j?^
 $& s; tf.v^:^ ^¥Sv5^^^^            -:;WJ^
 fe^^*;^£^: J^-?^:: ^^^^^T^ ;x-:t
                                 32    41    50     59    68    77

                                         DEGREES FARENHEIT

                                  0        4
                                       DISSOLVED OXYGEN (mg/L)
Figure 2-4. Thermally stratified lake in mid-summer (USEPA 1990). Curved solid line is water
temperature. Open circles are DO in an unproductive (oligotrophic) lake and solid circles are DO
in a productive (eutrophic) lake.

2.2. 1.4.3 Wetlands
Since 1979, the Fish and Wildlife Service's definition of a "wetland" has been accepted as a standard for
purposes of collecting information on the location, characteristics, extent, and condition of wetlands
(Tiner 2002):
           are lands transitional between terrestrial and aquatic systems where the water table is
   usually at or near the surface or the land is covered by shallow water. For purposes of this
   classification wetlands must have one or more of the following three attributes: 1) at least
   periodically, the land supports predominantly hydrophytes (plants adapted to grow in water or hydric
   soils); 2) the substrate is predominantly undrained hydric soil (waterlogged or flooded soils); and
   3) the substrate is nonsoil and is saturated with water or covered by shallow water at some time
   during the growing season of each year. "
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Monitoring and Evaluating Nonpoint Source Watershed Projects
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Three factors (hydrology, the presence of hydric soils, and the presence of hydrophytic vegetation) largely
determine the characteristics of wetlands, but hydrology is considered the master variable of wetland
ecosystems, driving the development of wetland soils and leading to the development of the biotic
communities (USEPA 2004). All three factors, however, serve as the foundation of any wetland condition
assessment method.

Wetlands can occur at numerous landscape positions (Figure 2-5) and are often classified according to
differences in hydrologic conditions (source of water, hydroperiod, hydrodynamics), vegetation
(emergent, shrub-scrub), topography (depressional, riverine), and to a lesser degree, soils (muck, peat,
unconsolidated) (USEPA 2004). Within the context of assessing wetland condition, classification is
intended to reduce variability within a class and enable more sensitivity in detecting differences among
impacted and impaired wetlands within the same classification. Different classes of wetlands may be
subject to different stressors and may vary in their relative susceptibility to particular stressors.
   Wetlands & Waterways
   of the Inland Bays Watershed
   ' 'i| ^ c'osest to landuses such as development and receive the highest
    concentrations of pollutants.
   . Forested buffers filter pollutants from surface water runoff and groundwater.
   . The roots, leaves, and branches from thef crested buffers slows water
    in thechannelfiltering morenutrients and decreasing pollution downstream.
                                        Larger
                                        SfnCtfin
                                        JHvvrfa* Wetfowfr
     . Arefed mostly by groundwater and floodwaters from u pstream.
     . 7h e wetlands fi rter pollutants and storefloodwatercfrom the stream.
     • Forested buffers protect stream channels and their wetlands because they
     work together to fitter nutrients.
                                                                     =
• Are very importantfor habitat and water quality, but marry are not legal ry protected
• In 'winter and summer they store and filter ground and surface water.
• In summerthey also can supply clean water to drinking water aquifers.
                                        Depmsional
                                        Wrtfanub
                                                       Sattmarshes filter and store great amounts of nutrients in their grasses and soils.
                                                       Saltmarshesneed wide buffers because they move landward as sea level rises.
                                                       Rising sea level reducessatt marsh area, which reduces capacity to filter nutrients.
                                                       Sea levels are expected to rise faster in the coming yean.

Figure 2-5. Wetlands and waterways of the Inland Bays watershed (DE CIB n.d.)

Due to the tremendous diversity among natural wetlands, a wetland monitoring program needs to be
based on a specific wetland's attributes. Strategies for designing an effective monitoring program build
from a hierarchy of three levels that vary in intensity and scale, ranging from broad, landscape-scale
assessments (Level 1), to rapid field methods (Level 2), to intensive biological and physico-chemical
                                                    2-13
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Monitoring and Evaluating Nonpoint Source Watershed Projects
                     Chapter 2
measures at Level 3 (USEPA 2004). One of the key considerations for wetlands monitoring is definition
of the assessment area, whether it is the entire wetland or a portion of the wetland. Rapid assessment
procedures have been shown to be sensitive tools to assess anthropogenic impacts to wetland ecosystems,
and can therefore be used to evaluate best management practices, to assess restoration and mitigation
projects, to prioritize wetland related resource management decisions, and to establish aquatic life use
standards for wetlands.

USEPA's 2006 Application of Elements of a State Water Monitoring and Assessment Program for
Wetlands provides states with information to plan a wetland monitoring program and includes a
discussion of the selection of indicators and metrics that reflect the unique characteristics of wetlands and
their response to human-induced disturbance (USEPA 2006a). Several "modules" have been developed
by EPA to support development of biological assessment methods to evaluate the overall condition and
nutrient enrichment of wetlands (USEPA 2002b). These modules can be found at
http://water.epa.gov/scitech/swguidance/standards/criteria/nutrients/wetlands/index.cfm.

Finally, because they are so biologically productive, wetlands tend to cycle sediment, nutrients, and other
pollutants very actively among physical (e.g., sediment), chemical (e.g., water column), and biological
(e.g., vegetation) compartments. Therefore, in a wetland monitoring program it may be  important to look
at each of these compartments, not treat the wetland as a simple input-output box. Moreover, because
vegetation is a key element of wetland systems, seasonality of vegetation growth and senescence may be
an important driver for nutrient cycling and therefore for monitoring design (USEPA 2002a).

2.2.1.4.4   Estuaries
Estuaries differ from freshwater bodies largely due to the mixing of fresh water with salt water and the
influence of tides on the spatial and temporal variability of chemical, physical, and biological
characteristics. Incoming tides affect estuaries by pushing salt water shoreward while fresh water is
entering from freshwater systems (Figure 2-6). Fresh water is lighter, so it flows over the top of salt
water, while the tide forces the salt water shoreward and under the inflowing fresh water. Outgoing tides
pull the entire water mass toward the ocean, and the freshwater input fills the gap left by the receding
submerged salt water. These processes affect daily and seasonal salinity distributions.
  River
Ocean
Figure 2-6. Mixing of salt water and fresh water in an estuary (after CBP 1995)
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Monitoring and Evaluating Nonpoint Source Watershed Projects                                       Chapter 2
Because of the dynamic interaction of fresh water and salt water, pollutants are not flushed out from
estuaries in the same manner as they are in most stream systems. Instead, an estuary often has a lengthy
retention period (Ohrel and Register 2006). Consequently, waterborne pollutants, along with
contaminated sediment, may remain in the estuary for a long time, magnifying their potential to adversely
affect the estuary's plants and animals. This retention period also introduces a lag time that must be
factored into monitoring plans intended to measure improvements  resulting from restoration or improved
land management.

The unique characteristics of each estuary must be recognized and understood when developing a
monitoring plan because of their impact on estuarine hydrology, chemistry, and biology. Basin shape,
mouth width, depth, area, tidal range, surrounding topography, and regional climate all play important
roles in determining the nature of an estuary (Ohler and Register 2006). The earth's rotation (Coriolis
effect), barometric pressure, and bathymetry (submerged sills and banks, islands) affect circulation and
spatial variability in estuaries. For example, Puget Sound's complex circulation pattern is driven by tidal
currents, the surface outflow of freshwater from Puget Sound rivers, the deep inflow of saltwater from the
ocean, wind strength and direction, and underwater sills (Gaydos 2009).

Freshwater inflow is a major determinant of the physical, chemical, and biological characteristics of most
estuaries. It affects the concentration and retention of pollutants, the distribution of salinity, and the
stratification of fresh water and salt water (NOAA 1990). These freshwater inputs typically vary
seasonally. For example, Figure 2-7 shows how salinity in the Chesapeake Bay is generally higher in fall
and lower in spring due to spring runoff (CBP 1995). Salinity and other characteristics of estuaries may
also vary spatially due to the location of freshwater inflows. The temporal variability of estuary condition
is also influenced by factors other than freshwater inputs. For example, temperature profiles vary
seasonally, and tidal cycles can affect the mixing of fresh and salt waters and the position of the fresh
water-salt water interface.
                                               2-15
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Monitoring and Evaluating Nonpoint Source Watershed Projects
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           Salinity: Spring and Fall
           KEY: Sail in pals per thousand     0-10

           Spring
10-20 • 2030
                                                               CHESAPEAKE BAV FOUNDATION
                                                                 Sawn* a National Tmuiirr
Figure 2-7. Salinity in the fall and spring in the Chesapeake Bay (CBF n.d.)

2.2.1.4.5 Nearshore Waters
The interplay of wind, waves, currents, tides, upwelling, tributaries, and human influences control water
quality - and monitoring requirements - in nearshore waters. For the purposes of this guidance, nearshore
waters include an indefinite zone extending away from shore, beyond the breaker zone (USEPA 1998);
the term applies to both coastal waters and large freshwater bodies such as the Great Lakes. Wind and
tides are the primary sources of energy in the coastal nearshore, and waves generated by the wind are
largely responsible for currents (SIO 2003). These waves also have a central role in the transport and
deposition of coastal sediments as well as the dispersion of pollutants and nutrients.

Upwelling brings cold, nutrient-rich waters to the surface, encouraging biological growth (Gaines and
Airame 2010). Upwelling is extremely variable in space and time, depending on factors such as the
strength and direction of the winds and the topography of the coastline  (Gaines and Airame 2010). The
spread of upwelled water down the coast of southern California can vary from a relatively narrow band
near the coastline to enormous filaments extending hundreds of miles from shore. Upwelling on the east
coast of Florida has been shown to be so dependent on the prevailing winds that it ceases as soon as the
driving force is terminated (Taylor and Stewart 1959). Upwelling also occurs in the Great Lakes (Blanton
1975, Plattner et al. 2006). For the period 1992-2000, the magnitude of upwelling events observed in the
southern basin of Lake Michigan tended to be greater than in the northern basin because the southern lake
surface is typically warmer than in the north, while the temperature of the hypolimnion is more balanced
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Monitoring and Evaluating Nonpoint Source Watershed Projects                                      Chapter 2
over the extent of the lake (Plattner et al. 2006). In Lake Ontario, upwellings caused rapid shifts in the
nearshore species composition of zooplankton and may be a mechanism for transport of certain diatom
species to the epilimnion from hypolimnetic waters (Haffner et al. 1984).

Nearshore currents and pollutant transport are also affected by tributaries and human-made structures.
Tributaries introduce fresh water to coastal waters and have varying potential to alter nearshore currents
depending on factors such as tide stage, wind conditions, and tributary flow rate. Headlands (narrow strips
of land that extend seaward), breakwaters (barriers built into the water to break the force of waves), and
piers can influence the circulation pattern and alter the direction of nearshore currents (SIO 2003). For
example, an obstruction on the down-current side of a linear beach will cause a pronounced rip current to
extend seaward.

Current patterns must be sufficiently understood to determine the best locations for monitoring and to
establish pollutant pathways and the likely relationships between land-based activities and nearshore
water quality. Because circulation and pollutant transport is so variable in nearshore areas, designing
monitoring plans based on assumptions about current patterns is not recommended. For example, a study
of nearshore coastal circulation at the mouth of the Kennebunk River in Maine showed that currents did
not carry river water directly to a local beach as expected (Slovinsky 2008); instead, river outflow
extended much farther offshore from the beach. Because the current system of nearshore waters drives the
relationship between land-based pollutant sources and receiving water quality, monitoring should include
provisions to track variables needed to characterize the current sufficiently to aid interpretation of other
chemical, biological, and physical data that are generated. Basic data on salinity, water temperature, and
depth are often essential to identify the source of the sampled water and characterizing current patterns.
The NOAA (U.S. Department of Commerce, National Oceanic and Atmospheric Administration) Great
Lakes Coastal Forecasting System forecasts surface currents, winds, water temperature, and water level
displacement, information that could be useful for sampling on any given date (GLERL 2011).

EPA, through its new Beaches Environmental Assessment, Closure and Health (BEACH) Program, is
working with state, tribal, and local governmental partners to make nearshore water quality information
available to the public. The BEACH Program provides a framework for local governments to develop
equally protective and consistent programs across the country for monitoring the nearshore water quality
along beaches and posting warnings or closing beaches when pollutant levels are too high. More
information on this program can be found at http://water.epa.gov/tvpe/oceb/beaches/beaches  index.cfm.

Note that because nearshore areas tend to be subject to heavy human use (e.g., swimming, boating,
shellfish production), special water quality criteria and standards may apply. Fecal bacterial criteria for
shellfish production, for example, tend to be far more restrictive than criteria for contact recreation. Such
criteria may require special monitoring programs.

2.2.1.4.6  Ground Water
Ground water is the source of much of the Nation's streamflow,  and ground water discharges often
sustain water levels in lakes and wetlands, particularly during dry periods (Taylor and Alley 2001). The
fact that the presence, quantity, and movement of ground water are not readily observable presents special
challenges for monitoring design.

Ground water occurs in two general types of aquifers - confined and unconfined. Unconfined (water
table) aquifers are in direct contact with the atmosphere through the soil and the elevation of the water
table surface (i.e., depth to ground water). They fluctuate freely in response to changes in recharge and
discharge (Figure 2-8). Confined (artesian) aquifers are separated from the atmosphere by an
                                               2-17
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Monitoring and Evaluating Nonpoint Source Watershed Projects
Chapter 2
impermeable layer (USDA-NRCS 2003) and may be under pressure that results in a flowing (artesian)
well if drilled into the aquifer. Perched water is held above the water table by an impermeable or slowly
permeable layer below and is often the source of springs.
            Perched Water Table
              Impermeable Layer
                                                                    Unsaturated Zone
                        Saturated Zone  (Unconfined Aquifer)
                       	•	
                     Impermeable Layer
                            Confined Aquifer
                             Regional Water Table
Figure 2-8. Basic aquifer types

Ground water levels are controlled by the balance among aquifer recharge, storage, and discharge (Taylor
and Alley 2001). This balance is affected by characteristics (e.g., porosity, permeability, and thickness) of
the rocks or sediments that compose the aquifer, as well as climatic and hydrologic factors (e.g., the timing
and amount of precipitation, discharge to surface-water bodies, and evapotranspiration). Ground water
moves along a hydraulic gradient from locations of higher hydraulic head to locations of lower hydraulic
head. The rate of ground water movement depends not only on hydraulic head but also on the hydraulic
conductivity (permeability) of the aquifer material; movement may be as rapid as 50 - 1000 m/day in a
coarse gravel aquifer or as slow as 0.001 - 0.1 m/day in a silt and clay formation. The direction of ground
water movement is not always obvious and not always consistent with the land surface topography.
Patterns of ground water movement must be determined in the field (usually by measuring hydrologic
head in numerous positions across a wide area) before determining sampling locations.

Ground water quality is influenced by a range of factors including aquifer type, native geology,
precipitation patterns, flow patterns, land use, pollutant sources, and pollutant characteristics such as
density and solubility (Scalf et al. 1981). Naturally, these factors can vary widely, even within a small
region. A study of two adjacent Maryland watersheds with similar topography, land use and soils found
that N yields differed significantly, largely due to the different characteristics of the aquifer underlying
the watersheds (Bachman et al. 2002).

A special case of ground water systems is karst topography. Karst is a geologic condition shaped by the
dissolution of channels or layers of soluble bedrock due to the movement of water. Karst regions typically
display such surface features as sinkholes and disappearing streams and may be underlain by extensive
cave systems. Aquifers in karst terrains are very sensitive to contamination because direct and rapid
connections exist between the land surface and ground water, via the dissolution channels. Sinkholes are,
                                              2-18
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Monitoring and Evaluating Nonpoint Source Watershed Projects                                      Chapter 2
for example, potentially direct pathways for sediment and chemicals to enter ground water without
filtration through the soil. Karst systems present special challenges for ground water monitoring efforts,
as sources of aquifer contamination may be widely dispersed and difficult to map.

The uncertainties regarding ground water flow patterns and the composition of underground materials,
coupled with seasonal patterns and the interplay of surface water and ground water, require that basic
knowledge of the particular ground water system under study be obtained before a monitoring program is
designed or initiated.

Regional or statewide ground water level recording and water quality monitoring networks are common
across the  nation, especially in regions where ground water is a primary source of drinking and  irrigation
water (FACWI 2013). These networks often detect contaminants via well monitoring and model
contaminant transport based on ground-water level data. Watershed-level monitoring of ground water,
however, is still relatively rare despite the frequently important interaction between ground water and
surface water. The interaction of surface water and ground water can be considered from the perspective
of surface  water recharging ground water or ground water discharging to a stream or lake (Goodman et al.
1996). The former is important when determining the impact of surface water on a ground water resource,
whereas the latter should be a key element of monitoring when ground water comprises a significant
portion of the water or contaminant budget of the surface water body (e.g., Schilling and Wolter 2001,
Schilling 2002). When conducted well, ground water monitoring data, coupled with agricultural and land
use data, can develop convincing evidence of the response of ground water quality to changes in
agricultural management (e.g., Exner et al. 2010).

While the  collection and analysis of groundwater data are not addressed in detail here, it is important that
the role of subsurface waters be factored into watershed-scale and field-scale monitoring efforts described
in this guidance. Several guidance documents are available for those seeking additional details regarding
ground water monitoring, including guidance on construction of monitoring wells and sampling
procedures (Scalf et al. 1981, Wilde 2006). The USGS has produced a series of groundwater technical
procedures documents (GWPDs) that describe measurement and data-handling procedures commonly
used by the agency in its groundwater monitoring activities (Cunningham and Schalk 2011). These
procedures address groundwater-site establishment, well maintenance, water-level measurements;
groundwater-discharge measurements, and single-well aquifer tests. In addition, guidance specific to
monitoring ground water in NFS studies was developed based on experiences in the Rural Clean Water
Program (Goodman et al.  1996).

Ground water monitoring is performed  for a number of purposes, including:
  •   To characterize background water quality.
  «   To determine the ground water component of a hydrologic/chemical budget for a surface
      waterbody.
  •   To document the impact of a polluting activity.
  •   To identify trends and variations  in water quality.
  •   To determine the effectiveness of BMPs.

Successful ground water monitoring design begins with a good understanding of the ground water system
and the establishment of specific monitoring objectives. Ground water monitoring often requires a two-
stage approach in which the first stage is a hydrogeologic survey to determine ground water surface
                                              2-19
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Monitoring and Evaluating Nonpoint Source Watershed Projects
                                                                           Chapter 2
elevations and flow rates and directions. First-stage surveys require numerous sampling stations because
aquifer water quality can vary considerably with depth and location (Figure 2-9). Ground water level data
can be used to determine ground water flow patterns as shown in Figure 2-10 (Winter et al. 1998).
                                                                   n= 1,276
                        Drinking Water Standard (10 nig/L)
             10   JO   JO    40   ?0   60    70   SO   90   100  110   HO  130  140   1?0   160

                                  Nitrate Concentration (mg/L)

Figure 2-9. IMOs concentration versus depth to water table (after Rich 2001)
      o
   13H
 feet. Da turn & aea level

13BOI4HO- WATEB FLOW
 LIME
Figure 2-10. Determining ground water flow patterns (Winter et al 1998)
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The second stage is an investigation of water quality, with stations selected based on monitoring
objectives and the results of the first stage (Goodman et al. 1996, USDA-NRCS 2003). Sampling
locations can and should be guided by knowledge of the hydraulic gradient, but the heterogeneous nature
of surbsurface environments makes appropriate location of sampling points a complicated and
unpredictable task (Scalf et al. 1981). Several sampling locations and sampling from multiple depths may
be required to characterize ground water and determine contaminant pathways (Goodman et al. 1996).
Ground water investigations in South Dakota, for example, have shown that nested wells may be
necessary for adequate examination of shallow aquifer water quality (SDDENR 2001).

In some cases it may be necessary to sample the unsaturated zone to get a true picture  of the threat to
ground water (Scalf et al. 1981, Goodman et al.  1996). In addition, long-term water level measurements
may be needed to show how contaminants are transported from their sources through the groundwater
system (Taylor and Alley 2001). It may even be possible to establish relationships between water levels
and contaminant concentrations, possibly indicating patterns associated with seasons or rainfall-events.

Because ground water monitoring is both complex and expensive, sophisticated geostatistical techniques
(e.g. Chiles and Delfmer 1999, Lee et al. 2005) are increasingly used both to build conceptual
hydrogeological models of ground water flow, quality, and contamination and to assess health and
environmental risk based on observed sample data (EPA Victoria 2006). Thus, modeling and spatial
analysis can be useful in designing ground water monitoring programs and in organizing and interpreting
results.

2.2.1.5  Climate
Climate is one of the principal determinants of the basic structure of a monitoring program. The
frequency, intensity, and duration of runoff-producing storm events affect sampling frequency and
duration, equipment selection, automatic sampler programming, and many other elements of a monitoring
program. Freezing conditions can have immense impact on the duration of the sampling season, the
design and cost of permanent sampling stations, and the operation and maintenance of sampling
equipment. Droughts and floods can be fatal to monitoring programs that have no budget flexibility, and
the lag time between BMP implementation and measurable water quality impacts can be changed
drastically by persistent changes in weather patterns.

Average precipitation patterns and the resulting average flow conditions are typically used to establish
sampling frequencies, the relative emphasis on base-flow and storm-event sampling, the location of
biological monitoring sites, and the design and siting of flow gaging stations. Precipitation patterns  over
any given study period, however, can vary significantly from long-term averages, as evidenced by a
seven-year study in Illinois in which annual precipitation was lower than the long-term average in all but
one year (Algoazany et al. 2007). An analysis of precipitation in the Minnesota River Basin for the period
1891-2003 showed a slightly increasing trend, with annual totals ranging from well under 400 mm to well
above 900 mm (Johnson et al.  2009). In a runoff study on a dairy in the Cannonsville Reservoir watershed
in New York, seven of the eight highest event flows occurred in the post-BMP period  despite the fact that
the pre- and post-BMP study periods exhibited similar scales and frequencies of precipitation and event
flow volume (Bishop et al. 2005). Short-term and long-term drought greatly influenced runoff events in
an 11-year study in Georgia (Endale et al. 2011). During the 86 months with below-average rainfall there
were only 20 runoff events, compared to 54 runoff events during the 46 months with average or greater
rainfall.
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Monitoring program managers must plan for a wide range of flow conditions, but flow is not the only
important consideration when designing a monitoring program. Climatic variability can also influence
aquatic organisms and land treatment programs. For example, the growth and development of riparian
buffers is dependent on adequate precipitation. No monitoring program can be designed to handle all of
the potential impacts of climatic variability, but all monitoring programs should be designed to account
for a foreseeable range of conditions. Design concerns can range from determining the size of a flume
required to measure edge-of-field runoff to planning budgets and time frames to allow the capture of a
sufficient number of high-flow events.

2.2.1.6  Soils, Geology and Topography
Soils, geology, and topography are local or regional features that must be considered in monitoring
program design (MacDonald et al. 1991). These characteristics influence the  hydrologic budget, potential
suspended sediment loading from erosion, background levels of nutrients and dissolved ions in ground
and surface waters, and other factors that drive monitoring program design.

The importance of soil groups is illustrated by a Pennsylvania study in which runoff was monitored from
two contrasting hillslope soil groups (colluvial and residual) that differed in subsurface morphological
characteristics such as the presence of a fragipan, the clay content of argillic horizons, and drainage class
(Needelman et al. 2004). Results showed greater runoff from the four colluvial sites for all significant
events, and overall runoff yields were also greater from the colluvial sites (average of 2.4 percent) than
from the two residual soil sites (average of 0.01 percent).

A study of an agricultural watershed in the coastal plain of Maryland showed the importance of near-
stream geomorphology and subsurface  geology in determining riparian zone  function and delivery of NOs
to streams (Bohlke et al. 2007). Stream NOs levels were higher during high flow conditions when much of
the groundwater passed rapidly across the riparian zone in a shallow, oxic aquifer wedge and higher
during low flow conditions when  stream discharge was dominated by upwelling from the deeper,
denitrified parts of the aquifer.

Slope must also be factored into the design of a monitoring program because slope and slope length affect
the rate and duration of runoff from a watershed, rate of erosion, depth of soil (steep slopes often have
less soil overlying the  bedrock), and stream characteristics. Slope also affects the likelihood of landslides
and debris flow, erosional processes, and weathering rates.

2.2.2 Monitor Source Activities
NFS pollution is highly variable and is  generated by activities on the land that vary in location, intensity,
and duration. To make the connection between pollutant sources and water quality observed through
monitoring, it is also necessary to monitor the activities on the land that generate NFS pollutants. In the
context of linking cause and effect, water quality monitoring data represent the effect, while source
activities represent a major component  of the cause. Put another way, to fully understand NFS pollution,
we must measure both the dependent variables (water quality) and the independent variables (source
activities).

In practice, monitoring pollutant source activities usually translates to land use and land management
monitoring. This means more than taking a static picture of land use/land cover in a watershed from a
satellite image (although that may be very useful for establishing a baseline condition). It means
monitoring dynamic pollutant-generating activities in time and space. Examples of NFS pollutant types
and common corresponding source activities to be monitored are shown in Table 2-2.
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          Table 2-2. Selected IMPS pollutants and watershed source activities to monitor
NFS Pollutant Type
Suspended sediment (field
erosion)
Suspended sediment
(streambank erosion)
Phosphorus
Nitrogen
Crop herbicides
Pathogens
Salt
Heavy metals
Stormwater flow
Potential Variables to Monitor
Cropland tillage, planting, harvesting, erosion control BMPs, precipitation.
Streamflow, stream morphometry, riparian condition, precipitation.
Manure applications, livestock populations, manure and fertilizer management, soil test P.
Fertilizer applications, legume cropping, manure and fertilizer management, groundwater
movement.
Herbicide application rates and timing, precipitation
Livestock populations, grazing practices, riparian condition, pasture fencing, manure land
application practices. Pet populations, wildlife/waterfowl activity, septic system
maintenance/failure, sewer maintenance, illicit discharge/connections.
Amount and timing of road salt used for deicing. Road salt contract amounts. Miles and
locations of roads salted. Irrigation return flows.
Vehicle traffic, highway infrastructure, street sweeping, stormwater management structures
and activities.
Impervious cover, stormwater management facilities, precipitation.
The practice of source activity monitoring is discussed in more detail in section 3.7 of this guidance.

2.2.3  Critical Details
Execution of a monitoring plan requires careful attention to some critical details, as the following
discussion reveals.

2.2.3.1  Logistics
Logistics are defined here as matters concerning the management of the flow of materials, information or
other resources from the point of origin to the point of use to meet the requirements of an enterprise. In
water quality monitoring, logistics refers specifically to supporting the basic functions of data collection.
  •   Supplying power to field stations.
  •   Ensuring access to sampling locations for sample collection and field measurements.
  •   Delivering, maintaining, and retrieving equipment, instruments, and supplies to and from the
      sampling sites.
  •   Providing communications and data links between a base and remote sampling stations.
  •   Having available, well-trained, and on-call field personnel.
  •   Traveling to and from sampling stations.
  •   Delivering samples to the laboratory on time and under appropriate chain of custody.

All of these elements must be addressed in the process of developing a monitoring plan. If necessary, how
will power (direct AC, solar, or battery) be supplied? Can desired sampling locations be accessed legally
and safely under the range of expected conditions (e.g., high flow, inclement weather)? If structures or
shelters are necessary, can the property owner's and municipality's permission be obtained? What is the
time and cost involved in traveling to and from a network of sampling locations? Is electronic
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communication between stations and a base (if desired) possible, considering distance and topography?
Can samples be delivered to the laboratory within the limits of required holding times?

These and other practical questions of how to carry out the physical tasks of monitoring need to be
considered in the planning stage. Some practical guidance for addressing such logistical issues is
presented by Harmel et al. (2006).

2.2.3.2  Quality Assurance/Quality Control and the Quality Assurance Project Plan
         (QAPP)
Data collected by a monitoring program must be of sufficient quality and quantity - with respect to
accuracy, precision, and completeness - to meet project objectives. Provisions for ensuring data quality
must be made during the monitoring design process, not after a plan is underway. These provisions fall
into two main categories. Quality control (QC) refers to a system of technical procedures developed and
implemented to produce measurements of requisite quality. QC activities typically include the collection
and analysis of blank, duplicate and spiked samples, analysis of standard reference materials, and
inspection/calibration/maintenance of instruments and equipment. Quality assurance (QA) is an integrated
system of management procedures and activities to verify that the QC system is operating within
acceptable limits and to evaluate and verify the quality of data collected. A QA system addresses the roles
and responsibilities of monitoring staff, required staff skills and training, tracks sample custody, sets data
quality objectives and procedures for data validation, and monitors QC activities, including actions taken
to correct problems. In general, each organization that conducts monitoring  should ensure that the
appropriate QA/QC measures are followed, but may vary among funding organizations.

All organizations conducting environmental programs funded by EPA are required to establish and
implement a quality system, a structured system that describes the policies and procedures for ensuring
that work processes, products, or services satisfy stated expectations or specifications (USEPA 2001).
EPA also requires that all environmental data used in decision making be supported by an approved
Quality Assurance Project Plan (QAPP) which documents the planning, implementation, and assessment
procedures for a particular project, as well as any specific quality assurance  and quality control activities
(USEPA 2008b). The purpose of the QAPP is to document planning results  for environmental data
operations and to provide a project-specific "blueprint" for obtaining the type and quality of
environmental data needed for a specific decision or use. In most monitoring programs, an approved
QAPP is required before data collection can begin; even in cases where a QAPP is not specifically
required, such a document is a valuable resource for documenting consistent monitoring procedures, and
therefore useful to prepare even if not required. Quality control, quality assurance and the QAPP process
are discussed in detail in chapter 8.

Other agencies including the United States Geological Survey (USGS) have issued guidance and
requirements regarding data quality (Wilde 2005). Every USGS study requires a sampling  and analysis
plan (SAP) and a quality-assurance plan (QAP) that include a description of the objectives, purpose, and
scope of the study and its data-quality requirements. In addition, each USGS Water Science Center
develops general quality-assurance plans that articulate its policies, responsibilities, and protocols.
Specific guidance on obtaining representative samples can be found in USGS's National Field Manual
(Wilde 2006). USGS quality control procedures emphasize generating information on bias and variability
because of their importance in proper and scientifically defensible interpretation of collected data.
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2.2.3.3  Data Management and Record-keeping
Even short-term monitoring efforts may generate tremendous quantities of data. A system for managing
that data stream must be included in an overall monitoring plan. Poorly recorded, misunderstood or even
lost data represent an irretrievable loss of information, a waste of resources, and a threat to program
objectives. Poor data management can also make the task of data analysis and interpretation more difficult
and challenging than necessary.

Good data management begins in the field, where a clear identification system is required to correctly
attribute data to their source. Field log sheets or notebooks are valuable tools for initial recording of field
data, sample identification and observations that may represent critical knowledge later. A good field log
can also serve as a guide or checklist for the  field technician.

Chain of custody records are essential where litigation is involved, but also useful for simply tracking
delivery of samples to the lab. It is also important to document assignment of lab sample numbers and
their correspondence to field identification codes.

The process and schedule of data reporting from the laboratory should be outlined and agreed upon.
Timely reporting of data from the lab is essential in providing  feedback to the monitoring and land
treatment program.

A good data management system should be implemented  in a simple,  consistent format (e.g., a
spreadsheet or a database form) that can accept both manually transcribed data (such as those from field
logs or lab data reports) and data already in electronic form (such as downloads from field instruments or
data loggers). Electronic data formats should be designed to be consistent with formats used for later
analysis (e.g., in a statistics package or uploads to STORET) to avoid  the cost and potential errors of
transcribing data from one format to another.

Data validation and error checking are essential and should be performed at an early stage. Validation
involves checking for correct transcription between data sources and data storage (e.g., between field logs
and electronic spreadsheets), checking for typographic errors, looking for extreme or impossible values,
and ensuring that all required data have been included. Validation should be performed on 100 percent of
the data, not just a spot-check. It is very important that validation be performed early in the process, as it
is costly and frustrating to have to repeat data analysis and presentation if errors are discovered late in the
process.

Data storage is also an important consideration. Paper records  such as field logs or lab data reports should
be archived and perhaps scanned for electronic storage. Both original  data and data derived from
calculations, analysis or other manipulations should be stored. Maintain a metadata file to record
important information about the data and the monitoring program,  QA/QC results, exceptions or unusual
occurrences, and any other important monitoring records. If data are stored in a national repository, such
as STORET, download the data and make sure they are identical to the data on your desktop. Electronic
data forms should be stored redundantly and protected with frequent backups. For long-term archiving,
select the storage medium carefully. Data from a 1985 project stored on 5.25 inch (in) floppy disks may
be nearly impossible to access in 2015; data recorded on a CD today may be unreadable in the future.

2.2.3.4  Roles and Responsibilities
Most monitoring programs involve cooperation among several different agencies, offices or individuals.
For example, a watershed project might include funding (USEPA), planning and implementation of
BMPs (USDA-NRCS, Soil and Water Conservation Districts), flow measurement (USGS), water
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chemistry sampling and analysis (Health Department), and biomonitoring (state environmental agency).
Even within a single activity, such as water chemistry monitoring, different individuals like field
technicians, laboratory analysts and graduate students may play different roles. A good monitoring plan
needs to specify the roles and responsibility of each participating entity and individual so that all
monitoring tasks can be accomplished smoothly. Perhaps even more important, a mechanism for
coordinating among the variety of agency and individual roles and responsibilities should be established
from the start. Strong leadership from an overall project director/coordinator can facilitate good
cooperation among a project team. In addition, frequent contact, progress reports, and regular meetings
among all project participants have been shown to be key ingredients for effective coordination.
2.2.3.5  Review of  Monitoring Proposals
Monitoring plans may be developed  and reviewed under a variety of different templates or formats.
Whether for an  internal check for completeness or for an external review in an approval process (e.g., for
state Section 319 funding), it is often useful to step back from the details and review the contents of a
monitoring plan to make sure that all necessary elements have been considered and addressed. Experience
of NFS monitoring efforts across the country suggests that confirmation of the following elements is
useful in review of monitoring plans:
  "  Watershed Identification and Characterization
      •   Descriptive  information on physiographic setting, water resources, land use/management
      •   Identification of stakeholders and project participants
  •  Problem Identification
      •   Clear identification of water quality problem(s)
      •   Documentation of impairment(s) and supporting data
      •   Known or suspected causes and supporting data
      •   Known or suspected sources of pollutants and supporting data
  *  Project Goals and Objectives
      •   Quantitative goals for water quality
          -   Tied to impairment, restoration of use(s)
          -   Including estimated load reductions as appropriate
      •   Quantitative goals for land treatment implementation
  *  Land Treatment(s) to be Implemented
      •   Identify critical areas and measures to be implemented
      •   Justification for specific practices selected
      •   Schedule and interim milestones/indicators of progress
      •   Availability of funds, personnel, and other resources
  •  Monitoring Plan
      •   Water quality
          -   Design
          -   Variables
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         -   Locations
         -   Frequency and duration
         -   Sample collection and analysis
      •   Land use/land treatment - process and responsibility
      •   Availability of funds, personnel, and facilities
  •   Data Management and Analysis
  •   Administration/Management/Coordination
  •   Reporting, Communication, Stakeholder Involvement
  •   Timetable and List of Deliverables
  "   Budget

Note that this checklist addresses several elements such as those associated with land treatment that may
seem to be outside the immediate realm of water quality monitoring, but these must also be considered
and coordinated with other project activities.

2.2.4 Feedback
Although implementation of BMPs on the landscape and monitoring water quality at various locations in
the watershed may seem to be separate activities that can proceed independently, successful NFS
watershed projects require effective coordination and collaboration among all activities. It is therefore
important to facilitate feedback of data and other information among different components of a watershed
project. For example, water quality monitoring staff should know where and when BMPs are
implemented in the watershed, and land treatment implementation should be guided by water quality data
where possible. Even within the monitoring program, it can be critical for biomonitoring staff to know the
results of water chemistry monitoring to fully understand what they observe in the biotic community.

Feedback mechanisms should be built into a watershed project from the beginning, not left to chance or
put off to the final project report. Frequent examination, presentation and discussion of monitoring data
will keep all project participants informed. Regular review of field data and observations can provide
evidence of events or conditions in the watershed that reveal  small problems before they become large.
Similarly, frequent examination of laboratory results can show evidence of analytical or QA/QC problems
before they result in major data loss. Feedback between water quality monitoring and land treatment
personnel can help fine tune BMP implementation to known  water quality problems and can provide
land-based data to improve understanding of observed patterns in water quality.

Feedback can be generated by requiring frequent reports (e.g., monthly) and meetings for all project
participants. The reports and meetings can be brief and follow a simple formula, but by requiring all
sectors to periodically compile, examine and present their data, feedback among all data streams can be
guaranteed.

2.2.5 Limitations of Monitoring
In practice, monitoring does not always answer all of the questions or achieve all of the objectives
because:
  •   Available resources may fall short of estimated costs.
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  •  A large number of different situations/scenarios create too many alternatives to evaluate cost-
      effectively.
  •  Socio-economic factors may require modification of monitoring or land treatment plans.
  •  Watershed size, access or other features impose significant logistical limitations.
  •  Water quality conditions (e.g., range of flows) are too variable to effectively monitor.
  •  Appropriate data quality cannot be achieved (e.g., volunteer monitoring).
  •  The actual desired response to treatment cannot be monitored due to flooding or other physical
      changes in the resource.
  •  The magnitude of change expected to result from treatment, especially in context of background
      levels or contributions from other uncontrolled sources, is small.
  •  Lag time between land treatment and water quality response exceeds the duration of monitoring.
  •  Random or catastrophic events (e.g., intense storms or chemical spills) overwhelm response to
      treatment.

In principle, if a project cannot afford monitoring that can be reasonably expected to achieve objectives
within the design parameters, it is recommended to forgo the inadequate monitoring that will not serve
project needs but will drain budget resources. It may be possible to narrow the monitoring objectives,
reduce the required precision, or reduce the scope of the monitoring effort to stay within budget, but such
compromises must be made within the context of designing  a plan that will meet stated objectives that
help the project meet its goals. Modeling may be an effective alternative, especially when numerous
alternative scenarios must be considered. However, note that proper model application (including
calibration and validation)  requires some data and considerable resources. See section 6.3 for ideas on
how to integrate monitoring and modeling.

2.3  Monitoring Scale Selection

2.3.1  General  Considerations
The scale of a  monitoring plan is the size of the area to be monitored, a spatial consideration. Selection of
the  appropriate scale depends on the study objectives, study duration, type of water resource monitored,
the  complexity of the project, and available resources (USDA-NRCS 2003). Monitoring scale is generally
locked in with the selection of monitoring design.

The choice of  scale affects monitoring costs, duration and logistics. The ability to isolate  the factors of
interest (e.g., BMP effectiveness, transport pathways) generally increases as scale  decreases, but the
transferability  of results generally decreases as scale decreases. Monitoring a set of 1 x 3  m plots, for
example, may  yield  good data on how cover crops reduce soil loss, but such data are very difficult to
extrapolate to a watershed-scale because small plots do not always reflect field-scale runoff processes or
watershed-scale transport and delivery processes. Analysis of long-term data collected in the Lake Erie
basin showed that watershed size had a much greater effect on concentration patterns than on unit area
loadings (Baker 1988). The greatest effect was on peak concentrations; as watershed size decreased, peak
concentrations of sediments, nutrients, and pesticides increased (Baker et al.  1985, Baker 1988). Pollutant
concentrations returned to baseline levels more quickly in smaller watersheds and  streams. It was also
determined that increasing  proportions of the annual load occur in decreasing proportions of time as
watersheds become  smaller, but that the high rates of export from small watersheds are distributed into
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larger numbers of individual events compared to larger watersheds. Because of these occurrences, it takes
more sampling effort to accurately measure the loads from a smaller watershed, and the likelihood of
missing high export rate events is greater (Baker 1988).

2.3.2  Options
Monitoring can be performed at scales ranging from national to single points, but the primary options for
the types of NFS monitoring studies addressed in detail by this guidance are the watershed and BMP
scales, the latter of which includes field and plot studies described by USDA-NRCS (2003). National and
statewide (or regional) monitoring scales are only briefly touched upon here, and point-scale sampling is
addressed primarily for explanatory variables. In fact, the data collected from point-scale sampling
performed in support of the types of monitoring described in this guidance will generally be extrapolated
to larger scales. For example, precipitation data  collected at a single point may be applied to a rooftop to
estimate flow volume handled by a green roof, or used to represent precipitation in either or both drainage
areas in an above/below design. Soil samples collected at single points in a field may be combined for
analysis to represent average conditions or analyzed separately with results interpolated to represent
varying conditions for plot or field stations. Soils and precipitation data are generally used as explanatory
variables in the statistical approaches discussed  in chapter 7.

2.3.2.1  Statewide or regional
Statewide monitoring designs generally emphasize larger streams and rivers, public lakes, and the outlets
of watersheds. States usually locate some of their monitoring at sites gaged by the USGS to take
advantage of the  flow data. Some monitoring stations at key locations are equipped with automatic
samplers or sondes for continuous monitoring, but cost and logistical constraints limit most monitoring
efforts to the collection of grab samples, a few field measurements (e.g., temperature, DO, conductivity),
and biological and habitat monitoring. With the  exception of the few stations with automatic or
continuous sampling, monitoring frequencies are generally low.

Statewide monitoring associated with NFS pollution is generally designed to assess current conditions. It
is unlikely that most statewide monitoring efforts can support trend analysis because of the strict
requirements (e.g., no breaks in the data, consistent methods over time, collection land use and other
covariate data) and the difficulty states have committing to consistent long-term monitoring efforts.
Recognizing this and related limitations, Congress in 2005 began appropriating additional funds within
Section 106 grants for an  initiative to enhance monitoring programs and provide statistically-valid reports
on water conditions (USEPA 2006b). In 2008, EPA amended its guidelines for this initiative to provide
incentives for states to implement state-wide statistically-valid monitoring surveys (USEPA 2008a).

2.3.2.2  Watershed
Watershed-level  monitoring for NPS assessments and the evaluation of project effectiveness has evolved
over the years as lessons have been learned from such programs as the Rural Clean Water Program
(USEPA 1993a), ACP-Special Water Quality Projects (Davenport 1984), Model Implementation Program
(NCSU and Harbridge House 1983), Nationwide Urban Runoff Program (USEPA 1983), and the Section
319 National NPS Monitoring Program (Tetra Tech n.d.). The current emphasis on Total Maximum Daily
Loads (TMDL)s  has changed watershed-level monitoring even further by placing a greater focus on
estimating the pollutant loads from each source  category in the watershed, setting numeric targets for
pollutant load reductions at a watershed level, and linking NPS and point source control efforts (USEPA
2012a).
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Watershed-level monitoring can be triggered by findings from state-level monitoring, but is even more
likely to develop from local stakeholder efforts to identify and solve problems that they care about
(USEPA 2012b). Infrequent grab samples, basic chemical and physical parameters, and rapid assessments
of aquatic biology conditions typically constitute the monitoring performed prior to or in the initial stages
of a watershed project (USEPA 2008b). Flow data are generally lacking unless the watershed has a USGS
gaging station, and monitoring stations are usually located where convenient for sampling, a situation that
can bias results.

As watershed projects evolve, the monitoring approach should change to address project goals (USEPA
2008b). Initial efforts generally focus on refining the problem definition, including characterizing the
water quality problem better, determining the major sources and causes of the problem, and providing
data to aid in the design of a plan to solve the problems. Monitoring during this phase of a watershed
project may include a synoptic survey, tests for toxicity, flow measurements at various points in the
watershed to support a load analysis, detailed habitat assessments, and higher level biological
assessments. Land use mapping, investigation of permitted discharger reports, and windshield surveys
may also take place to better characterize sources. Most of the monitoring during this phase of a
watershed project is short-term, with one or two sampling events at most. In rare cases, projects may
install "permanent" monitoring stations and factor long-term monitoring considerations into the short-
term effort. Such projects should ensure that all design requirements for long-term monitoring can be met,
however, before monitoring begins.

Watershed-level monitoring, as described in this guidance, typically begins after a watershed project
secures funding to address the identified problems. All too often project implementation begins before or
simultaneously with monitoring, complicating efforts to assess the effectiveness of the project. The
watershed monitoring plan should be coupled with and  complementary to the watershed project or
management plan (USDA-NRCS 2003). Depending on the specific objectives, the size and characteristics
of the watershed, and the parameters of concern, watershed-level monitoring can take various forms. A
key difference between watershed- and state-level monitoring is the narrowing of focus and increased
intensity of watershed-level monitoring. Because the questions to be addressed by monitoring are more
specific at the watershed level, each watershed monitoring effort is  unique.

Watershed-level monitoring to assess project effectiveness generally requires a control condition to serve
as a benchmark. This is not unlike the use of reference conditions for biological monitoring, but whereas
reference conditions are generally sought in areas with minimal human impact (see chapter 4), the control
conditions for watershed-level monitoring are usually found within or very near to the watershed being
treated. In some cases, watershed projects will have  relatively pristine conditions that serve as the control,
but usually the control is an upstream area or a paired watershed within a short drive of the treatment
watershed. Data from the control are used to isolate the effects of the BMP implementation in the study or
treated area. This type of monitoring is not generally performed in statewide monitoring programs.

Another approach used at the watershed level is long-term trend monitoring at a single station where there
is no control condition to serve as a benchmark. As described in section 2.4.2.5 and noted above for
statewide monitoring, this approach has a relatively high risk of failure because the requirements of the
method are difficult to satisfy over the long term. When performed  at the watershed level, rather than the
statewide level, however, the risk of failure can be reduced because more frequent sampling is done
(e.g., once per two weeks) and the timeframe for demonstrating results is shortened. Still, the risk of
failure with this approach remains generally  greater than for designs using controls because the timeframe
for monitoring at the watershed level can still be on the order of a decade.
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One of the biggest challenges at this scale is to determine when a watershed is too large for successful
monitoring (USDA-NRCS 2003). The upper limit on watershed size will depend on the monitoring
objectives. For trend analysis, there may be no upper size limit if monitoring will continue for decades. If
the goal is to attribute trends to changes in land use or land management, the cost of tracking such
information at a sufficient level of detail may be prohibitive for extremely large watersheds that cover
100,000 acres or more (-40,500 ha).

Problem assessment can be done at various levels of detail for various purposes. If the assessment is to
form the basis for an action plan that will require the obligation of substantial resources, it may be wise to
limit the monitored watershed size to 50,000 acres (-20,200 ha) or less (i.e., the HUC-12 level). If, for
example, the watershed or basin plan is for an area of 500,000 acres (-202,000 ha), monitoring would be
performed at ten subwatersheds of 50,000 acres each. Watersheds larger than 50,000 acres are considered
very large and may be inappropriate for assessment monitoring  because of their likely heterogeneity in
land uses (USDA-NRCS 2003).

Pollutant load measurement can be performed at watersheds of any size, but attribution of those loads to
specific sources or source categories becomes more difficult for large watersheds. A limit of 50,000 acres
may be appropriate for load estimation within the context of watershed plans or TMDLs where load and
wasteload allocations will be made. The exact size limit for any situation will depend on a wide range of
factors including whether or not watershed modeling is part of the effort.

The appropriate watershed size for evaluating watershed projects is probably 25,000 acres (-10,000 ha) or
less based on experiences in the NNMP and RCWP. The actual size depends on a number of factors
including average annual precipitation, the type and degree of use impairment, lag time, study duration,
the potential for making improvements with BMPs, the number and location of monitoring stations, and
sample type and frequency.

In comparison to statewide monitoring, watershed-level monitoring generally involves a shorter
timeframe (3-10 years), fewer stations, more frequent sampling of both storm events and base flow, and a
targeted and unique set of monitoring variables. Flow is usually measured in watershed-level monitoring.
Due to automation of monitoring stations, primarily to monitor  storm events, the annual cost per
monitoring station is generally greater than routine grab sampling programs. On the other hand, biological
data are typically collected only once or twice per year, and as a result, the timeframe for demonstrating
results is quite dependent on the specific problem identified and the treatment plan designed to solve the
problem. Where stream and habitat restoration has been done in watershed projects, biological monitoring
has often been successful in documenting benefits, but in the various, long-term, watershed-based NPS
monitoring programs listed above (e.g., Rural Clean Water Program), there have been very few instances
where biological monitoring has demonstrated the effectiveness of watershed projects that did not involve
such in-stream work. As a result, this guidance recommends that biological monitoring be coupled with
physical/chemical monitoring of the stressors targeted in watershed projects.

2.3.2.3  BMP or practice
Monitoring at the BMP level is generally the most intensive of the levels described here. The scale for
BMP monitoring can vary from plot studies to the inflow and outflow of a multi-acre constructed wetland
to the influent and effluent of a manufactured stormwater treatment device. Monitoring variables are
selected based upon the specific sources treated by the BMP, including such possibilities as metals and
organic chemicals in an urban setting; bacteria and BOD near shellfish beds or livestock operations;
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nutrients and sediment on cropland; temperature, DO, and biological communities for a stream
restoration; and pH in abandoned coal mining areas. Flow needs to be monitored in most BMP studies.

BMP-level monitoring is generally storm-event driven, with little or no base-flow sampling. Exceptions
include monitoring of constructed wetlands or wet ponds where it is essential that time-of-travel is
assessed to provide for "matching" inflow and outflow samples to estimate pollutant removals. In
addition, monitoring of stream restoration often includes both storm events and base  flow. Each condition
presents a different combination of stressors on the biological community that is typically monitored for
such projects.

While sampling is frequent and the schedule hectic, the duration of BMP-level monitoring is usually
short, typically ranging from one to two years to no more than five years. Paired studies, random block
designs and  similar research-type study designs are often used when inflow-outflow monitoring is not
possible. The intent of these studies is to eliminate all factors other than the BMP itself, so samples are
collected as  close as possible to the BMP.

Composite samples are collected in many BMP studies,  but because it is often desirable to assess
contaminant levels at different stages of the hydrograph, BMP studies may include analysis of several
samples per storm event. BMP monitoring is often more expensive per site than either statewide or
watershed monitoring.

2.3.2.3.1 Plot
The plot scale is generally used in monitoring designs that feature replication, particularly for research
objectives. This scale is not appropriate for problem assessment, pollutant load estimation, or trend
analysis but can be used for preliminary assessment of the effectiveness of BMPs.

Monitoring at this scale will focus on storm-event monitoring, generally requiring automatic samplers,
continuous flow measurement, and considerable annual  expense. Rainfall simulation is often used at this
scale to control study conditions. Monitoring duration is generally less than three years (USDA-NRCS
2003).

2.3.2.3.2 Field
Field scale study units are  larger than individual plots but can vary considerably in size (USDA-NRCS
2003).  Field scale studies in urban settings include parking lots, rooftops and street segments. In
agricultural settings, field scale studies include cropland field segments, paddocks and barnyards. A key
characteristic of most field studies is that samples are taken from episodic runoff, not from waterbodies.

Monitoring at the field scale is useful for the following objectives:
  «  Problem assessment, especially source characterization.
  •  Load allocations.
  *  BMP effectiveness.

Field scale monitoring is not recommended for trend analysis or determining the effectiveness of
watershed projects.

Monitoring at this scale is  usually  completed in less than five years,  and studies of individual  BMPs can
be completed in three years or less. Sampling is typically focused on storm events, with either discrete or
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Chapter 2
composite sampling depending on the specific needs of the study. Flow measurement is essential to most
field scale monitoring plans.

2.3.2.4  Summary
Table 2-3 matches monitoring objectives with the appropriate monitoring scale. The best scales for
assessing BMP effectiveness are plot and field, whereas the watershed scale is best for evaluating
watershed projects. Both field and watershed scales can be useful for problem assessment and load
estimation, whereas the watershed scale is generally best for trend analysis.

                      Table 2-3. Monitoring scale as a function of objective
Scale
Plot
Field
Watershed
Objective
Problem
Assessment

X
X
TMDL Loads

X
X
Trends


X
BMP
Effectiveness
X
X


Watershed
Project
Evaluation


X
2.4  Monitoring Design Selection

2.4.1 General Considerations
As discussed above, monitoring objectives drive decisions on the details of a monitoring program. There
are several experimental designs that can be applied to meet monitoring objectives, and some of the
choices are obvious. All else being equal, the monitoring design selected should be the one that best
matches available resources and presents the fewest logistical obstacles. Although this may seem obvious,
it is important that monitoring design be determined before monitoring begins to ensure that suitable data
are collected to meet monitoring objectives. Our discussion addresses monitoring design as a direct
function of monitoring objective, modeled after guidance developed for agricultural monitoring projects
(USDA-NRCS 2003).

2.4.2 Design Options
The design options discussed in this section are:
  •   Reconnaissance or synoptic
  •   Plot
  •   Paired
  •   Single watershed before/after
  •   Single-station long-term trend
  •   Above/below
  •   Side-by-side
  •   Multiple
  •   Input/output
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All of the above designs are targeted designs, which are the most common type of design used to evaluate
BMPs or plan and evaluate watershed projects. With the exception of the input/output design, all of the
designs are applicable to biological monitoring; however, the discussions that follow focus on the
measurement of chemical and physical parameters via in-stream measurements and the collection of
water samples. Chapter 4 provides more discussion on biological monitoring and provides some
discussion on probabilistic designs, which are particularly useful for providing unbiased assessments of
conditions in a waterbody or across a large geographic area. Section 2.5 of the 1997 guidance (USEPA
1997) also provides a detailed discussion of probabilistic monitoring designs. Detailed discussions of
statistical tests recommended for each design can be found in chapter 7.

2.4.2.1  Reconnaissance  or Synoptic
Reconnaissance or synoptic studies are designed to provide a preliminary, low-cost overview or summary
of water quality conditions in the area of interest. Reconnaissance surveys are often used to (USDA-
NRCS 2003):
  «  Determine the magnitude and extent of a problem.
  •  Obtain preliminary data where none exist.
  «  Target critical areas.

Data collected from reconnaissance surveys are generally used in the problem assessment and planning
phases of watershed projects but can also be used to help design projects to evaluate BMPs.
Reconnaissance surveys typically involve a relatively large number of sampling sites distributed across
the study area, low sampling frequencies (e.g., one or two samples at each site for high/low flow or
seasons), a core set of common monitoring variables with or without additional variables (e.g., pesticide
scans) selected based on knowledge of specific problems or sources, and a short study duration
(e.g., completed in under 12 months) (Figure 2-11). A common strategy is to sample significant
tributaries, longitudinally along primary streams, and at locations indicative of selected land uses
(including undisturbed).

Permanent monitoring stations are not installed for reconnaissance surveys and grab sampling is typically
used. Integrated grab samples, sediment samples, the use of multi-parameter probes, and instantaneous
flow measurements may be part of the sampling scheme depending upon the purpose of the study.

Because sample sizes are generally small, statistical analyses are not usually performed on reconnaissance
data. Instead, it is common to gather all potentially useful data on water quality and land use and
management, summarize the data in graphic or tabular form, and then interpret the data using best
professional judgment. For example, pollutant concentrations can be plotted against water quality criteria
to identify potential problem areas. Water chemistry data might be examined to see where pollutant
concentrations are highest and lowest and whether any patterns might exist along the course of a stream
or in tandem with patterns found in biological monitoring data. By superimposing water quality data
summaries on top of a land use map, it may be possible to identify critical areas where BMPs are needed,
particularly in cases where information has also been gained from visual surveys.
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Monitoring and Evaluating Nonpoint Source Watershed Projects                                     Chapter 2
                                      Monitoring  Stations
Figure 2-11. Reconnaissance sampling design

2.4.2.2  Plot
Plot studies are often used to:
  •  Assess soil conditions including nutrient levels.
  •  Assess pollutant transport pathways.
  •  Determine the effects of BMPs on pollutant transport.

Plot studies are typically used in research and are sometimes established to provide information that is
important to a broader watershed study. For example, the effectiveness of agricultural drainage water
management practices, which is important to the broader Mississippi River Basin nutrient management
effort (MRGOMWNTF 2008), could be addressed with plot studies. The ability of catch or cover crops to
reduce N loading to the  Chesapeake Bay is another example of a research question that could be
addressed with plot studies. Findings from the plot studies could be incorporated into planning and
modeling efforts for the large basins.

Nonpoint source plot studies typically employ a randomized complete block design or other, more
complicated statistical design such as the Latin square and split-plot designs, or a factorial arrangement of
treatments (USDA-NRCS 2003). Blocking is the arranging of study plots in groups (blocks) that are
similar to one another (e.g., slope, soils, pavement type). The  blocking factor is generally a source of
variability that is not of primary interest to the investigator. The study plots within each block include a
control and a treatment that is of primary interest such as different levels or forms of nutrient  application,
different cover crops, different levels of crop residue, or different street sweeping frequency. Each set of
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Monitoring and Evaluating Nonpoint Source Watershed Projects                                      Chapter 2
treatments is generally replicated at least three times, and each block is considered a replicate. The
number of plots required for the study is:

Plots = txb

Where  t = number of treatments including the control
        b = number or blocks

Data from plot studies are typically analyzed using analysis of variance methods which are described both
in chapter 7 and in chapter 4 of the 1997 guidance.

Plot studies using randomized complete block designs are common in the literature (Lentz and Lehrsch
2010, Wilson et al., 2010). Runoff plot studies usually require construction of permanent monitoring
stations equipped with weather stations, automatic sampling equipment, and continuous flow
measurement devices. In some cases remote access is provided. Samplers may be programmed to collect
either discrete samples over the course of individual storm events or composite samples. Sampling is
intensive and storm-event based, but studies are generally completed in three or fewer years.

Advantages of plot studies include the use of replicates and tight experimental control, but the results of
plot studies are not generally widely transferable (USDA-NRCS 2003).

2.4.2.3  Paired
Paired-watershed design is the most powerful design
option that is used with any frequency to evaluate the
impacts of BMPs or projects (USDA-NRCS 2003,
USEPA 1993b, Hewlett and Pienaar 1973). It has been
used with success in a number of Section 319 NNMP            „  ..      „    . .
   ...,,.   T  ,   -     /-r//-i      -mmx   j        •  Similar runoff and base flow patterns
projects, including Jordan Cove, CI (Clausen 2007) and
Morro Bay, CA (CCRWQCB and CPSU 2003). The basic
Selection of Paired Watersheds
•  Similar size and location
•  Similar slope, soils, and land cover
                                                            Similar relationships between
 ,.        ..      4.   u j  /4.   4.    4   j    4  i\           monitored variables and flow
design requires two watersheds (treatment and control)
and two time periods (calibration and treatment) as             *  Abilityto contml and document land
illustrated in Figure 2-12, but the design can include more        USe an   an  rea men  m
than one treatment watershed. The discussion here is
limited to a two-watershed design. Paired samples are
collected from both watersheds during the calibration period and regression analysis is used to test for a
relationship between the paired samples (USEPA 1993b). After the calibration period relationship is
established, BMPs are implemented in the treatment watershed, paired samples are collected, and a new
relationship is established between paired samples collected during the treatment period. At the end of the
treatment period, the significance of the effect of the BMPs is determined using analysis of covariance
(see  section 7.8.2). A helpful narrated video describing the paired-watershed design can be found at the
Jordan Cove, CT project website (Dietz 2006).

A variation on the paired-watershed design is the nested-paired-watershed design in which both
monitoring stations are located in the same watershed (Hewlett and Pienaar 1973). In this design, it is
preferred that the upper sub-watershed is used as the control, and the lower portion of the watershed is
treated. This alignment will reduce the chances that the treatment basin will influence the control as the
treatment effect passes through it (Hewlett and Pienaar 1973). The nested-paired watershed design is
essentially equivalent to the above/below-before/after design described in section 2.4.2.6.
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                                                          Chapter 2
The first step in establishing a paired study is the selection of two watersheds or study areas that are likely
to have qualitatively similar responses to precipitation events. This means that both watersheds will
generally have runoff when it rains, base or no flow in the absence of runoff events, and a relationship
between the concentrations of measured variables and flow. If these general responses to precipitation
patterns differ significantly, good calibration may not be possible. Several attempts at paired studies have
failed because the watersheds could not be calibrated. Choosing watersheds that are similar in size, slope,
location, soils and land cover will increase the chances of selecting a good pair (USEPA 1993b).
       Study
Control
Study
Control
           Calibration  Period
                                Treatment  Period
             Treated Area
     ^       Monitoring  Stations

Figure 2-12. Paired sampling design

Note that the control watershed can either be in a similar impaired condition as the treatment watershed or
in the improved condition desired for the treatment watershed. In the first case, the expected result is that
conditions in the treated watershed get better than in the control watershed, whereas in the second case the
expected result is that conditions  in the treated watershed become more like those in the control
watershed. In VT, for example, Meals (2001) successfully used a highly-impaired agricultural watershed
as a control, measuring the beneficial effects of treatment against that watershed. The essential
characteristic of a control watershed is that it does not receive BMP treatment during the life of the
monitoring project.

After a suitable pair is found, the  next important hurdle is to ensure that land use and land treatment
activities at both watersheds can be documented and controlled through study's duration. Too often
investigators have achieved control over the "treatment" watershed without securing equivalent control
over the "control" watershed, only to find that activities in the control watershed (e.g., housing
developments springing up on cropland or voluntary adoption of the BMPs under study) have
compromised the study.
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The New York NNMP project successfully paired a dairy farm watershed with a forested watershed, but
concluded that it was important to include matched measurements of flow volumes, and farm watershed
measurements of event peak flow and event flow rate to account for inherent differences in watershed
characteristics and hydrologic response in the analysis of P load data (Bishop et al. 2005). A forested
watershed was selected as the control watershed because no significant changes were expected, as
opposed to a farmed watershed where operational practices could be modified or the farm could go out of
business during the 8-year study period.

Monitoring for paired studies can range from biological monitoring to grab sampling to automated
sampling with permanent monitoring stations. Given the difficulty associated with orchestrating paired
studies at the watershed scale, it is recommended that higher-level biological and/or automated sampling
be performed to ensure collection of robust datasets. Well-designed paired studies can generally be
completed in five to seven years. Projects can be extended or even interrupted in cases where BMP
implementation is a lengthy process.

Advantages of this design for evaluating the effectiveness of BMPs and projects include control for
hydrologic variation and inherent watershed differences and the potential for clear attribution of water
quality response to the BMPs. It is also possible to examine the magnitude of the treatment effect for
large versus small events or baseflow conditions, but this study requires adequate sample sizes for each
data subset (Lewis 2006). Further, as demonstrated by the New York NNMP project, water quality
monitoring can be suspended during BMP implementation without compromising the study design
(Bishop et al. 2005). This can help address the problem of diluting the overall effect  of treatment by
lumping together data collected during the treatment implementation with post-treatment data (Lewis
2006). Land management tracking in both the control and treatment watersheds is recommended during
BMP implementation to aid in data  interpretation after water quality monitoring is resumed. Depending
on the location of monitoring stations, pollutant loads estimated from paired studies could be  used to
support TMDLs.

Based on the NNMP experience, the greatest practical disadvantage of the paired design is the difficulty
finding pairs, particularly at the watershed scale. Even when pairs are found, it is often difficult to control
both watersheds, so land use and land management changes occur where and when needed to support the
study. This challenge becomes even greater if multiple treatment watersheds are included in the design. A
disadvantage of paired studies versus multiple-watershed studies is the conclusions pertain only to the
specific watersheds and treatments tested; there is generally no practical ability to predict effectiveness of
the treatments or differing levels of treatment in other watersheds from the same population (Lewis 2006).
This disadvantage is because descriptors of the treatment (e.g., percent impervious area or percent of
cropland in no-till) are generally not included in the statistical model tested.

2.4.2.4  Single Watershed Before/After
In this design, a single monitoring station is located at the outlet of the study area. Sampling is performed
before and after the implementation of BMPs. In watersheds subject to TMDLs, this design may be
considered to measure pollutant loads before and after implementation of the TMDL to determine if loads
have been reduced or TMDL load targets achieved. Typically the investigator is expecting to  detect step
changes in the target parameter with the monitoring program.

This design is not recommended for BMP effectiveness studies because there are no  control stations (as in
the paired design described earlier); and BMP effectiveness cannot easily be distinguished from other
confounding effects (USDA-NRCS  2003). For example, if the "before" years are relatively dry and the
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Monitoring and Evaluating Nonpoint Source Watershed Projects                                      Chapter 2
"after" years are relatively wet or vice-versa, the differences in water quality and loads could be due to
differences in weather rather than the effects of implemented BMPs. Generally, this design does not
collect data that can be used to directly separate the effects of the BMPs from those of climate and is
therefore a poor choice for assessing the effectiveness of BMPs or watershed projects. However, in the
case of TMDLs, it would remain possible to compare measured loads versus target loads to see if goals
have been achieved although attributing success or failure to TMDL implementation would be difficult. A
possible exception would be the fortunate situation where water quality improved and pollutant loads
were reduced despite increased runoff during the "after" years.

While this design is generally not recommended for BMP effectiveness studies, analysis of covariance (see
section 7.8.2) can be used to provide some indication of BMP effects (USDA-NRCS 2003). Under this
approach, a water quality variable could be related to a climate variable such as precipitation using a method
described by Striffler (1965). A change in this relationship could be attributed to the BMP, but there would
be no direct estimate of reduction of the water quality variable of interest. This approach would require a
longer calibration period and results are not transferable to other areas (USDA-NRCS 2003).

Depending upon the study purposes, single watershed designs may or may not require construction of
permanent monitoring stations, weather stations, automatic sampling equipment or continuous flow
measurement devices. For some applications, grab samples will be sufficient, whereas composite sampling
with automatic samplers will usually be needed in applications that require pollutant load estimation.

2.4.2.5  Single-Station Long-Term Trend
This design has been a staple of water quality monitoring for decades and can be used to determine
changes in water quality or pollutant loads over time. Single-station trend monitoring generally cannot be
used to determine if BMPs improve water quality unless a very long water quality and concurrent land
use/treatment monitoring program can be sustained.

Trend monitoring is similar to single watershed before/after monitoring except that there is no planned
before/after period and the study duration is expected to be much longer (decades versus years).
Advantages of this design include the single monitoring station, wide applicability, and the ability to
account for lengthy lag times (see section 6.4) and gradual implementation of BMPs (USDA-NRCS
2003). Because of the expected longer study durations,  additional considerations may be necessary due to
major land use changes and data gaps; long-term commitment of resources; consistency in sampling and
analytical methods overtime; and tracking land use, land treatment, precipitation, and flow.

Simple statistical analyses can be used to detect trends;  however, they do not indicate why the trend
exists. Adjusting the trend data set for hydrologic influences can help in that regard. For example, using
streamflow as an explanatory variable,  it was possible to document a statistically significant reduction in
sediment and TP load in Willow Creek, Michigan, storm runoff over the eight years of monitoring
(Suppnick  1999). These reductions were then correlated with the percent of land in no-till.

See Meals et al. (2011) and chapter 7 for additional information on trend analysis.

2.4.2.6  Above/Below
Above/below monitoring has been used with success to assess the water quality impact of isolated sources
and determine the effectiveness  of BMPs at isolated sources.

In this method, design stations are located upstream (or up-gradient) and downstream (or down-gradient)
of the area or source that will be treated with BMPs  (Figure 2-13). When used in the planning phase of a
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Monitoring and Evaluating Nonpoint Source Watershed Projects                                     Chapter 2
watershed project, grab samples and instantaneous flow measurements for one or two sampling events
may be sufficient, but in most cases permanent monitoring stations are constructed and equipped with
automatic sampling equipment, continuous flow measurement devices, and sometimes remote access.
Depending on study objectives and flow conditions, samplers could be programmed to collect discrete
samples over the course of individual storm events, weekly composite samples, or some other variant.
Weather stations are usually included, but one station is often sufficient for both sampling sites. Study
duration for evaluating BMP effectiveness is generally three to five years, but potentially longer for larger
areas with multiple sources treated with a range of BMPs.

Samples are  "paired" in this design, with the intent to sample the same unit of water when it is above and
then below the study area. If monitoring is performed both before and after the BMPs are implemented
(above/below-before/after design), this design becomes equivalent to a nested-paired-watershed design
and can be treated statistically as if it were a paired-watershed design (see 2.4.2.3).
         Above  Station
                                                                  Treatment Area
   Below Station.
                             XL/'
                                          (A Monitoring  Stations

Figure 2-13. Above/below sampling design

Advantages of this design include:
  •   It is widely applicable.
  •   It is not as vulnerable to climate variability as the single watershed design.
  •   It is useful for isolating critical areas in the watershed.
  •   It can use the same statistical procedures (e.g., analysis of covariance) as a paired-watershed design
      because monitoring is performed before and after BMP implementation.
  •   Load measurements can be useful in TMDL watersheds depending on station location.

A significant disadvantage of this design is the potential for upstream conditions (e.g., high stream
temperatures or pollutant concentrations) to overwhelm downstream conditions, masking both the inputs of
the isolated source or area and the effects of the implemented BMPs. This risk can be addressed by ensuring
the isolated area contributes substantially to the downstream flow, but this may not be feasible without
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Monitoring and Evaluating Nonpoint Source Watershed Projects                                      Chapter 2
sacrificing the extent of the area is isolated. It is recommended that a reconnaissance effort be undertaken
with sampling at both low flow and high flow conditions to see if the site is suitable for this design.

Other disadvantages include the possibility that inherent differences between the two stations
(e.g., geology) or interactions between the BMPs and the watershed could be causing the measured
differences in water quality (USDA-NRCS 2003). Monitoring before and after BMP implementation can
help to address these issues. Absent complete knowledge of the interaction between subsurface and
stream flows, there is always the risk that unsuspected sources are impacting downstream water quality as
was found in the Maryland Section 319 National NFS Monitoring Program (NNMP) project where it was
determined that ammonia from the upper portion of the  watershed passed through nitrifying zones and
was converted to NOs, thus elevating NOs levels at the outlet of the watershed (Shirmohamadi et al.  1997,
Shirmohammadi and Montas 2004). A major ramification of this discovery was that nutrient management
had to be added to dairy manure management and stream fencing both as a BMP needed to solve the
nutrient problems in the watershed and as a factor influencing stream N levels.

2.4.2.7  Side-by-Side Before/After
Monitoring watersheds that are adjacent to each other without calibrating paired samples before treatment
is equivalent to having two separate single-watershed studies as described under section 2.4.2.4. This
design is not recommended for evaluation of BMPs or watershed projects because it is highly likely  that
there will be no way to distinguish among causal factors such as BMPs or land treatment, inherent
watershed differences, or an interaction between BMPs  and watershed differences (USDA-NRCS 2003).

2.4.2.8  Multiple
Where resources allow, monitoring of multiple watersheds (or fields) may be used to demonstrate the
effectiveness of BMPs (USDA-NRCS 2003). This design requires that more than two watersheds are
selected for monitoring within the geographic area of interest. Two different treatments and perhaps  a
control are replicated across the monitored watersheds in roughly equal numbers. Treatments may already
be in place when the study begins or may be implemented after a pre-treatment monitoring period. An
effective design would typically include monitoring over several years.

One advantage of using this approach with a large number of monitored watersheds (e.g., 10 or more) is
that variability among watersheds can be estimated. Furthermore, observing water quality changes of
similar direction and magnitude occurring with land treatment changes across several watersheds serves
to substantiate the evidence for BMP effectiveness and robustness of the BMP over a range of watershed
conditions. A major problem with this design is the practical limitations and cost associated with
controlling and monitoring many watersheds or fields. It is possible, however, that the increased annual
sample size associated with the inclusion of multiple watersheds could reduce the overall timeframe  for
monitoring.

Lewis (2006) describes a multiple-watershed approach in which 3 of 13 watersheds are used as controls,
5 are fully treated, and 5 are partially treated. He argues that this design has a significant advantage over
paired-watershed studies in that it allows for prediction under different conditions or treatment levels,
whereas prediction based on paired-watershed study results requires the assumed treatments are identical
to the treatments used in the study.

2.4.2.9  Input/Output
Input/output design is used to evaluate the effects  of individual  BMPs on water quality. It is not generally
useful for any of the other objectives addressed in this guidance.
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Monitoring and Evaluating Nonpoint Source Watershed Projects
                    Chapter 2
Under this design, paired samples are collected at the inflow and outflow of the BMP as illustrated in
Figure 2-14. For some BMPs such as manufactured devices used for urban runoff, the inflow and outflow
are clearly defined and collecting paired samples is simple. For practices such as constructed wetlands,
however, the inflow and outflow may be clearly defined, but collecting paired samples is challenging
because retention time may not be known or may vary between runoff events. Other practices such as rain
gardens can be difficult to evaluate because inflow may occur at several points or as sheet flow
(Figure 2-15), and outflow may not be directly measurable because underdrains are not used.
   Inflow
Outflow
Figure 2-14. Input/output sampling design
Figure 2-15. Multiple input pathways for rain garden
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Monitoring and Evaluating Nonpoint Source Watershed Projects                                      Chapter 2
Most sampling under this design will be storm-event based. Grab or automatic samples can be taken
depending upon the specific practice evaluated, and either discrete or composite samples could be
appropriate based upon specific study objectives. Flow measurements are essential to most BMP
evaluations because performance usually varies with both flow rate and influent pollutant concentration.

With the exception of larger practices such as constructed wetlands and animal waste management
systems, the possibility of having replicates and controls is an advantage of this design. No calibration
period is required for inflow/outflow studies and measured pollutant reductions can be clearly attributed
to the practice. A disadvantage of this design is the likelihood that results may not be widely transferable,
but the relevance of this disadvantage will vary depending on the practice and study specifics. For
example it may be possible to evaluate a manufactured urban stormwater device over a wide range of
flow and influent concentrations at a single location, resulting in fairly widespread applicability.

Statistical methods commonly used for input-output studies are described in chapter 7 and include the
paired t-test, nonparametric t-tests and the calculation of pollutant removal efficiencies. The effluent
probability method is described in section 7.7.2.

2.4.2.10 Summary
Table 2-4 matches monitoring objectives with appropriate monitoring designs. Reconnaissance is best for
the assessment phase of a watershed project, but above/below monitoring can also be helpful  in providing
information about the isolated source or area. The paired, above/below-before/after, plot, and input/output
designs are generally the best designs for evaluating the effectiveness  of BMPs or watershed projects. All
but reconnaissance, plot, and input/output monitoring can provide useful load estimation in support of
TMDLs if flow and the relevant variables are monitored. Not surprisingly, the trend design is the best for
trend detection.

Some monitoring designs can be used for more than one objective depending  upon the location of
monitoring stations, the schedule for BMP implementation, and the duration of the monitoring program.
Both the single watershed and side-by-side watershed designs could be used for trend detection if
monitoring is continued in a consistent manner over a longer than planned timeframe. It may  be wise to
actually plan for an extension of monitoring  duration for trend detection under these two designs if they
fail to yield results under a before/after monitoring design. Even above/below and paired designs could be
extended for trend detection but the cost associated with continued monitoring over a longer timeframe
would be very high; an alternative would be  to consider extended monitoring  for only the downstream
(for above/below) or treatment (for paired) stations with some reduction in the set of monitoring variables
or monitoring frequency to reduce costs. Any changes in monitoring frequency, however, would be
contingent upon the ability to meet the requirement of consistent methods throughout a trend  study.
                                               2-43
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Monitoring and Evaluating Nonpoint Source Watershed Projects
Chapter 2
                     Table 2-4. Monitoring design as a function of objective
Design
Reconnaissance
Plot
Paired
Single watershed
before/after
Single-station
long-term trend
Above/below-
(before/after)
Side-by-side
before/after
Multiple
Input/output
Short Description
Multiple sites distributed across study
area and monitored for a short
duration (<12 months)
Traditional research study design with
varying treatments (BMPs) replicated
in randomized block design
Treatment and control watersheds
monitored during a control and
treatment period
Single station at study area outlet
monitored before and after BMP
implementation
Single station at study area outlet
monitored before and after BMP
implementation
Stations, with paired sampling, located
upstream (up-gradient) and
downstream (down-gradient) of BMP
Same as single watershed since there
are no calibrating paired samples
Multiple watersheds monitored in two
or more groups: treatment and control
Stations located at the input and
output of an individual BMP
Variations


Variation: nested-paired
watershed— monitoring
stations are in the same
watershed

Same as single watershed
before/after without BMP
implementation
Same as nested-paired-
watershed design if sampled
before and after BMP
implementation



Objective
Problem
Assessment
X




X




•a
TO
O
_l
_l
o
i—


X
X
X
X
X


in
-a
1



X
X

X


BMP or Project
Effectiveness

X
X


X

X
X
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       Assessing Wetland Condition. EPA/620/R-04/009. U.S. Environmental Protection Agency,
       National Health and Environmental Effects Laboratory, Corvallis, OR.

USEPA (U.S. Environmental Protection Agency). 2006a. Application of Elements of a State Water
       Monitoring and Assessment Program For Wetlands. U.S. Environmental Protection Agency,
       Office of Water, Office of Wetlands, Oceans, and Watersheds, Wetlands Division, Washington,
       DC.

USEPA (U.S. Environmental Protection Agency). 2006b. Guidelines for the Award of Monitoring
       Initiative Funds under Section 106 Grants to States, Interstate Agencies, and Tribes. U.S.
       Environmental Protection Agency, Washington, DC. Federal Register, March 29, 2006,
       71:15718-15724. Accessed January 28, 2016. https://federalregister.gov/a/E6-4585.

USEPA (U.S. Environmental Protection Agency). 2008a. Amendment to the Guidelines for the Award
       ofMonitoring Initiative Funds under Section 106 Grants to States, Interstate Agencies, and Tribes.
       U.S. Environmental Protection Agency, Washington, DC. Federal Register, July  17, 2008,
       73:41069-41076. Accessed January 28, 2016. https://federalregister.gov/a/E8-16385.

USEPA (U.S. Environmental Protection Agency). 2008b. Handbook for Developing Watershed Plans to
       Restore and Protect Our Waters. EPA 841-B-08-002. U.S. Environmental Protection Agency,
       Office of Water, Washington, DC. Accessed February 10, 2016. http://www.epa.gov/polluted-
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USEPA (U.S. Environmental Protection Agency). 2011. Aquatic Resource Monitoring- Frequently Asked
       Questions - Survey Sampling. U.S. Environmental Protection Agency, Office of Research and
       Development, National Health and Environmental Effects Research Laboratory, Western Ecology
       Division, Corvallis, OR. Accessed January 28, 2016.
       http://archive.epa.gov/nheerl/arm/web/html/designfaqs.html.
                                              2-52
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USEPA (U.S. Environmental Protection Agency). 2012a. EPA Program Guidance. U.S. Environmental
       Protection Agency, Washington, DC. Accessed January 28, 2016.
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USEPA (U.S. Environmental Protection Agency). 2012b. Starting Out in Volunteer Monitoring. EPA
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       Accessed January 28, 2016.

Wagner, R.J., R.W. Boulger, Jr., C.J. Oblinger, and B.A. Smith. 2006. Guidelines and Standard
       Procedures for Continuous Water-Quality Monitors—Station Operation, Record Computation,
       and Data Reporting. Techniques and Methods 1-D3. U.S. Geological Survey, Reston, VA.
       Accessed January 28, 2016. http://pubs.usgs.gov/tm/2006/tmlD3/.

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       Wisconsin Association of Lakes, Madison, WI. Accessed January 28, 2016.
       http://www.wisconsinlakes.org/index.php/the-science-of-lakes/21-lake-types.

Welch, E.B. and G.D. Cooke. 1999. Effectiveness and longevity of phosphorus inactivation with alum.
       Journal of Lake and Reservoir Management 15:5-27.

Wetzel, R.G. 1975. Limnology. W.B. Saunders Company, Philadelphia, PA.

Wilde, F.D. 2005. Preparations for Water Sampling. Book 9, Chapter Al in Techniques of Water-
       Resources Investigations. U.S. Geological Survey, Reston, VA. Accessed January 28, 2016.
       http://pubs.water.usgs.gov/twri9Al/.

Wilde, F.D., ed. 2006. Collection of Water Samples (Version 2.0). Book 9, Chapter A4 in Techniques of
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       2016. http://pubs.water.usgs.gov/twri9A4/.

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       and nitrogen uptake by potato. Journal of Environmental Quality 39:2: 492-499.

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       Single Resource. Circular 1139. U.S. Geological Survey, Denver. Accessed January 28, 2016.
       http://pubs.usgs.gov/circ/circ 113 9/index.html.

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       Limnology and Oceanography 21(6):804-813.
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Monitoring and Evaluating Nonpoint Source Watershed Projects                                      Chapter 3
3   Monitoring  Plan  Details

     By D.W. Meals, S.A. Dressing, J. Spooner, and J.B. Harcum

In chapter 2 we discussed monitoring objectives, the fundamentals of good monitoring, the selection of an
appropriate geographic scale for monitoring, and the selection of a basic monitoring design. In this
chapter we discuss the nuts and bolts of monitoring, beginning with the selection of variables and
concluding with data reporting and presentation. Because the emphasis of this guidance is placed on
monitoring for watershed-level problem assessment, load estimation, trend analysis, and the effectiveness
of BMPs or watershed projects, the details that follow in this and subsequent chapters will be centered on
these objectives.

3.1    Variable Selection
Monitoring variables are often grouped into three general categories:
  •  Physical (e.g., flow, temperature, or suspended sediment)
  •  Chemical (e.g., DO, P, atrazine)
  •  Biological (e.g., E. coll bacteria, benthic macroinvertebrates, fish)

It is usually most appropriate for projects to monitor a mix of variables, although some  projects may
focus in one specific area such as physical measurements. Variables are often interrelated across these
categories. For example, DO concentrations and temperature influence the fish assemblage present at a
site.

Selection of the appropriate variables to monitor is a crucial task. A monitoring program cannot afford to
measure every single variable nor should a project attempt to do so because some variables contribute to
achieving project goals more than others. It is wasteful to measure characteristics that are unimportant or
irrelevant to project objectives, and it is equally problematic to fail to measure key variables. In general, it
is better to monitor a minimum set of variables well than a large number of variables poorly
(e.g., minimal sampling frequency and/or duration).

3.1.1    General Considerations
The selection of which variables to measure in a monitoring program requires consideration of several
important factors. It is important to resist the temptation to measure more variables than are needed for
the project or to adopt a generic list of traditionally monitored water quality variables. The final design of
a monitoring program often represents a compromise based on balancing information requirements,
budget, personnel, and other constraints. Excess resources spent on analyzing unnecessary variables may
force a reduction in the number of sampling stations, the sampling frequency, or the duration of
monitoring, which can threaten program effectiveness.

The following sections discuss important factors to be considered when selecting variables to monitor.
Variables commonly measured in watershed nonpoint source monitoring efforts are also discussed.
                                               3-1
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Monitoring and Evaluating Nonpoint Source Watershed Projects                                      Chapter 3


3.1.2  Selection Factors

Several factors should be considered when selecting variables to measure in a monitoring project. These
factors, discussed below, are:
  «   Program objectives
  •   Waterbody use
  •   Water resource type
  •   Use impairment
  •   Pollutant sources
  •   Expected response to treatment
  «   Difficulty or cost of analysis
  •   Logistical constraints
  •   Need for covariates
  •   Priorities

3.1.2.1  Program Objectives
The overarching principle  of monitoring variables selection is that the variables should be tied directly
to the program objectives with due consideration of the other factors described in this section. In many
cases, the stated program objective will clearly indicate the appropriate variable(s) to monitor. For
example, an objective to document the effectiveness of BMPs on E. coll levels at a public beach clearly
calls for measurement of E. coll bacteria. An objective to reduce TP loading to a lake would suggest
measuring TP (perhaps not dissolved P) concentration and measuring flow because both concentration
and flow data are required to calculate load (see section 3.8  and section 7.9). An objective to restore a
fishery might require, at a minimum, monitoring the fish population as well  as chemical (e.g., DO,
ammonium) and physical (e.g., temperature, substrate) variables that support acceptable fish habitat.

It is more challenging to select monitoring variables when program objectives are  less specific. For
monitoring aimed at assessing water quality standards compliance or TMDL implementation, the selected
variables should focus on what is required to assess water quality standards violations or TMDL
achievement. For monitoring objectives that involve watershed reconnaissance or characterization, other
factors such as the nature of the impairment, type of water resource, or likely pollutant sources must be
considered.

3.1.2.2  Waterbody Use
Variable selection may be driven by a waterbody's designated use. Designated uses are one of three
elements contained in water quality standards. The other elements are water quality criteria to protect
those uses and determine if they are being attained, and antidegradation policies to help protect high
quality water bodies (USEPA 201 Ic). States and tribes designate water bodies for specific uses based on
their goals and expectations for their waters. Typical designated uses include:
  •   Protection and propagation of fish, shellfish, and wildlife
  «   Recreation

  •   Public water supply
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Monitoring and Evaluating Nonpoint Source Watershed Projects                                       Chapter 3
  •  Agricultural, industrial, navigational, and other purposes.

Numeric and narrative water quality criteria are set to protect each designated use by describing the
chemical, physical and biological conditions necessary for safe use of waters by humans and aquatic life.
These criteria should be used to help guide variable selection and other monitoring details (e.g., sampling
period and frequency) where use attainment or protection is the primary monitoring concern. Failure to
meet some or all of the applicable water quality criteria can result in less than full support of designated
uses.

For example, monitoring of a waterbody used for recreation might emphasize sediment, nutrient, or
bacteria variables because these help define the aesthetic and health-related character of the waterbody.
However, variables for monitoring irrigation water might include total dissolved solids and salinity
variables and exclude less relevant biological variables. General applicability of water monitoring
variable groups to selected designated uses is shown in Table 3-1.

3.1.2.3  Waterbody Use Impairment
Monitoring of waterbodies with documented use impairments can differ substantially from monitoring to
assess use attainment or protection. For example, the impairment could be the result of a single pollutant
(e.g., violation of a turbidity criterion) or failure to achieve one portion of a narrative criterion (e.g., fish
assemblage), rather than a failure to meet all applicable criteria. In these situations, monitoring can be
focused on the specific variables violating criteria instead of all potential variables indicated by the
applicable water quality standard. While the variable list associated with criteria may be narrowed,
additional variables should be considered to address the causes of the violation(s). For example, turbidity
problems could be caused by streambank erosion, high phytoplankton production, or wash from
impervious surfaces.  Fish assemblage could be impacted by a number of factors such as lack of suitable
flow or cover, water quality, or physical obstructions. For projects with an objective to relate water
quality changes to pollution control efforts, it is essential to track variables associated with the causes of
identified water quality problems.

3.1.2.4  Type of Water Resource Sampled
Variables monitored  should be suitable for the type of waterbody under study. Appropriate variables often
differ significantly between surface and ground water and between streams and lakes. Examples of
variable groups that can be applicable to different water resource types are shown in Table 3-2.

3.1.2.5  Pollutant  Sources
Variables monitored  should reflect the nonpoint sources known or suspected to be present in the
watershed. Crop agriculture, for example, is likely to influence suspended sediment, turbidity, nutrients
and pesticides measured in water. The presence of intensive livestock agriculture in a watershed would
justify measuring biochemical oxygen demand (BOD), nutrients and indicator bacteria. Urban stormwater
sources are likely to influence variables such as discharge, temperature, turbidity, metals and indicator
bacteria. Examples of variable groups that can be responsive to different nonpoint source activities are
shown in Table 3-3.
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Monitoring and Evaluating Nonpoint Source Watershed Projects
Chapter 3
   Table 3-1. Monitoring variable groups by direct relationship to selected designated water use
                               (adapted from USDA-NRCS 2003)
Variable
Designated Use
Aquatic life
support
Contact
recreation
Aesthetics
Irrigation
Drinking
water
supply
Physical
Discharge
Dissolved oxygen (DO)
Salinity
Secchi disk transparency
Specific conductance
Suspended sediment
Temperature
Total dissolved solids (TDS)
Turbidity
X
X
X
X
X
X
X
X
X



X

X


X

X

X

X


X


X

X


X


X
X

X
X

X
X
Chemical
BOD
Inorganics (Cl, F)
Metals (As, Cd, Cr, Cu, Fe, Hg, Pb, Zn)
Nutrients (N, P) - dissolved
Nutrients (N, P) - total/particulate
pH
X
X
X
X
X
X


X



X


X
X


X
X
X

X

X
X


X
Biological
Benthic macro! nvertebrates
Chlorophyll a
Fish
Indicator bacteria (fecal coliform, £ coli)
Macrophytes
Pathogens (Giardia, Cryptosporidium)
Plankton (algae)
X
X
X

X

X

X

X

X


X


X

X








X

X

X
X
3.1.2.6  Response to Treatment
In a monitoring program designed to evaluate water quality response to management measure
implementation, it is critical that monitored variables focus on dimensions of water quality expected to
change in response to treatment. For example, an agricultural watershed uses conservation tillage as the
principal management measure implemented to address an erosion problem. The water quality monitoring
program should measure flow, peak flow, suspended sediment, and turbidity as variables likely to respond
to widespread changes in tillage practices. It would be less appropriate to monitor for E. coli, even if
E. coli standards are also violated in the watershed, unless land application of organic wastes in the
watershed occurs in the watershed.
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Monitoring and Evaluating Nonpoint Source Watershed Projects
Chapter 3
Table 3-2. Monitoring variables by selected water resource types (adapted from USDA-NRCS 2003)
Variable
Discharge
Dissolved oxygen
Habitat
Riffle/pool ratio
Salinity
Secchi disk transparency
Specific conductance
Substrate characteristics
Suspended sediment
Temperature
Total dissolved solids
Turbidity
BOD
Inorganics (Cl, F)
Metals (As, Cd, Cr, Cu, Fe, Hg, Pb, Zn)
Nutrients (N, P) - dissolved
Nutrients (N, P) - total/particulate
PH
Benthic macro! nvertebrates
Chlorophyll a
Fish
Indicator bacteria (fecal coliform, E. coif)
Macrophytes
Pathogens (Giardia, Cryptosporidium)
Plankton (algae)
Lake
X
X
X

X
X
X
X
X
X
X
X
X

X
X
X
X
X
X
X
X
X
X
X
Stream
X
X
X
X
X

X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
Wetland
X
X
X

X

X
X
X
X
X
X
X
X
X
X
X
X
X

X
X
X
X
X
Ground
Water

X


X

X



X


X
X
X

X



X

X

Research has shown that some BMPs can have unintended side effects. For example, increasing
conservation tillage may result in increased herbicide use or increased concentrations and delivery of
soluble nutrients. While conservation tillage has been shown to greatly reduce sediment bound P, P can
become concentrated at the soil surface because of the lack of mixing by tillage, resulting in significant
losses of soluble P in runoff (Beegle 1996). In these situations, it is advisable to monitor either TP or both
particulate and dissolved P to ensure that BMP effectiveness is accurately assessed. Decisions on whether
to track these variables, including adding subsurface monitoring sites, should be made at the beginning of
a monitoring program.
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Monitoring and Evaluating Nonpoint Source Watershed Projects
Chapter 3
Table 3-3. Monitoring variable groups by selected nonpoint source activities (adapted from USDA-
                                          NRCS 2003)
Variable
Nonpoint Source Activity
Crop
Agriculture
Livestock
Agriculture
Construction
Mining
Urban
Stormwater
Physical
Discharge
Dissolved oxygen
Salinity
Secchi disk transparency
Specific conductance
Suspended sediment
Temperature
Total dissolved solids
Turbidity
X
X
X
X

X

X
X
X
X
X
X

X

X
X
X


X

X
X
X
X
X
X


X
X
X
X
X
X
X

X
X
X
X
X
X
Chemical
BOD
Inorganics (Cl, F)
Metals (As, Cd, Cr, Cu, Fe, Hg, Pb, Zn)
Nutrients (N, P) - dissolved
Nutrients (N, P) - total/particulate
PH
X


X
X

X


X
X




X
X


X
X


X
X
X
X
X
X

Biological
Benthic macro! nvertebrates
Chlorophyll a
Fish
Indicator bacteria (fecal coliform, £ coli)
Macrophytes
Pathogens (Giardia, Cryptosporidium)
Plankton (algae)
X
X
X

X

X
X
X

X
X
X
X
X
X
X




X
X
X

X

X
X
X
X
X
X
X
X
3.1.2.7  Difficulty or Cost of Analysis
The difficulty and cost of analysis must be considered in the selection of variables to monitor. While other
factors like program objectives and pollutant sources should be more important criteria in the selection
process, cost of analysis often drives choices among suitable variables because of budget constraints.
Analytical costs will vary by region of the country and by laboratory. In-house laboratories, such as a
university or a state agency, may have lower unit costs than an independent contract laboratory.

Some representative analytical costs are shown in Table 3-4. For several monitoring objectives,
alternative monitoring variables that are lower cost may be available. For example turbidity analysis is
half the cost of suspended sediment; a total dissolved solids measurement is about twice the cost of a
laboratory analysis of specific conductance. Field measurement of conductivity is even cheaper if the
equipment is available. These pairs of variables are likely to be highly correlated, making the lower cost
alternative possibly the best choice  (see section 3.1.3.3 for a discussion of surrogates). However, this will
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Monitoring and Evaluating Nonpoint Source Watershed Projects
Chapter 3
not always be the case and cost alone should not be a primary criterion for variable selection. For
example, a lower-cost analysis for NOs-N ($17) measures an entirely different form of nitrogen from
TKN.

     Table 3-4. Representative laboratory analytical costs for selected water quality variables.
                   Costs will vary by region and by laboratory (Dressing 2014)
Variable
NOs-N
TKN
TN
Soluble reactive P
Total P
Turbidity
Suspended sediment
Specific conductance
Total dissolved solids
Pesticide scan
COD
Oil and grease
Lead (ICP)
Invertebrates
Cost per analysis ($)
17
35
20
15
22
8
16
8
15
135
25
45
15
150
It should also be noted that many variables can be analyzed by different methods that have both different
costs and different levels of sensitivity. For example, a lead analysis by inductively coupled plasma (ICP)
has a cost of $15/analysis using EPA method 200.9 (Barnstable County 2016) and a method detection
limit of 0.7 ug/L (Creed et al. 1994). Compare this to a lead analysis by EPA method 200.5 with a method
detection limit of  1.1 ug/L (Martin 2003) at a cost of $29/analysis (PSU 2016). Project objectives, data
quality objectives  and pollutant sources would factor into the trade-off between cost and sensitivity.
Specific analytical methods can be further investigated in the National Environmental Methods Index
(NEMI) at www.nemi.gov.

Finally, it should be noted that analytical costs, while potentially high, are often considerably lower than
other categories of project costs, particularly personnel costs (see chapter 9). While cost alone is an
important consideration, it cannot be the primary driver of variable selection. If monitoring of the
appropriate variables cannot be correctly performed, money spent on monitoring is wasted.

3.1.2.8  Method Comparability
Advances in sampling and analytical methods are common. While these advances are welcomed on the
one hand by reducing interference and improving reliability and accuracy, they can introduce challenges
during the course  of the project or when trying to design a new project that takes advantage of existing
data. For example, it is wrong to compare historical turbidity data determined by the Jackson Candle
method (units: Jackson Turbidity Unit or JTU) with turbidity data collected from a calibrated
nephelometer (units: Nephelometric Turbidity Units or NTU).  This caution extends to practically every
phase of the monitoring program, from field sampling, sample  preservation, and laboratory procedures.
Ensuring that data from multiple methods can be compared is critical. One approach is to perform a
comparability study by implementing both methods with laboratory splits and comparing the resulting
                                               3-7
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Monitoring and Evaluating Nonpoint Source Watershed Projects
Chapter 3
paired data. Depending on the results, it is prudent for projects with limited duration to continue with an
older method rather than updating to a new method.

3.1.2.9  Logistical Constraints
Logistical issues like refrigeration availability at a sampling station or travel time between field sites and
the laboratory may constrain selection of monitoring variables. Most water quality variables have
specified permissible holding times and holding conditions. These parameters determine the length of
time a sample can be stored after collection and prior to analysis without significantly affecting the
analytical results. Maximum holding times and storage conditions have been established by the U.S. EPA
(40 CFR 136.3, USEPA 2008b). Examples of these specifications are shown in Table 3-5.

Holding times and conditions will influence the choice of analytical variables. Unless samples can be
delivered to the laboratory within six hours, E.  coll analysis may be impractical. The demand for
immediate filtration of samples for orthophosphate analysis may restrict that analysis to grab samples,
while samples for TP can be held for 28 days. Samples for metals analysis can be held for up to six
months before analysis, offering flexibility in analytical schedules and laboratory selection.

  Table 3-5. EPA-recommended preservation conditions and hold times for selected water quality
                             variables (40 CFR 136.3 and NEMI 2006)
Variable
PH
Ammonia
Nitrate
Orthophosphate
Total Phosphorus
Total Dissolved Solids
Specific Conductance
Turbidity
Total Suspended Solids
Pesticides
COD
Oil and Grease
Soluble metals (except Hg, B)
£. co//'
Preservation
None
Cool, <6 °C, H2S04 to pH<2
Cool, <6 °C
Filter immediately, Cool, <6 °C
Cool, <6 °C, H2S04 to pH<2
Cool, <6 °C
Cool, <6 °C
Cool, <6 °C
Cool, <6 °C
Amber glass bottle, sealed, Cool, 4 °C
Cool, 4 °C, H2S04 to pH<2
Cool, 4 °C, H2S04 to pH<2
HN03topH<2
Cool, <10°C
Maximum Holding Time From Sample
Collection
15 minutes
28 days
48 hours
48 hours
28 days
7 days
28 days
48 hours
7 days
4 to 7 days depending on method
28 days
28 days
6 months
6 hours
                                               3-8
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Monitoring and Evaluating Nonpoint Source Watershed Projects                                      Chapter 3
All of these constraints will drive station location, field schedules and staff requirements in a monitoring
project. For example, in the St. Albans Bay Rural Clean Water project, samples from four tributary
monitoring stations were analyzed for both orthophosphate and TP. This work required sample collection
by a field technician two to three times each week in order to collect and retrieve samples and deliver
them to the laboratory 28 mi (45 km) away (Vermont RCWP Coordinating Committee 1991). In contrast,
the Lake  Champlain Basin Agricultural Watersheds NNPSMP project collected weekly composite
samples for P analysis that were analyzed for TP only, requiring a single trip by a field technician each
week to retrieve samples and deliver them to the laboratory (Meals and Hopkins 2002). In both examples,
power from the electrical grid  was available to run the refrigerated samplers required to maintain sample
temperatures at <6 °C. Without power, there would be additional logistical challenges to keeping samples
cold with ice or visiting stations more frequently.

3.1.2.10 Need for Covariates
It is important to consider monitoring variables not directly required by project objectives or pollutant
sources but that may be important in understanding or explaining the behavior of other critical variables.
Such explanatory variables that vary in concert with critical project variables are called covariates. Some
covariates are obvious. For nonpoint source issues, precipitation and other weather variables are usually
important covariates (see section 2.2.1).  Even where load measurement is not required, flow (or stage)
should always be measured, for example, as a key covariate in explaining observed patterns of suspended
sediment or particulate P that are delivered predominantly in surface runoff in high-flow events. A
monitoring program for a lake impaired  by eutrophication may benefit from measurement of temperature,
chlorophyll a, and algae, even if the focus is on  reducing nutrient loads. In cases where paired watersheds
are expected to have somewhat dissimilar hydrologic responses to precipitation events, it may be helpful
to monitor additional variables such as instantaneous peak flow rate and average flow rate for inclusion in
data analysis approaches (see section 7.8.2.2).

3.1.2.11  Set Priorities
Because numerous potential water quality variables exist and because selection criteria may conflict or
overlap, it is useful to take a deliberate approach to setting priorities when designing a monitoring
program. There are several ways to begin this approach. The USDA National Handbook of Water Quality
Monitoring (USDA-NRCS 2003) recommends formulating a written justification for each candidate
variable.  If the justification is weak, the  variable may be of low priority and might not be essential. A
ranking system may be useful, where a minimum set of essential variables are identified (e.g., flow and
TP for a TMDL aimed at a eutrophic lake), followed by a set of additional, justifiable variables to be
monitored if other constraints allow (e.g., orthophosphate, nitrogen, Secchi disk transparency, and
chlorophyll a). Finally, systematic evaluation of correlations among candidate variables may suggest that
one variable (e.g., turbidity) is highly correlated to another (e.g., TSS) so that both need not be measured.
Examination of such correlations may also show that  some variables do not have direct covariates (e.g.,
NO3-N) and should be given priority. Because some relationships between variables (e.g., turbidity and
suspended sediment) can change as a result of watershed plan implementation (e.g., turbidity correlation
with suspended sediment increases as nutrient levels and biological component of turbidity decrease), it
may be appropriate to monitor both variables.
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Monitoring and Evaluating Nonpoint Source Watershed Projects                                      Chapter 3


3.1.3  Physical and Chemical Water Quality Data

3.1.3.1  Measuring Surface Water Flow
Measuring surface water flow is an important component of many NFS water quality monitoring projects.
Flooding, stream geomorphology, and aquatic life support are directly influenced by streamflow. Runoff
and streamflow drive the generation, transport, and delivery of many NFS pollutants. Pollutant load
calculations require knowledge of water flow (see section 3.8 and section 7.9).

Surface water flow is simply the continuous movement of water in runoff or open channels. This flow is
often quantified as discharge, the rate of flow or the volume of water that passes through a channel cross
section during a specific period of time. Discharge can be reported as total volume (e.g., acre-foot [ac/ft]
or millions of gallons) or as a rate such as cubic feet per second (ft3/s or cfs) or cubic meters per second
(m3/s). The depth of flowing water (m or ft) is commonly measured as stage, the elevation of the water
surface relative to an arbitrary fixed point. Stage is itself important.  Peak stage may exceed the capacity
of stream channels, culverts, or other structures. Very low stage may stress aquatic life.

Flow data can be used for a variety of purposes, including problem assessment, watershed project
planning, assessment of treatment needs, targeting source areas, design of management measures, and
project evaluation. The  selection of appropriate flow variables depends on the specific purpose and
situation. Two  common uses of flow data by  watershed monitoring projects are pollutant load calculation
(see section 7.9) and model calibration. Pollutant loads are critical elements of TMDL development and
implementation. A pollutant load reduction is often one of the principal measures of success in NPS
watershed projects. Discharge data are essential for the estimation of loads of sediment or chemical
pollutants exported from a river or stream.

Evaluation of specific BMPs or watershed-scale  BMP implementation often requires measurement of
both pollutant concentration and flow. Many  BMPs, particularly stormwater practices in urban settings,
are  designed to reduce total flow, peak flow,  and/or flow velocity, as well as pollutant concentrations. The
degree to which these practices achieve pollutant load reductions due to changes in flow versus changes
in pollutant concentration varies. Careful consideration of the expected impacts of specific BMPs or
combinations of BMPs  should help guide decisions regarding flow variables to be monitored.

Basic principles of discharge measurement. Discharge is typically calculated as the product of velocity
and cross-sectional area (Figure 3-1). Surface water velocity is the direction and speed with which the
water is moving, measured in feet per second (ft/s) or meters per second (m/s). The cross-sectional area of
an open channel is the area (ft2 or m2) of a slice in the water column made perpendicular to the flow
direction.

Determination  of discharge (usually symbolized  as Q) thus requires two measurements: the cross-
sectional area of the water in the channel (A,  e.g., in m2) and the area-weighted average velocity of
moving water (V, e.g., in m/s). The product of these two measurements gives  discharge in volume per
unit time:

                                          Q = VXA

For example,
                                       m       _       m3
                                   1.25 —x 36m2 =45 —
                                         s                 s
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                                         Streams
                                    Cross-Sectional Area
             Water Velocity
                                                                Pipes
Figure 3-1. Cross-sectional area and water velocity for streams and pipes

It is important to recognize that the velocity of moving water varies both across a stream channel and from
the surface to the bottom of the stream because of friction and irregularities in cross section and alignment
-hence the use of average velocity in the above equation (see section 2.2.1.4.1). Friction caused by the
rough channel surfaces slows the water near the bottom and sides of a channel so that the fastest water is
usually near the center of the channel and near the surface. On a river bend, the water on the outside of
the bend moves faster than the water on the inside of the bend, as it has to cover more distance in the
same time frame. Clearly, more than a single measurement is needed to accurately characterize the
velocity of water moving down the stream, particularly when the stream channel is irregular.

Flow measurement in water quality monitoring projects can take several forms, from a single
measurement of peak stage during a high-flow event to continuous recording of stream discharge. Various
approaches to measuring flow are described below.

Peak stage measurement. How high the water reaches during a storm event or flood, also known as peak
stage, is often crucial information. In urban watershed projects where reduction of peak stormwater flows
is a major goal, tracking peak stream stage (and precipitation) during storm events before and after
watershed treatment can be a simple and inexpensive surrogate for monitoring actual streamflow. Peak
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Monitoring and Evaluating Nonpoint Source Watershed Projects                                       Chapter 3
stage is important to determine for stream restoration projects where high flows shape the physical habitat
of the stream. Peak stage is also essential to determine in flood planning, especially for flood frequency
statistics, floodplain management, and design/protection of structures.

Peak stage can be observed by several informal means such as high water marks and debris lines on
buildings or vegetation. More precise records of peak stage can be obtained using specialized crest gages
(Figure 3-2). Information about crest gages is available at http://pubs.usgs.gov/fs/2005/3136/fs2005-
3136-text.htm.
Figure 3-2. Traditional crest-stage gage

Instantaneous flow measurements. It is often necessary to estimate or measure discharge at a particular
site at a particular time, either to document flow under certain conditions or to develop a data base for
further analysis. There are several ways to determine instantaneous discharge, varying in accuracy and in
applicability by the size of the stream.
  • Manning's Equation. Discharge may be computed based on a slope-area method using the
     Manning equation:
                                               /1.486\   2  i
                                         Q =  	MR3S2
                                               \   n  /
       Where:
       Q = discharge in ft3/s
       A = mean area of the channel cross section in ft2
       R = mean hydraulic radius of the channel in ft
       S = slope of the water surface in ft/ft
       n = roughness factor depending on the character of the channel lining
       1.486 = conversion factor in ft1/3/s

The n factor can be estimated from tabular values and depends on the character of the channel, varying
between 0.01 for smooth concrete to 0.10 for weedy streams with deep pools. The proper selection of a
roughness factor is difficult in many cases and discharge determined by this method is only approximate.
  •   Volumetric measurement. For very small flows, e.g., low-flows in ditches or small streams or
      discharge from drain outlets, the most accurate method of discharge measurement is to simply
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     measure the time required to fill a container of known volume. In some circumstances, it may be
     necessary to use sandbags to temporarily channel flow to a practical collection point.
     Dilution methods. Dilution methods of discharge measurement consist of adding a concentrated
     tracer solution (salt or dye) of known strength to the stream and by chemical analysis determining
     its dilution after it has flowed far enough to mix completely with the stream and produce a uniform
     final concentration in the stream. Discharge is calculated as:
       Q = q * (Ci - C2)/(C2 - Co)

       Where:
       Q = stream discharge
        q = tracer injection rate
       Ci = tracer concentration in injection
       €2 = final concentration of tracer in the stream
       Co = background tracer concentration in the stream

     The particular tracer selected should be conservative (i.e., slow to decay and not taken up by
     sediments or living organisms in the stream) and should be easily measured in the laboratory or
     field. Salt (NaCl) and rhodamine dye are commonly used tracers. Rhodamine  dye can be analyzed
     in the field by fluorescence.

     When using tracers it is important to inventory all downstream uses of the water and check for
     notification requirements. Downstream users should be given advance notice of the study, including
     use of clear signage and other methods of communication.

     Weirs and flumes. For long-term projects, discharge can be measured using a weir or a flume,
     structures that water flows through or over that have a known relationship between stage and flow.
     If such a device is used,  discharge measurement can be as simple as observing the stage of water
     just upstream of the device and consulting a table or using a simple equation to calculate discharge.
     Weirs are essentially dams built across an open channel over which water flows through a specially
     shaped opening or edge. Weirs are classified according to the shape of their opening - e.g., a 90°
     V-notch weir has a notch shaped like an inverted right triangle, whereas a rectangular weir has a
     rectangular notch. Figure 3-3 shows a 120° V-notch weir in operation. Each type of weir has an
     associated equation for determining the discharge rate, based on the depth (stage) of water in the
     pool formed upstream of the weir (see Rantz et al. 1982 for examples). In practice, weirs can range
     from small wood or metal plates temporarily mounted across small ditches or  streams to more
     permanent installations involving concrete walls and other structures. Note that erecting any
     obstruction in a stream will create a pool upstream and care must be taken to avoid creating the
     potential for flooding during high flows.

     Flumes are specially shaped  open channel flow sections that restrict the channel area, resulting in
     increased velocity and a change in water level as water flows through a flume. The discharge
     through a flume is determined by measuring the stage in the flume at a specific point, depending on
     the type of flume (see Rantz  et al. 1982 for examples). In general, flumes are used to measure
     discharge where weirs are not feasible; flumes are often used to measure field runoff where flows
     during storm events can be collected and channeled through the device. Commonly used flumes
     include the Parshall (Figure 3-4) and Palmer-Bowlus (Figure 3-5). The H-flume is a special flume
     developed for agricultural field research that can measure discharge over a wide range with good
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Chapter 3
     accuracy. Figure 3-6 shows an H-flume in operation in a field runoff monitoring project. Flumes
     come in a wide range of sizes denoting the maximum depth of flow they can accommodate and can
     be purchased as prefabricated units or built on-site. While flow control structures such as weirs and
     flumes can be pre-calibrated, the accuracy of discharge measurements can be compromised by
     faulty installation (Harmel et al. 2006, Komiskey et al. 2013).
     Figure 3-3.120° V-notch weir, Englesby Brook, Burlington, VT
     Figure 3-4. Field application of small Parshall flume
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     Figure 3-5. Palmer-Bowlus flume
     Figure 3-6. 2-foot (0.6 m) H-flume in place for edge-of-field
     monitoring, East Montpelier, VT
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     Area-velocity technique. The most common method of measuring discharge in open channels is
     by measuring the cross-sectional area and the water velocity, as generally described earlier (Figure
     3-7). Discharge in a small, wadeable stream can be measured by the following process:
     •   Select location. Choose a straight reach, reasonably free of large rocks or obstructions, with a
         relatively flat streambed, away from the influence of abrupt changes in channel width.
     •   Establish cross-section. Determine the width of the stream and string a cable or measuring
         tape across the stream at a right-angle to the flow. Divide the width into 20 to 25 segments
         (streams less than 10 ft [3 meters (m)]) wide may not allow as many segments) using tape or
         string to mark the center of each segment on the cable (Figure 3-8). Typically the stream is
         divided into enough segments so that each one has no more than 10 percent of the total
         streamflow.
     •   Measure depth of each  segment. At each mark across the stream, measure the depth from the
         water surface to the bottom with a graduated rod or stick (Figure 3-9).
     •   Measure water velocity. At each mark, measure the velocity of the water (see below). Where
         depth is less than 2.5 feet (ft) (0.8 m), a single velocity measurement at 0.6 of the total depth
         below the water surface gives a reasonable estimate of the  average velocity with respect to
         depth. For depths of 2.5 ft or more, the average of velocity measurements taken at 0.2 and 0.8
         of depth is preferred.
     •   Calculate discharge for each segment. For each segment, stream discharge is the product of
         width of the segment and the measured depth (giving area) multiplied by the velocity for that
         segment.
     •   Sum discharges. Total stream discharge is the sum of all segment discharges.
     Figure 3-7. Measuring stream discharge (USGS)
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     While wading is the preferred method for accurate discharge measurement, there are safety
     considerations that limit the flows at which wading can be accomplished. The USGS has a rule of
     thumb that prohibits wading if the product of depth (in ft) and velocity (in ft/s) exceeds 8 anywhere
     in the cross-section. Discharge measurement in larger rivers or at high flows follows the same
     principles of area and velocity but requires specialized techniques. These include suspension of
     equipment from bridges (Figure 3-10), cranes (Figure 3-11), or cableways, use of weighted
     sounding lines,  and the use of heavy equipment for velocity measurement (Turnipseed and Sauer
     2010).
                                                    Area =    [W, * D,)
     Figure 3-8. Delineation of stream-width segments for discharge measurement
     Figure 3-9. Measuring the cross-section profile of a stream channel
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     Figure 3-10. Measuring discharge from bridge using an ADCP (acoustic Doppler
     current profiler) unit (USGS 2007)
     Figure 3-11. Measuring discharge from a bridge using a current meter and crane (USGS n.d.)
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Monitoring and Evaluating Nonpoint Source Watershed Projects
Chapter 3
      Accurate velocity measurement is a critical component of the area-velocity technique. A variety of
      instruments are available to measure water velocity, from traditional mechanical current meters to
      electronic sensors (Turnipseed and Sauer 2010). Velocity measurement technology is evolving. For
      example, acoustic Doppler technology can measure velocity distributions within the flow,
      eliminating the need for wading or suspending instruments into the water (Fulton and Ostrowski
      2008).

Continuous flow measurements. A single instantaneous measurement of stream discharge provides
limited value because it provides information about only a single point in time. It is usually necessary to
monitor discharge continuously when a project attempts to measure pollutant load over time or assess
relationships between stream discharge and pollutant concentrations or aquatic life.

Continuous discharge measurement in open channels usually requires that the stage-discharge relationship
is known, either through the installation of a weir or flume or through development of a stream rating. A
stream rating is an equation determined for a specific site that relates discharge to stage based on a linear
regression of a series of concurrent measurements of stage and discharge (e.g., by the area-velocity
technique). Stage can be measured by a staff gage, a rigid metal plate graduated in meters or feet attached
to a secure backing, linked through survey to a fixed elevation and located in a part of the stream where
water is present even at low flows (Figure 3-12). Stage can also be read by measuring the distance from a
fixed overhead point to the water surface (e.g., using a weighted wire or tape lowered from a bridge beam
or using an ultrasonic sensor).
Figure 3-12. Staff gauge in stream
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Chapter 3
The rating equation should be based on measurements taken over a full range of streamflow conditions. It
is usually unacceptable to extrapolate the rating equation beyond the range of observations that it is based
on. As shown in the stream rating curve in Figure 3-13, stage-discharge relationships usually have a log-
log form. With a valid stream rating, discharge can be determined simply from a stage observation
plugged into the equation or read from a table. For more information on stage-discharge ratings, see
http://training.usgs.gov/TEL/Nolan/SWProcedures/Index.html. Note that stream rating curves should be
checked periodically, especially after major high-flow events. Rating curves frequently shift due to
changes in streambed slope, channel roughness, and filling, scouring, or reshaping of streambanks.

Harmel et al. (2006) recommended against using Manning's equation in lieu of direct streamflow
measurements to establish a stage-discharge relationship because  it results in unacceptable uncertainty. In
their analysis of various methods to estimate discharge they found that streamflow estimation with
Manning's equation with a stage-discharge relationship for an unstable, mobile bed and a shifting channel
resulted in a probable error range of ±42 percent. This compares with a range of 6 percent to 19 percent
for typical scenarios using other methods.

Once the stream rating has been developed, continuous discharge measurement becomes an exercise in
continuously measuring stream stage. Continuous measurement of stage is also used to record discharge
through weirs or flumes where the rating is already known. Depending on the installation, this continual
measurement can be accomplished in a number of ways.

A stilling well is a vertical tube or pipe hydraulically connected to the channel such that the level of water
in the stilling well matches that in the channel, but the transient variations due to waves or turbulence are
damped out (Figure 3-14). Stilling wells can range from an 8-inch-diameter (in) (20 centimeters [cm])
pipe connected to the side of a flume to  a 3-ft-diameter (0.9 m) pipe placed in the ground and connected
by pipes to a stream. Several devices exist to measure and record  stage in a stilling well. Traditionally,
this method was conducted using a float attached to a pulley that rose and fell with the water level in the
well and moved a pen on a clock-drive chart recorder (Figure 3-15). There are modern versions that use
electric chart drives or digital recording  systems.
     E
     a
     o
     (P
     co
     £
     w
                 0.01
                                 Stream stage H (m)
                                                             = 42.17(H)
                                                                            1.302
Figure 3-13. Example of a stage-discharge rating for a stream
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Chapter 3
               •Recorder
               Shelf
Figure 3-14. Stilling well design schematic (Wahl et al. 1995)
Figure 3-15. Traditional clock-drive chart recorder at a stilling well

Other approaches to measuring and recording level, either in stilling wells or directly in the channel include:
  •  Bubblers. Air or an inert gas is forced through a small diameter bubble line submerged in the flow
     channel. The water level is measured by determining the pressure needed to force air bubbles out of
     the line.
  •  Pressure transducers. A probe fixed to the bottom of the channel senses the pressure of the
     overlying water.
  •  Ultrasonic sensors. The sensor is mounted above the flow stream, and transmits a sound pulse that
     is reflected by the surface of the flow. The elapsed time between sending a pulse and receiving an
     echo determines the level in the channel.
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Output from level recording sensors can either be recorded directly into a data logger (an electronic
device connected to an instrument or sensor that records data over time) for later processing or into a
specialized flow meter. There are several manufacturers of such meters; the meters often include the
facility to calculate and record discharge and summary statistics, record other data such as precipitation,
and interact with other devices such as automated water samplers.

Additional information on flow measurement can be obtained from:
  •   USDI Bureau of Land Reclamation.  Water Measurement Manual.
      http://www.usbr.gov/tsc/techreferences/mands/wmm/WMM  3rd 2001.pdf.
  •   USGS Measurement and Computation of Streamflow. http://pubs.usgs.gov/wsp/wsp2175/

Streamflow measurement in a natural channel can be challenging for the novice, and mistakes made by
technicians can greatly increase measurement uncertainty beyond the ranges reported by Harmel et al.
(2006). This can result in highly unreliable stage-discharge relationships, inaccurate estimates of pollutant
load, and spurious relationships between flow and other measured parameters. For these reasons, cost
savings, and convenience, many monitoring teams seek flow data from USGS wherever possible. Real
time daily stream flow data from USGS stations are available at http://waterdata.usgs.gov/usa/nwis/rt.

3.1.3.2  Commonly Measured Physical and Chemical Water Quality Constituents
Selected physical and chemical characteristics and constituents commonly measured in NFS monitoring
programs are listed in Table 3-6. This is by no means an exhaustive list. These and other water quality
variables are discussed in detail in the following sources:
  •   Standard Methods for the Examination of Water and Wastewater (Rice et al., 2012)
  •   U.S. EPA Clean Water Act Analytical Methods
      (http://water.epa.gov/scitech/methods/cwa/index.cfm)
  •   National Environmental Methods Index (www .nemi. gov/)

There are several complex issues associated with chemistry and analysis of some constituents that should
be clarified. Below are a few brief discussions of some of the most  important issues that those creating
monitoring systems for NFS might encounter.

The traditional measurement of particulate matter suspended in water has been TSS, measured by filtering a
subsample of water through a glass fiber filter and weighing the dried residue captured on the filter. In the
last decade, research has reported a significant bias in the TSS analysis (Gray et al. 2000). The TSS analysis
typically involves subsampling an aliquot from a bulk sample by pipette or pouring from an open container.
This method often results in a significant underestimate of heavier particles (i.e., sand) in the sample and
thus an underestimate of the total amount of suspended material in the original water. In contrast, the
suspended sediment concentration (SSC) analysis entails measurement of the entire mass of sediment and
the net weight for the entire sample, capturing all the particles in the  original sample. An extensive
comparative analysis (Gray et al. 2000) concluded that the TSS method frequently underestimates
suspended sediment concentration and is fundamentally unreliable for the analysis of natural water samples.
In contrast, the SSC method produces relatively reliable results for samples of natural water, regardless of
the amount or percentage of sand-size material in the samples. SSC and TSS data collected from natural
water are not comparable and should not be used interchangeably. NFS monitoring projects should monitor
SSC, not TSS, to conduct accurate monitoring of suspended sediment loads. However, if comparability with
past monitoring is required, it still may be necessary to measure TSS.
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  Table 3-6. Selected physical and chemical water quality variables commonly measured in IMPS
                               watershed monitoring programs
Variable
Abbreviation
Units
Definition
Notes
Physical Characteristics
Salinity
Secchi disk
transparency
Specific
conductance
Total dissolved
solids
Total suspended
solids
Suspended
sediment
concentration
Temperature
Turbidity
Volatile suspended
solids
Nonvolatile
suspended solids


COND
TDS
TSS
SSC
T

VSS
NVSS
g/kg
mg/L
m
mS/m
pmhos/
cm
mg/L
mg/L
mg/L
°C
NTU
mg/L
mg/L
A measure of the total level of salts
such as chlorides, sulfates, and
bicarbonates in water.
A measurement of water transparency
in lakes using a black and white disc
lowered into the water; the secchi
depth is noted as the depth at which
the pattern on the disk is no longer
visible.
A measure of the ability of water to
pass an electrical current; affected by
the presence of inorganic dissolved
solids.
The sum of all dissolved matter (e.g.,
Ca, Cl, N03, P, Fe, S, and other ions)
in a sample.
A measure of the weight of all
particulate matter suspended in water
obtained by separating particles from
an aliquot of a water sample using
filtration.
A measure of the weight of all
suspended sediment in water
obtained by separating particles from
the entire water sample by filtration.
A measure of the thermal energy
content of water.
A measure of water clarity, i.e., how
much suspended particulate material
in water decreases the passage of
light.
A measure of the organic portion of
TSS.
A measure of the inorganic portion of
TSS, usually calculated as the
difference between TSS and VSS.
Affects suitability of water (especially
groundwater) for drinking, irrigation, and
industrial use.
A common, inexpensive measurement of
turbidity and an indicator of trophic status of
lakes.
Indirect measure of dissolved solids in
water, highly correlated with salinity.
Indirect indicator of salinity; affects
suitability of water for drinking, irrigation,
industrial use.
Affects water clarity, aquatic life support,
suitability for drinking water and/or irrigation;
indicates sediment from field and/or
streambank erosion; particles carry other
pollutants, e.g., P, metals, toxicants. It is a
measure of wastewater treatment efficiency.
Related to TSS, but considered more
representative of full range of particle sizes
present in water because the entire sample,
not a subsample, is filtered.
Rates of biological and chemical processes
depend on temperature. Solubility of oxygen
is determined by temperature. Aquatic
organisms from microbes to fish depend on
certain temperature ranges for their optimal
health, reproduction, and survival.
Indirect measure of suspended solids in
water; particles may include soil particles,
algae, plankton, microbes, and other
substances.
Indicate what portion of TSS is organic in
origin such as algal cells or organic wastes.
Indicate what portion of TSS is comprised of
inorganic materials such as soil particles.
Chemical Characteristics
Biochemical Oxygen
Demand
Dissolved Oxygen
BOD
DO
mg/L
mg/L
The amount of dissolved oxygen
consumed by microorganisms in
water in the decomposition of organic
matter.
Oxygen dissolved in water.
Indirect measure of organic pollutant levels.
Usually referenced by oxygen consumed
over specified time, e.g., 5-day BOD
(BODs).
Supports aquatic life; influences form and
availability of other pollutants.
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Variable
Metals
Nitrogen - Ammonia
Nitrogen -
Ammonium
Nitrogen - Nitrite
Nitrogen - Nitrate
Nitrogen - Nitrite +
Nitrate
Nitrogen - Organic
Nitrogen - Total
Kjeldahl
Nitrogen - Total
Phosphorus -
Orthophosphate
Phosphorus -
Soluble Reactive
Phosphorus - Total
PH
Abbreviation
(various)
NHs-N
NH4+-N
N02-N
NOs-N
N02-N+N03-N

TKN
TN
OP
P04-P
SRP
TP

Units
mg/L or
^g/L
mg/L
mg/L
mg/L
mg/L
mg/L
mg/L
mg/L
mg/L
mg/L
mg/L
mg/L

Definition
Metals are trace elements having
atomic weight from 60 - 200 (e.g., As,
Cd, Cr, Cu, Hg, Ni, Pb, Zn). Metals
exist in surface waters in colloidal,
particulate, and dissolved phases;
dissolved concentrations are
generally low.
Unionized form of N produced by
microbial mineralization of organic N.
Ionized form of N produced by
microbial mineralization of organic N.
A partially-oxidized form of N that is a
short-lived product of mineralization
and nitrification of N from organic
materials, usually rapidly further
oxidized to NOs-N.
An oxidized form of N that is a
common component of inorganic
fertilizer; also produced by the
mineralization and nitrification of N
from organic materials.
Sum of nitrite and nitrate N in a
sample.
Nitrogen in a complex organic form
(e.g., proteins) prior to mineralization
to ammonia.
TKN is the sum of organic N and
ammonia-N.
Total N is the sum of all forms of N in
a water sample.
The simplest and most stable of
inorganic P compounds, HsP04.
A dissolved form of P operationally
defined as the P that reacts with
specific reagents in a laboratory
analysis.
Total P is the sum of all forms of P in
a water sample, as determined by
chemical digestion to a dissolved
form.
A measure of the acidity or basicity of
water, expressed as the negative log
of the H- ion concentration.
Notes
Behavior and toxicity varies by element, but
metals generally exert chronic and/or acute
health effects on aquatic organisms and
humans. Presence of elevated
concentrations may indicate influence of
industrial waste, landfill leachate, or urban
stormwater runoff.
Important plant nutrient; may contribute to
eutrophication. Toxic to fish at high levels
Results reported for ammonia N typically
include both NHs-N and NIV-N forms.
Important plant nutrient. Under typical
conditions, most ammonia in surface waters
occurs as ammonium.
Nitrites have similar behavior and toxicity to
nitrates; significant levels are rarely found in
surface waters as they are rapidly converted
to NOs-N in aerobic environments.
Nitrates are highly soluble and mobile in
surface and ground water; excess amounts
can promote eutrophication and pose a
health threat to humans and animals in
drinking water.
Nitrite and nitrate are often analyzed
together, depending on laboratory method
The presence of organic N indicates recent
presence of organic wastes.
TKN includes all forms of N except N02-N
and NOs-N
TN can be determined directly through
chemical analysis or calculated as the sum
ofTKN+N02-N+NOs-N
Ortho P (also referred to as "reactive" P) is
a plant nutrient that may contribute to
eutrophication.
Related and functionally similar to ortho
phosphate, usually measured on a filtered
sample.
Generally includes both particulate and
dissolved P, unless operationally separated
into dissolved and particulate forms in the
laboratory.
Affects chemical form of some pollutants,
may have direct effects on aquatic life.
Indicator of mine drainage.
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Monitoring and Evaluating Nonpoint Source Watershed Projects
                                     Chapter 3
Nitrogen (N) undergoes a complex cycle in the environment that includes both air and water pathways,
mediated by microorganisms. A simplified N cycle is illustrated in Figure 3-16. Forms of N commonly
measured as chemical water quality variables (Table 3-6) track the aqueous components of this cycle
well. It should be noted that the term "ammonia" commonly refers to two chemical species that are in
equilibrium in water (NH3, un-ionized and NFl4+, ionized). Water quality analyses for ammonia usually
measure and report total ammonia (NH3 plus NFl4+).  The toxicity of ammonia is primarily attributable to
the un-ionized form (NHs), as opposed to the ionized form (NFl4+) (NCSU 2003). Ambient conditions of
pH determine the net toxicity of total ammonia in water; in general, more un-ionized NHs and therefore
greater toxicity exist at higher (alkaline) pH. NFS monitoring projects concerned with nitrogen should
monitor total N, either as a discrete analysis or by measuring TKN and NCh+NOs and  summing the two
for an estimate of total N unless there is a compelling reason to select different N variables.
                                 Nitrogen Gas
                                      N2
  Denitrification
            Nitrite, Nitrate
              N02- NOs-
         N fixation
Organic Nitrogen
       Nitrification^
                                  Ammonium
                                     NH4+
          Mineralization
Figure 3-16. Simplified version of the nitrogen cycle

Phosphorus (P) undergoes a somewhat simpler cycle (USEPA 2012), lacking the atmospheric component,
but the analytical scheme does not correspond perfectly to that cycle. In water quality monitoring, P is
reported largely on an operational basis corresponding to sampling and laboratory procedures, rather than
to specific points on a biogeochemical cycle.

P in freshwater systems exists in either a particulate phase or a dissolved phase. Particulate matter
includes living and dead plankton, precipitates of P, P adsorbed to particulates, and amorphous P. The
dissolved phase includes inorganic P (generally in the soluble orthophosphate form), organic P excreted
by organisms, and macromolecular colloidal P. Most of these forms, however, are rarely analyzed
specifically.

For some purposes, a water sample may be split between dissolved and particulate forms by filtration
prior to further analysis. Thus, it is important to specify and know whether a specific P analysis is done
for the dissolved phase, the particulate phase, or on the total sample. Ortho phosphorus is frequently
analyzed as the primary dissolved form of P and is readily available to algae and aquatic plants. Most of
the P discharged by wastewater treatment facilities is in the dissolved form. Another P fraction is also
sometimes defined operationally as "reactive P" because it reacts with certain reagents in chemical
analysis to form a color, resulting in reporting as Soluble Reactive P (SRP), which is related to but not
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Monitoring and Evaluating Nonpoint Source Watershed Projects                                       Chapter 3


exactly equivalent to the ortho-P analysis1. To gauge the potential impact of a P discharge on
eutrophication, "bioavailable P" is sometimes evaluated by measuring a sample's potential to support
algae growth in a bioassay. Bioavailable P does not usually correspond exactly to a form of P directly
measurable in chemical analysis.

Because the organic and inorganic particulate and soluble forms of P undergo continuous transformations
(e.g., through uptake and release by algae and other plants or by chemical sorption and desorption on
soils, suspended sediment, and other particulate material), many monitoring programs measure TP rather
than individual forms to determine the amount of nutrient that can potentially support the growth of
aquatic plants and contribute to eutrophication. The TP analysis uses digestion by acid and strong
chemicals to convert all P in a sample to a soluble reactive form that can be easily measured in the
laboratory (USEPA 2012). Several  different digestion procedures are available and a monitoring program
should be sure to specify the appropriate method for their situation.

3.1.3.3  Surrogates
In some cases, it may be preferable to use surrogate variables to represent other variables that may be
mentioned specifically in project objectives but are difficult or expensive to measure. In some cases, it is
necessary to use surrogates because a desired response variable is a complex composite of many
individual factors. If, for example, the objective is to monitor the condition of salmon spawning areas,
surrogate measures are necessary because the quality of spawning areas responds to many influences.
Good surrogate variables would be stream bank undercut, substrate embeddedness, and vegetative
overhang (Platts et al. 1983).

Two important criteria must be met by surrogate measures:
   *  A strong and consistent relationship must exist between the surrogate and the primary variable(s) of
     interest. Such a relationship can be established by simple linear regression  using a local data set.
   •  A scientific basis is needed to assert that the surrogate and primary variable(s) will respond
     similarly to environmental management (e.g., BMP implementation) and change (i.e., the
     relationship remains the same). This assertion should be confirmed with data collected after such
     management or change.

While some surrogate relationships are widely appropriate in principle, the specifics of the relationship
vary from site to site  and, in most cases,  should be based on locally derived data. For example, the
relationship between turbidity and TP is  usually highly specific to an individual watershed and should be
used only in the system where the relationship can be documented. It is very important that the physical,
chemical, or biological relationships between candidate surrogates  and primary variables are considered
in some depth to ensure that plausible relationships exist. For example, while erosion and sedimentation
rates are often related in principle, using measured or estimated field erosion rates as  a surrogate for
watershed sediment load, for example, is likely to give poor results because the relationship between the
two variables (i.e., the sediment delivery ratio)  is not known.
1 The term "orthophosphate" is a chemistry-based term that refers to the phosphate molecule all by itself (USEPA
2012). "Reactive phosphorus" is a corresponding method-based term that describes what you are actually measuring
when you perform the test for orthophosphate. Because the lab procedure is not perfect, you get mostly
orthophosphate but you also get a small fraction of some other forms.
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Cost and ease of analysis are the primary reasons why specific conductance (fast and easy to measure
with an electronic instrument) is often used as a surrogate for total dissolved solids (TDS) that requires
measuring, drying and repeated weighing of a sample in a laboratory. In addition, specific conductance
can be expected to respond to environmental management in the same way as TDS in many cases.
Improved irrigation management, for example, might be expected to reduce levels of both actual TDS and
specific conductance generated by the dissolved ions. Specific conductance can be expected to reflect the
effect of irrigation management on TDS.

Indicator bacteria like E. coll are commonly used to indicate the likely presence of true pathogens in
water because indicators are relatively fast and inexpensive to measure compared to pathogens. A good
application of E. coll as a surrogate would be a study evaluating the effects of fencing livestock from
streams because reductions in direct manure deposition to the stream would be expected to reduce both
E. coll bacteria and manure-borne pathogens.

Turbidity is fast and easy to measure directly in the field and can be recorded continuously by field
instruments. It is often highly correlated with TSS or SSC and can be used as a surrogate for these more
expensive analyses when such correlations are established with local data. For example, if turbidity data
will be used to predict or estimate TSS concentrations or loads (e.g., through a regression equation), the
specific parameters of the equation must be documented in the local system because soils and suspended
sediment vary widely among watersheds. In addition, turbidity can be a poor surrogate for SSC if the
particles causing turbidity are not consistently related to those comprising SSC. Management changes that
reduce SSC through trapping of larger sediment fractions, for example, may change the relationship
between SSC and turbidity, which is more strongly linked to finer sediment. When turbidity is used as a
surrogate for either TSS  or SSC it is recommended that the relationship between the surrogate and
primary variable is checked throughout the monitoring period to determine if changes have occurred.

3.1.4  Biological Data
Biological data, including aquatic organisms, habitat, and pathogens, are often central to NFS monitoring
efforts. Selected biological characteristics commonly measured in NFS monitoring programs  are listed in
Table 3-7. This is not an exhaustive list. These and other water quality variables are discussed in detail in
chapter 4 and in the following sources:
  •  Rapid Bioassessment Protocols for Use in Streams and Wadeable Rivers: Periphyton, Benthic
      Macroinvertebrates and Fish (Barbour et al. 1999).
  •  The Qualitative Habitat Evaluation Index (QHEI): Rationale, Methods, and Application (Rankin
      1989).
  «  Methods for Assessing Habitat in Flowing Waters: Using the Qualitative Habitat Evaluation Index
      (QHEI) (MBI 2006).

Aquatic organisms are particularly useful because they integrate the exposure to various NPS pollutant
stressors overtime. Measures of biological communities can integrate the effects of different  pollutant
stressors like excess nutrients, toxic chemicals, increased temperature, and riparian degradation. They
provide an aggregate measure of the impact of stressors from the watershed. When the objectives of a
NPS watershed project focus on biological response (e.g., restoration offish in a stream)  or when
treatment in the watershed focuses on in-stream practices like habitat restoration, biological monitoring is
essential.
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         Table 3-7. Selected biological water quality variables commonly measured in
                            IMPS watershed monitoring programs
Variable
Units
Definition
Notes
Habitat variables
Bottom
substrate
Embeddedn
ess
Flow velocity
Channel
alteration
Pool/riffle
ratio
Qualitative
Habitat
Evaluation
Index
(QHEI)1
Qualitative
score
Qualitative
score
cm/s
Qualitative
score
Qualitative
score
Numerical
score
Percent rubble, gravel; presence of undercut
banks, woody debris
Percent gravel, cobble, and boulder particles
surrounded by fine sediment
Range of current velocity
Channelization, presence of point bars, silt
deposition
Variety of pool/riffle environments
Multiple metric index of habitat variables
including substrate, cover, channel quality,
riparian condition, bank erosion, pool/riffle
distribution, drainage area, and gradient
Quality and diversity of substrate influences suitability for
fish reproduction and habitat quality for benthic
invertebrates.
Substrate condition influences suitability for fish
reproduction and habitat quality for benthic invertebrates.
Prevailing current velocity influences suitability for stream
biota.
Altered channels may reduce habitat diversity; sediment
deposition can render substrate unsuitable for fish or
invertebrate communities.
A diversity or lack of pool and riffle environments
influences suitability of a stream environment for fish and
other biota.
The QHEI is composed of an array of metrics that
describe attributes of physical habitat that may be
important in explaining the presence, absence, and
composition of fish communities in a stream. A significant
correlation between QHEI and IBI has been documented
in Ohio.
Microorganisms
Indicator
bacteria
Pathogens
Microbial
Source
Tracking
nOOml
cfu/100ml
MPN/100
ml
#/L
MPN/100
ml

Bacteria of fecal origin whose presence is
indicative of the probability of existence of true
pathogens, e.g., fecal coliform, E. coli,
enterococci
Waterborne microorganisms that cause
disease in humans or animals, including
bacterial pathogens like £ coli 0157:H7 and
Salmonella and protozoans like Giardia and
Cryptosporidium
Use of DMA, antibiotic resistance, or other
techniques to attribute bacteria found in water
to specific host group, e.g., human, cow,
waterfowl
Use of indicator bacteria is based on rapid, inexpensive
analysis, presumed association with true pathogens, and
some epidemiological evidence of gastrointestinal
disease.
Rarely analyzed as a routine because of expense and
required expertise.
Increasingly used in situations of significant
microbiological impairment where multiple sources are
possible and specific cause(s) of impairment is unknown.
Plants
Chlorophyll
a
Algae
Macrophytes
mg/L

(various)
Measurement of chlorophyll a pigment
extracted from algae collected in a water
sample
Identification and classification of algae taxa
found in a sample of lake water
Identification, classification, of macrophyte
taxa and measurement of extent and
abundance in a lake or stream
Used as an indicator of biological productivity or trophic
state of lakes and as a surrogate for algal biomass. Often
correlated with other measures of lake eutrophication
such as P load and secchi disk transparency.
Presence and/or dominance of certain algal taxa may
indicate trophic status of lakes, e.g., presence of
cyanobacteria (blue-green algae) often indicate
eutrophication due to excess P concentrations.
Presence of extensive growth of some species is
considered a nuisance, especially invasive species;
extent and abundance of other species is considered
ecologically desirable.
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Variable
Units
Definition
Notes
Benthic Macroinvertebrates
#of
organisms
Taxa
richness
Biotic Index
EPT Index
Functional
feeding
groups
#/m2
# of families
Numerical
score
# of taxa
(various)
Number of organisms found per unit area
Number of families present
Index based on tolerance of taxonomic groups
(e.g., family) to organic pollution
Number of distinct taxa within the
Ephemeroptera, Plecoptera, and Trichoptera
groups
Classification of organisms by feeding style,
e.g., shredders, scrapers, filter-feeders,
predators
Provides crude estimate of biomass for comparison
between sites or over time.
Reflects general health of community; generally
increases with improving water quality, habitat diversity,
and habitat suitability.
Indicates general impacts of organic pollution on
invertebrate community; values of the Bl increase with
decreasing water quality.
Summarizes taxa richness within the insect groups that
are generally considered to be pollution sensitive.
Certain feeding groups indicate certain impairment types,
e.g., shredders are sensitive to riparian zone impacts
that change the inputs of coarse particulate organic
matter to a stream.
Fish
#of
individuals
# of species
Index of
Biotic
Integrity
#/m2
# of species
Numerical
score
Number of individuals found per unit area
Number of different species present
Integrated index of multiple metrics of species
richness and composition, trophic
composition, and fish abundance and
condition
Reflects crude estimate of fish population size and
biomass for comparison between sites or over time.
Reflects general health offish community; generally
increases with improving water quality, habitat diversity,
and habitat suitability. Presence/absence of particular
species can be associated with water quality or particular
stressors.
Individual component metrics can be used. IBI is
adaptable and often modified on a regional basis.
1 Developed by Ohio EPA (Rankin 1989)

    Biological monitoring of macroinvertebrates, fish, and other aquatic biota must consider more dimensions
    than is the case for most physical and chemical monitoring. For example, the presence or absence of
    certain species or assemblages is not simply an indicator of ambient water quality or water quality
    impairment. The biotic community present at a particular location is always a reflection of the available
    habitat required to support those life forms. Data on aquatic biota cannot be interpreted without reference
    to the habitat at a particular site and is the reason that several habitat metrics are listed in Table 3-7. The
    nature of aquatic communities is also strongly determined by ecoregion. A warm-water river in Iowa is
    not capable of supporting the same biotic community as a Rocky Mountain stream in Montana.  This is
    not necessarily because of a water quality impairment but because climate, watershed, soils, vegetation,
    and other factors differ between the two ecoregions. In NFS watershed monitoring projects, it is common
    practice to monitor biological variables at impaired or treated sites and at reference sites within the
    ecoregion indicating the best biological condition that can be expected in the subject watershed. For this
    reason, data on biological variables are often presented in comparison with data on the same variables
    collected at one or more reference sites. See chapter 4 for additional details on biological monitoring.

    The use of indicator bacteria in biological monitoring is an evolving issue (Meals et al. 2013). Organisms
    like fecal coliform and E. coll are not themselves pathogenic but  are assumed to have  a significant
    association with the presence of true pathogens. Empirical evidence has suggested a statistical probability
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Monitoring and Evaluating Nonpoint Source Watershed Projects                                      Chapter 3
of increased incidence of gastrointestinal disease at some threshold of indicator bacteria count (Dufour
1984). However, the adequacy of the association between indicator bacteria and true pathogenic
microorganisms has been increasingly challenged in recent years (Harwood et al. 2005). Indicator
organisms have been found in high numbers where few pathogens were detected and pathogens have been
documented when a waterbody meets water quality standards for indicator bacteria. True pathogens like
Cryptosporidium have been shown to survive considerably longer than E. coli in animal waste spread on
agricultural land (Hutchison et al. 2005). Furthermore, the traditional presumption that indicator bacteria
indicate recent fecal pollution is increasingly in doubt as fecal coliform and E. coli have been shown to
survive for long periods and even reproduce in aquatic sediments, beach sands, and urban storm drains
(Jiang et al. 2007, Yamahara 2009). However, other research continues to support an association between
both E. coli and enterococci and the incidence of gastrointestinal disease (Arnone and Walling 2007), so
the matter is far from resolved.

Indicator bacteria will likely continue to be widely used monitoring variables in the future. Water quality
standards for shellfishing continue to be based on fecal coliform counts. TMDLs for bacteria are nearly
always focused on fecal coliform, E.  coli or some other indicator organism. As microbial source tracking
becomes more widely cost-effective, that technology may become more important than simply measuring
indicator bacteria counts at a sampling station (USEPA 2005a, 201 Ib). Furthermore, when waterborne
disease outbreaks are an immediate concern, evaluation of true pathogens could be warranted.

See chapter 4 for a detailed discussion of biological monitoring approaches.

3.1.5  Weather Data
Weather is an essential variable set for NFS monitoring projects. Precipitation drives NFS pollutant
generation and delivery and patterns of wet/dry weather, seasonality, and extremes are major influences
on NFS loads. Actual weather data during a watershed project are needed to place the monitoring period
in context with long-term average conditions. Weather is often a critical covariate  in NFS projects, as
unusually dry or wet weather may  exaggerate or mask response to treatment. Precipitation variables like
total rainfall, rainfall intensity, storm duration, and  storm interval are often key design components in
urban stormwater/LID practices. Temperature may  be an important response variable in restoration of
stream habitat and for implementation of urban  stormwater BMPs. Finally, good weather data are usually
key drivers for modeling and the extent and quality of precipitation data often determines the success of
model calibration.

Variable selection is largely driven by specific project needs. In most cases, at least daily precipitation
totals are needed.  Data on storm intensity, duration, and frequency may also be needed where pollutant
delivery is highly episodic and monitoring is focused on storm events. Air temperature (daily minimum,
maximum, and mean)  data may be needed because temperature drives evapotranspiration. In northern
regions air temperature determines the form of precipitation as rain or snow and controls snowmelt. The
majority of the annual NFS pollutant load in northern regions may be delivered by winter and spring
snowmelt events (Hanson et al. 2000, Panuska et al. 2008). Other weather variables may be required by
specific project objectives. For example, monitoring of stream fishery status after restoration of forested
riparian buffers may benefit from data on solar radiation to correlate to shading and stream water
temperature  (Whitney 2007). A study of bacteria survival  and transport in field runoff might need to
monitor solar radiation, relative humidity, wind velocity, and soil temperature in addition to basic
precipitation and air temperature as variables that affect microorganism survival after manure application.
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Monitoring personnel should query local sources of weather data to determine the need for additional
weather stations. A source of information for this step is:
  •  NOAA Earth System Research Laboratory, http://www.esrl.noaa.gov/psd/data/faq/
     •   Provides information and links for locating climate and weather data and information.
Sources of current and historical weather data include:
  •  NOAA National Weather Service Internet Weather Source, http://weather.noaa.gov/
     •   Provides weather conditions for the past 24 hours, forecasts, watches, and warnings. Data are
         easily copied and pasted into a spreadsheet.
  •  NOAA National Centers for Environmental Information Climate Data Online.
     https: //www .ncdc .noaa. gov/cdo-web/
     •   Provides for historical data retrievals and download to a comma delimited file.
  •  NOAA Meteorological Assimilation Data Ingest System (MADIS).
     https: //madis .ncep .noaa. gov/index. shtml
     •   MADIS is a meteorological observational database and data delivery system that provides
         observations that cover the globe. Data are available from July 2001 to the present.
  •  Weather Underground, http://www.wunderground.com/
     •   Provides current and historical data that can be downloaded to a comma delimited file. Weather
         data come from more than  180,000 weather stations across the country.

3.1.6   Watershed Characterization
In designing any watershed monitoring program, it is essential to characterize the watershed to identify
causes and sources of NPS pollution, understand how water and pollutants are transported through the
watershed, and determine where and how to implement a monitoring program. In any specific project,
data on particular watershed characteristics like geology or impervious cover may be needed, but in
nearly all NPS projects, data on topography, soils, surface and subsurface drainage, hydrology
(e.g., NHDPlus), and land use/land cover will be necessary. These data are often collected as part of the
watershed project planning process described in detail by U.S. EPA (2008a).

3.1.6.1   Topographic Data
Topographic data may be needed to determine water flow paths, including mapping subcatchments, and
to identify areas of steep slope, critical elevation, or particular aspect. Application of simulation models
like SWAT (Soil and Water Assessment Tool) and AGNPS (Agricultural Non-Point Source Pollution
Model) requires detailed topographic data. The main sources of topographic data in the recent past were
published topographic maps.  Today topographic data are readily available as Digital Elevation Models
(OEMs) derived from remote sensing and assembled in a geographic information system (GIS). OEMs
are commonly available from state or local agencies and, once imported into a GIS, can be readily
manipulated to generate derived data on drainage area boundaries, hydrography, elevation, slope, and
aspect.

A major consideration in DEM data for monitoring programs is resolution. Standard OEMs generally
offer 30-meter resolution (i.e., vertical accuracy of ±30 meters), with  10-meter resolution possible in
some cases, providing an improved representation of landscape features. Recent advances in using
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Monitoring and Evaluating Nonpoint Source Watershed Projects                                      Chapter 3


LiDAR (Light Detection and Ranging, a remote sensing system using aircraft-mounted lasers) can
provide DEMs with a resolution of 1 meter or better. High-resolution DEMs can be useful in locating and
mapping very small-scale landscape features such as drainage ditches, swales, and ephemeral gullies, all
of which can be important in understanding runoff and pollutant transport and identification of critical
source areas to design land treatment.

USGS provides information on several sources of free geospatial data at:

http://education.usgs.gov/lessons/geospatialwebsites.html

3.1.6.2  Soil Characteristics
Data on soil physical characteristics and soil chemistry may be required for some NFS monitoring
projects. Physical characteristics like hydrologic soil group strongly influence where surface runoff
commonly occurs. Soil type and factors (e.g. soil erodibility) influence erosion and soil loss and are
sometimes used as parameters to identify critical source areas of NFS pollutants in a watershed. Soil and
vadose zone variables like permeability, hydrologic  conductivity, or depth to water table may be
important to determine in ground water monitoring efforts. Soil chemistry data (e.g., soil test P, organic
matter, cation exchange capacity) may be essential to identify important source areas and understand
pollutant transport over and through watershed soils. Testing for soil P levels can be helpful at the
beginning of a project to ensure that paired watersheds, for example, are suitably matched (Bishop et al.
2005).

Data on soil characteristics may be available from specific studies in local areas or can be obtained from
national databases such as the USDA State Soil Geographic (STATSGO) and Soil Survey Geographic
(SSURGO) (http://soils.usda.gov/survey/geography/).

3.1.6.3  Land Use/Land Cover
Land use/land cover data includes  information on the natural and cultural character of the land surface
(e.g., forest, grassland, wetland, water, pavement) and on the activities taking place on the land (e.g., crop
agriculture, pasture, residential, commercial, highways). Because NPS pollution is predominantly a
function of land use, detailed knowledge of land uses and their spatial distribution is critical in developing
a watershed monitoring program.

Land use/land cover data are usually derived from remote sensing data, either aerial photography or
satellite imagery.  Specific classification of land use/land cover types vary according to project objectives.
For an urban stormwater/LID monitoring effort, data on many classes of developed land may be needed,
as well as aggregate variables like  impervious cover. In urban watersheds, the percent of direct and
indirect impervious  cover and other metrics of urban land use have been clearly documented as a
determinant of many dimensions of stream impairment (Paul and Meyer 2001, Roy et al. 2003). In
contrast, an agricultural NPS monitoring effort may  need detailed information on many agricultural land
uses like corn, soybeans, hay, pasture, farmstead but may lump urban land uses into a single broad
category. A common land use/land cover classification scheme is shown in Table 3-8.
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  Table 3-8. Anderson Level II land use and land cover classification system for use with remote
                              sensor data (Anderson et al. 1976)
1 Urban or Built-up Land







11 Residential
12 Commercial and Services
13 Industrial
14 Transportation, Communications, and Utilities
15 Industrial and Commercial Complexes
16 Mixed Urban or Built-up Land
17 Other Urban or Built-up Land
2 Agricultural Land



21 Cropland and Pasture
22 Orchards, Groves, Vineyards, Nurseries, and
Ornamental Horticultural Areas
23 Confined Feeding Operations
3 Rangeland



31 Herbaceous Rangeland
32 Shrub and Brush Rangeland
33 Mixed Rangeland
4 Forest Land



41 Deciduous Forest Land
42 Evergreen Forest Land
43 Mixed Forest Land
5 Water




51 Streams and Canals
52 Lakes
53 Reservoirs
54 Bays and Estuaries
6 Wetland


61 Forested Wetland
62 Nonforested Wetland
7 Barren Land







71 Dry Salt Flats
72 Beaches
73 Sandy Areas other than Beaches
74 Bare Exposed Rock
75 Strip Mines, Quarries, and Gravel Pits
76 Transitional Areas
77 Mixed Barren Land
8 Tundra





81 Shrub and Brush Tundra
82 Herbaceous Tundra
83 Bare Ground Tundra
84 Wet Tundra
85 Mixed Tundra
9 Perennial Snow or Ice


91 Perennial Snowfields
92 Glaciers

The land use/land cover variables of interest for watershed characterization are mainly static but are
spatially referenced. A single map of current watershed land use/land cover may suffice for designing a
water quality monitoring program; an annual update may be useful to relate to observed trends in water
quality over time. Such data are distinct from land use activity data needed on a fine scale to relate to
observed water quality at a site level, which include a critical temporal element. This kind of land use data
monitoring is discussed later in section 3.7.

3.2   Sample Type Selection

3.2.1   General Considerations
The goal of collecting water samples is to obtain information representative of the target population for
the monitoring effort.  If monitoring is directed only at storm flows, the goal is to collect samples
representative of storm flow conditions. If base flows are of greatest importance, then samples need to
represent base flow conditions. For pollutant load estimation, it is most important that samples represent
flow conditions that generate the greatest share of the pollutant load most strongly related to the identified
problem. When monitoring is directed at specific conditions that threaten or harm aquatic life, sampling
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Chapter 3
may need to favor extreme conditions such as low flow or high temperature. Sample type choices can be a
major determinant of the success or failure of a monitoring program.

As described in chapter 2, water quality varies both temporally and spatially. The extent that water quality
spatial variability is addressed in a monitoring program is determined by the station location and the
sample type. Station location determines where on the landscape a particular sample is taken, whereas
sample type determines the spatial representation of each sample taken at that location. Similarly,
sampling frequency and duration combine with sample type to determine the extent of temporal
variability of water quality captured by the monitoring program. Sampling duration defines the timeframe
for sampling, and sampling frequency determines how many times samples are collected during that
timeframe. Sample type determines the degree to which temporal variability is captured within each
sampling event.

There are generally four types of water quality samples (USDA-NRCS 2003):
  •   Grab. A discrete sample taken at a specific point and time.
  •   Composite. A series of grab samples collected at different times and mixed together.
      •   Time-weighted: A fixed volume  of sample collected at prescribed time intervals and then
         mixed together.
      •   Flow-weighted: A series of samples each taken after a specified volume of flow has passed the
         monitoring station and then mixed together.

  •   Integrated. Multi-point sampling to  account for spatial variations in water quality within a water
      body.
  •   Continuous. Truly continuous or very frequent sequential measurements using electrometric
      probes.

Each sample type has advantages and disadvantages and is discussed in the remainder of the next section.
Ultimately, the selection of the appropriate sample type is determined by  study objectives, variable(s)
sampled, and whether concentration or mass is of interest (USDA-NRCS  2003). Integrated samples are
generally preferred when suspended sediment is  measured, and grab samples are preferred for bacteria.
Generally appropriate sample type selection as a function of monitoring objective is illustrated in Table 3-
9.

  Table 3-9. Sample type as a function of monitoring objective (adapted  from  USDA-NRCS 2003)
Objective
Problem Identification & Assessment
N PS Load Allocation
Point Source Wasteload Allocation
Trend Analysis
Assess Watershed Project Effectiveness
Assess BMP Effectiveness
Assess Permit Compliance
Sample Type
Grab
X


X


X
Composite
Time-
Weighted
X

X
X
X
X
X
Flow-
Weighted
X
X
X
X
X
X
X
Integrated
X


Continuous
X


X






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Monitoring and Evaluating Nonpoint Source Watershed Projects
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Objective
Validate or Calibrate Models
Conduct Research
Sample Type



Composite
Time-
Weighted
X
X
Flow-
Weighted
X
X
Integrated
Continuous


3.2.2   Types

3.2.2.1  Grab
Grab samples are discrete samples taken from a specific point and time (USDA-NRCS 2003). For this
reason, grab samples provide the narrowest representation of the spatial and temporal variability of water
quality conditions. Grab samples are usually obtained manually with plastic or glass bottles or jars  but can
also be taken with automatic samplers. Grab sampling typically occurs in wadeable streams or from boats
on lakes, but sampling can also be taken from bridges during high flows for larger streams and rivers. It is
important to document both when and where grab samples are taken. Location can be recorded by
recording depth and position along the width of the stream or depth and coordinates on a lake.

The specific method used to collect grab samples can have a significant influence on the content of the
sample. Wilde et al. (2014) define samples for which the velocities of the stream and water entering the
sampler intake are the same and different as isokinetic and nonisokinetic, respectively. Because the
suspension of particulate materials depends largely on stream velocity, an isokinetic sample may therefore
have a different and more accurate sediment concentration compared to a nonisokinetic sample.
Isokinetic, depth-integrated samplers are described in section 3.2.2.3. Nonisokinetic samplers include the
hand-held bottle, the weighted-bottle sampler, the BOD sampler, and the so-called "thief samplers" such
as the Kemmerer and Van Dorn samplers that are often used for lake sampling at specific depths (Wilde
etal. 2014).

3.2.2.2  Composite
Composite  samples are generally considered a series of simple grab samples taken overtime and lumped
together (USDA-NRCS 2003). Isokinetic, depth-integrated samples collected to produce a discharge-
weighted sample may also be included in this grouping (Wilde 2006). Composite samples are usually
collected with automatic samplers (see section 3.6.2.4), but passive  samplers (Bonilla et al. 2006) and
labor-intensive manual methods can also be used. Composite samples derived from simple grab samples
are  taken from a single location and do not address the spatial variability of water quality conditions.
When automatic samplers with fixed-depth intake(s) are used, the sample is considered by USGS to be a
point-integrated sample (Wilde et al. 2014).

Sample preservation is always a concern but is of particular importance when automatic samplers are
used for composite sampling. Analyte loss can occur between sample collection and laboratory analysis
because of physical, chemical, and biological processes that result in chemical precipitation, adsorption,
oxidation, reduction, ion exchange, degassing, or degradation (Wilde et al. 2009). Acidification and/or
refrigeration is required for many monitoring variables.

The trigger for collecting samples distinguishes time-weighted from flow-weighted composite sampling.
Time-weighted composite samples are derived from samples collected at pre-determined intervals such as
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Monitoring and Evaluating Nonpoint Source Watershed Projects                                      Chapter 3
hourly or daily samples taken and composited in a single container (Stuntebeck et al. 2008). Because flow
is not considered (but could be measured) in the sampling scheme, time-weighted composites are
generally inappropriate for load estimation (see section 7.9) in nonpoint source applications. Where flow
is constant, however, time-weighted composites would be useful for load estimation. If flow is measured
in a time-weighted sampling scheme where samples are collected in multiple bottles, it is possible to
make up a flow-weighted composite samples from individual discrete samples by adding amounts of
individual samples in proportion to the flow that occurred over the collection interval (Stuntebeck et al,
2008). Peak pollutant concentrations may be missed in a time-weighted sampling design, however,
resulting in low estimates of pollutant load.

Flow-weighted or flow-proportional samples are better for capturing the influence of both peak
concentrations and peak flows, resulting in more accurate estimates of pollutant loads (see section 7.9).
Collecting flow-weighted samples requires an established stage-discharge relationship, prediction of flow
conditions during the period between sample collections, continuous flow measurement, and
instantaneous and continuous calculation of flow volume that has passed the sampling station. Any
fouling of the stage measurement by backflow, icing or other causes will result in incorrect flow volume
calculations and the collection of non-representative samples. Remote access to the monitoring station
provides some capability to address these potential problems. Flow-weighted composite sampling has as
many applications as time-weighted composite sampling, with the additional advantage of being useful
for pollutant load estimation (Table 3-9). The cost for flow-weighted sampling will exceed that of time-
weighted sampling that does not include flow measurement. Both composite sampling types offer reduced
laboratory costs per unit of temporal information gained when compared to grab sampling over the same
time period because fewer samples are analyzed. Compositing results in information loss, however, as the
individual samples are averaged either by time or flow. This information loss corresponds with reduced
sample-to-sample variability which can be helpful in efforts to evaluate BMP and project effectiveness.
For the same number of samples, composite sampling  also offers the advantage of fewer trips to the field
compared to grab sampling, reducing labor costs (see chapter 9 for a discussion of monitoring costs).

Advances in remote access and  control of automatic sampling equipment have made it possible to adjust
the sampling program based on  current knowledge of weather conditions and discharge (Stuntebeck et al.
2008). This technology provides considerable flexibility for the researcher, including the  ability to change
flow-weighted sampling if flow conditions differ markedly from those assumed when the sampler was
programmed.

3.2.2.3  Integrated
Grab samples can be integrated  over depth and/or width. At flowing-water sites, USGS collects an
isokinetic, depth-integrated, discharge-weighted sample as standard procedure (Wilde 2006). However,
such a sample would not integrate temporal variations (USDA-NRCS 2003). Depth integration in lake
sampling can be achieved by mixing grab samples taken from each lake stratum, by obtaining a
simultaneous sample of the entire  water column with a hose, or by automatic devices that collect at
different depths overtime (USDA-NRCS 2003).

Isokinetic, depth-integrating methods are designed to produce a discharge-weighted (velocity-weighted)
sample (Wilde 2006). Using this method, each unit of stream discharge is equally represented in the
sample, either by dividing the stream cross section into intervals of equal width (EWI) or equal discharge
(EDI) (Wilde 2006). With the EWI method, depth integrated samples are collected at equally spaced
intervals at the cross section and then composited (USDA-NRCS  2003). Under the EDI method,
knowledge of stream discharge  is used to divide the  cross section into equal discharge subsections for
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sampling. In theory, the two methods will produce composite samples with identical constituent
concentrations. The instantaneous load could be determined by multiplying the analyte concentration by
the measured instantaneous stream discharge (Wilde 2006). If nonisokinetic sampling methods are used,
the method will not result in a discharge-weighted sample unless the stream is completely mixed laterally
and vertically.

An isokinetic, depth-integrating sampler is designed to accumulate a representative water sample
continuously and isokinetically from a vertical section of a stream while transiting the vertical at a
uniform rate (Wilde et al. 2014). Isokinetic, depth-integrating samplers are categorized into either hand-
held samplers or cable-and-reel samplers. The USGS provides details on how to use these devices for
both isokinetic and nonisokinetic sampling (Wilde et al. 2014).

Integrated samples may be the best approach for situations where water quality is known to be spatially
variable, e.g., vertical integration for lake sampling, or horizontal integration for river sampling. Given
that the temporal variability of lake conditions is generally not as great as that in streams, integrated grab
samples may be the most useful sample type for lakes. Grab samples at various lake depths, however,
may provide necessary information that integrated samples "average out," so both types of samples could
be appropriate depending upon the monitoring objectives. A combination of seasonal, integrated and
simple grab samples taken at representative depths could be the best approach for problem assessment
and trend analysis for lakes and other still water bodies. Composite or continuous sampling under these
conditions would be likely to generate datasets with substantial serial correlation issues at a cost far
greater than simple or integrated grab sampling.

The best approach for lake sampling will depend on project monitoring objectives and lake
characteristics. Because sampling throughout the entire water column is not always necessary to
characterize conditions of interest, integrated sampling can be unimportant. For example, when
monitoring a vertically stratified lake for nutrient problems, it may be most desirable to collect surface
grab samples for chlorophyll a and use meters to develop depth profiles of temperature, pH, conductivity,
and DO. Nutrients could be monitored with surface grab samples only unless project objectives dictated
that bottom samples were also necessary. Pairing the chlorophyll a and nutrient data from grab samples
taken at various surface locations would be appropriate for analysis  in most cases.

3.2.2.4  Continuous
Continuous sampling is not usually used in nonpoint source pollution studies, but the USGS uses
continuous water-quality monitors in its national assessment of surface waters (Wagner et al.  2006). A
commonly used configuration for USGS data collection is the four-parameter monitoring system, which
collects temperature, specific conductance, dissolved oxygen, and pH data. Devices currently on the
market have sensors for DO, conductivity, pH, turbidity, depth, chlorophyll a, blue-green algae,
ammonia, NOs, Cl"1, total dissolved gas, temperature, and other parameters. Sondes are available that can
measure  15 parameters simultaneously. Some instruments can store measurements to internal or external
memory in a format compatible with a hand-held display, personal digital assistant (PDA), or laptop
computer (Gibs et al. 2007).

Continuous sampling can be performed during short-term or long-term periods depending upon the
monitoring objectives. Like grab and composite sampling, continuous monitoring provides no
information about the spatial aspects of water quality conditions. Continuous sampling also has the
potential to create information overload if carried out during a long period, with the potential  consequence
of expensive data reduction requirements, including addressing the problem of autocorrelation.
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Monitoring and Evaluating Nonpoint Source Watershed Projects                                      Chapter 3
Other challenges associated with continuous sampling include the need for careful field observation,
cleaning, and calibration of the sensors (Wagner et al. 2006). Despite manufacturer claims, even "self-
cleaning" sensors require cleaning. Most electrodes are temperature dependent and many cannot be
placed in areas of high stream velocity (USDA-NRCS 2003), but flow-through systems can be designed
to address the stream velocity issue (Wagner et al. 2006). An advantage to continuous sampling is the
ability to track the duration of values exceeding thresholds, in particular, those with significant diurnal
variability.

With the exception of flow, continuous sampling is not frequently used in nonpoint source monitoring. It
may be useful for variables such as temperature or dissolved oxygen, which should be measured in situ
and for which minimum and maximum daily values are critical concerns. Continuous monitoring cost
considerations include the cost of sondes and sensors, labor associated with keeping the sensors clean and
operative, and costs associated with reducing the datasets for statistical analysis. Problem assessment and
research are two areas for which continuous measurement could be highly appropriate. Continuous
sampling could also track the exposure of aquatic organisms to harmful levels of temperature or DO,
providing a very useful tool for trend analysis or an assessment of BMP or watershed project
effectiveness.

3.3   Station  Location
Monitoring station locations must be determined at two distinct scales. At the macro-scale, sampling
locations must be determined by monitoring objectives, experimental design and resource type. The
micro-scale issues of site access and physical configuration will drive the final selection of station
locations.

3.3.1   Macro-scale
At the watershed or macro-scale, monitoring design (see section 2.4) will control station location. A
single-watershed or trend design will require a station to be located at a watershed outlet where collected
data represent water quality from the entire drainage area. An above-below or input-output design calls
for two or more stations bracketing a treated area or an individual BMP to compare concentrations or
loads entering and leaving the area. A synoptic or reconnaissance design will need numerous stations,
located at areas that can isolate particular drainage areas or NPS pollutant source areas (Figure 3-17).
Ground water monitoring for flow and/or mass determination will require an extensive network of
monitoring wells to determine flow into and out of the area and to map hydrogeologic properties of the
aquifer.
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Monitoring and Evaluating Nonpoint Source Watershed Projects
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                                                                    Monitoring Sites
                               Residential
                               Construction   Dairy
Figure 3-17. Possible sampling locations fora synoptic survey

Water body type is another macro-scale factor in station location. On stream or river networks, station
locations might be selected to either capture or avoid the effects of tributary streams, to isolate sub-
catchments, or to focus on areas of particular characteristics, e.g., high-quality regional biological
reference sites. In lakes and reservoirs, monitoring stations at each major tributary discharge may be
required to effectively measure load for a TMDL. In the lake itself, lake morphology, vertical
stratification, and currents may require samples in several lake regions and/or at several depths in order to
adequately represent water quality (Figure 3-18). Lake sampling designs and factors to consider when
selecting sampling locations are described in detail by Nevers and Whitman (n.d.), and U.S. EPA (1998)
provides guidance on sampling designs and locations for bioassessments.
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Monitoring and Evaluating Nonpoint Source Watershed Projects
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            Natural Lake                               Impoundment
               Water Column Sampling
               Locations
Figure 3-18. Potential lake monitoring locations

In its 2012 National Lakes Assessment, U.S. EPA randomly selected 904 natural lakes, ponds, and
reservoirs across the lower 48 states using a probability based survey design (USEPA 201 la). To be
included in the survey, these lakes (excluding the Great Lakes and the Great Salt Lake) had to be at least
one meter deep and greater than 2.5 ac (1 ha) in size. In addition to these 904 sites, some sites were
resampled for quality assurance purposes, and reference sites representing least-disturbed conditions
were also sampled. A variety of field measurements were taken at "index sites" which are either the
deepest point in a natural lake or the middle of a reservoir (USEPA 201 la). If the deepest point exceeded
50 m in depth, the index site was set as close to the middle of the lake  as field staff could go without
exceeding 50 m in depth. In addition, conditions of the littoral zone and shoreline were documented from
stations around the lake.

The location of monitoring stations in ground water systems is determined by aquifer type and vertical,
horizontal, and longitudinal variability in both water quality and water quantity (Figure 3-19). Both
USDA-NRCS (2003) and Lapham et al. (1997) provide additional information on well selection for
ground water monitoring.

In some cases, it may be appealing to adopt sampling stations that were part of a past monitoring network
or are active in another project or program. Piggy-backing on past or existing monitoring stations may offer
advantages of an historical data record or significant cost-savings. A prime example is co-locating with an
operating USGS station. High-quality continuous flow data (sometimes in real-time through a
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Monitoring and Evaluating Nonpoint Source Watershed Projects
                                     Chapter 3
website) is a major benefit for a monitoring program because flow data are challenging and expensive to
acquire. However, adopting sites from past or other monitoring programs must be carefully evaluated
before decision making. Such stations may not be located optimally for the current monitoring program's
objectives, and data collected for other purposes, objectives and schedules, or by other methods, may not
be useful for current needs.
   a  Ground water aquifers
        Land surface
                Confined aquifer
       Bedrock
   b  Monitoring source areas
      Equipqtential
      lines
c Multilevel wells
                                                                              Well
                                                                              t
                                                               Sand-
                                                               pack-
               JL
               I
                                                                ScreenH >
                                                                                    "Seal
d Vertical locations
                                                                Well
                                                        Perched
                                                        water table
                   Surface
                                                                                   Source
                                                          Clay
                                                                         Water table
Figure 3-19. Possible groundwater monitoring locations (after USDA-NRCS 2003)

3.3.2  Micro-scale
Some general considerations apply to choosing the location of sampling stations at the local scale, and
some specific factors apply to locations for flow measurement and biological monitoring.
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Monitoring and Evaluating Nonpoint Source Watershed Projects
                                                                                        Chapter 3
                                                               Micro-Scale Site Location
                                                                    Considerations
                                                                Representativeness
                                                                Easy access
                                                                Safety
                                                                Power
                                                                Permission
                                                                Security
3.3.2.1  General Considerations
Stations must be located so samples and other data can be
collected that are representative of the conditions being
monitored according to project objectives. In practice, this
means that stream stations should be located on relatively
straight runs, away from obvious eddies or backwaters, far
enough from major obstructions that prevent adequate
mixing, and far enough downstream of tributary or other
inputs to ensure complete mixing before samples are
collected. Lake stations should be located far enough into
open water to avoid obvious near-shore influences and
outside of confined embayments unless near-shore or
embayment conditions are of primary interest. In lakes of
complex morphometry, multiple sampling stations may be required to collect representative data. Ground
water sampling wells should be arrayed and installed in locations (both horizontally and vertically) that
represent the resource of interest, e.g., a known  contaminant plume or a regional aquifer system.

Many relevant general considerations for local-scale station location relate to practical matters of logistics
(see section 2.2.3.1). Access, in terms of both travel from a base to the site and foot access to the stream
and/or station facilities, is critical. Considering the safety of field staff, especially in harsh seasons or
inclement weather, is vital. The availability of power and communication links may be essential to some
station types. Security, from both human interference and natural threats like flooding, is important as is
land ownership. In some cases, stations can be located in the highway right-of-way or on a bridge
structure, avoiding the need for negotiations with private landowners (although permission/approval from
the state or local transportation agency is usually required). In some cases, permission from or lease or
rental agreements with property owners may be required. Finally, if buildings, electrical power, or other
physical structures are to be installed, local land use permits may be required.

3.3.2.2  Locations for Flow Measurement
There are some special considerations for locating stream stations at which flow will be measured in open
channels.
  •  Select a straight reach, reasonably free of large rocks or obstructions, with a relatively flat
      streambed, away from the influence of abrupt changes in channel width.
  •  Avoid culverts, waterfalls, and bridges where obstructions or degraded structures may cause
      hydraulic anomalies that interfere with a stable stage-discharge relationship.
  •  Seek an area with a stable cross-section and avoid areas subject to frequent deposition of sand or
      gravel bars or severe bank erosion.
  •  Look for an area where depth and velocity measurements can be conducted safely at low flows.
  •  Look for an area where a bridge crossing  or walkway allows safe velocity measurements at high
      flows.
  •  Look for areas where stage can be measured and/or recorded continuously, e.g., protected area for a
      staff gage.

Where flow is to be measured at the edge of a field or elsewhere using a weir or a flume, look for sites
where flow can be collected and/or diverted into the device, where ponding caused by a weir will not
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Monitoring and Evaluating Nonpoint Source Watershed Projects                                      Chapter 3
cause problems, and where concentrated discharge from a flume can be safely conveyed away
downstream. See section 3.1.3.1 for additional information on flow measurement.

3.3.2.3  Locations for Biological Monitoring
Rapid Bioassessment Protocols (Barbour et al. 1999) lists several important considerations for locating
biomonitoring sites.
  "   Ensure a generally comparable habitat at each station. Otherwise, differences in biology attributable
      to local habitat alone will be difficult to separate from differences or changes in response to water
      quality degradation due to NFS pollution.
  "   Locally modified sites, such as small impoundments and bridge areas, should be avoided unless
      project objectives are to assess their effects.
  "   Sampling near the mouths of tributaries entering large waterbodies should be avoided because these
      areas will have habitat more typical of the larger waterbody.
  •   Biological monitoring programs generally require a reference site to provide data on the best
      attainable biological conditions in a local or regional system of comparable habitat.

See chapter 4 for additional information on locating biological monitoring sites.

3.4   Sampling  Frequency and Duration

The questions of how often to collect samples (the sampling frequency or interval between samples) and
how long to conduct a sampling program are critical and without simple or stock answers. The choice of
sampling frequency depends on program objectives, type of water body involved, variables measured, and
available budget.

3.4.1  General Considerations
In general,  sampling frequency must be relatively high (e.g., daily to weekly) for monitoring to evaluate
effectiveness of a single BMP or to document the mechanisms controlling water quality at a particular
site. Automatic samplers with flow meters  that can collect composite, flow-weighted, samples over storm
events and  collect weekly or biweekly samples enable effective sampling for concentration and load data
to evaluate  BMP effectiveness. They also reduce the high cost of retrieving and analyzing samples
collected more frequently. A program with an objective  of detecting a long-term trend or evaluating
watershed program effectiveness can accept longer intervals (e.g., weekly to monthly) between samples.
Considerations specific to monitoring for load estimation are discussed in section 3.8.

Sampling frequency must also be  determined based on the type of waterbody being monitored, and in
particular the variability of water quality in the waterbody. Greater variability requires higher sampling
frequency to obtain a reasonable picture of water quality. For example, water quality in edge-of-field
runoff from cropland is likely to be considerably more variable and require considerably more frequent
sampling than water quality in a large lake or a regional  aquifer. Water quality in intermittent streams is
usually more variable than in large river systems. A general guide to the relationship between system
variability and sampling interval is illustrated in Figure 3-20.
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Monitoring and Evaluating Nonpoint Source Watershed Projects
                 Chapter 3
   o
   c
   CD
   D
   CT
   CD
   O)
    _
   E
   CD
  C/)
        O)
        ±
Overland
  Flow
               Ground Water
               Low
    High
                                    System Variability

Figure 3-20. Schematic of sampling frequency as a function of system type (after USDA-NRCS
2003)

Project budgets, staff availability, and laboratory capability typically put limitations on sampling
frequency, but financial resources should not be the primary basis for decisions on sampling frequency. A
sampling program that cannot achieve desired objectives because of inadequate sampling frequency is not
cost-effective. Where resources are limited, consider reducing the list of variables analyzed or even the
number of stations before cutting back on the sampling frequency that is required to meet project
objectives. Use of less expensive surrogate variables, simplifying field instrumentation, and the use of
composite sampling programs are all ways to reduce costs while maintaining the critical sampling
frequency.

Calculation of appropriate sampling frequency varies with the statistical objective for the monitoring data
and sampling regime. Following are examples of how sampling frequency in the context of simple
random sampling can be calculated for estimating the mean and for detecting trends.

3.4.1.1   Estimating the Mean
A common objective for monitoring data is to be able to estimate the mean value of a water quality
variable with a specific level of confidence in the estimate. The equation for calculating the sample size
(Reckhow and Chapra 1983, USDA-NRCS 2003) is:
                                           n =
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Monitoring and Evaluating Nonpoint Source Watershed Projects                                       Chapter 3


       where:
       n = the calculated sample size
       t = Student's t at n-1 degrees of freedom and a specified confidence level
       s = estimate of the sample standard deviation
       d = acceptable difference of the estimate from the estimate of the true mean, or !/> of the
       confidence interval from the mean

The t value is taken from a table of Student's t at the desired confidence level (P) (typically 0.05 or 0.10).
In general, a two-tailed t-test should be used because we are usually interested in error on both sides of
the mean.  The estimate of the population standard deviation is best obtained from baseline data from the
monitored water body; if such data are lacking, an estimate from a comparable nearby system can be
used. The  acceptable confidence interval from the true mean can be expressed as a percent of the  mean.
The actual calculation may be an iterative process because the value oft may change with the particular
value of n chosen. See file nmean.pdf for an example.

3.4.1.2   Detecting a Step or Linear Trend
Another objective for monitoring data might be to detect a change or trend in the value of a water quality
variable with a specific level of confidence (see section 7.8.2.4 for a discussion of trend analysis
techniques).

Commonly in watershed studies, there are two types of change in the water quality variable studied:
   •  a step change that compares the pre- and post- water quality mean values
   «  a linear (gradual, consistent) trend over time

To determine sample size to detect a step change (e.g., comparing the change in baseline mean due to
implementation of land management changes), the detectable change must first be calculated based upon
the standard deviation of the difference between the pre- and post- means with an anticipated number of
samples. See section 3.4.2.3 for an example calculation to determine the detectable step change with a
given sample size. With an iterative process of trying different pre- and post- sample sizes, a sample size
to detect a step change difference of acceptable magnitude can be estimated. See file ntrend.pdf for an
example.

As with documenting a step change between pre- and post- BMP periods, monitoring for trend detection
must be sensitive enough to detect the level of water quality change likely to occur in response to
management changes. For a linear trend, this monitoring is based upon the confidence interval on the
standard deviation of the slope. The standard deviation of the slope (Sbi) is a function of both the  square
root of the MSB  (which is the standard deviation of the water quality data with any linear trend removed),
as well as the spread of the X's (in this case, length of monitoring:
Where: MSB   = standard deviation of the water quality data with any linear trend removed
       X;      = X value at time i, X is the average X values.
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Typically, for watershed studies, X is expressed as a 'DATE' value which represents 1 day. The slope is
therefore expressed as change per day. To express as a change per year over N number of years, the
slope/per day would be multiplied by 365 days/year and N number of years.

Therefore, for a linear regression of water quality values vs. time, one-half of the confidence interval on
the slope is:
!/2 confidence interval = (N) *
(n*N-2)df '
365 * Sbi    [same as Minimal Detectable Change]
Where: ^n*N-2)df = One-sided Student's ^-statistic (a=.05)
       N       = Number of monitoring years
       n        = Number of samples per year
       df       = degrees of freedom
       365     = Correction factor to put the slope on an annual basis when DATE is entered as a Date
       (day) variable, e.g., the slope is in units per day. If DATE values were 1-12 for months and the
       slope was expressed 'per month' then this value would be "12."

The sample size could therefore be calculated interactively by trying various sample frequencies and
durations until the watershed monitoring would be able to detect the amount of change anticipated by
BMP implementation.

If pre-BMP data exist, the sample variance can be used to estimate MSE  (or capture the MSE by running
the sampled data through a linear regression computer program. Table 3-10 gives sample size for
common sample intervals and durations. Table 3-11 provides example values of £(Xi — X )2 for
biweekly sampling that were generated using P concentration data from a long-term NPS monitoring
project. This information is used in the linear trend example in file ntrend.pdf. Note that the required
sample duration will increase when corrected for autocorrelation (See section 3.4.2).

See Spooner et al. (2011) for more details on calculating the minimum detectable change (MDC) for
linear trends.  See file ntrend.pdf for an example (hyperlink to be added).

                        Table 3-10. Number of total samples per indicated
                             sample frequency and number of years

Number of years, N
1
2
3
4
5
6
7
8
9
10
Total number of samples, n
Weekly
52
104
156
208
260
312
364
416
168
520
Biweekly
26
52
78
104
130
156
182
208
234
260
Monthly
12
24
36
48
60
72
84
96
108
120
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Monitoring and Evaluating Nonpoint Source Watershed Projects
Chapter 3
    Table 3-11. Values of V£(Xi - X)2 for biweekly sampling for selected monitoring durations,
                      assuming Xi is measured as a 'Date' or daily variable
Number of years, N
2
4
8
j£(Xi-X)z
1,472
4,224
15,955
3.4.2  Minimum Detectable Change (MDC) Analysis

3.4.2.1  Definition and Overview
The MDC is the minimum change in a pollutant concentration (or load) during a given time period
required for the change to be considered statistically significant. Most of the material presented is taken
from Spooneretal. (2011) where the reader will find a more detailed discussion, relevant equations, and
illustrative examples.

The calculation of MDC has several practical uses, including determining appropriate sampling
frequencies (discussed here) and assessing whether a BMP implementation plan will be sufficient for
creating change that is measurable with the planned monitoring design (see section 7.6.3). The same basic
equations are used for both applications with the specific equations depending primarily on whether a
gradual (linear) or step trend is anticipated. The reader is referred to Meals et al. (201 Ib) for a discussion
of these types of trends. In simple terms, one can estimate the required sampling frequency based on the
anticipated change in pollutant concentration or load, or turn the analysis around and estimate the change
in pollutant concentration or load that is needed for detection with a monitoring design at a specified
sampling frequency. The basic steps for conducting MDC analysis and consideration of matters such as
the availability of representative data, the distribution of available data, independence of data values, the
need for data transformation, and level of statistical significance are touched upon lightly here, but
described and illustrated in detail in Spooner et al. (2011).

Sampling frequency determination is very closely related to MDC calculations. Sample size
determination is usually performed by fixing a significance level, power of the test, the minimum change
one wants to detect, the duration of monitoring, and the type of statistical test. MDC is calculated
similarly, except that the sample size (i.e., number of samples), significance level, and power are fixed
and the minimum  detectable change is computed. In short, MDC is the amount of change you can detect
given the sample variability.
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3.4.2.2  Steps to Calculate the MDC
The calculation of MDC or the water quality concentration change required to detect significant trends
requires several steps described by Spooner et al. (1987 and 1988) for a power of 50 percent. This general
procedure varies slightly based upon:
  •  Whether the appropriate statistical model assumes a step or linear trend.
  •  Whether the data used are on the original scale (e.g., mg/L or kg) or log transformed.
  •  Incorporation of time series to adjust for autocorrelation.
  •  Addition of explanatory variables such as streamflow or season.
  •  Whether an alternative power is selected.

The following assumptions are made in the calculation of MDC.
  •  Historical sample measurements  are representative of the  temporal and spatial variation of the past
     and future conditions.
  •  Variability due to sampling, transport or laboratory error is negligible compared to variability over
     time.

3.4.2.2.1  Step 1. Define the Monitoring  Goal and Choose the Appropriate Statistical
           Trend Test Approach.
One goal may be to detect a statistically significant linear trend  in the annual mean (geometric mean is
using log transformed data) pollutant concentrations that may be related to land treatment changes. A
linear regression model using log-transformed  data would be appropriate. An alternative goal to detect a
statistically significant change in the post-BMP period as compared to a pre-BMP period would require a
step change statistical test such as the t-test or ANCOVA.

3.4.2.2.2  Step 2. Exploratory Data Analyses.
The water quality data sets are examined  to verify distributional assumptions  required for parametric
statistical procedures. Specific attention is given to  the statistics on normality, skewness, and kurtosis.
Preliminary data inspections are used to determine if the residuals follow a normal distribution with
constant variance, both of which are required for the parametric analyses to be used. Both the original and
logarithmic transformed  values are tested. See  section 7.10 for a list of available software packages.
Options for exploratory data analysis (EDA) include Minitab Basic Statistics (Minitab 2016) and the SAS
procedure PROC UNIVARIATE (SAS Institute 2012).

3.4.2.2.3  Step 3. Data Transformations.
Water quality data often  follow log-normal distributions. In these cases, use the base 10 logarithmic
transformation for the dependent variables (e.g., TP) to minimize the violation of the assumptions of
normality and constant variance. Explanatory variables in statistical trend models do not have any
distributional requirements because it is only the distribution of the residuals that is  crucial. However, if
they do exhibit log normal distribution, exploratory variables (e.g., upstream concentrations, flow) are
also log-transformed which usually helps with the distribution of the residuals. When log transformation
is required for the dependent variables, the log-transformed data are used in all MDC calculations leading
to Step 7.
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3.4.2.2.4  Step 4. Test for Autocorrelation.
Perform tests for autocorrelation on the water quality time series. An autoregressive, lag-1 (AR(1))
structure in biweekly or weekly samples is common. The tests usually assume samples are collected with
equal time intervals. Methods to test for autocorrelation are described in detail in section 7.3.6.

3.4.2.2.5  Step 5. Calculate the Estimated Standard Error.
The variability observed in the historic or pre-BMP water quality monitoring data is used to calculate the
MDC estimate. The estimated standard error is obtained from using the same statistical model selected in
Step 1.

For a linear trend, use regression models with a linear trend, time series errors, and other optional
explanatory variables to obtain an estimate of the standard deviation on the slope over time. If adjusting
for autocorrelation, use a software procedure such as SAS's PROC AUTOREG to get the correct standard
error on the slope. Alternatively, you can use the standard error adjustment for autocorrelated data given
below in this step. For a step trend, use a t-test or ANCOVA with appropriate time series and explanatory
variables to estimate the standard deviation of the difference between the mean values of the pre-BMP vs.
post-BMP data (s^ re_x ostp. In practice, an estimate is obtained by using the following formula:
                                              (MSB   MSE
                                   s-XpOSt) -  , Uma   LLpost


Where: npre + npost = the combined number of samples in the pre- and post-BMP periods

       sf r  + r   "i= estimated standard error of the difference between the mean values in the pre- and
         ^ •x-pre* Apost)
       the post- BMP periods.

       MSE= sP2= Estimate of the pooled Mean Square Error (MSE) or, equivalently, weighted average
       ("pooled") of the variances within each period. The MSE estimate is obtained from the output of
       a statistical analysis using a t-test or ANCOVA with appropriate time series and explanatory
       variables. If post-BMP data are not available, no autocorrelation is present, and no explanatory
       variables are appropriate (i.e., the simplest case), MSE or sp2 can be  estimated by the variance
       (square of the standard deviation) of pre-BMP data.

The standard error on the trend estimate for simple trend models (e.g., step,  linear, or ramp trends) with
AR(1) error terms is larger than that  (incorrectly) calculated by software procedures that do not include a
correction for autocorrelation. The following adjustment can be applied to obtain the correct standard
error for  weekly or biweekly water quality data (Matalas, 1967; see Spooner et al. 2011 for additional
details):

                          std. dev.corrected=std. dev.uncorrected  \-^


Where: std.devCOrrected   = true standard deviation of the trend (slope or difference between 2 means)
       estimate
       std.devuncorrected = incorrect standard deviation of the trend estimate calculated without regard to
       autocorrelation
       p = autocorrelation coefficient for autoregressive lag 1, AR(1)
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3.4.2.2.6  Step 6. Calculate the MDC.
For a power of 50 percent, the MDC is essentially one-half of the confidence interval for the slope of a
linear regression trend or for the step trend difference between the mean values of the pre-and post-BMP
periods. For a linear trend, the MDC is equal to one-half of the confidence interval on the slope obtained
by multiplying the estimate standard deviation of the slope by the /-statistic, the total monitoring
timeframe, and a correction factor for the additional planned monitoring years (see Spooner et al. 2011 for
formulas). For a step trend, the MDC is one-half of the confidence interval to detect a change between the
mean values in the pre- vs. post- BMP periods.

3.4.2.2.7  Step 7. Express MDC as a Percent Change.
If the data analyzed were not log-transformed, this is just the MDC divided by the average values in the
pre-BMP period expressed as a percentage. If the data were log-transformed, a simple calculation can be
performed to express the MDC as a percent decrease in the geometric mean concentration relative to the
initial geometric mean concentration or load. The calculation is (see details and examples below):

                                  MDC% =  (l - 10-MDC') X 100

where MDC' is the MDC on the log scale and MDC% is a percentage.

3.4.2.3  Examples
The simplest example of an MDC calculation assumes a step trend, no autocorrelation, no covariates or
explanatory variables, and Y values on the original scale (i.e., not transformed); see  Spooner et al. (2011)
for examples of linear trends with autocorrelation and covariates, as well as a paired watershed study or
above/below-before/after studies. In this simple example, the planned comparison would be to detect a
significant change in the average values between the pre- and post-BMP periods. The pre- and post-
periods can have different sample sizes but should have the same sample frequency. Note: in this
simplified example, the MDC would be equivalent to the Least Significant Difference (LSD) and would
be calculated with a power of 50 percent as:
                               MDC = trn re+n ost_2\  * srx re+x  gst\

Or, equivalently:

                                 MDC= t
                                                              post
Where: trn   , n    7\ = one-sided2 Student's /-value with (npre + r\post-2) degrees of freedom.
        \llpre~t"llpost~<:'J                                  ^ F     F    /   o
        %re + ftpost = the combined number of samples in the pre- and post-BMP periods
        s(x   +x   t)= estimated standard error of the difference between the mean values in the pre- and the
        post- BMP periods.
        MSE= sp2= Estimate of the pooled Mean Square Error (MSE)
2 The choice of one- or two-sided /-statistic is based upon the question being asked. Typically, the question is
whether there has been a statistically significant decrease in pollutant loads or concentrations and a one-sided /-
statistic would be appropriate. A two-sided /-statistic would be appropriate if the question being evaluated is whether
a change in pollutant loads or concentrations has occurred.  The value of the /-statistic for a two-sided test is larger,
resulting in a larger MDC value.
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Calculation Example #1 (post-BMP data not available): It is assumed that there will be two years of pre-
BMP monitoring following by five years of post-BMP monitoring. For this example calculation, we
assume bi-weekly sampling to avoid serious autocorrelation concerns and the need for adjustment.
Example #2 illustrates an approach to address autocorrelation associated with weekly sampling.

npre =   26 samples/yr x 2 yr = 52 in the pre-BMP period
np0st =  26 samples/yr x 5 yr = 130 in the post-BMP period
Mean X = 36.9 mg/1, mean of the 52 samples in the pre-BMP period
5P = 21.2 mg/L = standard  deviation of the 52 pre-BMP samples
MSE=  Sp2 = 449.44
t(npre+npost-2)= ^80=  1.6534 (one-sided)

The MDC would be:
                              MDC = t
                                                          pre
             lpost
                                   MDC = 1.6534
                                                     449    449

                                         MDC = 5.7 mg/1
                          Percent change required = 100 x (5.7/36.9) = 15%
So, in this example, sampling bi-weekly before
(2 years) and after (5 years) BMP implementation
would require a 15  percent change in
concentration to be detectable at the 95 percent
confidence level and a 50 percent power. If a
smaller change was anticipated, then sampling
frequency (or duration) would need to be
increased to adjust  for autocorrelation. If a
decrease of more than 15 percent was expected,
then sampling frequency could be decreased and
the MDC recalculated to determine if the reduced
sampling frequency would be adequate to meet
project goals. Because MDC analysis is used to
"estimate" detectable change it is recommended
that estimated sampling frequency needs are
assumed to be higher than calculated to reduce the
risk of failure.

Calculation Example #2 (post-BMP data not
available, similar data distribution as in example
#1): It is assumed that there will be two years of
pre-BMP monitoring following by five years of
post-BMP monitoring. For this example
              Autocorrelation
Essentially means that subsequent samples are influenced
by previous samples. These subsequent samples contain
less new information than would otherwise be obtained
from a completely independent additional sample (i.e.,
there is information overlap). The result is that
autocorrelation reduces the effective sample size
compared to the situation with no autocorrelation.
Rho(p) is the coefficient of autocorrelation, and
basically describes the relationship between the current
and its past values.
  •  Rho increases as the strength of the relationship
     between current and past samples increases.
  •  Larger rho means that each collected sample has
     less new information (i.e., effective sample size is
     reduced).
  •  So, the relative improvement in estimates of a mean
     or a minimum detectable change decreases as
     sample size increases.
  •  Rho is used to adjust the standard deviation for
     inclusion in the step-change MDC calculations
     demonstrated in this section.
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calculation, we assume weekly sampling and address autocorrelation by assuming an autocorrelation
coefficient of p=0.3 (common for NFS projects with weekly sampling). A corrected standard deviation is
calculated as (see Spooner et al. 2011 for additional details):
                                                  ,    ,             1 + 0.3
             pooled std. dev.corrected = 21.2 x  |-	= 21.2 x  \-	—— = 28.9
                                                                    J_   \J m ij
Therefore:
npre =  52 sample s/yr x 2 yr = 104 in the pre-BMP
 period
ftpost =  52 samples/yr x 5 yr = 260 in the post-BMP
               period
Mean X = 36.9 mg/1, mean of the 52 samples in the pre-
               BMP period
sp = 28.9 mg/L = corrected standard deviation of the 52
               pre-BMP samples
MSE= sP2 = 834.67
t(npre+npost-2)=^62= 1.6491 (one-sided)

The MDC would be:
                                                      MSB   MSB
                             MDC=tr_   _   t_2)   	+	
                                                     I npre   npost
                                                   835   835
                                  MDC = 1.6491 I— + —


                                       MDC = 5.5 mg/1

                         Percent change required = 100 * (5.5/36.9) = 15%

So, in this example, sampling weekly before  (2 years) and after (5 years) BMP implementation would
also require at least a 15 percent change in concentration to be detectable at the 95 percent confidence
level and a 50 percent power. In essence, autocorrelation results in diminishing returns for higher sample
frequencies. However, it should be noted that even biweekly sample frequency such as used example #1
also have autocorrelation, just a lesser amount (e.g., p=0.1)  which would have resulted in a MDC estimate
for biweekly sampling in example #1 of 17.2 percent.

3.4.2.4  Factors Affecting the Magnitude of the MDC
Up to this point the discussion of MDC analysis has been based on simplifying assumptions. The reality,
however, is that the true MDC value for a specific significance level varies as a function of pollutant
variability, sampling frequency, length of monitoring time, other factors (e.g., potential explanatory
variables such as season, meteorological, and hydrologic variables), the magnitude and structure of the
autocorrelation (see Calculation Example #2  above), and the statistical techniques used to analyze the
data. Variations in water quality measurements are due to several factors including:
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  •  A change in land treatment or land use resulting in decreased (hopefully) concentrations and/or
     loadings to receiving waters (determining the amount of water quality change is usually a key
     objective of a watershed project).
  •  Sampling and analytical error.
  «  Monitoring design (e.g., sampling frequency, sampling location, variables measured).
  •  Changes in meteorological and hydrologic conditions.
  •  Seasonality.
  «  Changes in input to and exports from the system. For example, changes in upstream concentrations
     can affect the downstream water quality.

The bottom line is that the magnitude  of MDC is often larger than expected but can be reduced by:
  •  Accounting for changes in discharge, precipitation, ground water table depth or other applicable
     hydrologic/meteorological explanatory variable(s).
  •  Accounting for changes in incoming pollutant concentrations upstream of the BMP implementation
     subwatershed (i.e., upstream concentrations).
  «  Increasing the length of the monitoring period.
  «  Increasing the sample frequency.
  •  Applying the statistical trend technique that best matches the implementation of BMPs and other
     land use changes.

Figure 3-21 through Figure 3-24 illustrate how MDC varies with sampling frequency/duration,
confidence level (expressed as percent), coefficient of variation (CV), and autocorrelation coefficient (p),
respectively using a 50 percent power. These examples all assume a step trend and no covariates or
explanatory variables and use the basic equation found in section 3.4.2.3. The CV is used in lieu of
standard deviation because it has broader applicability (CV=std.dev./mean). Figure 3-22 to Figure 3-24
assume a seven-year monitoring program (two pre-BMP and  five post-BMP) with the same sampling
frequency each year. Data are assumed to follow a normal distribution and pre- and post-BMP CVs are
assumed to be the same. In Figure 3-21 through Figure 3-23, the values of p were assumed to be 0.1 and
0.3 for sampling frequencies of 26 and 52 times per year, respectively. No autocorrelation was assumed
for less frequent sampling.
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                                                                •26x/yr
       0
           0
      5             10
Total Years Monitoring
15
Figure 3-21. MDC versus frequency and years of monitoring. Assumes p=0.1 for 26x/yr and
0.3 for 52x/yr, CV=0.7, and 95% confidence level.
                                                                •4x/yr
                                                                •12x/yr
                                                                •26x/yr
                                                                •52x/yr
       0
          75       80       85       90       95
                     Confidence Level (%)
                                  100
Figure 3-22. MDC versus confidence level. Assumes p=0.1 for 26x/yr and 0.3 for 52x/yr, 7 years
of monitoring, and CV=0.7.
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                                                                 —4x/yr
                                                                 —12x/yr
                                                                 —26x/yr
                                                                 —52x/yr
          0
             0
 0.5          1          1.5
Coefficient of Variation
Figure 3-23. MDC versus coefficient of variation. CV calculated using unadjusted std. dev.
Assumes p=0.1 for 26x/yr and 0.3 for 52x/yr, 7 years of monitoring, and 95% confidence level.
        0
           0
0.2
0.4         0.6
   p value
0.8
Figure 3-24. MDC versus coefficient of autocorrelation (p). Assumes 7 years of monitoring, 52x/yr,
CV=0.7, and 95% confidence level. MDC = 13% if no autocorrelation is assumed.
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Figure 3-21 shows that the change in MDC is less pronounced with increasing duration for designs with
more frequent sampling. For example, MDC drops from 78 percent to about 62 percent when monitoring
is extended from three to four years with quarterly sampling; the corresponding change for weekly
sampling is only 5 percent. In addition, the change in MDC is minor after seven years for monthly or
more frequent sampling. Figure 3-22 illustrates the benefits of considering the statistical confidence
needed in changes that might be documented. Sampling 26 times/year over seven years, the MDC drops
from 21 to 11 percent when the confidence level is changed from 95 to 80 percent, respectively. In some
cases management decisions can be based on less than 95 percent confidence. These changes are more
pronounced at lower sampling frequencies. Figure 3-23 illustrates the importance of having a good
estimate of variance when calculating MDC. An assumption that the CV=0.5 when it is actually 1.5 could
result in a monitoring plan designed to detect an MDC of 15 percent at 26 samples per year when the
actual MDC is 45 percent. Finally, Figure 3-24 illustrates the impact of autocorrelation on MDC
estimates. In this example (52 samples/year for seven years), the MDC increases with increasing
autocorrelation, with an MDC of 20 percent at p=0.4 and an MDC of 32 percent at p=0.7. Testing for
autocorrelation is an important element of using existing data to aid in monitoring plan development,
particularly when anticipated sampling frequencies exceed about 25 or more per year.

The reader is referred to Spooner et al. (2011) for additional details on estimation of MDC.

3.4.3   Sampling Duration
How long should a monitoring program be conducted? The answer is essentially: as long as needed to
achieve the objectives or document a change. Following are basic guidelines for ensuring that a planned
monitoring program has a reasonable chance of success.
  •   Capture at least one full cycle of natural or cultural variability. Especially for NFS situations,
      monitoring should be conducted long enough to capture the full range of expected variability from
      weather, seasons and cultural factors such as cropping patterns or construction management.
      Similarly, if the first year of monitoring is done in a notable drought period, it would be wise to
      extend monitoring to capture a more representative set of weather conditions.
  •   Use statistical tests to evaluate the adequacy of a monitoring period. Data from some
      monitoring designs can be tested statistically to determine if an adequate database exists.  For
      example, data from a paired-watershed design (see section 2.4.2.8) can be tested to determine if
      acceptable calibration has been achieved and if treatment can begin (USEPA 1993b).  Pre-treatment
      data from a before/after design can be evaluated for MDC to  help determine if it is likely that
      enough data exist to document an expected change.
  •   Consider lag time. Lag time between land treatment and water quality response is a common
      phenomenon (see section 6.2). Knowledge of key lag time factors can help determine the required
      duration of a monitoring program.  For example, if groundwater travel time from an agricultural
      field through a riparian forest buffer to a stream is known to be five to 10 years, it is reasonable to
      expect to continue monitoring at least that long. Similarly, a lake with a flushing rate of 1.5 years
      may respond much more quickly to changes in pollutant inputs and a shorter monitoring program
      could suffice.

3.5   Monitoring Station  Construction and Operation
This section discusses the design and operation of physical facilities involved in fixed monitoring
stations. The type of station required depends on both project objectives and the nature of the resource
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being monitored. Not all monitoring designs require fixed station facilities, e.g., synoptic/grab sampling,
lake monitoring, biological monitoring. When physical facilities are required, several important principles
apply, regardless of station type.
  "  Select monitoring sites according to specific criteria based on program objectives and needs
      (see section 3.3).
  "  Design the station to collect representative samples from the target population under
      foreseeable circumstances. Make certain that measurements and samples are taken from areas that
      represent the resource or problem of interest, e.g., from the main flow of a stream, not an eddy;
      from a well-mixed area below a discharge; from the geologic formation transmitting subsurface
      flow. In situations where vertical or horizontal variability exists, depth-integrated samples or
      several discrete samples may be required. Physical facilities should allow access and sample
      collection during anticipated high flows, harsh climates, or inclement weather.
  "  Strive for simplicity. While sophisticated technology offers many capabilities and advantages,
      power failures and unexpected errors may occur and cause problems in complex designs. When
      possible, the simple alternative may well be the best choice. A passive crest gage may provide
      necessary information on peak stream stage more reliably than an electronic sensor. In addition,
      monitoring systems with data loggers and real-time internet uplinks may function well most of the
      time, but there is often no substitute for a regular visit by a field technician to maintain equipment
      and to record key data and observations.
  "  Include redundancy. When possible, provide a backup means of collecting essential samples or
      data. This may mean including a passive sampling device like a US U-59 single stage sampler
      (Wilde et al. 2014) as a backup to an autosampler. A flow totalizer on a flow meter provides data on
      total event discharge in case a data logger fails or a file is corrupted and the continuous stage and
      flow data are lost.
  "  Provide security.  Monitoring instruments and equipment need to be protected both from the
      elements and from potential vandalism. Field technicians need safe access and protection from
      inclement weather and other hazards. The integrity of samples and accumulated data should be
      protected so that adequate chain of custody is maintained.

The following sections discuss important aspects of monitoring station design for several common
applications including streams and rivers, edge of field, and individual structures or BMPs. The following
are  examples of comprehensive references that provide additional detail on these and other matters of
monitoring station design.
  "  USDA Field Manual for Research in Agricultural Hydrology (Brakensiek et al. 1979)
  "  USDA-NRCS National Handbook of Water  Quality Monitoring (USDA-NRCS 2003)
  "  USGS National Field Manual for the Collection of Water Quality Data (USGS variously dated)

When selecting specific instrumentation and equipment for monitoring stations, review manufacturer
information for features and specifications to be sure that equipment can do the jobs required.

3.5.1   Grab Sampling
Even though monitoring programs based exclusively on grab sampling may not require "stations" with
physical facilities, grab sampling  stations must be located and identified so that samples can be repeatedly
collected from the same location.  Such locations may be  fairly obvious such as road crossings on streams
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or pipes delivering flow to or from a stormwater treatment system. These sampling locations can simply
be recorded on a map or in a standard operating procedure. In lakes, however, repeated navigation to a
specific location will likely require use of a global positioning system (GPS) device. Determination of
sampling depth will also be required at some lake stations, using a weighted line or an electronic depth
sounder. There are numerous devices available to collect grab  samples. The choice will depend on water
resource characteristics, the type of sample desired (e.g., surface vs. depth-integrated), and on the
variable(s) to be  monitored (see section 3.6.2.1).

3.5.2  Perennial Streams  and Rivers
Long-term stations to continuously record streamflow and collect periodic water samples require
structures  and facilities to house monitoring equipment. Specific considerations for flow measurement
have been discussed previously (see sections 3.1.3.1 and 3.3.2.2). Stations for continuous flow
measurement require a staff gage  and a means of continuously recording stage, e.g., using a stilling well
with a float or bubbler or directly in the channel using a bubbler, pressure transducer, or ultrasonic device.
The traditional float gage in a stilling well is highly reliable and is protected from turbulence, ice and
debris in the stream channel. Advantages of bubblers, transducers or ultrasonic devices are they can be
placed directly in the stream channel, data can be logged electronically, and flow data can be linked to an
autosampler. A diagram of a stream station with an in-stream pressure transducer and staff gages is shown
in Figure 3-25 (Freeman et al. 2004).

Water samples at continuous monitoring stations are typically  collected by autosamplers. Autosamplers
commonly pump samples from the stream through plastic tubing and collect the water in one or more
bottles. Modern autosamplers are sophisticated instruments that can collect timed samples of specific
volume based on their own internal  programs or collect storm-event or flow-proportional samples when
linked to a flow recorder or other triggering device (see section 3.6.2.4). One common issue associated
with pumping autosamplers is the nature and placement of the intake. Sampler intake is usually fixed at
some point in the stream and may not collect a sample representative of vertical or horizontal variability.
Some depth-integrated intake devices have been proposed and tested with success (Eads and Thomas
1983), but some  of these devices can require frequent maintenance and can be impractical in northern
climates where ice is a problem. Selbig and Bannerman (2011), however, demonstrated the idea of
vertical  stratification of solids in storm sewer runoff using a fully-automated, depth-integrated  sample arm
(DISA) for collecting integrated samples within pipes (Figure 3-26). Subsequent laboratory testing
showed that the DISA was belter  able to characterize suspended-sediment concentration and particle size
distribution compared to fixed-point methods (Selbig et al. 2012).

Some variables (like temperature, turbidity, specific conductance, and dissolved oxygen) can be
monitored in situ without collecting actual water samples using sensors deployed directly in the stream.
Installation and operation of such sensors for continuous monitoring requires consideration of site-
specific characteristics related to exposure of the sensors to the water, mounting platforms, protection
from fouling and impact from debris, calibration, and maintenance. Consult manufacturer
recommendations and additional resources for specific guidance on sensors (e.g., Miles 2009, USEPA
2005b).
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                  -Telephone or
                  Satellite Telemetry
                  Instrument Shelter
                                  Staff and
                                        stage
                                           Water
                                           Surface
                           Submersible
                           Transducer
                              . A
Figure 3-25. Monitoring station with submersible transducer in stream (Freeman et al. 2004)
Figure 3-26. Drawing and field installation of depth-integrated sample arm for automatic samplers
(photo by R.T. Bannerman, Wisconsin DNR)
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The major advantage of autosamplers and recording sensors is that they can operate unattended for
extended periods. Autosamplers, for example, can remain dormant for weeks and triggered by
precipitation or rising flow independent of personnel action. This is particularly important when
monitoring transient storm events is an objective. However, such equipment is expensive and requires
regular maintenance and calibration.

Modern monitoring instruments can be linked together with a data logger (either a separate unit or part of
either the flow meter or autosampler) for sampling control and data storage. Where resources are
available, stations can be equipped to communicate through cell phone systems or Internet in real time. In
such cases data can be downloaded and commands for sampling or recording data can be sent remotely. If
this kind of system is used, issues of communication linkage such as line-of-sight for radio or
connectivity for cell phones must be considered during station design.

Unless a completely passive, mechanical system is devised, most water quality monitoring stations will
require electrical power. Power can be provided with deep-cycle automotive or marine batteries, but
servicing and recharging batteries may be problematic and battery power may be inadequate for running
refrigeration or heating. For long-term application, it is desirable to obtain AC power from either the
electrical grid or a properly designed solar charging system. It should be cautioned, however, that
electronic monitoring instruments are often vulnerable to voltage spikes that may occur, especially in
rural areas, and computer-type power surge protectors should be used to prevent instrument damage.

Finally, it should be noted that stream monitoring stations face a number of challenges in northern
climates. Ice in the  stream channel can disrupt a stage-discharge rating (see section 3.1.3.1) and disable or
destroy sampling lines or instruments located in the stream. Winter weather may require robust shelter
and prolonged low  temperatures may require heat from heating tape or propane heaters to prevent
samples and equipment from freezing. Conversely, stations in hot climates may require special cooling
and/or ventilation for proper operation. Such requirements must be considered in designing monitoring
stations.

3.5.3  Edge of Field
"Edge of field" generally describes a situation where flow is intermittent and may or may not move
through defined channels. For the purposes of this manual, this includes monitoring in waterways or
points of concentrated flow at the edges of agricultural fields or in intermittent streams in any location
associated with field drainage. Edge of field monitoring stations share many common requirements with
stations on perennial streams, i.e., the need to measure flow (when it occurs), the need to collect
representative water samples and other data, the need for power, and challenges of extreme weather.
Edge-of-field stations face several additional challenges including:
  «   Lack of a defined drainage channel, requiring measures such as wingwalls or berms to direct
      flow into and/or out of the station.
  «   Intermittent flow, requiring that monitoring equipment be prepared for activation (e.g., by
      precipitation  or flow) at any time.
  •   Unpredictable timing and magnitude of flow, requiring wide tolerances in flow and sampling
      capacity.
  *   Remote location, usually lacking easy access and power from the grid.
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Stuntebeck et al. (2008) provides a comprehensive discussion of how these challenges were met in edge-
of-field monitoring stations at the Discovery and Pioneer Farms in Wisconsin. Typical edge-of-field
stations included these elements:
  •  Enclosures consisting of custom-made, aluminum, clam-style structure to house equipment
     designed to measure stage, collect water samples, and provide two-way telecommunication.
  "  Stage and discharge equipment including
     •   Wingwalls and berms to  collect overland flow.
     •   A flume for discharge measurement.
     •   A discharge outlet to prevent erosion and ensure proper flume operation.
     •   A bubbler gage, pressure transducer, or acoustic sensor for water level recording.
     •   A crest gage as a backup and calibration check for recorded stage data.
  "  Sampling equipment including an autosampler and sample intake line protected from freezing by
     using a down-gradient slope, heat tape, and foam insulation.
  "  Data logging and control instruments.
  •  Communications including  radio modem and datalogging communications software.
  "  Power including solar-charged DC batteries for electronics operation and an AC generator for
     heating and sample refrigeration.
  "  Digital time-lapse camera to periodically record field conditions.

Finally, it should be noted that edge-of-field stations typically require more maintenance than continuous
stream stations. Edge-of-field stations may have to remain dormant but ready for activation over extended
periods between events, and regular maintenance visits are required even when inactive. This is
particularly true in northern climates where removal of ice and  snow in preparation for monitoring critical
winter thaw or spring runoff events is especially labor-intensive.

Figure 3-27 shows examples of edge of field monitoring stations.

3.5.4  Structures/BMPs
Monitoring stations for specific BMPs or stormwater treatment structures are similar in many respects to
edge-of-field stations, but require some additional considerations because of site characteristics and
constraints.

Many individual BMP monitoring efforts have similar requirements for flow measurement, water
sampling, data logging, communications, and security as other station types, but are often constrained by
physical characteristics. Monitoring inflow and outflow from a constructed wetland is generally
comparable to monitoring flow in an intermittent stream. Runoff from a parking lot entering an
infiltration BMP, however, may be very difficult to quantify and sample, and outflow from the BMP may
be carried in an underground pipe.  Some specialized equipment for such monitoring has been developed,
including passive runoff samplers (Figure 3-28) and flume inserts for pipes with integrated stage sensors
(Figure 3-29). In a review of passive samplers for urban catchment studies, Brodie and Porter (2004)
classified them based on the main hydraulic principle applied in their design: gravity flow, siphon flow,
rotational flow, flow splitting, and direct sieving. In two Wisconsin studies, Parker and Busch (2013)
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demonstrated the capabilities and limitations of a crown divisor sampler in the laboratory and at the edge
of a small field, while Graczyk et al (2000) compared siphon samplers to automatic samplers in a stream
setting. In urban settings, much of the monitoring equipment may need to fit into a catch basin or storm
sewer access point. Station enclosures and security in urban areas may present additional challenges.
Figure 3-27. Edge-of-field monitoring stations, a, b, Wisconsin Discovery and Pioneer Farms
(Stuntebeck et al. 2008); c, d, Vermont (Meals et al. 2011 a).
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            Plywood—-•
       Polyvinyl chloride
      (PVC) Schedule 40
       pipe, 4-in. (inch)
       nominal diameter
                                        3/8-inch thread
                                        for removing cap
          750 milliliter
          plastic wide-
          mouth bottle
       A. Siphon Sampler
                                                                                  6-inch PVC coupling
                                                                                  Polycarbonate set
                                                                                  screw used to adjust
                                                                                  size of drain hole
                                                                                  1.5-liter sample bottle

                                                                                  6-inch-diameter
                                                                                  PVC sleeve


                                                                                  Quick-set grout

                                                                                  Undisturbed asphalt
                                                                                  Asphalt subgrade
                                                                                  material
B. Street Runoff Sampler
  C. Coshocton Wheel
                                                            D. Multi-slot Sampler
Figure 3-28. Examples of passive runoff samplers that can be used for edge-of-field or BMP
studies (A-Graczyk et al. 2000, B-Waschbusch et al. 1999, C-Brakensiek et al. 1979, and D-Parker
and Busch 2013; photo D by P. Parker, University of Wisconsin-Platteville)
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                             Chapter 3
                                    Manhole
         Flow Meter
        Mounted on
           Ladder
 Water Quality
Sampler on Shelf
      Ultrasonic Flow
           Sensor
Figure 3-29. Flow measurement and water quality sampling in stormwater pipes

In urban runoff monitoring, the first flush phenomenon (the initial surface runoff from a rainstorm
carrying high levels of pollutants that accumulated on impervious surfaces during dry weather) requires
special consideration because pollutant loads during the first part of an event may be much larger than
those in the later flows. Several approaches have evolved to monitor this phenomenon. Low-cost passive
first-flush samplers are available that capture early surface runoff, then close when filled (Figure 3-30).
Waschbusch et al. (1999) used a range of passive samplers to monitor street runoff, driveway runoff, lawn
runoff (Figure 3-31), roof runoff, and parking lot runoff. Some modern autosamplers offer special settings
for activation of intensive sampling programs at certain flow levels, then scale back sampling frequency
later in the event (Figure 3-32).
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         A. Nalgene® first-flush sampler.
         Installed below grate (at right).
     B. Edge-of-road sampler.
                                            C. GKY first-flush sampler.
Figure 3-30. Examples of first-flush runoff samplers (A-Nalgene 2007, B-Barrett 2005, C-GKY2014)
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                               Chapter 3
                                  Flow Direction
 PVCCap
                      PVC TUbmg Wrapped Top VlCW
                   5   in Nylon Screenmg       _,.         _
                      '-                   SbeontulwjgtoCaiiy
                                            Water to Bottle
                 PVC Cap with
                  V-notch for
                  Tygon'Ribe
 TWo Rows of 2.5-inch
  Slots for Water Entry
                     PVC Coupling with  /*]
                     Slot for lygon TUbe
                                 PVC
                         1-Quart Qass Bottle
                                                         PVC Coupfag with
                                                         Slotfoi
              to Cany Water
                to Bottle
                                       Cross-Sectional View       Side View

Figure 3-31.  Passive sampling setup for lawn runoff (after Waschbusch et al. 1999)
     Sigma 900 MAX Portable Standard
                   Sampler
Isco6712 Portable
     Sampler
Figure 3-32. Examples of automatic samplers with capabilities for variable sampling frequencies
(Hach® 2013a, Teledyne Isco 2013a)
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Many BMP monitoring efforts follow an input/output design, where water quality (i.e., concentration or
load) is measured entering and again leaving the structure to assess pollutant reduction performance. Such
cases not only require two monitoring stations but also require that the stations be coordinated so that
water actually treated by the BMP is sampled properly. If sampling is conducted simultaneously at the
entrance and exit of a stormwater BMP, for example, the outflow sample may represent "old" water
pushed out of the BMP by "new" inflow, rather than new inflow after treatment by the BMP. Similarly,
water quality measured simultaneously upstream and downstream of a feedlot may not reflect the
influence of the feedlot, at least early in a storm event. Time of travel or residence time in the BMP must
be considered in setting up monitoring stations. This can be accomplished by linking the above and below
stations to better coordinate downstream with upstream sampling. Stuntebeck (1995), for example,
modified the basic above/below design in a Wisconsin barnyard runoff study by setting the samplers to be
activated by precipitation and programming them to collect time-integrated samples for an initial period.
This modification allowed for sampling of barnyard runoff in the receiving stream before streamwater
level increases could be sensed, thereby effectively isolating the barnyard runoff from nonpoint-pollution
sources upstream. Secondly, this approach allowed sampling during small storms in which local inputs
from the barnyard were apparent, but little storm runoff from the upstream areas of the watershed were
observed. A second modification took advantage of the close proximity of the two stations to create a
direct electronic connection between the stations for collection of concurrent samples.

3.5.5  Meteorology
Meteorological data, particularly precipitation data, are nearly always relevant to NPS monitoring projects
(see section 3.1.5). The nature and extent of meteorological monitoring will vary according to monitoring
objectives.  Precipitation data are useful in driving event sampling and for documenting rainfall conditions
relative to long-term averages. Particular monitoring objectives may require monitoring of other
meteorological variables. A study of indicator bacteria runoff from agricultural fields, for example, may
call for monitoring of weather conditions that influence bacteria survival in the field, such as air
temperature, soil temperature, solar radiation, relative humidity, and wind velocity.

Guidance for meteorological monitoring is given in the USDA Agricultural Handbook 224 (Brakensiek et
al. 1979) and in the National Weather Service  Observing Handbook No. 2 (NWS 1989). Probably the
most important criterion for precipitation measurement is location. For BMP or field monitoring efforts, a
single meteorological station may be sufficient. For larger watershed monitoring, multiple stations are
usually necessary to account for variation of weather with elevation, and other geographic factors.
Multiple precipitation stations are especially important in monitoring efforts designed to provide  data for
model  application. Successful application of watershed models such as SWAT is highly dependent on
accurate precipitation data (Gassman et al. 2007). Precipitation monitoring stations must be located so
that there are  no obstructions within 45° of the lip of the gage (USDA-NRCS 2003). A more restrictive
general rule, illustrated in Figure 3-33, indicates that an obstruction should not be closer to the gage than
two to four times the obstruction's height above  the gage (Brakensiek et al. 1979).

A variety of instrumentation is available for meteorological monitoring, including many electronic
instruments that record directly into dataloggers. Tipping bucket rain gages measure both total
accumulated rainfall and rainfall rate and can be connected to other monitoring instruments to log data
and/or trigger sample collection. For winter operation, tipping bucket gages must be heated electrically. A
weighing bucket precipitation gage can measure both rain and snow if it is charged with anti-freeze in the
winter. It is generally a good idea to provide a manual (non-recording) rain gage on the station site as a
backup and calibration check  for the recording instrument.
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                      2X to  4X
                                    Obstruction
Figure 3-33. Precipitation gage placement relative to obstructions

An example of a meteorological station measuring precipitation, air temperature, solar radiation, relative
humidity, and wind velocity is shown in Figure 3-34.
Figure 3-34. Photograph of a meteorological monitoring station
(Meals etal. 2011 a)
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3.6   Sample Collection and Analysis Methods
Collection and analysis of samples, and obtaining measurements and other data from monitoring stations
is an exacting task that requires training, appropriate equipment, careful adherence to standard
procedures, and detailed record-keeping. This guidance discusses basic principles and important rules of
thumb. Other sources such as the USGS National Field Manual for the Collection of Water Quality Data
provide specific information and procedures.

3.6.1   General Considerations
This section presents some general aspects of sample collection and is primarily focused on preparation to
collect specific types of samples. A preliminary step in determining  sample collection and analysis
methods for a new monitoring project is to examine how sampling was performed under other past or
current monitoring efforts in the area or in other locations you may be interested in. As noted in the
discussion of trend monitoring (section 2.4.2.4) changes in methods over time can doom the analysis, so it
can be very important to align your methods with those used in the past. Unless there is a compelling
reason to use different sample collection and analysis methods from those used to generate past data, it
may be best to simply use the same methods to increase the likelihood of data compatibility.

3.6.1.1  Documentation and Records
Because field personnel may rotate assignments in a monitoring project, it is critical that field procedures
be documented clearly to ensure consistency, both day-to-day and over the long term. Preparation of field
manuals and written standard operating procedures (SOPs) will help supplement the basic training that
will be required for field personnel. Field personnel should also keep meticulous sample collection
records to support and explain the data being collected. These records should include a logbook of
calibration and maintenance records for field instruments and notes concerning variations from SOPs,
errors, extreme events and field conditions.

3.6.1.2  Preparation for Sampling
Preparation for a sampling trip includes activities such as cleaning, calibrating, and testing field
instruments and sampling equipment as well as making certain that all needed supplies and equipment are
assembled. The USGS recommends that a formal checklist be filled out in preparation for each sampling
trip to make sure that nothing essential is forgotten (Wilde variously dated).

3.6.1.3  Cleaning
Sample containers must be clean to avoid contamination and preserve sample integrity (Wilde 2004).
Most water quality variables have specific requirements for the type and composition of sample container
and the cleaning process appropriate for that constituent (see section 3.6.3.2 for references to sources of
information on analytic methods). Field personnel must ensure that sample containers they take are
prepared for use. In the field, most polyethylene sample bottles and those glass sample bottles that are
designated for analysis of inorganic constituents should be field rinsed with the same water that will
ultimately fill the sample bottle. Specific field rinsing procedures recommended by USGS are described
in Table 5-2 of Wilde et al. (2009).
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3.6.1.4  Safety
Field personnel are subject to the basic safety policies and regulations of their employer. In addition, field
work for water quality monitoring presents special hazards and considerations that should be addressed.
Some important safety protocols include:
  •   Field personnel should not work alone, should have capacity for communication, and should leave
      contact and itinerary information with their base.
  •   Pay attention to inclement weather, especially when sampling from boats in open water or sampling
      in flashy urban streams. Seek shelter or head back to shore if threatening conditions approach.
  •   When wading to collect samples or make measurements, wear a personal flotation device (PFD)
      and do not attempt to wade a stream where the depth exceeds 4 ft or where the product of depth (in
      ft) times velocity (in ft/s) equals or exceeds 8 anywhere in the cross section. This guidance is based
      on a study that tested the stability of human subjects over a velocity range of 1.2-10 ft/s and a depth
      range of 1.6-4 ft (Abt et al. 1989).
  •   When electrofishing (see section 3.6.2.6 and chapter 4), always work in teams of two properly
      trained technicians and use proper protective equipment.
  •   Follow standard safety procedures around mechanical equipment and hazardous chemicals.
  •   Use caution and extra protection when working with water known or suspected to contain high
      levels of pathogens.

These and other important procedures are documented in detail in chapter 9 of the USGS National Field
Manual (Lane and Fay 1997).

3.6.2  Field Procedures
General procedures are discussed below for different types of sampling. The reader is encouraged to
consult other resources (e.g., Barbour et al. 1999; USGS variously dated) for more detailed information
on specific sampling procedures. A detailed discussion of sample types can be found in section 3.2.

3.6.2.1  Field Measurements
Collection of data on some water quality characteristics must be based on field measurements, rather than
samples collected for later analysis in a laboratory. Variables such as water temperature and dissolved
oxygen concentration must be measured directly in the waterbody (Figure 3-35). Other properties such as
pH, specific conductance, and turbidity can be measured either in situ or immediately on the site using a
sample taken from the source, depending on the specific instruments involved.

An in situ measurement is made  by immersing one or more instrument sensors directly into the
waterbody.  In flowing water, a single sampling point in a well-mixed area is generally used to represent
an entire cross-section, often after a preliminary investigation of variability has been made from repeated
measurements at points along the cross-section. In lakes or other still water, field measurements may be
made at multiple locations and depths, depending on monitoring objectives and the variability of the
waterbody.  It is important to record the results of individual measurements from the field, not averaged
values. Field measurements in ground water generally require purging the monitoring well of standing
water before taking measurements so that the measurements accurately represent the properties of the
water in the geologic formation at the time of collection. Following purging, field measurements are
performed either above ground by pumping water from the well or downhole, using submersible sensors.
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Figure 3-35. Measuring dissolved oxygen, specific conductance,
pH, and water temperature using a hand-held probe

Detailed procedures for making field measurements are presented in chapter 6 of the USGS National
Field Manual (Wilde variously dated).

3.6.2.2  Grab Sampling
There are a variety of devices available to collect grab samples from waterbodies for different purposes
(Wilde etal. 2014).
  •   Isokinetic depth-integrated samplers are designed to accumulate a representative water sample
      continuously and isokinetically (water approaching and entering the sampler intake does not change
      in velocity) from a vertical section of a stream while transiting the vertical at a uniform rate.
      Isokinetic samplers may be hand-held or used with cable systems. Such devices are often used for
      suspended sediment sampling because maintaining constant velocity facilitates the collection of a
      sample that is representative of all suspended matter moving in the water column. Some examples
      of isokinetic samplers are shown in Figure 3-36.
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                                    Chapter 3
                   AUSDH-81 sampler
B. US DH-95 sampler
                    C, US D-95 sampler
                                                           US D-96 sampler
                       £. US D-99 sampler
                                                        F. US DH-2 sampler
                                                                      Not to scale
       Figure 3-36. Examples of isokinetic depth-integrating samplers (Wilde et al. 2014)

     Nonisokinetic samplers are sampling devices in which the sample enters the device at a velocity
     that differs from ambient. Nonisokinetic samplers include ordinary hand-held open-mouth bottles,
     weighted bottles on cables, and specialized BOD and volatile organic compound (VOC) samplers
     for collecting non-aerated samples.
     Depth-specific samplers (also called "thief samplers") are used to collect discrete samples from
     lakes, estuaries and other deep water at a known depth. Common samplers of this type (another
     form of nonisokinetic sampler) include the Kemmerer and Van Dorn samplers (Figure 3-37).
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        A. Kern in e rer sam pier   B. Van Do rn sample r
       Figure 3-37. Depth-specific samplers for lake sampling (Wilde et al. 2014)

3.6.2.3  Passive Sampling
Passive samplers are devices to collect unattended grab samples without reliance on external power or
electronic activation. They offer the convenience of unattended operation, however in most cases the
exact time and circumstance of sampling is unknown unless other data are taken at the same time. Some
passive samplers are also limited to collecting samples from the rising limb of the hydrograph, so
resulting data may be biased compared to samples collected during the full event. Examples of passive
samplers include:
  •  Runoff samplers are used to collect overland flow from urban or rural areas. A first-flush sampler
     is often a bottle buried so that its mouth is flush with the ground (see Figure 3-30). When the bottle
     is filled, a check-valve closes, preventing subsequent flow from entering. Another type of runoff
     sampler/flow splitter collects overland flow and splits off a subsample into a down-slope container.
     Examples are shown in Figure 3-38.
  •  Single-stage samplers  (Figure 3-39) are designed to collect unattended samples for suspended
     sediment or other constituents from streams during storm events. Multiple units can be mounted
     above each other to collect samples from different elevations or times as stream stage increases.
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                                                                         Entrance Box
Figure 3-38. Examples of passive samplers, a, Passive runoff sampler/flow splitter, University of
Georgia, Tifton, GA (photo by D.W. Meals); b, Multi-slot divisor (after Brakensiek et al. 1979);
c, Water and sediment sampler (Dressing et al. 1987, photo by S.A. Dressing).
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                (C
                           Exhaust
                           port
                           Intake
                           nozzle
        Figure 3-39. Single-stage passive sampler (diagram: Wilde et al. 2014, photo by D.W.
        Meals)

     Tipping-bucket samplers are mechanical devices that capture water flowing from a pipe or other
     concentrated discharge in one of two pans that tip back and forth on an axis as one pan fills and the
     other discharges to a large pan. Slots or a funnel can passively convey a sample to a collection
     bottle; the resulting sample is a flow-proportional composite. A tipping bucket sampler has the
     additional feature that total discharge can be measured by counting the number of tips with a
     mechanical counter. An example of design and application of a tipping-bucket system for sampling
     field runoff and suspended sediment with a pipe collector is given by Kahn and Ong (1997).
     Coshocton wheel samplers are rotating wheels driven by the force of water discharging from a pipe
     or flume (see Figure 3-28). A standing slot collects a sample each time it rotates under the
     discharge.  Coshocton wheels collect a sample volume proportional to the total discharge (usually 1
     percent of the discharge) and therefore can provide an estimate of total event discharge.
     Lysimeters are devices buried in the ground to sample soil water moving through the vadose zone,
     the area between the ground  surface and water table (Figure 3-40). Lysimeters may be entirely
     passive ("zero-tension lysimeters") collecting gravitational water in funnels, pans or troughs.
     Alternatively, tension lysimeters extract a sample of soil water by applying suction through porous
     plates or cups.
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        Figure 3-40. Lysimeters before and after installation (photos by R. Traver, Villanova
        University)
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3.6.2.4  Autosampling
Autosamplers generally consist of an intake line submerged in the waterbody or the flow through a pipe
or flume, a peristaltic or submersible pump that pumps water to the sampler, one or more bottles to
contain collected samplers, and electronic controls to initiate sample collection and record data. Some
autosamplers may be refrigerated to preserve samples for extended periods. Some may be designed
specifically to fit into storm drains and catch basins. Most operate with either DC or AC power. Examples
of autosamplers are shown in Figure 3-41.
Figure 3-41. Examples of portable and refrigerated autosamplers
(Hach® 2013b, Teledyne Isco 2013b)

Autosamplers can be set to take time-based samples either continuously, i.e. collect a sample every eight
hours, or as initiated by an external trigger such as detection of rainfall or rising stream stage. Some
samplers can be set in variable time programs, e.g., to collect samples every 15 minutes during the early
part of a storm event, then take hourly samples as the event subsides. When connected to a flow meter,
autosamplers can take flow-proportional samples, collecting a subsample for every m3 that passes the
station during a set time period or during a discrete storm event. Flow-proportional sampling may be the
most appropriate way of sampling for many NFS pollutants, where high concentrations are associated
with high flows and where events that could be missed by timed sampling carry the bulk of the pollutant
load (see section 3.2.2.2).

Most autosamplers can collect discrete samples in individual bottles so that a picture of constituent
concentration variation across a time period or storm event (i.e., a chemograph) can be plotted and the
relationships among time, flow and concentration evaluated. Autosamplers can also combine individual
samples into a single larger container to yield a composite sample that represents an extended time period
(see section 3.2.2.2). Collecting composite samples can reduce analytical costs by sending a single sample
(representing the time period or the storm event) to the laboratory. A flow-proportional sample provides
an event mean concentration (EMC) with a single analysis and facilitates load estimation by providing a
single EMC result that can be multiplied by the total period or event flow for a load estimate (see section
3.8 and section 7.9).

The flexibility, capacity for self-contained unattended operation, and potential linkage to flow data are
major advantages of autosamplers. There are also a few disadvantages with autosamplers. First,
autosampler intakes are generally fixed in one position in a waterbody and may therefore not be fully
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representative of variability, especially where strong vertical or horizontal gradients exist. Second, the
size of the intake line and the velocity achieved by the autosampler pump, as well as the position in the
streamflow, may prevent the collection of a representative sample, especially of suspended sediment and
particulate-bound pollutants. Third, monitoring for some pollutants like volatile organics or pathogens,
may be challenging because of special limitations for materials contacting the sample and requirements
for sterilization between sample intake events. Finally, because samples are taken at intervals, regardless
of whether an autosampler collects on a time- or flow-based program, the possibility always exists that a
transient pulse of a pollutant (e.g., from a spill or first-flush) may pass by unsampled. This of course is
also a risk in manual sampling.

Autosamplers must be maintained properly to ensure that sample collection is reliable and performed in
accordance with programming instructions. Routine maintenance, sample volume calibration, and probe
calibration procedures specified in user manuals should be strictly followed.

3.6.2.5  Benthic Macroinvertebrate Sampling
Sampling of benthic macroinvertebrates from aquatic substrates like stream bottoms and lake beds must
consider not only how to physically collect samples but also the diversity of stream habitats that influence
the numbers and types of organisms to be sampled. Different types of assemblages of macroinvertebrates
inhabit different aquatic habitats (Hawkins et al. 1993). While a monitoring program need not necessarily
sample all these habitat types, the habitats sampled should be based on monitoring objectives and on
regional stream or lake characteristics. Two distinct types of stream habitats are generally sampled: riffles
(shallow areas of fast-moving water, generally with a stony or gravelly bottom) and pools (areas of
deeper, slow-flowing water, generally with a softer sediment substrate) (Figure 3-42). In lakes, near-shore
areas offer different substrates and habitats from those in deeper lake regions that might lack light,
vegetation, and oxygen.  Different groups of organisms tend to occupy these habitats,  and different
approaches for sampling them are required.

The Rapid Bioassessment Protocols (RBPs) recommended by U.S. EPA (Barbour et al. 1999) specify
many of the parameters of benthic macroinvertebrate sampling. These issues are discussed in greater
detail in chapter 4 of this guidance. In general, benthic macroinvertebrates can be collected actively or
passively. In rivers and streams, active collection is often accomplished by disturbing the streambed and
capturing the  dislodged organisms in a net as the current carries them downstream (Figure 3-43). Kick-
seines, D-frame nets, and Surber square-foot samplers are common devices used (Figure 3-44).
Regardless of the specific device, it is important to quantify both the area of the streambed disturbed and
the time/effort of sampling so that results can be quantified (e.g., organisms/m2), repeated and compared
among different sampling events overtime. In lakes, active sampling in shallow areas can be done by
similar methods. Grab samplers such as the petite ponar (Figure 3-44) or larger dredges are used for
taking sediment samples from hard bottoms such as sand, gravel).

Passive sampling for benthic macroinvertebrates often uses artificial substrates like the Hester-Dendy
plate sampler or rock baskets (Figure 3-44) that are anchored in the waterbody. Organisms colonize the
devices and then the devices are retrieved to collect and enumerate the organisms.

All of these techniques have advantages and disadvantages that are discussed in chapter 4.
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Figure 3-42. Preparing to take samples in a low-gradient stream
Figure 3-43. Using a D-frame net to sample a gravel bottom
stream for benthic macroinvertebrates
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Figure 3-44. Sampling devices for biological and habitat variables, a, D-frame net; b, Surber sampler
(Rickly 2016); c, Ponar dredge (Rickly 2016); d, Hester-Dendy artificial substrate (Rickly 2016); e, Rock
basket artificial substrate (Ben Meadows 2016).


   3.6.2.6  Fish Sampling
   As with benthic macroinvertebrates, distinct fish assemblages are found in different habitat types. For fish,
   characteristics like water temperature, flow velocity, dissolved oxygen levels, cover and shade, in addition
   to substrate type, are important habitat characteristics. In general, biomonitoring efforts should sample fish
   habitats based on project objectives and resource characteristics. Major habitat types like riffles, pools and
   runs (stream reaches between riffles and pools) should normally be sampled. Habitats and the size of
   sampling areas should be consistent between sampling events to allow long-term comparisons.

   Fish are most commonly sampled by electrofishing, where a portable generator system introduces an electric
   current into the water, temporarily stunning fish within a certain range (Figure 3-45). In practice, the ends of a
   sampling reach (approximately 30 m in length) are closed off with nets and a sampling crew walks through
   the reach. One person runs the shocker, while the others retrieve stunned fish into buckets. When collection is
   complete, the fish are  counted and identified (usually to species), then returned to the stream (Figure 3-46).
   The process may be repeated at several different sites of similar habitat to ensure a representative sampling
   has been achieved. Other approaches to fish sampling include use of seines, gill nets, traps, or underwater
   observation. For a discussion of the advantages and limitations of different fish sampling gear, see Klemm
   etal. (1992). Ohio EPA (OEPA 1987) discusses electrofishing techniques for bioassessment.
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Figure 3-45. Backpack electrofishing (USEPA)
Figure 3-46. Field processing offish sample: taxonomic identification and data recording
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3.6.2.7  Aquatic Plant Sampling
Aquatic plants sampled for water quality monitoring include algae (small free-floating plants), periphyton
(the community of algae, microbes, and detritus attached to submerged surfaces), and macrophytes (large,
plants rooted in aquatic sediments). Many of these plants are good indicators of nutrient enrichment and
ecosystem condition. Algae are usually evaluated in lakes or other bodies of standing water and are
sampled using a plankton net towed through the water column (Figure 3-47). Collected organisms are
identified and counted under a microscope. As a surrogate for algal biomass, chemical analysis of a water
sample for chlorophyll a may be performed. Periphyton biomass is usually measured in streams, either by
scraping known areas of rock surfaces or by use of artificial substrates (typically glass microscope slides)
placed in the stream and retrieved after a specified period. Aquatic macrophytes, often monitored in near-
shore areas of lakes or in large rivers, may be surveyed to assess species composition, quantified in small
plots by counting individual plants or harvesting vegetation, or mapped by remote sensing to document
areal extent of growth.
   X T
Figure 3-47. Plankton nets (NOAA 2014)

3.6.2.8  Bacteria/Pathogen Sampling
Collection of water samples for monitoring indicator bacteria, pathogens, or other microorganisms is
usually conducted by grab sampling. Samples for fecal coliform and E. coll bacteria analysis typically
require small volumes (e.g., 100 milliliter [ml]). Samples for detection and enumeration of protozoan
pathogens like Giardia and Cryptosporidium may require up to 20 Liter (L)  of sample. Sterile sample
containers such as autoclaved polyethylene containers or pre-sterilized single-use bags or bottles are
required. Sample collection should be done by clean technique, with samples allowed to contact only
sterile surfaces; field personnel should wear gloves when collecting grab samples, both to protect
themselves from water-borne pathogens and to prevent sample contamination. Samples for bacteria
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and/or pathogens might require more rapid delivery to the laboratory than samples from physical and
chemical analysis (see section 3.6.3).

3.6.2.9  Habitat Sampling
Assessment of aquatic habitat may be essential to interpretation of data collected from monitoring of
benthic invertebrates and fish. Habitat characteristics might also be an important response variable to land
treatment or stream restoration efforts. Habitat quality may be measured in three dimensions: habitat
structure, flow regime, and energy source. Habitat structure includes physical characteristics of stream
environment such as channel morphology, gradient, instream cover (boulders and woody debris),
substrate types, riparian condition, and bank stability. Flow regime is defined by velocity and volume of
water moving through a stream, both on the average and during extreme events (wet or dry). Energy
enters stream systems through nutrients from runoff or ground water, as leaves and other debris falling
into streams, or from photosynthesis by aquatic plants and algae.

Some important metrics of habitat sampling were shown in Table 3 -7 in section 3.1.4. Many habitat
characteristics are quantified by direct measurement in representative stream reaches, e.g., by surveying,
substrate sampling, and soil/geophysical measurements. Sets of habitat measurements are often
incorporated into indices that facilitate comparison between sites and between sampling times. For
example, the Qualitative Habitat Evaluation Index (QHEI) used by Ohio EPA (Rankin 1989) includes
measurements of:
  •   Substrate: type and quality
  •   Instream cover: type and amount
  •   Channel morphology: sinuosity, development, channelization, stability
  •   Riparian zone: width, quality, bank erosion
  •   Pool quality:  maximum depth, current, morphology
  •   Riffle quality: depth, substrate stability, substrate embeddedness
  •   Map gradient

Habitat assessment is discussed further in chapter 4 of this guidance. The reader is referred to additional
resources for more information on habitat sampling:
  •   Rapid Bioassessment Protocols for use in streams and wadeable rivers: periphyton, benthic
      macroinvertebrates and fish (Barbour et al. 1999)
  •   The Qualitative Habitat Evaluation Index (QHEI): rationale, methods, and application (Rankin  1989)
  •   Methods for assessing habitat in flowing waters: using the Qualitative Habitat Evaluation Index
      (QHEI) (OEPA 2006.).

3.6.2.10 Specialized Sampling
Specialized sampling techniques may be required for unusual or emerging pollutants. For example,
microbial source tracking analyzes DNA to attribute indicator bacteria to specific  host sources (USEPA
201 Ib, Meals et al. 2013). This method requires water sampling and might also involve collection of fecal
material from human and animal sources in the watershed.

Urban stormwater monitoring may test for optical  brighteners (fluorescent whitening agents added to
laundry detergent) in stormwater as indicators of wastewater or septic effluent contamination. Because
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these chemicals are absorbed by fabric, cotton pads are deployed in streams for several days, then
collected and tested for fluorescence with a UV source (Gilpin et al. 2002).

Sentinel chambers, dialysis membrane diffusion samplers, polar organic chemical integrative samplers
(POCIS), and other passive sampling devices have been used to passively sample low-concentration
pollutants like volatile organic compounds, estrogen analogs, endocrine disrupters, and other emerging
pollutants in a variety of settings (Vrana et al. 2005, Liscio et al. 2009, Kuster et al. 2010).

3.6.3  From Field to Laboratory
There are several important steps to consider between sample collection and analysis including sample
processing, sample preservation and transport, sample custody tracking, and performance audits. Quality
assurance and quality control procedures are described in detail in chapter 8.

3.6.3.1  Sample Processing
Sample processing refers to the measures taken to prepare and  preserve a water sample at or after
collection, and before it is delivered to the laboratory for analysis. The goals of sample processing are to
prepare samples for appropriate analysis (e.g., dissolved vs. TP), prevent contamination and cross-
contamination, and preserve sample integrity until analysis. The USGS National Field Manual includes
detailed sample processing procedures for many specific analytes, and recommends the following order of
sample processing: organic fraction, organic C, inorganic constituents, nutrients, radiochemicals, isotopes,
and then microorganisms (Wilde et al. 2009).

Samples requiring filtration (e.g., dissolved P, dissolved organic C) must be filtered during or
immediately after collection (Wilde et al. 2009). Surface water samples may be composited or
subsampled in the field using an appropriate device, such as a churn or cone splitter (Figure 3-48).
Ground water samples are not composited but are pumped either directly through a splitter or through a
filtration assembly into sample bottles unless a bailer or other downhole sampler is used to collect the
sample.
Figure 3-48. Churn and cone splitters (FISP 2014)

3.6.3.2  Sample Preservation and Transport
Water samples to be analyzed for most water quality variables have specified permissible holding time
and holding conditions that determine the length of time a sample can be held between collection and
analysis without significantly affecting the analytical results. Maximum holding times and storage
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conditions have been established by the EPA (40 CFR 136.3, USEPA 2008b) and are shown in Table 3-
12. Storage and preservation for most analytes involve cooling to below 6 °C; chemical preservatives such
as nitric acid (HNOs) or sulfuric acid (tbSCX) may also be used, depending on the analyte (Wilde et al.
2009).

Samples should be packaged and transported to the laboratory for analysis as soon as possible. The
shorter the time between sample collection and analysis, the more reliable the analytical results will be. If
samples must be shipped to a laboratory, check to insure that sample containers are sealed, labeled, and
packed to prevent breakage. It is necessary to follow receiving laboratory protocols for labeling,
documenting, and packaging samples.

           Table 3-12. Required containers, preservation techniques, and holding  times
Parameter number/name
Container 1
Preservation 2>3
Maximum holding
time4
Table IA— Bacterial Tests:




1-5. Coliform, total, fecal, and E. coli
6. Fecal streptococci
7. Enterococci
Q.Salmonella
PA, G
PA, G
PA, G
PA, G
Cool, <10°C, 0.0008% Na2S2035
Cool, <10°C, 0.0008% Na2S2035
Cool, <10°C, 0.0008% Na2S2Os5
Cool, <10°C, 0.0008% Na2S2Os5
8 hours.22.23
8 hours.22
8 hours.22
8 hours.22
Table IA— Aquatic Toxicity Tests:

9-12. Toxicity, acute and chronic
Table IB— Inorganic Tests:
















1 . Acidity
2. Alkalinity
4. Ammonia
9. Biochemical oxygen demand
10. Boron
1 1 . Bromide
14. Biochemical oxygen demand, carbonaceous
15. Chemical oxygen demand
16. Chloride
17. Chlorine, total residual
21. Color
23-24. Cyanide, total or available (or CATC)
and free
25. Fluoride
27. Hardness
28. Hydrogen ion (pH)
31, 43. Kjeldahl and organic N
P, FP, G

P, FP, G
P, FP, G
P, FP, G
P, FP, G
P, FP, or
Quartz
P, FP, G
P, FPG
P, FP, G
P, FP, G
P, G
P, FP, G
P, FP, G
P
P, FP, G
P, FP, G
P, FP, G
Cool, <6 °C 16

Cool, <6 °C 18
Cool, <6 °C 18
Cool, <6 °C 18, H2S04o pH <2
Cool, <6 °C 18
HNOsto pH <2
None required
Cool, <6 °C 18
Cool, <6 °C 18, H2S04o pH <2
None required
None required
Cool, <6 °C 18
Cool, <6 °C 18, NaOH to pH >10 56, reducing
agent if oxidizer present
None required
HNOsor H2S04o pH <2
None required
Cool, <6 °C is, H2S04o pH <2
36 hours.

14 days.
14 days.
28 days.
48 hours.
6 months.
28 days.
48 hours.
28 days.
28 days.
Analyze within 15
minutes.
48 hours.
14 days.
28 days.
6 months.
Analyze within 15
minutes.
28 days.
Table IB-Metals: 7



18. Chromium VI
35. Mercury (CVAA)
35. Mercury (CVAFS)
P, FP, G
P, FP, G
FP, G; and
FP-lined
cap17
Cool, <6 °C 18, pH = 9.3-9.7 20
HNOsto pH <2
5ml/L12NHCIor5ml/LBrCM7
28 days.
28 days.
90 days.17
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Parameter number/name

























3, 5-8, 12, 13, 19, 20, 22, 26, 29, 30, 32-34, 36,
37, 45, 47, 51, 52, 58-60, 62, 63, 70-72, 74, 75.
Metals, except boron, chromium VI, and
mercury
38. Nitrate
39. Nitrate-nitrite
40. Nitrite
41. Oil and grease
42. Organic Carbon
44. Orthophosphate
46. Oxygen, Dissolved Probe
47. Winkler
48. Phenols
49. Phosphorous (elemental)
50. Phosphorous, total
53. Residue, total
54. Residue, Filterable
55. Residue, Nonfilterable (TSS)
56. Residue, Settleable
57. Residue, Volatile
61. Silica
64. Specific conductance
65. Sulfate
66. Sulfide
67. Sulfite
68. Surfactants
69. Temperature
73. Turbidity
Container 1
P, FP, G
P, FP, G
P, FP, G
P, FP, G
G
P, FP, G
P, FP, G
G, Bottle and
top
G, Bottle and
top
G
G
P, FP, G
P, FP, G
P, FP, G
P, FP, G
P, FP, G
P, FP, G
P or Quartz
P, FP, G
P, FP, G
P, FP, G
P, FP, G
P, FP, G
P, FP, G
P, FP, G
Preservation 2'3
HNOsto pH <2, or at least 24 hours prior to
analysis 19
Cool, <6 °C 18
Cool, <6 °C 18, H2S04to pH <2
Cool, <6 °C 18
Cool to <6 °C 18, HCI or H2S04to pH <2
Cool to <6 °C 18, HCI, H2S04, or H3P04to pH
<2
Cool, to <6 °C 18>24
None required
Fix on site and store in dark
Cool, <6 °C 18, H2S04to pH <2
Cool, <6 °C 18
Cool, <6 °C is, H2S04to pH <2
Cool, <6 °C 18
Cool, <6 °C 18
Cool, <6 °C 18
Cool, <6 °C 18
Cool, <6 °C 18
Cool, <6 °C 18
Cool, <6 °C 18
Cool, <6 °C 18
Cool, <6 °C 18, add zinc acetate plus sodium
hydroxide to pH >9
None required
Cool, <6 °C 18
None required
Cool, <6 °C 18
Maximum holding
time4
6 months.
48 hours.
28 days.
48 hours.
28 days.
28 days.
Filter within 15
minutes; Analyze
within 48 hours.
Analyze within 15
minutes.
8 hours.
28 days.
48 hours.
28 days.
7 days.
7 days.
7 days.
48 hours.
7 days.
28 days.
28 days.
28 days.
7 days.
Analyze within 15
minutes.
48 hours.
Analyze.
48 hours.
Table 1C— Organic Tests: 8





13, 18-20, 22, 24-28, 34-37, 39-43, 45-47, 56,
76, 104, 105, 108-111, 113. Purgeable
Halocarbons
6, 57, 106. Purgeable aromatic hydrocarbons
3, 4. Acrolein and acrylonitrile
23, 30, 44, 49, 53, 77, 80, 81, 98, 100, 112.
Phenols 11
7, 38. Benzidines1112
G, FP-lined
septum
G, FP-lined
septum
G, FP-lined
septum
G, FP-lined
cap
G, FP-lined
cap
Cool, <6 °C « 0.008% A/a2S2C>35
Cool, <6 °C 18, 0.008% Na2S2035, HCI to pH
29
Cool, <6 °C 1S, 0.008% A/a2S203, pH to 4-510
Cool, <6 °C 1S, 0.008% Na2S203
Cool, <6 °C 18, 0.008% Na2S2035
14 days.
14 days.9
14 days.10
7 days until
extraction, 40 days
after extraction.
7 days until
extraction.13
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Parameter number/name















14, 17, 48, 50-52. Phthalate esters 11
82-84. Nitrosamines1114
88-94. PCBs 11
54, 55, 75, 79. Nitroaromatics and isophorone 11
1, 2, 5, 8-12, 32, 33, 58, 59, 74, 78, 99, 101.
Polynuclear aromatic hydrocarbons 11
15, 16, 21,31,87. Haloethers11
29, 35-37, 63-65, 107. Chlorinated
hydrocarbons 11
60-62, 66-72, 85, 86, 95-97, 102, 103.
CDDs/CDFs11
Aqueous Samples: Field and Lab Preservation
Solids and Mixed-Phase Samples: Field
Preservation
Tissue Samples: Field Preservation
Solids, Mixed-Phase, and Tissue Samples: Lab
Preservation
114-118. Alkylated phenols
119. Adsorbable Organic Halides (AOX)
120. Chlorinated Phenolics
Container 1
G, FP-lined
cap
G, FP-lined
cap
G, FP-lined
cap
G, FP-lined
cap
G, FP-lined
cap
G, FP-lined
cap
G, FP-lined
cap

G
G
G
G
G
G

Preservation 2'3
Cool, <6 °C 1S
Cool, <6 °C18, store in dark, 0.008% Na2S2Os5
Cool, <6 °C 18
Cool, <6 °C 18, store in dark, 0.008%
Na2S2Os5
Cool, <6 °C 18, store in dark, 0.008%
A/a2S2035
Cool, <6 °C 18, 0.008% Na2S2035
Cool, <6 °C 1S

Cool, <6 °C 18, 0.008% Na2S2035, pH <9
Cool, <6 °C ™
Cool, <6 °C 18
Freeze, <-10°C
Cool, <6°C, H2S04o pH<2
Cool, <6 °C, 0.008% Na2S203HN03to pH <2
Cool, <6 °C, 0.008% Na2S203H2S04o pH <2
Maximum holding
time4
7 days until
extraction, 40 days
after extraction.
7 days until
extraction, 40 days
after extraction.
1 year until
extraction, 1 year
after extraction.
7 days until
extraction, 40 days
after extraction.
7 days until
extraction, 40 days
after extraction.
7 days until
extraction, 40 days
after extraction.
7 days until
extraction, 40 days
after extraction.

1 year.
7 days.
24 hours.
1 year.
28 days until
extraction, 40 days
after extraction.
Holdof leastt days,
but not more than 6
months.
30 days until
acetylation, 30 days
after acetylation.
Table ID— Pesticides Tests:







1-70. Pesticides11
Table IE— Radiological Tests:
1-5. Alpha, beta, and radium
Table I H— Bacterial Tests:
If. co/i
2. Enterococci
Table I H— Protozoan Tests:
G, FP-lined
cap

P, FP, G

PA, G
PA, G

Cool, <6 °C 1S, pH 5-9-1s

HNOsto pH<2

Cool, <10°C, 0.0008% Na2S2Os5
Cool, <10°C, 0.0008% Na2S2Os5

7 days until
extraction, 40 days
after extraction.

6 months.

8 hours.22
8 hours.22

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Parameter number/name


Q.Cryptosporidium
S.Giardia
Container 1
LDPE; field
filtration
LDPE; field
filtration
Preservation 2'3
1-10 °C
1-10 °C
Maximum holding
time4
96 hours.21
96 hours.21
1  "P" is for polyethylene; "FP" is fluoropolymer (polytetrafluoroethylene (PTFE); Teflon®), or other fluoropolymer, unless stated otherwise in this Table II;
  "G" is glass; "PA" is any plastic that is made of a sterilizable material (polypropylene or other autoclavable plastic); "LDPE" is low density polyethylene.
2  Except where noted in this Table II and the method for the parameter, preserve each grab sample within 15 minutes of collection. For a composite
  sample collected with an automated sample (e.g., using a 24-hour composite sample; see 40 CFR122.21 (g)(7)(i) or 40 CFR Part 403, Appendix E),
  refrigerate the sample at < 6 °C during collection unless specified otherwise in this Table II or in the method(s). For a composite sample to be split into
  separate aliquots for preservation and/or analysis, maintain  the sample at < 6 °C, unless specified otherwise in this Table II  or in the method(s), until
  collection, splitting, and preservation is completed. Add the preservative to the sample container prior to sample collection when the preservative will
  not compromise the integrity of a grab sample, a composite sample, or aliquot split from a composite sample within 15 minutes of collection. If a
  composite measurement is required but a composite sample would compromise sample integrity, individual grab samples must be collected at
  prescribed time intervals (e.g., 4 samples over the course of a day, at 6-hour intervals). Grab samples must be analyzed separately and the
  concentrations averaged. Alternatively, grab samples may be collected in the field and composited in  the laboratory if the compositing procedure
  produces results equivalent to results produced by arithmetic averaging of results of analysis of individual grab samples. For examples of laboratory
  compositing procedures, see EPA Method 1664 Rev. A (oil and grease) and the procedures at 40 CFR 141.34(f)(14)(iv) and (v) (volatile organics).
3  When any sample is to be shipped by common carrier or sent via the U.S. Postal Service, it must comply with the Department of Transportation Hazardous
  Materials Regulations (49 CFR part 172). The person offering such material for transportation is responsible for ensuring such compliance. For the
  preservation requirement of Table II, the Office of Hazardous Materials, Materials Transportation Bureau, Department of Transportation has determined that
  the Hazardous Materials Regulations do not apply to the following materials: Hydrochloric acid (HCI) in water solutions at concentrations of 0.04% by weight
  or less (pH about 1.96 or greater; Nitric acid (HN03) in water solutions at concentrations of 0.15% by weight or less (pH about 1.62 or greater); Sulfuric acid
  (H2S04) in water solutions at concentrations of 0.35% by weight or less (pH about 1.15 or greater); and  Sodium hydroxide (NaOH) in water solutions at
  concentrations of 0.080% by weight or less (pH about 12.30 or less).
4  Samples should be analyzed as soon as possible  after collection. The times listed are the maximum times that samples may be held before the start of
  analysis and still be considered valid. Samples may be held for longer periods only if the permittee or monitoring  laboratory has data on file to show
  that,  for the specific types of samples under study, the analytes are stable for the longer time, and has received a variance from the Regional
  Administrator under Sec. 136.3(e). For a grab sample, the holding time begins at the time of collection. For a composite sample collected with an
  automated sampler (e.g., using a 24-hour composite sampler; see 40 CFR 122.21(g)(7)(i)or40 CFR  part 403, Appendix E), the holding time begins at
  the time of the end of collection of the  composite sample.  For a set of grab samples composited in the field or laboratory, the holding time begins at the
  time  of collection of the last grab sample in the set. Some samples may not be stable for the maximum time period given in the table. A permittee or
  monitoring laboratory is obligated to hold the sample for a shorter time if it knows that a shorter time is necessary to maintain sample stability. See
  136.3(e) for details. The date and time of collection of an individual grab sample is the date and time at which the sample is collected. For a set of grab
  samples to be composited, and that are all collected on the same calendar date, the date of collection is the date on which the samples are collected.
  For a set of grab samples to be composited, and that are collected across two calendar dates, the date of collection is the dates of the two days; e.g.,
  November 14-15. For a composite sample collected automatically on a given date, the date of collection is the date on which the sample is collected.
  For a composite sample collected automatically, and that  is collected across two calendar dates, the date of collection is the dates of the two days; e.g.,
  November 14-15. For static-renewal toxicity tests, each grab or composite sample may also be used to prepare test solutions for renewal at 24 h, 48 h,
  and/or 72 h after first use, if stored at 0-6 °C,  with  minimum head space.
5  ASTM D7365-09a specifies treatment  options for samples containing oxidants (e.g..chlorine). Also, Section 9060A of Standard Methods for the
  Examination of Water and Wastewater (20th and 21st editions) addresses dechlorination procedures.
6  Sampling, preservation and mitigating  interferences in water samples for analysis of cyanide are described in ASTM D7365-09a. There may be
  interferences that are not mitigated by the analytical test methods or D7365-09a. Any technique for removal or suppression of interference may be
  employed, provided the laboratory demonstrates that it more accurately measures cyanide through quality control measures described in  the analytical
  test method. Any removal or suppression technique not described in D7365-09a or the analytical test method must be documented along with
  supporting data.
7  For dissolved metals, filter grab samples within  15 minutes of collection and before adding preservatives. For a composite sample collected with an
  automated sampler (e.g., using a 24-hour composite sampler; see 40 CFR 122.21(g)(7)(i)or40 CFR  Part 403, Appendix E), filter the sample within 15
  minutes after completion of collection and before adding preservatives. If it is known or suspected that dissolved sample integrity will be compromised
  during collection of a composite sample collected automatically over time  (e.g., by interchange of a metal between dissolved and suspended  forms),
  collect and  filter grab samples to be composited (footnote 2) in place of a composite sample  collected automatically.
8  Guidance applies to samples to be analyzed by GC, LC, or GC/MS for specific compounds.
9   If the sample is not adjusted to pH 2, then the sample must be analyzed within seven days of sampling.
10 The pH adjustment is not required if acrolein will not be measured. Samples for acrolein receiving no pH adjustment must be analyzed within  3 days of
  sampling.
11 When the exfractable analytes of concern fall within a single chemical category, the specified preservative and maximum holding times  should be
  observed for optimum safeguard of sample integrity (i.e.,use all necessary preservatives and hold for the shortest time listed). When the analytes of
  concern fall within  two or more chemical categories, the sample may be preserved by cooling to < 6 °C, reducing residual chlorine with  0.008% sodium
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  thiosulfate, storing in the dark, and adjusting the pH to 6-9; samples preserved in this manner may be held for seven days before extraction and for forty
  days after extraction. Exceptions to this optional preservation and holding time procedure are noted in footnote 5 (regarding the requirement for
  thiosulfate reduction), and footnotes 12,13 (regarding the analysis of benzidine).
12 If 1,2-diphenylhydrazine is likely to be present, adjust the pH of the sample to 4.0 ± 0.2 to prevent rearrangement to benzidine.
13 Extracts may be stored up to 30 days at < 0 °C.
14 For the analysis of diphenylnitrosamine, add 0.008% Na2S203and adjust pH to 7-10 with NaOH within 24 hours of sampling.
15 The pH adjustment may be performed upon receipt at the laboratory and may be omitted if the samples are extracted within 72 hours of collection. For
  the analysis of aldrin, add 0.008% Na2S203.
16 Place sufficient ice with the samples in the shipping container to ensure that ice is still present when the samples arrive at the laboratory. However,
  even if ice is present when the samples arrive, immediately measure the temperature of the samples and confirm that the preservation temperature
  maximum has not been exceeded, in the isolated cases where it can be documented that this holding temperature cannot be met, the permittee can be
  given the option of on-site testing or can request a variance. The request for a variance should include supportive data which show that the toxicity of
  the effluent samples is not reduced because of the increased holding temperature. Aqueous samples must not be frozen. Hand-delivered samples used
  on the day of collection do not need  to be cooled to 0 to 6 °C prior to test initiation.
17 Samples collected for the determination of trace level mercury (<100 ng/L) using EPA Method 1631 must be collected in tightly-capped fluoropolymer or
  glass bottles and preserved  with  BrCI or HCI solution within 48 hours of sample collection. The time to preservation may be extended to 28 days if a
  sample is oxidized in the sample bottle. A sample collected for dissolved trace  level mercury should be filtered in the laboratory within 24 hours of the
  time of collection. However,  if circumstances preclude overnight shipment, the sample should be filtered in a designated clean area in the field in
  accordance with procedures given in Method 1669. If sample integrity will not be maintained by shipment to and filtration in the laboratory, the sample
  must be filtered in a designated clean area in the field within the time period  necessary to maintain sample integrity. A sample that has been collected
  for determination of total or dissolved trace level mercury must be analyzed within 90 days of sample collection.
18 Aqueous samples must be preserved at < 6 °C, and should not be frozen unless data demonstrating that sample freezing does not adversely impact
  sample integrity is maintained on file and accepted as valid by the regulatory authority. Also, for purposes of NPDES monitoring, the specification of "<
  °C" is used in place of the "4 °C" and "< 4 °C" sample temperature requirements listed in some methods. It is not necessary  to measure the sample
  temperature to three significant figures (1/100th of 1 degree); rather, three significant figures are specified so that rounding down to 6 °C may not be
  used to meet the <6 °C requirement. The preservation temperature does not apply to samples that are analyzed immediately (less than 15 minutes).
19 An aqueous sample may be collected and shipped without acid preservation. However, acid must be added at least 24 hours before analysis to
  dissolve any metals that adsorb to the container walls. If the sample must be analyzed within 24 hours of collection, add the acid immediately (see
  footnote 2). Soil and sediment samples do not need to be preserved with acid.  The allowances in this footnote supersede the preservation and holding
  time requirements in the approved metals methods.
20 To achieve the 28-day holding time,  use the ammonium sulfate buffer solution specified in EPA Method 218.6. The allowance in this footnote
  supersedes preservation and holding time requirements in the approved hexavalent chromium methods, unless this supersession would compromise
  the measurement, in which case requirements in the method must be followed.
21 Holding time is calculated from time of sample collection to elution for samples shipped to the laboratory in bulk and calculated from the time of sample
  filtration to elution for samples filtered in the field.
22 Sample analysis should begin as soon as possible after receipt;  sample incubation must be started no later than 8 hours from time of collection.
23 For fecal coliform samples for sewage sludge (biosolids) only, the holding time is extended to 24 hours for the following sample types using either EPA
  Method 1680 (LTB-EC) or 1681 (A-1): Class A composted, Class B aerobically digested, and Class B anaerobically digested.
24 The immediate filtration requirement in orthophosphate measurement is to assess the dissolved or bio-available form of orthophosphorus (i.e.,that
  which passes through a 0.45-micron filter), hence the requirement to filter the sample immediately upon collection (i.e..within 15 minutes of collection).
  [38 FR 28758, Oct.  16,1973]
  Source: Electronic Code of Federal Regulations, U.S. Government Printing Office (http://www.ecfr.gov)
  Title 40: Protection  of Environment
  PART 136-GUIDELINES ESTABLISHING TEST PROCEDURES FOR THE ANALYSIS OF POLLUTANTS . § 136.3  Identification of test procedures.
 (Accessed January 29,2016).


    3.6.3.3  Sample Custody

    The location and status of collected samples must be tracked at all points between the source waterbody
    and the final data report (see chapter 8). The  purposes  of tracking sample custody are to prevent loss of
    samples and/or data, document the conditions under which the samples were held between collection and
    analysis, and preserve sample and data security and integrity.  The principal  goal is to be able to track each
    individual analytical result back through all the steps between collection and analysis  should any
    questions arise concerning analytical results. Records of sample custody are important in all monitoring
    programs, but are especially critical where data may be used for regulatory or litigation purposes.

    Sample custody starts  with a consistent numbering and labeling system that uniquely identifies each
    sample with respect to source, monitoring program, date  and time of collection, responsible person(s), and
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desired analysis. Custody is usually tracked through forms and other records that are signed and dated by
each individual in the chain. For example, in addition to field logs and notes, field personnel will
generally fill out a form upon delivery of samples to the laboratory documenting sample identification
numbers, program name, date and time of collection, date and time of delivery, and name of delivery
person. Laboratory staff will incorporate sample identification numbers into their own custody and data
tracking system.

3.6.3.4  Performance Audits
Regular field operations performance audits should be part of the overall quality assurance/quality control
process embodied in the QAPP (see chapter 8). These performance audits might include actions such as:
  •   Sample container and equipment blanks: distilled/deionized water is processed through sampling
      equipment and sample containers to rule out contamination.
  •   Trip blanks: distilled/deionized  water is transported from the laboratory through the field sampling
      process to document any potential contamination during travel and transport.
  •   Field duplicates: two grab samples are collected in quick succession to assess repeatability of
      sampling.
  •   Field splits: a collected sample is split into two subsamples to assess analytical performance by the
      laboratory or to make comparisons between labs.

3.6.4  Laboratory Considerations
Water quality samples collected from field sites are generally analyzed in a laboratory. While field test
kits are widely available and commonly used in volunteer/citizen monitoring, the accuracy and precision
generally required in NFS monitoring programs, especially those evaluating the effects of treatment or the
achievement of TMDL objectives, demand formal laboratory analysis. Laboratories used for NFS
monitoring projects may include those  operated by state agencies, universities, and private companies.

Specific analytical methods exist for all the  water quality variables discussed in this guidance. For all
monitoring efforts, analyses should be conducted by accepted laboratory methods. These methods are too
numerous to explore in this guidance. There are several resources available to learn about and select
appropriate analytical methods, including:
  •   U.S. EPA Approved Clean Water Act Methods http://www.epa.gov/cwa-methods
  •   Standard Methods for the Examination of Water and Wastewater, 22nd Edition, American Public
      Health Association, American Water Works Association, and Water Environment Foundation,
      http://www. standardmethods.org/ (Rice et al. 2012)
  •   National Environmental Methods Index (NEMI) https://www.nemi.gov/home/

Select a laboratory to analyze monitoring samples with care. While there is no national certification
program for water quality laboratories,  most states operate their own certification or registration
programs. U.S. EPA operates a Drinking Water Laboratory Certification Program in partnership with
EPA regions and states in which laboratories must be certified to analyze drinking water samples for
compliance monitoring. Certified laboratories must successfully analyze proficiency testing samples
annually, use approved methods, and successfully pass periodic on-site audits. Such certified laboratories
may also perform analyses on non-drinking water samples.
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When selecting a laboratory, look for one that is certified either by a state program or under the EPA
Drinking Water program, one that uses approved methods for analysis, and one that participates in
regional comparative proficiency testing programs, if available. In general, it is easier to locate a
laboratory to conduct physical and chemical analyses than one to perform analysis of benthic
macroinvertebrates, fish, and other aquatic biota. State environmental or natural resource agency
biomonitoring programs or university laboratories may be the best bet for bioassessment sample
processing. Any laboratory selected, however, should be able to provide documentation of methods and
QA/QC protocols used, as well as provide assurance that samples will be handled and processed
expeditiously. In making arrangements with the selected laboratory, consider the lab's data approval and
reporting system, particularly the likely delays between sample delivery and final data reporting. Long
delays in data reporting will inhibit the feedback between land treatment and water quality monitoring
that is critically important in watershed project management. Finally, while most water quality
laboratories are equipped to analyze water samples for common indicator bacteria like E. coli, analysis for
pathogens like E. coli O157:H7 or Cryptosporidium requires considerable expertise generally found only
in state health department or private consultant laboratories.

3.7   Land  Use and Land Treatment Monitoring

3.7.1    General Considerations
As discussed in section 2.2.1, NFS pollution is generated by activities on the land that vary in location,
intensity, and duration.  For all monitoring objectives addressed in this guidance (see section  2.1), it will
be important to track both land use and land treatment. Note that for the purposes of this guidance, the
term "land use" refers not only to the general category of land use or cover (e.g., residential,  row crop)
but also to land management or source activities (e.g., street sweeping, agrichemical applications, tillage).
Similarly, in many cases, the term "land treatment" refers not just to the existence of a specific treatment
or BMP (e.g., sediment basin, reduced tillage) but also to the management of the BMP (e.g.,  sediment
basin clean-out, tillage dates, or nutrient application rate, timing, and method). Land use/treatment
monitoring encompasses both land use  and land treatment.

In general, linking land treatment to water quality response requires both land use/treatment and water
quality monitoring. Specific needs for land use/treatment monitoring may differ by monitoring type. For
example, assessment monitoring often includes complete spatial coverage of source activities, but
temporal variability is not generally addressed because of the short timeframe for problem assessment.
Modeling is often used to address the long-term temporal aspects of source activities, including land use
changes like conversion of agricultural land to residential use. Evaluating the land uses of a watershed is
an important step in understanding watershed condition and source dynamics. Additional details
regarding the role of land use in watershed assessment can be found in U.S. EPA's Watershed Planning
Handbook (USEPA 2008a).

Understanding of pollutant loading patterns requires information on both the spatial and temporal
variability of source activities, particularly when load and wasteload allocations are developed as part of a
TMDL. The size of the  margin of safety in a TMDL is often directly related to the level of uncertainty
associated with the variability of nonpoint source loads (see USEPA 2008a for a discussion of margin of
safety [MOS]).

It is necessary to track land use/treatment when planning to attribute water quality trends to activities  on
the land (see section 2.4.2.4). Because monitoring for trend analysis can continue for decades, costs need
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to be factored carefully into decisions about the scope, level of detail, and frequency of land use/treatment
monitoring that will be done.

For individual BMP effectiveness monitoring, it is important to document:
  "   Design specifications of the practice evaluated;
  "   Degree to which the practice was implemented, maintained, and operated according to
      specifications;
  "   Management activities conducted under the scope of the practice; and
  "   Any situations where the BMP operated under conditions outside of the design range.

For example, it  is important to flag any monitoring data collected when the design capacity of a
stormwater runoff device is exceeded because performance will often suffer. These  same considerations
apply to all BMPs to be evaluated at the watershed scale, with the additional proviso that both the spatial
distribution and interrelationships of BMPs should be addressed.

Existing guidance provides recommendations for tracking the implementation of agricultural,
silvicultural, and urban BMPs (USEPA 1997b, USEPA 1997c, USEPA 2001b). This guidance addresses
data sources, methods of data collection, temporal and spatial scales of land use/treatment monitoring,
monitoring variables, and sampling frequency.

3.7.2  Basic Methods

3.7.2.1  Direct Observation
Personal observations may be the best way to track land use/treatment for plot and field studies. Studies at
this scale are frequently visited for equipment monitoring and  sample collection, so a good record of
source activities can be obtained. It is recommended that a form be developed and used to ensure that
tracking is complete and consistent overtime (USDA-NRCS 2003). Examples of such forms are shown in
Figure 3-49. Advantages of this method include the ability to schedule visits and the fact that the observer
controls the quality of data collected.
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Agronomic
Data Form
Site name









































MANURE APPLICATION

Date



Field #
(map)



Amt applied
(spreader #, loads)




Date
incorporated



Comments





Date



Field #
(map)



Crop or
stocking rate




Activity
(till, plant, harvest,
etc.)



Comments




Figure 3-49. Examples of agricultural activity data recording forms

Other forms of direct observation include windshield surveys such as those performed by the
Conservation Technology Information Center (www.ctic.purdue.edu/CRM/) (CTIC 2016). For some
applications, photography can be an important tool. At an edge-of-field monitoring station, an automated
digital camera can be installed to take periodic photographs looking up into the drainage area to record
crop growth or other visible information. A detailed discussion of the use of photo points for monitoring
is presented in chapter 5.

Disadvantages of direct observation methods include the potential for bias due to the observer's lack of
understanding of management activities, scheduling that misses important events, and the inability to
assess rate or quantity information based only on observation (USDA-NRCS 2003).

3.7.2.2  Log Books
Log books can be given to land owners and managers to record activities relevant to the monitoring study
(USDA-NRCS 2003). An advantage of this method is that the same individual who is responsible for the
activity does the reporting. However, it is difficult to guarantee compliance or consistent reporting across
individuals.

3.7.2.3  Interviews
For interviews, as for log books, reporting is performed by the individual responsible for the activity.
When conducted in person, interviews also offer the opportunity to gather additional information of
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importance to the study. Disadvantages include the potential for less than complete reporting of
information by the person interviewed, as well as potentially inadequate or uneven interview skills by
those conducting the interviews (USDA-NRCS 2003).

A combination of the log book and interview approach may work well in small watersheds with a
relatively small number of participants. A Vermont project (Meals 2001) successfully used a combination
of log books distributed to watershed farmers with an annual interview to collect the logbook and record
other information. Interviews were conducted by a local crop consultant who was known and trusted in
the region.

3.7.2.4  Agency reporting.
USDA maintains data on conservation practices implemented with USDA cost-share funds or technical
assistance. The utility of this information is limited for watershed projects, however, because Section
1619 of the Food. Conservation, and Energy Act of 2008 (section 1619) provides that USDA, or any
contractor or cooperator of USDA, may not generally disclose farm-specific information. Exceptions to
this prohibition include the disclosure of such information with consent of the producer or owner of the
land and statistical or aggregate summaries of the data by which specific farms are not identifiable.
Publicly-available data are typically aggregated at the county level and some implementation is not
reported due to confidentiality restrictions. In addition, cumulative implementation is difficult to ascertain
because maintenance and operation of practices is not tracked. Note also that the information in the
system is verified and finalized annually, so data within a current year may be incomplete or inaccurate.

State-level information on USDA conservation programs can be obtained through the RCA Report -
Interactive Data Viewer. This information may be useful during the project planning phase to determine
the level of program activity and degree to which specific practices are implemented in the state. Farm-
specific data, however, would need to be obtained directly from the producer or owner of the land or
through a section 1619 agreement with USDA. Hively et al. (2013) describe in detail several section 1619
agreements established within the Chesapeake Bay watershed.

There are also several survey-based inventories of land use information, including USDA's National
Resources Inventory (NRI) and the Census of Agriculture (USDA-NASS 2012). Because of
confidentiality requirements, the Census of Agriculture does not disclose information on animal
populations, crop acreage, or the like for counties with fewer than four individual producers. Data for
such non-disclosed counties may need to be estimated, using a variety of approaches (see section 3.7.6).

Other specialized land use datasets include NOAA's  Coastal Change Analysis Program's (C-CAP)
nationally standardized database of land cover and land change information for the coastal regions of the
U.S. Various historical GIS datasets are also available, including the National Land Cover Data and
USGS's Land Use and Land Cover data (USEPA 2008a). GIS data for mapping human population are
provided by the U.S. Census Bureau through the TIGER (Topologically Integrated Geographic Encoding
and Referencing) program. TIGER data consist of man-made features (such as roads and railroads) and
political boundaries. Population data from the 2010 Census can be linked to the TIGER data to map
population numbers and density for small (census blocks) and large areas (counties and states). In
addition, a number of states and counties also have statewide or local land use and land cover information
available.
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3.7.2.5  Remote Sensing
The basic categories of remote sensing are described in existing guidance (USEPA 2008a). Aerial
imagery includes images and data collected from an aircraft and involves placing a sensor or camera on a
fixed-wing or rotary aircraft. Space-based imagery includes images and data collected from space-borne
satellites that orbit the earth. A wide range of remote sensing datasets are available for free or at low cost,
including data products at the USGS's National Map Viewer and Download Platform or Earth Resources
Observation and Science (EROS) data center. Other datasets include Landsat data, elevation, greenness,
"Nighttime Lights," and coastal and Great Lakes Shorelines (USEPA 2008a). In some regions, FSA
conducts annual low-altitude aerial photography to assess compliance with crop insurance programs. If
this photography can be accessed with appropriate permissions, it can provide an annual record of crops
grown, changes in field boundaries, land development, and other features.

Commercial web-based resources such as Bing Maps and Google Earth can be useful tools for land use
monitoring. Although the date of the imagery in these or other resources may not exactly match what is
required for a specific project, features such as roads, farmsteads, rivers, and lakes are readily apparent
and general land use types (e.g., urban, agriculture, or forest) can be identified and mapped in preparation
for acquisition of more current detailed data.

Remote sensing can be useful for tracking practices and land management that are monitored visually. For
example, cover  crops are easily identified with remote sensing, but whether the cover crops have been
fertilized is not easily identifiable. McCarty et al. (2008) used remote sensing technologies to scale point
measurements of BMP effectiveness from field to subwatershed and watershed scales, demonstrating that
optical satellite  (SPOT-5) data and ground-level measurements can be effective for monitoring nutrient
uptake by winter cover crops in fields with a wide range of management practices. Hively et al. (2009a
and 2009b) combined cost-share program enrollment data with satellite imagery and on-farm sampling to
evaluate cover crop N uptake on 136 fields within the Choptank River watershed in Maryland. Annual
cost-share program enrollment records were used to locate cover crop fields and provide agronomic
management information for each field. Satellite imagery from December and March was used to
measure pre-winter and spring cover crop biomass, respectively. Data collected simultaneously from
fields were used to convert satellite reflectance measurements to estimates of biomass and nutrient
uptake, thus providing a means to estimate aboveground biomass and N uptake estimates for all fields
enrolled in the cover crop program.
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 Locating Best Management Practices by Three Methods
 Eagle Creek Watershed, IN — NIFA-CEAP Watershed Project

  Objective:  To assess the effects of BMPs on water quality, researchers needed to identify all BMPs
             implemented in an agricultural watershed since 1995 under a variety of state and federal
             programs
  Three approaches based on different data sources were used:
    •   Examination of government records (NRCS, FSA, Indiana Dept. of Env.  Mgt.)
    •   Interviews with producers (structural and operational, polygon and line format)
    •   Analysis of aerial photography (structural only)
                      Records           Interviews           Photos
  Structural BMPs
                          Practices identified by particular source only

                          Practices identified by source and at least one other source
 Operational BMPs
                            Records
                                            Interviews
  Observations:
    1.   NRCS data required processing to eliminate double-counting because each point potentially represented
        multiple practices. After eliminating all the double-counting, 107 structural practices were reduced to 48
        standard practices and 299 operational BMPs to 84 distinct practices.
    2.   Remote sensing picked up only 27 structural practices and no operational practices
    3.   Producer interviews detected 47 structural practices and 185 operational practices
    4.   Using all three sources of information, 94 structural practices and 215 operational practices were
        identified.
    5.   53% of the structural practices were identified by government records, while 67% were identified through
        producer interviews.
    6.   Operational practices were identified in  government records 76% of the time relative to 87% from
        producer surveys.
    7.   Researchers found that:
        •  Government records identified the majority of BMPs, but were incomplete and difficult to obtain
        •  Interviews were information-rich  but time-consuming to conduct
        •  Photos were effective to confirm and supplement records and interviews
        •  Combined data collection techniques provided a clearer picture of conservation practices in the
          watershed compared to any single approach.
                                                                                     (Gradyet al. 2013)
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3.7.3   Temporal and Spatial Scale
Land use/treatment monitoring should address the entire area contributing to flow at the water quality
sampling point. Depending on the specific study area and monitoring design, some parts of a larger area
may be emphasized more than other parts. For example, land nearest to the sampling point can sometimes
have a major effect on the measured water quality, so these areas must be monitored carefully.  Thus, the
spatial coverage of land use monitoring may range from a single field (or portion of a field) up to an
entire river basin.

In designing a land use/treatment monitoring system, it is logical to begin with the assumption that the
temporal scale of land use/treatment monitoring should match that of the water quality monitoring when
the data are to be combined for analyses. Data from weekly composite water quality samples, for
example, would be associated with weekly measures of source activity. However, this design should be
tempered by understanding the inherent variability of what is being measured (see section 3.7.5). Some
metrics of land use and land treatment do not in fact vary on a weekly time scale. It would be wasted
effort, for example, to determine and record the crop present in an agricultural field each week during a
single growing season or note that a residential subdivision is composed of moderate density detached
homes. On the other hand, some highly transient land management activities are very critical to water
quality. Manure application on cropland, tillage operations, and street sweeping are examples, and weekly
records of such phenomena would be important. Still other land management activities  may be important
to identify exactly in time and magnitude, for example in relation to a storm event. Herbicide losses from
cropland, for example, are strongly influenced by proximity of application to the first few runoff events;
pollution potential of pasture runoff may be influenced by the number of grazing animals around the time
of maj or runoff events.

A multi-level land use/treatment monitoring approach can address these multiple temporal concerns:
  "  Characterization: an initial snapshot of land use/land cover, focusing on relatively static
     parameters (at least relative to the project period) such as water bodies, highways, impervious
     cover, and broad patterns of urban,  agricultural, and forest land uses;
  "  Annual: an annual survey for annually-varying features such as crop type;
  *  Weekly: weekly observations or log entries to identify specific dates/times of critical activities like
     manure or herbicide applications, tillage, construction, and street sweeping; and
  "  Quantitative: data collection on rates and quantities (e.g., nutrient or herbicide application rates,
     number of animals on pasture, logging truck traffic).

The guiding principle of timing is to collect land use/treatment data at a fine enough time resolution to be
able to (at least potentially)  explain water quality observations (e.g., a spike in P concentration) as they
occur.

It is important to note that associations between land use/treatment observations and water quality
patterns can be confounded by the timing of the source activities (USDA-NRCS 2003). For example, road
salt is applied under icing conditions, while wash off tends to occur during periods of thawing or rainfall.
Matching weekly water quality and land use/treatment in this case could result in associating high salinity
levels with periods of no road salt application. As another example, nutrient concentrations peak during
wet periods, but manure  is not usually applied when fields are muddy. Using weekly data, high nutrient
concentrations would be associated with periods of no  manure application. An understanding of pollutant
pathways and lag time (section 6.2) and some creative  data exploration are often needed to effectively
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  pair land use/treatment observations with water quality data, but this becomes more difficult moving from
  the BMP level to the watershed scale. Such issues may be addressed by pairing annual water quality data
  with annual land use/treatment data (Meals 1992); although fine-scale relationships may be lost by this
  data aggregation, broad patterns of the influence of land use on water quality may be established.

  3.7.4   Monitoring Variables

  The appropriate set of land use/treatment variables for any monitoring plan will depend on the monitoring
  objectives, monitoring design and characteristics of the watershed or site to be monitored. The set of land
  use/treatment variables needed for problem assessment is usually broad (USEPA 2008a), whereas the set
  of variables for BMP effectiveness monitoring is tailored to the BMP and the conditions under which it is
  being evaluated.

  Table 2-2 in section 2.2.2 illustrates an important first step in selecting land use/treatment variables
  appropriate for the monitoring plan. The next step involves selecting the specific water quality variables
  and matching those with specific land use/treatment variables for which a relationship  is likely.
  Table 3-13 shows examples of pairing water quality and land use/treatment variables.

             Table 3-13.  Relationship of water quality and land use/land treatment variables
 Source
Water Quality
 Monitoring
  Variable
~Weekly Land Use/Treatment Monitoring
              Variables
 ~Annual Land Use/Treatment Monitoring
              Variables
 Cropland
 Erosion
Suspended
Sediment
•  Date of tillage operations;
•  Tillage equipment used;
•  Crop canopy development;
•  Cover crop density
•  Acreage (and percentage) of land under
   reduced tillage;
•  Acreage (and percentage) served by
   terrace systems;
•  Acreage (and percentage) of land
   converted to permanent cover;
•  Linear feet (and percentage of linear feet)
   of watercourse protected with riparian
   buffers
 Agricultural
 Cropland
Total Nitrogen
•  Manure and/or fertilizer application rates;
•  Manure and/or fertilizer forms;
•  Date of manure and/or fertilizer
   application;
•  Manure and/or fertilizer application
   methods
•  Number (and percentage) and acreage
   (and percentage) of farms implementing
   comprehensive nutrient management
   plans;
•  Annual fertilizer and manure N
   applications per acre;
•  Legume acreage;
•  N fertilizer sales
 Urban
Stream Flow
•  Operation and maintenance of
   stormwater system;
•  Functioning of stormwater diversions or
   treatment devices
•  Percent impervious cover;
•  Acreage (and percentage) served by water
   detention/retention;
•  Number and area of rain gardens or other
   infiltration practices
"-Weekly" variables are those that must be monitored frequently to record the exact date or quantity associated with the metric. "-Annual" variables
can be determined less frequently as they generally remain constant within a crop year.


  3.7.5    Sampling Frequency

  As discussed briefly in section 3.7.3, land use/treatment data can be either static or dynamic (USDA-
  NRCS 2003). Static land use/treatment data such as soil type and slope do not generally change with time,
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but dynamic land use/treatment data can vary with time. Examples of dynamic land use/treatment data
include the number of animals, crop rotations, cover crops, undisturbed area, nutrient and pesticide
applications, road salting, and irrigation schedules.

Sampling frequency will vary depending on the study design and source activity. For BMP effectiveness
studies at the plot or field scale, observations should be made each time the site is visited (USDA-NRCS
2003). It is possible to easily observe the entire study area at these scales, but observations made at
monitoring stations for larger-scale projects, although important to do, will not cover the entire study area.
The frequency for sampling dynamic data will vary depending on the type and magnitude of the
variable's impact on measured water quality. For example, construction activities occur on a daily basis at
any given construction site, but there are construction phases that are more important than others (e.g.,
site clearing) and therefore warrant closer attention. The availability of records should also be considered
when determining sampling frequency. Producers under many nutrient management plans, for example,
must keep field-by-field records of manure and chemical nutrient applications, so sampling can
theoretically be done on an annual basis assuming that the records are clear and accurate.

3.7.6   Challenges
There are many challenges associated with tracking land use/treatment, including gaining access to
locations for direct observation or communication with landowners or managers. Obtaining cooperation
on field logs also represents a major hurdle in many cases, especially when confidential business
information is involved. At the watershed scale, the task of checking all source activities of potential
interest can be difficult logistically, labor intensive, and complicated in a mixed use watershed where
different areas of expertise may be needed to track a wide range of source activities.

Data confidentiality can present major challenges to monitoring land management in a watershed project.
Confidentiality applies at many levels, from individual  landowners participating in USDA cost-share
programs through their local NRCS district to county or watershed-level data reported in the Census of
Agriculture. In small projects, a good way to overcome this obstacle is to obtain permission from the
landowner; with such permission, NRCS and FSA records of BMP implementation will be accessible. In
some field-scale projects, it may be possible to have the cooperating landowner(s) sign a release at the
beginning of the project to allow  access to their records, including nutrient management plans,
participation in cost-share programs, BMP installation,  etc.

Dealing with larger scale  agency  data is more problematic. As noted previously (section 3.7.2.4), data
reported by the Census of Agriculture are not disclosed if a limited number of producers are present in a
county or watershed. This data gap presents a challenge to determining basic characteristics of a county or
watershed such as cropland acres or animal populations. There are, however, some helpful approaches to
estimate the undisclosed data. For example, if dairy cow numbers are not disclosed for a county of
interest, it is possible to add up the numbers for reported counties, subtract that sum from the state total to
arrive at a number for the "remainder" dairy cows. If data from more than one county are non-disclosed,
the "remainder"  animals can be apportioned by county area, cropland acres, or other reported variable.
Although such procedures are cumbersome and add uncertainty, they often represent the best or only
source of data for a project area.
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   Estimation of non-disclosed Census of Agriculture data Nutrient Use
   Geographic Information System (NuGIS) International Plant Nutrition Institute

    Objective:  A major national study of fertilizer nutrient balance by county needed to
               derive estimates of fertilizer nutrients applied and removed in harvested
               crops for each U.S. county

     Standard procedures for estimating data missing due to non-disclosure in Census of
     Agriculture were developed:
      •  When Census ofAg Production data for a commodity were not disclosed for some counties in
         a state, subtracting the sum of disclosed production for a commodity from the state total
         production for that same commodity yielded a remainder - the 'State Production Remainder' -
         that represents the  sum of production in non-disclosed counties for that commodity. We
         apportion the State Production Remainder for this commodity to each county in a state with
         non-disclosed production for this commodity, based on each county's harvested acres of this
         commodity as reported in the Census of Ag or as estimated.
      •  For each commodity, the amount of State Production Remainder that is apportioned to each
         county with a non-disclosed production value was calculated using a 'Production to Harvested
         Acres coefficient'; this could also be thought of as an estimated yield. This coefficient was
         calculated, for each commodity, in each state, by dividing the (State Production Remainder)
         by the (Sum of Harvested Acres in counties with non-disclosed Production). The county crop
         production was then calculated using:
           (County Total Cropland Acres) X (Harvested Acres to Total Cropland Acres
           coefficient).

    Example:

         State Production Remainder for Corn =
         (State total production of corn) - (sum of corn production in counties with data
         disclosed)
         2 million bu corn - 1 million bu corn = 1 million bu corn
         State Production to Harvested Acres coefficient for corn =
         (State  Production  Remainder for Corn)/
         (Sum of Harvested Acres of Corn in counties with non-disclosed production of
         corn)=
         (1 million bu) / (5,000 Harvested Acres of Corn) = 200 bu corn / harvested acre of
         corn
         Estimated Production of Corn for County A =
         (Harvested Acres of Corn in County A) X (Production to Harvested Acres
         Coefficient for corn) =
         (3,000 Harvested acres) X (200 bu corn / harvested acre) = 600,000 bu of corn
         production

                                                                            (IPNI 2010)
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3.8   Special considerations for pollutant load estimation

Because of the central role pollutant loads and load reduction targets play in many watershed projects,
especially those with TMDLs, the accuracy of load estimates is very important to all project stakeholders.
Further, the potentially high relative cost of monitoring for load estimation (see chapter 9) places a
premium on cost-effectiveness. This section combines many of the observations made in this chapter
about monitoring for load estimation in one place to provide basic guidelines and considerations for this
special type of monitoring. Richards (1998)
provides a comprehensive discussion of pollutant
load estimation techniques and is the source of
much of the information presented here.
Pollutant flux (see Box) varies tremendously with
both flow and pollutant concentration. Because
we cannot measure flux directly or continuously,
we usually compute unit loads (e.g., daily or
monthly) as the product of discharge and
pollutant concentration, then sum these unit loads
to produce an estimate of annual load.
       Basic Pollutant Load Terms
Flux- instantaneous loading rate (e.g., kg/sec)
Flow rate - instantaneous rate of water
passage (e.g., L/sec)
Discharge - quantity of water passing a
specified point (e.g., m3)
Load - mass of substance passing a specified
point (e.g., metric tons).
The following steps are recommended to plan a monitoring effort for load estimation:

    1.  Determine whether the project goals require knowledge of load, or if goals can be met using
       concentration data alone. In many cases, especially when trend detection is the goal,
       concentration data may be easier to work with and be more accurate than crudely estimated load
       data. However, some concurrent hydrologic/meteorologic data (flow, stage height, rainfall, etc.)
       are often needed for some aspect of any watershed study.

    2.  If load estimates are required, determine the accuracy and precision needed based on the uses to
       which they will be put. This is especially critical when the purpose of monitoring is to look for a
       change in load. It is foolish to attempt to document a 25 percent load reduction from a watershed
       program with a monitoring design that gives load estimates ± 50 percent of the true load (see
       Spooneretal. 2011).

    3.  Decide which approach will be used to calculate the loads based on known or expected attributes
       of the data. This decision will also lead to choices on monitoring equipment (e.g., whether an
       automatic sampler will be used). See section 7.9.2 for a discussion of approaches to load
       estimation and see below for a discussion of sampling equipment.

    4.  Use the precision goals from Step 2 to calculate the sampling requirements for the monitoring
       program. Sampling requirements include both the total  number of samples and the distribution of
       the samples with respect to some auxiliary variable such as flow or season. See section 3.4 and
       below for information on sampling frequency and distribution.

    5.  Calculate the loads based on the samples obtained after the first full year of monitoring, and
       compare the precision estimates (of both flow measurement and the sampling program)  with the
       initial goals of the program. Adjust the sampling program if the estimated precision deviates
       substantially from the goals. See Interval Estimation (p. 4-18 of the 1997 guidance [USEPA
       1997a]) or Spooner et al. (2011) and section 3.4.2 for information relevant to this step.
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3.8.1  Sample Type and Sampling Equipment
The basic approaches for load estimation described in section 7.9 of this guidance are numeric
integration, regression, and ratio methods. With numeric integration, the goal is to collect representative
concentration samples for each sampling interval which is typically defined either by the calendar
(e.g., daily, weekly) or by the volume of flow that passes by the sampling point. In other words, there are
no data gaps. For both the regression and ratio methods, it is assumed that a strong relationship exists
between concentration and flow and that there will be sampling intervals for which only flow is measured
(i.e., no concentration samples taken). With the regression approach, the missing concentration values are
then estimated from the relationship of flow and concentration (when concentration samples were taken).
The ratio approach assumes that flow is measured for each sampling interval and that daily loads are
calculated for those days when concentration samples are taken. A flow ratio (annual flow/flow for days
with concentration samples) is then used in combination with a bias correction factor (to account for
correlation between discharge and load) to estimate annual pollutant load. Both the regression and ratio
methods can be performed using annual or seasonal relationships. These relationships may change over
time, particularly in cases where BMPs are implemented, so it is important that the relationships are re-
examined at least annually.

Autosamplers are typically required for numeric or composite integration because of the large number of
concentration samples needed. Grab sampling is typical for both the regression and ratio methods, but
autosamplers can be used. Continuous or near-continuous flow measurement is required for all three
methods but in some cases flow data are obtained from others (e.g., USGS).

Section 3.2 describes many options for sample type, the simplest of which is a grab sample. The specific
type of sample appropriate for each project will depend on the details of the load estimation objective. For
example, it may be desirable to track the variability of both concentration and load during the sampling
interval. In this case, multiple discrete samples over time would be preferred over composite samples; the
cost for sample analysis, however, would increase considerably. Where fluctuations within the sampling
interval are not of interest, composite samples would be recommended. Flow-proportional samples are
recommended for load estimation in these cases.

3.8.2  Sampling Frequency and Timing
Sample type is an essential consideration involved  in sampling  for good load estimation but sampling
frequency and sample distribution over time are equally important. The selection of sampling frequency
required for accurate estimation of pollutant loads is more challenging than for concentration because
load is a product of concentration  and flow, both of which usually vary significantly. Furthermore, in NFS
situations, because the majority of the annual pollutant load often occurs in a few major events, the choice
of when to sample is also critical.

Ideally, the most accurate approach to estimating pollutant load would  be to sample very frequently and
capture all the variability. Flow is relatively straightforward to measure continuously (see Meals and
Dressing 2008 and section 3.1.3.1), but concentration is expensive to measure and in most cases
impossible to measure continuously.  It is therefore critically important  to choose a sampling interval that
will yield a suitable characterization of concentration. Strategies for determining sampling frequency and
timing for accurate load estimation are described below; see Richards (1998) for additional information.

Sampling frequency determines the number of unit load estimates that can be computed and summed for
an estimate of total load. Using more unit loads increases the probability of capturing variability across
the year and not missing an important event; in general, the accuracy and precision of a load estimate
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increase as sampling frequency increases. For example, the top panel in Figure 3-50 shows load estimated
from weekly sampling superimposed on idealized daily load data. The bottom panel shows results plotted
from monthly and quarterly sampling on top of the same daily load data. The weekly data appear to
capture much of the variation of the daily series, but the monthly series does much more poorly. Quarterly
sampling clearly misses many important peaks and overstates periods of low flux.
  cc
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usually not a problem for monitoring programs but can be a concern when electronic sensors are used to
collect data nearly continuously.

The choice of when to collect concentration samples is critical. Most NFS water quality data have a strong
seasonal component as well as a strong association with other variable factors such as precipitation,
streamflow, or watershed management activities such as tillage or fertilizer application. Selecting when to
collect samples for concentration determination is essentially equivalent to selecting when the unit loads
that go into an annual load estimate are determined. That choice must consider the fundamental
characteristics of the system being monitored. In northern climates, spring snowmelt is often the dominant
export event of the year; sampling during that period may need to be more intensive than during
midsummer in order to capture the most important peak flows and concentrations. In southern regions,
intensive summer storms often generate the majority of annual pollutant load; intensive summer
monitoring may be required to obtain good load estimates. For many agricultural pesticides, sampling
may need to be focused on the brief period immediately after application when most losses tend to occur.
In arid areas, it may be more appropriate to collect storm composites, focusing sampling efforts on the
normal wet periods. Regardless of the approach chosen, it is essential that loads are calculated after the
first year in accordance with Step 5 above to determine if precision needs are met.

For both the regression and ratio approaches, determination of sampling frequency may assume a normal
distribution for concentration and random sampling. Several formulas are available to calculate the
number of samples (random or within strata) required to obtain a load estimate of acceptable accuracy
based on known variance of the system (see chapter 2 of the 1997 guidance). Stratification may improve
the precision and accuracy of the load estimate by allocating more of the sampling effort to the aspects
which are of greatest interest or which are most difficult to characterize because of great variability such
as high flow seasons.

3.8.3   Planning and Cost Considerations
As described here, the sampling regime needed for load estimation must be established in the initial
monitoring design, based on quantitative statements of the precision required for the load  estimate. In
many cases, the decision to calculate loads is sometimes made after the data are collected, often using
data collected for other purposes. At that point, little can be done to compensate for a data set that
contains too few observations of concentration, discharge, or both, collected using an inappropriate
sampling design. Many programs choose monthly or quarterly sampling with no better rationale than
convenience and tradition. A simulation study for some Great Lakes tributaries  revealed that data from a
monthly sampling program, combined with a simple load estimation procedure, gave load estimates
which were biased low by 35 percent or more half of the time (Richards and Holloway 1987).

Monitoring programs often struggle with a conflict between the number of observations a program can
afford and the number needed to obtain an accurate and reliable load estimate. Most use flow as a means
to estimate the best intervals between concentration observations. For example, planning to collect
samples every x thousand ft3 of discharge would automatically emphasize high flux  conditions while
economizing on sampling during baseflow conditions.

It is possible, however, that funding or other limitations may prevent a monitoring program from
collecting the data required for acceptable load estimation. In such a case, the question must be asked: is a
biased, highly uncertain load estimate preferable to no load estimate at all?  Sometimes the correct answer
will be no.
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3.9   Data Management

3.9.1   General considerations
Data management can be defined as the development, execution and supervision of plans, policies,
programs and practices that control, protect, deliver, and enhance the value of data and information assets
(Mosley et al. 2009). Small, short-term monitoring projects can often set up and operate their own
effective  data management system using basic tools like spreadsheets and paper files. Depending on the
magnitude and duration of the monitoring project, it may be advisable to go beyond immediate local data
storage and reporting practices and participate in and comply with ongoing USEPA data management
programs (e.g., USEPA 2010). Regardless of the magnitude of the monitoring effort, data management
must be part of initial project planning.

Data management planning should be an integral part of developing a monitoring plan as reflected by its
inclusion as a Group B element in QAPPs (USEPA 200 la). The aspects of data management to be
described in a QAPP include the path of the data from their generation to their final use or storage, the
standard record-keeping procedures, document control system, and the approach used for data storage and
retrieval on electronic media. In addition, the control mechanism for detecting and correcting errors and
for preventing loss of data during data reduction, data reporting, and data entry to forms, reports, and
databases are to be described in the QAPP. Examples of any forms or checklists to be used are also
required, as are descriptions of all data handling equipment and procedures to process, compile, and
analyze the data. This includes procedures for  addressing data generated as part of the project as well as
secondary data from other sources. Required computer hardware and software and any specific
performance requirements for the hardware/software configuration used are to be described. Data analysis
software  options are described in chapter 7.

3.9.2   Data acquisition
Sections 2.1 through 3.7 and chapters 4-5 address experimental design, sample collection, and sample
analysis methods for a wide range of nonpoint source monitoring projects. The data generated by these
monitoring projects must be collected (data acquisition) and transferred to the data management system
for storage and analysis.

Field and laboratory procedures may include the use of field books or data entry sheets to  record
observations and measurements and either paper or electronic data report forms. The transcription of data
reported in these fashions into a database is a potential source of typographic errors, switched digits, and
other errors in data entry. It is crucial that all data be error-checked after entry into electronic forms, but
before analysis and reporting. Finding errors in a dataset after analysis and reporting is underway can be
very frustrating.

Newer methods of data acquisition include the use of data loggers (either external loggers that record
multiple data streams or loggers directly built into sensor devices), laptops, tablets, and smartphones to
allow direct acquisition, transmission, and entry of data to electronic media. An advantage of using data
loggers is that manual data entry and the associated transcription errors are avoided (USDA-NRCS 2003).
Remote access allows direct transfer of field data from a data logger to the main data storage site. One
disadvantage of data loggers is that their storage capacity is limited; once full, new data may not be
recorded  or older data may be overwritten and thus lost. It is strongly recommended that monitoring
protocols include prompt and routine downloads of data from field data  loggers.
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Not all data are generated directly by the project. Element B-9 of a QAPP addresses data obtained from
non-measurement sources such as computer databases, programs, literature files, and historical data bases
(USEPA 2001a). Whenever data are obtained from other sources, it is important to determine the
sufficiency of the data for project purposes (USEPA 2008a). One of the challenges of using GIS data, for
example, is the need to ground-truth and fill gaps in the data layers (USDA-NRCS 2003). Johnson and
Zelt (2005) present a method for filling in a data gap of spatial scale in woodland LULC (land-use/land-
cover) between the land-cover data available from the 30-m  1990s National Land Cover Dataset (NLCD)
and the reach-level data available from the prescribed National Water Quality Assessment (NAWQA)
habitat assessment.

Data provided by others may have been collected at different locations, by different methods, or to serve
different objectives from those of the current project, so it is important to carefully review the data and
methods used for its collection. This situation is a common occurrence in the watershed project planning
phase during which projects often must use whatever data are available to characterize problems and
suggest actions to solve those problems. The QAPP should include acceptance criteria for the use of such
data in the project, as well as any data use limitations (USEPA 200la).

3.9.3  Data storage
Data storage includes both manual and computerized technologies (USDA-NRCS 2003).  All field and
laboratory notebooks must be fully documented and stored safely, and all data contained in the notebooks
should be backed up in paper or digital form, perhaps as scanned images.

A data inventory is important for monitoring projects, particularly those focused on problem assessment.
Information on ways to organize and manage a data inventory is provided in existing guidance (USEPA
2008a). Naming and labeling conventions should be established, and metadata (e.g., where,  how, why,
when and what was monitored) should be included with all datasets.

Spreadsheets might be adequate for data generated by small projects, but a relational database is usually
preferable for more complex projects involving many sites or variables (USEPA 2008a). A relational
database houses data, metadata (information about the data), and other ancillary information in a series of
relational tables including station information, sample information, analyses, methods used, and quality
control information.

All computerized data and electronic project files should be backed up using  one of many options,
including USB flash drives, external hard drives, CDs, remote servers via File Transfer Protocol (FTP),
and commercial data storage systems available on-line. All media have their advantages and
disadvantages. As technology changes, computerized data should be copied to the latest media using the
latest software. For archival purposes, data storage as paper printouts may be a preferred choice; consider
that 1985 data archived on 5.25-in floppy disks would be next to useless today. Daily backup of
computerized data and electronic project files is recommended. Where practical, backups should be stored
offsite for protection against theft, fire or water damage. Today, with the proliferation of relatively
inexpensive and free options for data backups, there is little excuse for losing data due to computer failure
once the data has returned from the field or laboratory.
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3.10  Data Reporting and Presentation

3.10.1  General considerations
Data reporting and presentation occur at multiple levels in many forms to address a wide range of
audiences and purposes. Communication with stakeholders is often best done on a frequent, informal
basis, whereas communication with outside audiences is more commonly accomplished via presentations
at professional meetings or publications of project findings.

Funding agencies generally include reporting requirements in their grants or contracts. Some include
requirements to upload the data to repositories such as EPA's STORET (http://www.epa.gov/storet/).
States receiving section 319 grants are required to use GRTS (Grants Reporting and Tracking System) to
report specific nationally mandated data elements (USEPA 2013).

3.10.2  Communicating with Stakeholders
Project managers should  schedule regular meetings with stakeholders to present available data and
discuss both successes and failures. Project staff will often find that stakeholders have information, ideas,
and resources they need to improve the project or make their objectives easier to accomplish. Quarterly
meetings are recommended, so those collecting and analyzing the data should examine the data frequently
to be familiar with the current status of the  project and to identify and fix problems. The USGS, for
example, recommends that field and laboratory results be  examined as soon as possible, preferably before
the next sample-collection field trip (Wilde 2005). Results indicating potential bias in the data may trigger
needed changes in equipment, equipment-cleaning procedures, or field methods used.

Communicating with groups of individuals with  varied levels of understanding and different learning
styles requires a multimedia approach that includes written materials, audio-visual presentations, and
face-to-face communication. Simple quarterly reports with easily interpreted graphs, summary tables, and
maps will enhance the communication. Reports should highlight observed patterns and both raw data and
metadata should be attached for those in the audience with more advanced understanding of project data.
A particularly powerful tool for presenting  information to any audience is a Geographic Information
System (GIS) that can be used to create watershed maps and display a variety of spatial information
(USEPA 2008a). Users can display selected data and a combination of spatial coverages tailored to the
specific audience and venue.

3.10.3  Final reports
Final reports are an essential element of all monitoring projects, but experiences of the Rural Clean Water
Program (USEPA 1993a) and similar watershed  programs show that project budgets frequently do not
provide sufficient resources for final data analysis and reporting. One way to address this problem is to
require quarterly reports and meetings as described in section 3.10.2. A major hurdle associated with final
reports is the task of pulling together all project data and performing the final analyses. This burden is
reduced substantially if reports and analyses have been generated on a regular basis since the beginning of
the project.

The basic elements of a project report are the title, abstract, introduction, body, summary and conclusions,
references,  forward, preface, appendices, glossary, tables, and illustrations (USGS 2008). The
introduction should include the purpose and scope of the report, and will usually include background
information pertinent to the study. The body of the report includes the purpose of the study, data
summaries, and the analyses and interpretation of the data. The summary and conclusions pull together
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the major results and conclusions described in the body. A concise Executive Summary is useful as a pull-
out section to distribute project results to a wide audience.

State and federal agencies have their own guidelines and reporting requirements. Professional
publications and journals specify reporting requirements at their websites.

3.11  References
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Barbour, M.T., J. Gerritsen, B.D. Snyder, and J.B. Stribling.  1999. Rapid Bioassessment Protocols for
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Brodie, I. and M. Porter. 2004. Use of Passive Stormwater Samplers in Water Sensitive Urban Design. In
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Gassman, P.W., M.R. Reyes, C.H. Green, and J.G. Arnold. 2007. The Soil and Water Assessment Tool:
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Gilpin, B. J., J. E. Gregor, and M. G. Savill. 2002. Identification of the source of fecal pollution in
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Harwood, V.J., A.D. Levine, T.M. Scott, V. Chivukula, J. Lukasik, S.R.  Farrah, and J.B. Rose. 2005.
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       2016. http://pubs.water.usgs.gov/twri9A4/.

Wilde, F.D., ed. variously dated. Field Measurements. Book 9, Chapter A6 in Techniques of Water-
       Resources Investigations. U.S. Geological Survey, Reston, VA. Accessed April 22, 2016.
       http://water.usgs.gov/owq/FieldManual/Chapter6/Ch6_contents.html#Chapter A6.

Wilde, F.D., D.B. Radtke, J. Gibs, and RT. Iwatsubo. 2009. Processing of Water Samples  (Version 2.2).
       Book 9, Chapter A5 in Techniques of Water-Resources Investigations. U.S. Geological Survey,
       Reston, VA. Accessed February 5, 2016. http://pubs.water.usgs.gov/twri9A5/.

Wilde, F.D., M.W. Sandstrom, and S.C. Skrobialowski. 2014. Selection of Equipment for Water
       Sampling (Ver. 3.1). Book 9, Chapter A2 in Techniques of Water-Resources Investigations. U.S.
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       Geological Survey, Reston, VA. Accessed February 5, 2016.
       http://water.usgs.gov/owq/FieldManual/Chapter2/Chapter2 V3-l.pdf.

Yamahara, K.M., S.P. Walters, and A.B. Boehm. 2009. Growth of Enterococci in unaltered, unseeded
       beach sands subject to tidal wetting. Applied and Environmental Microbiology 75(6): 1517-1524.
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Monitoring and Evaluating Nonpoint Source Watershed Projects                                      Chapter 4
4   Biological  Monitoring of Aquatic Communities
     By J. B. Stribling, C.J. Mi Hard, J.B. Harcum, and D.W. Meals


4.1  Overview
Biological monitoring uses surveys of resident biota (e.g., fish, benthic macroinvertebrates, periphyton,
amphibians) to characterize the structure and function of the assemblage and assess the condition of a
waterbody (USEPA 2013). The central purpose of assessing biological condition is to determine the
degradation level of an ecosystem (or water body) and the cumulative effects of physical, chemical,
hydrologic  or biological stressors on aquatic biota. Resident biota reflect the integrated effects of variable
magnitudes of these different stressors and stressor types, and thus provide an overall measure of
environmental quality. As such, biological assessment is a crucial monitoring tool used by all 50 states
and increasingly by tribes (USEPA 2002, 2011).

Biological assessments conducted by state water resource quality programs are often designed to assess
regional or state-wide condition as well as conditions in smaller targeted watersheds or projects. During
the past 20  years, these water quality agencies have invested resources to develop biological assessment
capabilities within their state or tribe. These capabilities include using scientifically defensible and
documented field and laboratory methods/protocols, establishing  reference sites, and
evaluating/developing metrics and indexes most suitable for assessing biological  condition, and
implementing them in routine monitoring programs or projects. "Index calibration" is a term
encompassing data analyses leading to establishing  scoring criteria and testing/selection of the suite of
metrics making up multimetric indexes (MMI; further discussed below). To the extent practical,
watershed projects or NGOs should use MMI that have been regionally calibrated based on broader
datasets of known quality. Use of MMI previously established by local, state, or regional agencies
requires that the same or similar methods be used for field sampling and laboratory processing for other
streams and sites being evaluated relative to BMP or other issues. This approach of using accepted
protocols and calibrated metrics and indexes, coupled with sufficient and appropriate quality control (QC)
checks, improves defensibility of assessment results and increases confidence in natural resource
management decision-making.

We recommend that biological monitoring be  coupled with physical/chemical monitoring, which includes
the stressor(s) of concern and focus on smaller watersheds (i.e., sub- hydrologic unit code [HUC] 12
watershed level) to document effectiveness of an individual BMP. Biological assessment is a useful tool
for evaluating overall ecological condition because it integrates multiple stressors overtime; however, it
does not  directly measure  changes in a specific stressor (e.g., decreased sediment loading resulting from
riparian buffers or point source discharge). The BMP could still be evaluated as effective in reducing
stressor load, even though a positive biological response might not be detected. And, linking detectable
changes in broad scale biological condition to  a particular small scale BMP is more difficult as the
potential increases for unknown and multiple stressors. Case Study 1 from Wisconsin illustrates the use of
several types of indicators for monitoring BMP effectiveness (Evaluating Effectiveness of Best
Management Practices for Dairy Operations in the  Otter Creek Watershed, Wisconsin).
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Monitoring and Evaluating Nonpoint Source Watershed Projects
                                                        Chapter 4
   CASE STUDY 1: EVALUATING EFFECTIVENESS OF BEST MANAGEMENT
   PRACTICES FOR DAIRY OPERATIONS IN THE OTTER CREEK WATERSHED,
   WISCONSIN
   Located in eastern Wisconsin, the Otter Creek watershed
   is a 24.6 km2 sub-basin tributary to the Sheboygan Creek
   watershed (Figure CS1-1), the latter ultimately feeding
   into Lake Michigan. Otter Creek has a total stream length
   of approximately 21 km and is a third-order watershed
   with a low to medium slope (2.5 - 5.4 m/km) throughout
   the area with a median wetted width of 4.2 m (Wang et al.
   2006). Stream bottom is composed mostly of sand, silt,
   and clay, with riffle areas of medium gravel. Land use and
   land cover during the study period was dominated by row
   crop agriculture (62 percent), forests (14 percent), and to
   lesser degrees by grasslands and wetlands (10 percent and
   6 percent, respectively).
   Corsi et al. (2005) noted
   that the basin was home
   to 64 farms averaging
   approximately 0.5 km2 in
   production.  In the basin,
   there were eight
   barnyards associated with
   dairy operations, with an
   average herd size of 45
   animals. The Wisconsin
   Department of Natural
   Resources reported in
   1993 that the primary
   problems in the basin
   were direct livestock
   access to streams,
   resulting in elimination of
   bank vegetation, fish
   habitat degradation,
   accelerated  and extensive
   bank erosion, and water
   temperature modification.
   Other acute problems
   associated with the
   livestock were barnyard
   manure runoff, upland
   delivery of sediment, and
   runoff from  areas of
   winter manure-spreading.
   In addition to degradation
   of physical habitat, these
                                 Dairy farms, barnyard
                                 runoff
                                 Manure storage, barnyard
                                 runoff control, stream bank
                                 protection, stream fencing
                                 and crossings, stabilization,
                                 buffer strips
                                 Fish assemblage monitoring
                                 Effectiveness evaluation
                                                        /'
       EXPLANATION
   	 Basin boundary
   	Subbasin boundary
     Monitoring loutnn
     2 • fisr a rb habitat station
     A Water quality
     T Rainfall
Figure CS1-1. Otter Creek watershed (Corsi et al. 2005)
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Monitoring and Evaluating Nonpoint Source Watershed Projects                                      Chapter 4
   sources and stressors led to organic and inorganic nutrient over-enrichment, and occasional, if
   not often, severe depletion of dissolved oxygen (Wang et al. 2006).

   The BMPs designed and installed were focused on buffering, reducing, or otherwise eliminating
   the stressors and included a combination of animal waste management, stream bank protection,
   and upland management. Waste management practices were developed as facilities that
   provided improved manure storage, better control of barnyard runoff, and treatment of
   milkhouse wastewater. Four different types of BMPs were implemented for protection of
   >1,900 m of stream bank: fencing (2,800 m), stream crossings, grade stabilization, and buffer
   strips. Upland management included 635 ha (6.4 km2) of nutrient management and sediment
   reduction of 250 metric tons/year achieved with changes in crop rotation, reduced tillage, critical
   area stabilization, grass waterways, and pasture management. Riparian and upland BMPs were
   implemented during 1993 and 1999.


   Monitoring and  Sampling  Design
   The goal of this monitoring project was to evaluate the effectiveness of multiple BMPs on the
   biological, habitat, and water chemistry characteristics in Otter Creek. Changes, if any, in instream
   habitat and biological characteristics relative to timing of BMP installation would be interpreted
   as BMP effectiveness at the watershed scale. Water chemistry evaluated using data from both
   base- and stormflow sampling would be used to establish changes in stressors impacting the fish
   assemblage.

   Annual fish and habitat evaluations occurred relatively continuously from 1990 to 2002, providing
   for pre- (1990 to 1992), during (1993 to 1999), and post-installation (2000 to 2002) monitoring at
   four stations. Stations 1, 3, and 4 were located in areas where streambanks were trampled from
   cattle with free access while station 2 was located in a wooded riparian area (Figure CS1-1).
   Streambanks at station 1 and 3 were fenced in 1993 and 1996, respectively, while station 4 was
   not fenced (Corsi et al. 2005). There were four streams in similar watersheds that also flow to
   Lake Michigan that were also monitored. Two watersheds, the Meeme and Pigeon Rivers,  were
   monitored as two control watersheds in a paired-watershed monitoring design with a single
   sampling station each (see section 2.4.2.3). Neshota and Trout Creeks (tributaries to the West
   River and Duck Creek, respectively) were also monitored with single stations.

   Fish sampling and physical habitat evaluations were generally conducted each year at four
   locations in the lower part of the watershed, during roughly a six-week period spanning August
   and September. Gradually increasing funding resulted in more complete sampling over time, and
   thus, the record for some of the sites is better than others. In Otter Creek,  1 station was sampled
   in 1990, 2 in 1991, and all 4 from 1992-2002. Fish were sampled from a reach length of 35 times
   the median wetted channel width, which was an actual  range from 105 to 234 m. Fish sampling
   used a single-tow barge electrofisherto cover the sampling reach in a single-pass/no block net
   approach. All fish captured were identified to species, counted, weighed (total, by species), and
   released unharmed back to the stream. Data were summarized by reach and sampling event as
   species lists, as proportions of individuals in the samples omnivores, insectivores, carnivores,
   simple lithophils, relative stressor tolerance, and as an IBI. The IBI had been previously calibrated
   for Wisconsin warm-water streams. Habitat features recorded for each sampling event at each
   location included turbidity, dissolved oxygen, specific conductance, and flow, along with 30
   habitat variables, encompassing channel morphology, bottom substrates, cover for fish, bank
   conditions, riparian vegetation, and land use. These data were summarize by calculating mean
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Monitoring and Evaluating Nonpoint Source Watershed Projects                                     Chapter 4
   and variance for each reach and sampling event, and used to calculate width/depth ratio and total
   habitat quality index.

   Monitoring also included a single-watershed, before/after study of water chemistry. A stream
   gage installed in 1990 at the base of Otter Creek just upstream of its confluence with the
   Sheboygan River was equipped to continuously record data on stage and for automatic activation
   to collect and refrigerate samples with every 0.2-ft. increase and 0.3-ft decrease in stage. Water
   chemistry constituents measured from these grab samples included TSS, TP, dissolved ammonia
   nitrogen (NH3-N), 5-day biochemical oxygen demand (BOD5), and fecal coliforms. Fixed interval
   grab samples, analyzed for the same constituents, were also taken throughout the pre- and post-
   BMP study period. Precipitation was measured at three locations in the watershed, one near the
   gaging station, and two others further up in the headwaters.


   Results
   For both fish and habitat data, analysis of covariance (ANCOVA) (see section 7.8) was used to
   relate the fish assessments and habitat variables and conditions of the Otter Creek sites to the
   other watersheds over time. The statistical significance of changes was evaluated  using the
   Wilcoxon rank-sum nonparametric test at 95 percent confidence. Changes were measured from
   different subsets of base- and stormflows, including some investigation of seasonal effects
   (vegetative vs. nonvegetative).

   Post-BMP implementation baseflow samples showed statistically significant lower concentrations
   of TSS and BOD5, higher fecal coliform, and no differences in dissolved NH3-N and  TP for the
   combined seasons (vegetative and nonvegetative), whereas samples from nonvegetative periods
   exhibited lower BOD5 and no changes in the other four analytes. TSS concentration was lower
   during the vegetative season. From several different analyses, BOD5 was demonstrated to have
   decreased substantially (by 45 percent median concentrations) in baseflow from the pre-to post-
   BMP periods. TSS was also found to be lower in post-BMP samples relative to those of pre-BMP,
   however, only actually evident in the full dataset, and not in the smaller data subsets
   (i.e., vegetative vs. nonvegetative). Dissolved NH3-N concentrations decreased  from pre-to post-
   BMP baseflow for the full dataset and nonvegetative season, but there were no differences for
   the vegetative season. Analyses showed none of these difference to  be statistically significant
   (p<0.05),  even with a measured 32 percent decrease in dissolved NH3-N. There was a significant
   (p<0.05) increase in median fecal coliform concentrations of 260 percent for pre- to post-
   samples, demonstrated in both the full and stratified (vegetative and nonvegetative) datasets.

   Analyses using pre- and post-BMP regressions across all monitored storms showed reductions in
   stormflow constituents, whether combined or stratified by vegetative vs. nonvegetative. For TSS,
   the pre/post reduction was 58 percent for the combined  nonvegetative and vegetative dataset,
   41 percent for the vegetative season, and 73% for the nonvegetative season. TP predictions for
   reductions were 48 percent, 34 percent, and 61 percent, while for dissolved NH3-N they were
   41 percent, 40 percent,  and 42 percent, respectively.

   Although  considerable variation in stream physical habitat was observed in the reference streams,
   there was clear post-BMP improvement in Otter Creek (Corsi et al. 2005). In particular, there were
   significant increases (p<0.05) in percent cobble and percent gravel, and significant decreases in
   percent embeddedness, sand,  and silt for stations 1, 2, and 3 (Wang  et al. 2006). These changes
   reflected the natural woody buffer (station 2) or exclusion fences (stations 1 and 3). Stream
   width-to-depth (W/D) ratio and percent bank erosion decreased significantly (p<0.05) for stations
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Monitoring and Evaluating Nonpoint Source Watershed Projects                                     Chapter 4
   1 and 3 where exclusion fences were installed. Other habitat variables that showed significant
   improvement for one or more of the sample reaches were sediment depth and percent riffles.
   ANCOVA showed that overall habitat quality improved in Otter Creek only for those reaches that
   had a natural riparian buffer or had exclusion fences installed. For stations 1, 2, and 3, most of the
   habitat variables (8 out of 10) improved significantly (p<0.05) with BMP implementation, with
   substrate embeddedness, bank erosion, sediment depth, silt and sand substrates, and W/D ratio
   decreasing and conductivity, gravel and rubble substrates, and overall habitat scores increasing.

   Cumulative fish species were similar for each of the Otter Creek locations, the two control
   watersheds (Meeme and Pigeon  Rivers), and the two additional watersheds (Neshota and Trout
   Creeks) (33, 29, and 31 species, respectively), and six species dominated in all streams. From pre-
   to post-BMP installation, fish abundance decreased by 79 percent in Otter Creek and by
   65 percent in the control watersheds. When sampling years were considered in combination, the
   percentages of stressor-tolerant fishes and omnivores increased in Otter Creek, while stressor
   tolerant fishes decreased and insectivores increased in control watersheds. In neither Otter Creek
   nor the control watersheds was there an obvious directional change in percentage of stressor
   intolerant fishes or IBI scores. ANCOVA showed significance (p<0.05) in a decrease of abundance
   and an increase  in percent omnivores; and, for one of the Otter Creek sample locations where
   riparian  pasture was dominant, there were significant decreases in number of fish species and
   percentage of darter individuals.

   Characteristics of physical habitat and water chemistry improved, apparently substantially so, as a
   result of the several BMPs that were implemented in Otter Creek. However, there was no
   significant improvement  in biological condition as measured by the fish community. This status of
   the fish assemblage even with habitat and water chemistry suitable for supporting much higher
   quality led the authors to conclude that there are some broader scale watershed or regional
   factors preventing more evident, positive changes in the fish community. Wang et al.  (2006)
   speculate that pollution intolerant species might have been largely eliminated in  the larger
   watershed and thus not able to colonize Otter Creek.


   Literature
   Corsi, S.R., J.F. Walker, L Wang, J.A.  Horwatich, and R.T.  Bannerman. 2005. Effects of Best-
        Management Practices in Otter Creek in the Sheboygan River Priority Watershed, Wisconsin,
        1990-2002. Scientific Investigations Report 2005-5009. U.S. Geological Survey (in
        cooperation with the Wisconsin Department of Natural Resources). Accessed February 9,
        2016. http://pubs.usgs.gov/sir/2005/5009/.

   Wang, L., J. Lyons, and P. Kanehl. 2006. Habitat and fish responses to multiple agricultural best
        management practices in a warm water stream. Journal of the American Water Resources
        Association 42(4):1047-1062.
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Whether indicators being monitored are based on biological, physical, chemical, or hydrologic data, or
targeted vs. probability-based site selection, questions required for monitoring designs are similar.
Targeted sampling designs are needed to answer questions on BMP effectiveness at a particular location
typically using a paired (treatment/control) watershed or upstream/downstream approach. Alternatively,
probability-based designs could also be used to evaluate the difference in biological condition from
randomly selected sample locations where a particular BMP (treatment) was implemented versus a
sample of non-treated (i.e., control) areas. Unlike traditional physical/chemical monitoring programs that
take measurements and collect samples frequently throughout a year, most biological monitoring
programs take samples once (or, rarely, twice) during specific periods annually because biota do not
usually vary dramatically in response to individual transient events. Biological data are converted into
indicator values (e.g., such as an MMI, also known as an Index of Biological Integrity [IBI]). As a result
of many of these protocol characteristics, variability that could otherwise cause data analysis issues
related to seasonality, autocorrelation, and non-normality is controlled (Fore and Yoder 2003).
Disaggregation of indexes to individual metrics or even taxa can allow for more detailed interpretation of
subtle biological changes relative to BMP effectiveness. However, that level of interpretation requires
access to a biologist with appropriate training and experience.

The concept of reference conditions has begun to emerge in the analysis of traditional physical/chemical
monitoring data. Reference conditions are those observed in unimpaired or minimally impaired
waterbodies in the region of interest and  are used as a benchmark against which to measure changes. As
previously stated, many  states and tribes  have established regional reference sites in support of their
ongoing assessment programs, and it may be useful and efficient to use them in a watershed monitoring
program.

This chapter presents basic information about biological monitoring and its applicability to NPS  and
watershed projects. Section 4.2 introduces the different types of biological monitoring and common terms
used in this chapter with an emphasis on benthic macroinvertebrates and periphyton. An overview of
monitoring design and assessment protocols is provided in sections 4.3 and 4.4, respectively.


4.2  Background
Natural biological communities are often diverse, comprising multiple species at various trophic levels
(e.g., primary producers, secondary producers, carnivores) and varying degrees of sensitivity to
environmental changes.  Adverse impacts from NPS pollution or other stressors, such as habitat alteration,
can reduce the diversity  of the biota, change the relative abundances of different taxa, or alter the trophic
structure. Biological  surveys of resident biota particularly sensitive to stressors, such as fish, benthic
macroinvertebrates, or periphyton, take advantage of this sensitivity as a means to evaluate the collective
influence of the stressors on the biota (Cummins 1994).

The central purpose of biological assessment is to characterize the condition of resident biota relative to
cumulative effects of stressors as the principal indicator of stream (or water body) condition. Monitoring
changes in biological condition can be particularly useful for determining the impacts, depending on the
frequency and duration of exposure, of episodic stresses (e.g., spills, dumping, treatment plant
malfunctions), toxic nonpoint source pollution (e.g., agricultural pesticides), cumulative pollution
(i.e., multiple impacts overtime or continuous low level stress), non-toxic mechanisms of impact
(e.g., trophic structure changes due to nutrient enrichment), or other impacts that periodic chemical
sampling might not detect (USEPA 2011).
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Monitoring and Evaluating Nonpoint Source Watershed Projects                                      Chapter 4


4.2.1  Types of Biological Monitoring
Different kinds of biological monitoring are defined by the particular indicators being used and the spatial
and temporal scales of questions being addressed. The most common biological indicator groups (or
assemblages) used for routine biological monitoring and assessment of freshwater ecosystems in North
America are benthic macroinvertebrates, fish, and periphyton (algae). Additionally, programs will often
collect data on physical, chemical, and hydrologic features of the systems being evaluated to provide
information  on environmental factors potentially affecting the biota. The scales of the questions being
addressed drive (or should drive) the number and distribution of locations, and the frequency and duration
of sampling, that is, the monitoring and sampling design. Further, efforts to control or better understand
the natural variability of the biota have led to different kinds of specific field and laboratory methods for
taking the samples or performing measurements. Controlling variability of indicator values means that
assessment data are of known quality, and leads to improved confidence in and defensibility of
management decisions.

The purpose of this document is to provide  users the information necessary for applying existing
biological indicators (metrics and indexes) to their ecosystems or water bodies of concern. It is not
intended to give a comprehensive review of all methods  and procedures used for  biological assessments.
By existing indicators, we mean those MMI or observed/expected (O/E) ratios that have been
appropriately calibrated for the region and water body type. The remainder of this section, and those that
follow, provide an overview of selected methods.


4.2.1.1  Benthic Macroinvertebrates
Stream environments contain a variety of macro- and microhabitat types including pools, riffles, and runs
of various substrate types; snags; and macrophyte beds (Hawkins et al. 1993). Relatively distinct
assemblages of benthic macroinvertebrates  inhabit various habitats, and it is unlikely that most sampling
programs would have the time and resources to sample all habitat types. Decisions on the habitats
selected for  sampling should be made with  consideration of the regional characteristics  of the streams. For
instance, higher-gradient streams (slope roughly  >1:1) often have riffle habitat with hard bottom substrate
(cobble or gravel riffles) that serve as excellent habitat for a diversity macroinvertebrates, whereas low-
gradient coastal streams lack riffles but have a very productive habitat including woody debris snags
(Figure 4-1), leaf packs, undercut banks, and shorezone vegetation. These two different stream types
could be sampled with different methods, during different times of the year, or with different biological
index periods,  but consideration should be given to protocols already being applied in the water body or
region. Many routine biological monitoring programs use multi-habitat composite sampling techniques,
e. g., the 20-jab method, because the intent  is to characterize the biota of the stream reach.


4.2.1.2  Fish
Fish surveys yield a representative sample of the species present at all habitats within a  sampling reach
that is representative of the stream.  If comparable physical habitat is not sampled at all stations, it will be
difficult to separate degraded habitat from degraded water quality as the factor limiting  the fish
community (Klemm et al. 1992).

At least two of each of the major habitat types (i.e., riffles, runs, and pools) should be incorporated into
the sampling as long as they are typical of the stream being sampled. Most species will  be successfully
sampled in areas where there is adequate cover, such as macrophytes, boulders, snags, or brush.
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Monitoring and Evaluating Nonpoint Source Watershed Projects
Chapter 4
Figure 4-1. Using a D-frame net to sample woody snag habitat for stream benthic
macroinvertebrates

Sampling near modified sites, such as channelized stretches or impoundments, should be avoided unless it
is conducted to assess the impact of those habitat alterations on the fish community. Sampling at mouths
of tributaries entering larger waterbodies should be avoided because these areas have habitat
characteristics more typical of the larger waterbody (Karr et al. 1986) and non-resident visitors from the
larger waterbody may be captured. Sampling reach lengths range from 100 to 200 m for small streams
and 500 to 1000 m for rivers. Some agencies identify their sampling reach by measuring a length of
stream that is 20 to 40 times the stream width.

For biological assessments of the entire assemblage, the gear and methods used should ensure that a
representative sample is collected.

Fish can be collected actively or passively. Active  collection methods involve the use of seines, trawls,
electrofishing equipment, or hook and line. Passive collection can be conducted either by entanglement
using gill nets, trammel nets, or tow nets,  or by entrapment with hoop nets or traps. For a discussion on
the advantages and limitations of the  different gear types, see Klemm et al. (1992).  IBI emphasizes active
gear, and electrofishing is the most widely used active collection method. Ohio EPA (1987) discusses
appropriate electrofishing techniques for bioassessment. Other sources for sampling method discussions
are Allen et al. (1992), Dauble and Gray (1980), Dewey et al. (1989), Hayes (1983), Hubert (1983),
Meador et al. (1993), and USFWS (1991). Fish are generally identified to the species or subspecies level.


4.2.1.2.1  Length, Weight, and Age Measurements
Length and weight measurements can provide estimations of growth,  standing crop, and production of
fish. The three most commonly used length measurements are standard length, fork length, and total
length. Total length is the measurement most often used.
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Monitoring and Evaluating Nonpoint Source Watershed Projects                                       Chapter 4
Age may be determined using the length-frequency method, which assumes that fish increase in size with
age. However, this method is not considered reliable for aging fish beyond their second or third growing
season. Length can also be converted to age by using a growth equation (Gulland 1983).

Annulus formation is a commonly used method for aging fish. Annuli (bands formed on hard bony
structures) form when fish go through differential growth patterns due to the seasonal temperature
changes of the water. Scales are generally used for age determination, and each species offish has a
specific location on the body for scale removal that yields the clearest view for identifying the annuli.
More information on the annulus formation method and most appropriate scale locations by species can
be found in Jerald (1983) and Weatherley (1972).


4.2.1.2.2 Fish External Anomalies
The physical appearance offish usually indicates their general state of well-being and therefore gives a
broad indication of the quality of their environment. Fish captured in a biological assessment should be
examined to determine overall condition such as health (whether they appear emaciated or plump),
occurrence of external anomalies, disease, parasites, fungus, reddening,  lesions, eroded fins, tumors, and
gill condition. Specimens may be retained for further laboratory analysis of internal organs and stomach
contents, if desired.


4.2.1.3  Periphyton
Periphyton is an assemblage of organisms that adhere to and  form a surface coating on stones, plants, and
other submerged objects in aquatic habitats. These can take the form of soft algae, algal or filamentous
mats, or diatoms. The  advantages of using the periphyton assemblage as an indicator include:
  •  Rapid reproduction rates and short life cycles and thus  quick response to perturbation, which makes
     them valuable indicators of short-term impacts.
  •  Primary producers are ubiquitous in all waters, and they are directly affected by water quality.
  •  Rapid periphyton sampling requiring few personnel, with easily quantifiable results.
  •  A list of the taxa present and their proportionate abundance can be analyzed using several metrics
      or indices to determine biotic condition and diagnose specific stressors.
  •  The periphyton community contains a naturally high number of taxa that can usually be identified
     to species.
  •  Tolerance of or sensitivity to changes in environmental conditions that are known for many species
      or assemblages of diatoms.
  «  Periphyton is sensitive to many abiotic factors that might not be detectable in the insect and fish
      assemblages.

The state of Kentucky, for example, has developed a Diatom Bioassessment Index (DBI), currently used
in water quality assessments. Metrics used to construct the DBI include diatom species richness, species
diversity, percent community similarity to reference sites, a pollution tolerance index, and percent
sensitive  species. Scores for each metric range from 1 to 5. The scores are then translated into descriptive
site bioassessments that are used to determine aquatic life use.
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In Montana, Bahls (1993) developed metrics for diatoms that included a diversity index, a pollution
index, a similarity index, and a siltation index. Three other metrics—dominant phylum, indicator taxa,
and number of genera—were used for soft-bodied algae to support the diatom assessment. Further study
and refinement of these metrics has led to the development of diatom biocriteria (Teply and Bahls 2005)
and an evaluation of their discrimination efficiency (Teply and Bahls 2007). Periphyton data collections
currently comprise routine monitoring of the Statewide Monitoring Network (SWM) and support the
determination of designated aquatic life uses.


4.2.2 Linkages to Habitat
The quality of the physical habitat is an important factor in determining the structure of benthic
macroinvertebrate, fish, and periphyton assemblages (Southwood 1977). The physical features of a
habitat include substrate type, quantity and quality of organic debris  (leaf litter, woody materials) in the
waterbody, exposure to sunlight, flow regime, and type and extent of aquatic and riparian vegetation.
Even though there might not be sharp boundaries between habitat features in a stream, such as riffles and
pools, the biota inhabiting each feature are often taxonomically and biologically distinct (Hawkins et al.
1993). Habitat quality is assessed during biological assessment.

Habitat features are  generally associated with biological diversity, (Southwood 1977, Raven et al.  1998)
and their quality largely determines both the structure and function of benthic macroinvertebrate and fish
assemblages. Habitat quality refers to the extent to which a suitable environment for a healthy biota
exists. It encompasses five factors: habitat structure, flow regime, energy source, biotic interactions (such
as invasive species and disease), and chemical water quality. Habitat structure refers to the physical
characteristics of stream environments. It comprises channel morphology (width, depth, sinuosity),
floodplain shape and size, channel gradient, instream cover (boulders, woody debris), substrate types and
diversity, riparian vegetation and canopy cover, and bank stability. Flow regime is defined by the patterns
of velocity and volume of water moving through a stream over time. Energy enters streams as the input of
nutrients in runoff or ground water, as organic debris (e.g., leaves) falling into streams, or from
photosynthesis by aquatic plants and algae. Biotic interactions  include issues such as invasive species and
disease while water quality includes a range of issues from nutrient sources to toxicants.

These factors are interrelated and make stream environments naturally heterogeneous. Habitat structural
features that determine the assemblages of macroinvertebrates can differ greatly within small areas—or
microhabitats—or in short stretches of a stream. For instance, woody debris in a stream affects the flow in
the immediate area,  provides a source of energy, and offers cover to  aquatic organisms. Curvature
(sinuosity) in a stream affects currents and thereby deposition of sediment on the inner and outer banks, in
turn influencing the  character of the streambed. Rocks and boulders create turbulence, which affects
dissolved oxygen levels. Shallow stream reaches where water velocity is relatively high (riffles) provide
areas of high dissolved oxygen and a gravel or cobble bottom ; deep, wide portions (pools) are areas of
lowered velocity where material can settle out of the water, streambeds are composed of soft sediments,
and increased decomposition occurs.

Aspects of habitat structure are separated into primary,  secondary, and tertiary groupings corresponding
to their influence on small-, medium-, and large-scale aquatic habitat features (Barbour et al. 1999). The
status or condition of each aspect of habitat is characterized along a continuum from optimal to poor. An
optimal condition would be one that is in a natural state. A less than  optimal condition, but one that
satisfies most expectations, is suboptimal. Slightly worse  is a marginal condition, where  degradation is for
the most part moderate, but is  severe in some instances. Severe degradation is characterized as a poor
                                               4-10
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Monitoring and Evaluating Nonpoint Source Watershed Projects                                       Chapter 4
condition. Habitat assessment field data sheets (see Plafkin et al. 1989) provide narrative descriptions of
the condition categories for each parameter. Habitat can be assessed visually, and a number of biological
assessment methods incorporate assessments of the surrounding habitat (Ball 1982, Barbour et al. 1999,
OEPA1987,Plattsetal. 1983).

A clear distinction between impacts due to watershed (i.e., large-scale habitat), stream habitat, and water
quality degradation is often not possible, so it is difficult to determine with certainty the extent to which
biological condition will improve with specific improvements in either habitat or water quality.
Generally, if the biological community condition varies directly with the habitat quality, then water
quality is not the principal factor affecting the biota. The opposite is considered true if the biological
condition is degraded relative to the potential of its habitat. Some measures of biological condition in
comparison to habitat condition can be used as indicators of organic enrichment or energy source
alteration (Barbour and Stribling 1991).

Biological and habitat data collected from numerous sites in good or near-natural condition can be used to
determine the type of biological community that should be found in a particular aquatic habitat. This
natural condition has been referred to as reflecting biological integrity, defined by Karr and Dudley
(1981) as "the capability of supporting and maintaining a balanced, integrated, adaptive  community of
organisms having a species composition, diversity, and functional organization comparable to that of the
natural habitat of the region." Highly detailed biological assessments are comparisons of biological
conditions at a test site to the expected natural community and are thus a measure of the degree to which a
site supports (or does not support) its "ideal" or potential biological community (Gibson et al. 1996).
Other types of biological assessment involve comparisons of impacted sites to control sites, the latter
being sites that are similar to monitored sites but are not affected by the stresses that affect the monitored
sites (Skalski and McKenzie 1982). Knowledge of the natural condition is still valuable for accurate data
interpretation when control sites are used (Cowie et al. 1991).


4.2.3 Limitations of Biological Assessments
Although biological assessment is useful for detecting and prioritizing severity of aquatic ecosystem
degradation, it does not necessarily provide a direct linkage to or measurement of specific stressors. Thus,
it usually does not provide definitive information about the cause of observed water body degradation,
i.e., the specific pollutants or their sources. Monitoring of chemical water quality and toxicity may also be
necessary to design appropriate pollution control programs.

Prior to routine application in monitoring and assessment, it is necessary to calibrate biological indicators
for the water body types, geographic and ecological regions, and to structure sampling and analysis
programs to appropriate address management objectives. Thus, establishing a biological assessment and
monitoring program can require a significant investment of time, staff, and money. However, the majority
of these are one-time, up-front investments dedicated to the establishment of reference conditions,
standard operating procedures, and a programmatic quality assurance and quality control plan.

Finally, there can often be a lag between the time at which a toxic contaminant or some other stressor is
introduced into a water body and a detectable biological response. Consequently, biological monitoring
may not always be appropriate for determining system response due to short-term stresses, such as
storms. Similarly, there is often a lag time in the improvement of biological condition following habitat
restoration or pollution abatement. The extent of this lag time is difficult to predict; but, it should be
recognized and anticipated. Other factors also determine the rate at which a biological community
                                               4-11
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Monitoring and Evaluating Nonpoint Source Watershed Projects
Chapter 4
recovers, e.g., the availability of nearby populations of species for recolonization following pollution
mitigation and the extent or magnitude of ecological damage done during the period of perturbation
(Richards and Minshall 1992). Both the possibility of the lack of detectable recovery from perturbation
and unpredictable lag times before improvement is noticeable have obvious implications for the
applicability of biological monitoring to some NFS pollution monitoring objectives. Table 4-1
summarizes the strengths and limitations of the biomonitoring approach.
 Table 4-1. General strengths and limitations of biological monitoring and assessment approaches
Strengths
Properly developed methods, metrics, and reference
conditions (i.e., calibration) provide a means to assess the
ecological condition of a waterbody
Biological assessment data can be interpreted based on
regional reference conditions where reference sites for the
immediate area being monitored are not available
Bioassessments using two or more organism groups at different
trophic levels provide improved confidence in interpretation of
assessment results
Biological condition is an indicator of cumulative effects from
both short- and long-term stressors

Limitations
Development of regional methods, metrics, and reference
conditions takes considerable effort and an organized and well-
thought-out design
Rigorous bioassessment can be expensive and requires a high
level of training and expertise to implement
Basic biological assessment information does not provide
information on specific cause-effect relationships
There may be a lag time between pollution abatement or BMP
installation and community recovery, so monitoring over time is
required for trend detection
Biological assessment does not always distinguish between the
effects of different stressors in a system impacted by more than
one stressor
4.2.4  Reference Sites and Conditions
Biological condition assessments often compare metrics of observed assemblages to the expectations for
those assemblages in the absence of environmental disturbance. Those expectations are based on samples
taken from water bodies that are minimally degraded, have low stressor loads, or are considered "best
available". They constitute the reference condition, which is often derived from observations collected
from reference sites with minimal levels of disturbance (Hughes 1995, Stoddard et al. 2006, Gibson et al.
1996 [see Figure 4-2]).

A reference condition is a composite characterization of the natural biological condition in multiple
ecologically homogeneous reference sites. The overall goal of establishing a reference condition is to
describe the natural potential of the biota in the context of natural variation. Minimally disturbed
reference sites are those with habitats assumed to fully support a natural biota. The greater the difference
is between indicator characteristics of reference and monitored samples, the more disturbed the monitored
samples are considered. The disturbance responsible for the difference might be a habitat change,
pollution, or some other stress. Another approach is describing expectations relative to a complete
gradient of disturbance conditions, and thus, interpreting biological conditions relative to that gradient,
the biological condition gradient (BCG) (Davies and Jackson 2006 [see section 4.4.3]). Site classification
is integral to the reference condition concept (Gerritsen et al. 2000, Hawkins et al. 2000a). Site
classification accounts for natural biological variability prior to evaluating potential effects of human
disturbance. The objective  of classification is to group water bodies with similar reference biological
                                               4-12
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Monitoring and Evaluating Nonpoint Source Watershed Projects
            Chapter 4
                                 Preliminary Resource Assessment
Reference Sites
i
r
Minimally
Disturbed
i
r
Where natural
sites exist,
establish
expectations
i

Set thresholds
so that most
reference site
pass

Biological
Integrity
Expectation

1 r


Best
Attainable

Where no
natural sites
exist, select
the best
available

Set thresholds
expecting
several
reference sites
to fail

Interim
Expectation
                                            Reference
                                               Sites
                                            Available?
No Reference Sites
                                                                            Ecological
                                                                            Modeling
                                                                            Where no
                                                                           natural sites
                                                                         exist, sample a
                                                                         full gradient of
                                                                           disturbance
                                                                               Use
                                                                           neighboring
                                                                          classes, expert
                                                                            consensus,
                                                                             hindcast
                                                                              model
                                                                           Hypothetical
                                                                           Expectation
Figure 4-2. Approach to establishing reference conditions (after Gibson et al. 1996)
                                            4-13
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Monitoring and Evaluating Nonpoint Source Watershed Projects                                       Chapter 4
characteristics together, allowing formulation of precise indicator expectations within each group (Hughes
et al. 1986). Site classification can be categorical or continuous. Categorical classification groups distinct
site types with similar biological and environmental characteristics. Continuous classification recognizes
the gradation of site types. Categorical classification is generally used for MMIs, and continuous
classification is often associated with predictive models  of observed and expected taxa. Reference sites
can be defined identically for either indicator type. Developing reference condition/expectations and site
classification are both critical components of index calibration and will have already been accomplished
by regional environmental resource management agencies, universities, or other monitoring entities.

In the sense of biological monitoring and assessment, reference sites are traditionally thought of as having
been used in the index calibration process, and thus, sampling of them for a particular site specific
assessment is not required. In addition to the calibrated metric and index scoring framework used in
assessments, individual control sites could add potentially useful information for specific stressor inputs
to streams or other water bodies. Control sites could be positioned to be upstream/downstream, or pre-
and post-implementation, of input points or zones from the land use/land cover of concern, including
BMP or other stressor source control activities. In this situation, it would be necessary to have multiple
reaches sampled to represent different assessment areas.


4.3  Biomonitoring Program  Design
As described in chapter 2, different monitoring designs might be applicable depending on the objectives
of individual monitoring projects with consequent implications for site selection process, number of sites
sampled, what features are monitored, and time and frequency of sampling. The sampling design used in
NFS biological monitoring might consist of either targeted or probabilistic  designs. Targeted monitoring
designs are normally chosen for site-specific objectives such as whether biological impairment exists at a
given site or whether impairment has been reduced by a watershed project. See section 2.4 for a more
detailed discussion of targeted site selection design; however in brief, site locations are selected based on
the purpose of the project. Targets might include vulnerable areas with known or suspected perturbations
(stressors), planned point source controls, or waterbodies in areas treated with BMPs.  NPDES permits,
urban stormwater sites, timber harvest areas, rangeland,  row crop farming,  and construction sites are
examples of known stressor sites. Upstream/downstream sampling stations, before-and-after site
alterations, or recovery zones (sampling at established distances from sources) are types of sampling
locations for known stressor sites. Ecologically sensitive sites that may or may not be  affected by
stressors and reference sites with minimal disturbance might also be chosen as with targeted monitoring.

While targeted monitoring designs  are often used for site-specific purposes, the results from those studies
cannot be extended to  other sites in the region (Fore and Yoder 2003). Alternatively, probabilistic designs
are useful for providing unbiased assessments of conditions across a water  body or large geographic area.
In a probabilistic sampling program, the entity about which inferences are made is the population or target
population and consists of population units. The sample  population is the set of population units that are
measured. As an example, in a watershed impacted by nonpoint sources, the target population could be
the biological condition of all 1st-, 2nd-, and 3rd- order streams. Benthic macroinvertebrates, selected water
chemistry, and physical habitat quality are then collected at randomly selected sites drawn from the
population of 1st, 2nd, and 3rd order streams. By sampling and statistically evaluating randomly selected
population units, inferences can be  made about the entire waterbody. The advantages and disadvantages
of targeted and probabilistic sampling are summarized in Table 4-2. In some cases,  a monitoring program
may have a combination of targeted and probabilistic sites.
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Monitoring and Evaluating Nonpoint Source Watershed Projects
                                                                                    Chapter 4
            Table 4-2. Comparison of probability-based and targeted monitoring designs
                              Advantages
                                                              Disadvantages
 Probabilistic
 Design
Provides unbiased estimates of status for a valid
assessment on a scale larger than that of the
individual sample location.
Can provide large-scale assessment of status and
trends of resource  or geographic area that can be
used to evaluate effectiveness  of environmental
management decisions for watersheds, counties,  or
states over time.
Stratified random sampling can improve sampling
efficiency, provide  separate data on each stratum,
and enhance statistical test sensitivity by separating
variance among strata from variance within strata.
Small-scale problems will not necessarily be identified
unless the waterbody or site happens to be chosen in
the random selection process.
Cannot track restoration progress at an individual site
or site-specific management goals.
 Targeted
 Design
Targeted sampling along a stream or river provides
an efficient means of detecting pollution sources
(Gilbert 1987).
Identifies small-scale status and trends of individual
sites, which can be used to assess potential
improvements due to stressor controls and other
management activities.
Contributes to understanding of responses of
biological resources to environmental impact.
A targeted design will not yield information on the
condition of a large-scale area such as the watershed,
county, state, or region.
It cannot specifically monitor changes from
management activities on a scale larger than site-
specific.
Resource limitations usually make it impossible to
monitor effects of all pollutant sources using a targeted
design.
Targeted sampling can result in biased results if there is
a systematic variation in the sampled population.
For probability-based designs, simple random sampling is not optimal. It can produce clusters of
sampling sites that might not be representative of the larger scale area of interest (e.g., Hurlbert 1984).
Therefore, some sort of stratification is preferred for ensuring a dispersed distribution of site locations
(Stevens and Olsen 2004). The approach of stratifying the target population and then randomly sampling,
referred to as stratified-random sampling, is often more efficient than simple random sampling. This is
because a target population is recognized to consist of groups that each have internal homogeneity
(relative to other groups), and stratifying the target population will tend to minimize within-group
variance and maximize among-group variance (Gilbert 1987, Fore and Yoder 2003). Case Study 2 from
Maryland provides  an example of secondary uses of data and assessment results from stratified-random
sampling. Another approach for randomly selecting sites, used by EPA's national aquatic resource
surveys, that allows for random sampling while ensuring representation from all relevant site types and
locations is the unequal probability Generalized Random Tessellation Stratified (GRTS) spatially-
balanced survey design (Stevens and Olsen 2004).
                                                   4-15
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Monitoring and Evaluating Nonpoint Source Watershed Projects
                                         Chapter 4
   CASE STUDY 2: EFFECTIVENESS OF STORMWATER PONDS IN ENHANCING
   INSTREAM BIOLOGICAL CONDITIONS IN MARYLAND URBAN WATERSHEDS
   Prince George's County, Maryland covers 1,290 km2 of
   the mid-Atlantic Coastal Plain, and is the suburban
   jurisdiction immediately east of the  District of
   Columbia (Figure CS2-1). It has more than 965
   kilometers of stream channels draining to the Patuxent
   River on the east, Anacostia River in the west and
   northwest, Potomac River on the southwest, and
   Mattawoman Creek in the south. The northeast
   corridor of the U.  S. has undergone heavy and nearly
   continuous urbanization, resulting in an increasing
   percentage of landscape covered with impervious
   surfaces. Increased imperviousness reduces the
   capacity for land to absorb rainfall and causes storm
   water to be instantly converted to runoff. This can
   result in accelerated erosion and severe channel
   instability in watersheds that contain large
   proportions of impervious cover. Surface
   runoff can also transport solid trash and
   contaminants from parking lot asphalt,
   sidewalks, and rooftops. These stressors
   can combine to cause substantial harm to
   aquatic communities. The primary
   objective for this project was to assess the
   effectiveness of stormwater
   detention/retention ponds in protecting
   and enhancing instream biological
   condition.
   Stream and watershed assessments have
   been conducted in Prince George's County
   for nearly two decades and have fully
   documented spatial patterns of aquatic
   biological conditions. The initial round of
   county-wide assessments (1999 to 2003)
   showed that more than half of the stream
   length (52.5 percent) was biologically
   degraded, with the majority of impaired
   stream reaches located in the western part
   of the county near major regional
   transportation thoroughfares. Higher
   quality streams predominantly rated as
   "fair" and "good" were found to be in the
   eastern part of the county along the
   drainage to the Patuxent River, and in the
   south, near the border with Charles County
                Urbanized watersheds, elevated
                flashiness, channel instability,
                habitat degradation
                Stormwater retention ponds
                Multiple watershed monitoring
                Benthic macroinvertebrates,
                physical habitat
                Random site selection, post-
                stratification
                Assessing effectiveness
               06-008
                              04-009
            08-i
                                     08-034
        N
  24-003
                                         27-070
                                            36-006
   Group 1 - Reference (Low Imperviousness)
   Group 2 - SW WITHOUT Treatment Ponds
   Group 3 - SW WITH Treatment Ponds
Figure CS2-1. Prince George's County, Maryland.
Distribution of sample locations used as part of this
analysis.
                                             4-16
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Monitoring and Evaluating Nonpoint Source Watershed Projects                                     Chapter 4
   and Mattawoman Creek. This pattern was expected because greater development intensity
   typically follows transportation corridors with the attendant increases in impervious surfaces.

   One of the principal BMPs used for stormwater management in the 1990s was stormwater
   detention/retention ponds (SWPs). Many managers designing and installing SWPs had multiple
   objectives including, but not limited to, collecting and slowing runoff from impervious surfaces,
   allowing suspended particulates to settle out, collecting trash solids, and providing features that
   helped foster lower water temperatures and elevated levels of dissolved oxygen. BMP siting
   decisions were often based on stream problems brought to the attention of county managers by
   the public, citizens' environmental groups, or stream monitoring data. Thus, it was not always
   straightforward to identify specific management objectives or goals associated with an individual
   SWP. Nonetheless, the authors of this study (Stribling et al. 2001) assumed that improving
   instream biological condition was among the objectives for SWP implementation.


   Monitoring and Sampling Design
   Routine county-wide monitoring performed by the Prince George's County Department of
   Environmental Resources (PGDER) to assess stream and watershed biological  conditions is based
   on a long-term, probability-based, rotating basin plan. Stratified (by wadeable stream order)
   random sampling was used to select sampling sites for this study, with the number of potential
   sites for  each stream order within each of the 41 subwatersheds set proportional to the number
   of stream km in each order (map scale = 1:100,000). There are 50 to 60 sites sampled per year for
   biological (benthic macroinvertebrates), selected water chemistry, and physical habitat quality
   variables. Benthic macroinvertebrates are collected over 100-m channel reaches by making
   20 1-m linear sweeps (jabs) with a 500 u. mesh D-frame net distributed among different habitat
   types (such as snags, leaf packs, vegetated/undercut banks, bottom, riffle/cobble) in proportion
   to their frequency of occurrence at each site. To minimize the effects of seasonal variability, all
   sampling occurs during the Maryland Department of Natural Resources' Biological Stream Survey
   (MBSS)-specified index period, which is March 01 to April 30. Ten percent of the sampling
   segments are randomly selected for replicate reaches, which provides information necessary for
   quality control and for calculating field sampling precision. Data on physical habitat quality and
   water chemistry (pH, conductivity, water temperature,  and dissolved oxygen) are collected at
   each site for their potential in explaining biological condition. Benthic data (number of taxa,
   number of individuals of each, per sample) are used to  calculate the benthic-index of biological
   integrity (B-IBI) developed by the MBSS. In general, the B-IBI and the physical  habitat quality
   index, as applied by the PGDER program, have 90 percent confidence intervals of ± 0.67 points on
   a 5-point scale, and ±6.7 points on a 200-point scale, respectively.

   The purpose of this study (Stribling et al. 2001) was to determine effectiveness of stormwater
   detention ponds in protecting and/or enhancing in-stream biological condition. The physical
   habitat and biological data were segregated into the following three treatment groups and
   directly compared using percentile distributions of measurement values:
   Group 1     Streams with minimal stormwater stressors (0-5 percent impervious surface),
   Group 2     Streams with substantial  stormwater stressors (>12 percent impervious surface) and
               without SWP, and
   Group 3     Streams with stormwater stressors (>12 percent impervious surface) and with SWP.

   Using GIS analysis, upstream drainage areas were delineated for all sites sampled in 2000, and
   land use/land cover (LULC) determined for each, including  calculation of impervious surface. From
                                              4-17
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Monitoring and Evaluating Nonpoint Source Watershed Projects
                                                                             Chapter 4
   these data and the existing dataset, all sites were screened to define a set of sites that would
   reasonably represent each of the groups (Table CS2-1), resulting in five sites per treatment group.
   Group 1 was represented by sites with drainage areas (DAs) ranging from 31 to 585 ha and
   imperviousness of 1.0 to 4.7 percent. Group 2 had DAs ranging from 248 - 634 ha and
   imperviousness of 17 to 34 percent, and Group 3  had DAs from 76 to 568 ha and 11.2 to
   34.9 percent imperviousness. The ages (time elapsed since installation) of the  SWPs of Group 3
   ranged from 7 to 13 years, so all SWPs should have had ample opportunity to "mature"  through
   multiple growing seasons. SWP functionality was  not assessed as part of this study.

  Table CS2-1. Sampling sites in Prince George's County including drainage area, percent
  imperviousness in the drainage, and proportions of different land use/land cover types in the
  drainage area
                                   DA
                                 Date
                                  of
                                                             Percent Land Use/Land Cover
  Site ID Grp
     Site Name
(ha)  Imper  Pond AGR BAR  COM  FOR HDR  IND  LDR  MDR  OS
  08-034   1   Beck Branch
                      60    1.0    NA  19.5  0    0   80.5  0
                                           0
                     0    0
       0
  08-040   1   UT to Upper
              Beaverdam Creek
                     148    2.7    NA  27.8  0    1.7  70.5  0
                                                 0    0
  33-007   1
UT to Lower Potomac
River
284    2.1    NA  12.7  0    0   80.3  0
                     5.2   1.7   0
  36-005   1   Black Swamp Creek     585   4.7   NA  18.2   5.9   1.3  64.3   0
                                                                      7.8   2.4   0
  36-006   1
UTto Black Swamp
Creek
 31    2.4    NA  28.3 10.0  0   61.6  0
                     0    0
  04-009   2   Crows Branch
                     297   34.1    NA   0
                       0
7.3  19.9 20.9  0    17.9  29.5   4.4
  08-014   2
UTto Upper
Beaverdam Creek
476   27.6    NA   2.7  0.7 21.2 57.7 16.9  0
                     0    0
      0.8
  08-039   2   UTto Upper
              Beaverdam Creek
                     599   17.2    NA  13.1  0.7  21.3  64.7  0
                                                 0.3   0
  22-003   2   Watts Branch
                     248   26.2    NA   0
                       0
7.5  37.5  2.9  0
43.1   0
  28-007   2   UT to Broad Creek
                     634   20.5    NA   6.4  0.1  10.6  47.9  0
                                           0
                     7.9  26.9   0
  06-008   3   Bear Branch
                     167   20.5   1987  3.9 20.2   3.8  51.3  5.6 15.2   0
                                                      0
                               0
  24-003   3   UTto Carey Branch
                      76   34.9   1987  0    0    0.1   4.6  0    0    13.3  81.9   0
  24-011   3   UT to Henson Creek
  	(Broad Creek)	
                     120   31.3   1993  000   13.6  0    0    13.1  73.3   0
  27-070   3   UT to Piscataway
              Creek
                     515   11.2   1989 15.7  6.2   0.7  52.2  1.9  0
                                                 5.2  18.1   0
  29-003   3   Hunters Mill Branch
                     568   11.7   1987 12.1  0.1   0.5  59.7  0    0.9   2.7  24.1   0
  Abbreviations: Grp-treatment group; DA-drainage area; % Imper-percent imperviousness; AGR-agriculture; BAR-bare
  ground; COM-commercial; FOR-forest; HDR-high density residential; IND-industrial; LDR-low density residential; MDR-
  medium density residential; OS-open space
                                                4-18
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Monitoring and Evaluating Nonpoint Source Watershed Projects
                                                           Chapter 4
                                3.6
                                3.2
                                2.8
                             ~  2.4
CD
u
                             Q>
                             CD
                                2.0
                                 1.6
                                 1.2
                                0.8
                                   40
            60    80    100    120    140
              Total Habitat Score
160
Minimum SW stressors
SW without ponds
SW with ponds
Results
As is common in many stream assessments, biological conditions (B-IBI scores and assessments)
of the stream groups were plotted against the overall scores for physical habitat quality.
Expectations are that biological conditions would be elevated in the presence of good habitat
quality, which generally held true with this  dataset (Figure CS2-2). Those locations with higher
scores  and ratings for biological condition tended to be those with better habitat quality and
falling in the upper right
hand quadrant of the
chart; those sites also
tended to be in the
Group  1 set of sites, that
is, with lower stormwater
stressors. Those sites
with lower biological
condition scores and
ratings were generally in
the central and  lower left
portions of the chart, and
tended to be members of
site groups 2 and 3 which
were in drainages with
substantial impervious
surface areas (>11
                          Figure CS2-2. Relationship of biological condition (benthic index of biological
                          integrity [B-IBI]) to physical habitat quality (total habitat score). The 90
                          percent confidence intervals (Cl) are 0.67 points on a 5-point scale for the B-IBI
                          and 6.7 points on a 200-point scale for the habitat quality index.
Both habitat quality and
biological condition indicated higher quality for streams with minimal impervious (<5 percent)
(Figures CS2-3 and CS2-4).  Habitat quality was slightly better in Group 1 streams (with scores
ranging from 91 to 150) than in Groups 2 and 3 where scores ranged from 55 to 116 and 78 to
129, respectively (Figure CS2-3). The biological condition of Group 1, however, was more strongly
separated from the other two groups (Figure CS2-4). The ranges of B-IBI scores were 1.57 to 3.29
for Group 1, 1.0 to 2.14 for Group 2, and 1.29 to 2.43 for Group 3. There was very little difference
between the two groups of streams exposed to stormwater stressors, whether with SWPs (Group
3) or without (Group 2), suggesting that the ponds, in themselves, do not have a strong effect on
improving the quality of instream biological conditions. While the SWPs buffered some stressors
arising  from  impervious surface runoff, it is likely that there were other stressors that were
unrecognized and not addressed by the SWPs. These other stressors could  include upstream or
atmospheric chemical contamination, excessive suspended particulates, altered energy input (i.e.,
leaf litter or woody materials), or habitat alteration. Potential shortcomings of this study include
lack of  focus on evaluating changes of stressor loads from individual SWPs through, for example,
pre- and post-implementation data on stressors for streams where SWPs were implemented; and
lack of  information regarding the design specifications of the SWPs. The investigators recognized
that evaluating the effectiveness of individual  BMPs required more intensive load monitoring
around the BMP(s) of concern, but, nonetheless, concluded that isolated BMPs were not likely to
enhance instream biological condition on their own and that restoration and protection of natural
resources requires management on the watershed scale and dealing with multiple and complex
stressors and stressor sources.
   percent) and the
   attendant storm water
   stressors.
                                              4-19
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                                                                        Chapter 4
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Monitoring and Evaluating Nonpoint Source Watershed Projects                                       Chapter 4
Monitoring performed at different spatial scales can provide different types of information on the quality
and status of water resources. Conquest et al. (1994) discuss a hierarchical landscape classification
system, originally developed by Cupp (1989) for drainage basins in Washington State that provides an
organizing framework for integrating data from diverse sources and at different resolution levels.
Assessments of waterbodies on a large scale such as an ecoregion, subregion, state, or county provide
information on the overall condition of waterbodies in the respective unit. Assessment on a small
geographic scale may involve a whole stream, river, or bay or a segment (reach) of the waterbody. A
targeted sampling design applies to monitoring waterbodies within a watershed that are exposed to known
stressors. Known disturbances, such as point sources, specific NFS inputs, or urban stormwater runoff,
can all be targeted for small-scale assessments. At this scale the effectiveness of specific pollution
controls, BMP installation/implementation, natural resource management activities, or physical habitat
restoration can be monitored. This scale is also where a paired-watershed design can be useful. See Case
Study 3 from Pennsylvania for an example of interpreting results from this type of assessment design.
Table 4-3 summarizes a waterbody stratification hierarchy for streams and rivers, lakes, reservoirs,
estuaries, and wetlands.  Further, the sampling site, or the portion of the water body to be sampled, is
defined based on technical objectives and programmatic goals of the assessment and/or monitoring
activity (Flotemersch et  al. 2011). For example, EPA defined a sample reach as 20 times the mean wetted
width for its national surveys of lotic waters (streams and rivers) (USEPA 2009); however, many
individual states use a fixed 100 m as the sampling reach (Barbour et al.  1999, Carter and Resh 2013).

Depending on  the waterbody, subsequent stratification levels may vary in number and may be quite
different across waterbodies at a given level in the hierarchy. For example, a state or regional monitoring
program designed to  assess the status of biological communities in streams might need to be stratified to
the level of segments, whereas monitoring to assess the efficacy of specific stream restoration measures
might need to be stratified to the macro- or microhabitat level. If data collected by a particular design are
so variable that meaningful conclusions cannot be drawn, post-stratification of the data set might be
required. If stratification to  the level of microhabitat is needed, the sampling and analysis methods used at
higher levels might be inappropriate or inadequate.
                                               4-21
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Monitoring and Evaluating Nonpoint Source Watershed Projects                                     Chapter 4
   CASE STUDY 3: EFFECTS OF STREAMBANK FENCING ON BENTHIC
   MACROIN VERTEBRATES
   Big Spring Run Basin, a subbasin of Mill Creek
   Watershed located in Lancaster County, Pennsylvania
   (Figure CS3-1) is dominated by agricultural land use,
   much of which is adjacent to aquatic systems. The Mill       ' Pasture animal exclusion
Creek Basin falls in the Susquehanna River Basin, which
ultimately feeds into the Chesapeake Bay. The most
common agricultural NPS control measures
implemented in the watershed were barnyard runoff
                                                        SE Pennsylvania
                                                           from stream access
                                                           Paired-watershed
                                                           monitoring
   control and streambank fencing. This study was               Nested experimental design
   designed to provide land managers information on the
   effectiveness of streambank fencing in controlling NPS pollution. While the project addressed
   both water quality and biological condition, the emphasis here is on results associated with
   macroinvertebrate monitoring.


   Monitoring and Sampling Design
   The objective of this monitoring program was to document the effectiveness of streambank
   fencing of pasture land on the quality of surface water and  near stream  ground water. The
   primary monitoring design was a paired-watershed design,  but above/below monitoring was also
   included to provide multiple opportunities for comparisons to ensure that the effects of fencing
   could be documented.

   The Big Spring Run Basin consists of two similar subbasins ideal for paired-watershed analysis; one
   was chosen as the treatment basin and the other as the control. The treatment basin was 3.6 km2
   with 4.5 km of stream, of which approximately 70 percent of the streams run through open
   pasture. The control basin was 4.7 km2 with 4.3 km of stream and consisting of approximately
   70 percent of streams running through open pasture. Elevation and geologic makeup were also
   nearly identical for both basins, with stream gradients ranging from 0.3 to 0.6 m elevation change
   for every 30 m of channel. Temperate zone climate was typical for the study basins, with an
   average precipitation of 104 cm and an average temperature of 11°C. Agriculture accounted for
   over 80 percent of the land use in each subbasin.

   Surface water monitoring stations for the  paired-watershed analysis were located at the outlets
   of the control (C-l) and treatment (T-l) subbasins (Figure CS3-1). Site T-l was to also be used with
   T-3 in the treatment subbasin for an above/below study; streambank fencing was to be installed
   between T-l and T-3. A site (T-2) was also added at a visually degraded upstream tributary for
   comparison with C-l in another paired-watershed analysis.  Site T-4 was added  to determine the
   effects of new construction that began two years into the study. Surface water samples were
   collected every 10 days from April to November (about 25 to 30 samples per site per year)
   because this was when dairy cows and heifers were pastured. Monthly base-flow samples were
   collected during the remaining part of the year. Storm event samples  were collected at all sites
   except T-3, with from 35 to 60 percent of the storm events  sampled over the entire study period.
   Monitoring variables included total and dissolved nutrients, suspended sediment concentration,
   field parameters (low flow only), fecal streptococcus (low flow only), and discharge.
                                            4-22
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Monitoring and Evaluating Nonpoint Source Watershed Projects
                                                          Chapter 4
   A nested well approach
   was used to monitor near-
   stream groundwater
   parameters, including
   water quality and level,
   flow directions, age dating,
   and chemical quality. Two
   nested wells were placed
   in the treatment basin at
   sites T-l and T-2. At each
   location, three shallow
   wells and one deep well
   were clustered together.
   One of the shallow wells at
   each ground water
   monitoring location was
   placed outside of the
   fenced area as a control.
   Water level was measured
   continuously and wells
   were sampled monthly
   during periods of little to
   no recharge. The resulting
   samples were analyzed for
   nutrients and fecal
   streptococcus.

   Benthic macroinvertebrate
   sampling was conducted in
   May and September of
   each year at five different
   locations (Figure CS3-1):
   one at the outlets of the
           Ground-water well nests are
           located at surface-water
           sites T-1 and T-2
                                  -  '-^	^ 76" 15'    New residents
                                  I       .'"M.   --"K. devel°Pmenl
                                  'TREATMENT BASlft '-/	
         EXPLANATION
      D AGRICULTURE
      • WATER
      D DEVELOPED
      • FORESTED
      D OTHER GRASS (LAWNS. PARKS. GOLF COURSES)
      • WETLAND
        STREAM
     	BASIN BOUNDARY
      A SURFACE-WATER-QUALITY SITE

      • SURFACE-WATER BENTHIC-MACROINVERTEBRATE SITE
®        SURFACE-WATER-QUALITY AND
        BENTHIC-MACROINVERTEBRATE SITE
      O- SPRING
           Street
                                         15 KILOMETERS
Figure CS3-1. Land-use map of study area and location of surface-
water sites, ground-water well nests, and selected springs in the Big
Spring Run Basin, Lancaster County, PA
   control and treatment basins (sites T-l and C-l), two upstream in the treatment basin (sites Tl-3
   and T2-3), and one upstream in the control basin (site Cl-2). Stream pool and riffle habits were
   sampled using the kick-net method; the USEPA Rapid Bioassessment Protocols (RBP) was used to
   characterize habitat; and water quality samples and stream measurements were also taken. Most
   metrics were applied to taxonomic identifications to the family level. The list of metrics includes
   percent dominant taxon (genus and family), EPT (Ephemeroptera, Plecoptera, and Trichoptera)
   index, generic EPT/Chironomidae ratio, EPT/total number, percent Chironomidae, shredders/
   total taxa ratio, scrapers/filterers ratio, Hilsenhoff Biotic Index (HBI) (genus and family), taxa
   richness (genus and family),  and percent Oligochaeta.

   All monitoring was conducted both before and after installation of streambank fencing which
   occurred in the treatment subbasin from May 1997 through July 1997. About 3.2 kilometers of
   fencing was installed along riparian zones in pastured areas to create a 1.5- to 3.6-m-wide stream
   buffer strip. Each pastured fenced had an average of two cattle crossings through the stream to
   allow the animals to migrate between pasture locations and access a water supply. Monitoring
   was carried out before and after fencing was installed. The pre-treatment period was from 1993
   to 1997 and post-treatment  monitoring was carried out from  mid-July 1997 through June 2001.
                                                4-23
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Monitoring and Evaluating Nonpoint Source Watershed Projects
                                                                         Chapter 4
   Due to their potential effect on the quality and quantity of the water and habitat, basin-wide
   covariate data were also collected during the study period, such as precipitation, inorganic and
   organic nutrient applications, and the number of cows present. Precipitation and agricultural data
   were obtained, respectively, using precipitation gauges (logging at 15-minute intervals), and from
   farm operators (monthly records).
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Monitoring and Evaluating Nonpoint Source Watershed Projects                                     Chapter 4
   Results
   Data collected from the pre-treatment period (1993 to 1997) were compared to those from the
   treatment and control basins to determine the effectiveness of streambank fencing. Changes in
   the yields of nutrients and suspended sediment during low flow and storm flow events were
   quantified using ANCOVA. ANCOVA was also used to quantify changes between pre- and post-
   treatment concentrations of nutrients, water quality, and fecal streptococcus in collected water
   samples, as well as nested wells inside and outside of the treatment area. Canonical
   correspondence analysis (CCA) was used to determine the effects of streambank fencing on
   instream biological conditions as characterized by benthic macroinvertebrates. A brief overview
   of major findings from analysis of water chemistry data is presented here, followed by a more
   detailed summary of results from macroinvertebrate monitoring.

   It was concluded that water chemistry results indicated that riparian fencing had fairly consistent
   effects on suspended sediment but less clear effects on nutrients. Post-treatment period
   improvements were evident at site T-l for both nutrients and sediments; however, site T-2
   showed reductions only in suspended sediment. The average reduction in suspended sediment
   yield for the treated sites was about 40 percent. N species at T-l showed reductions of 18 percent
   (dissolved NO3) to 36 percent (dissolved ammonia); yields of TP were reduced by 14 percent.
   Conversely, site T-2 showed increases in N species of 10 percent (dissolved ammonia) to 43
   percent (total ammonia plus organic N), and a 51-percent increase in yield of TP. The different
   results for nutrients at T-2 and T-l were attributed to ground water contributions and the failure
   to implement nutrient management along with the fencing. Shallow ground water flow
   contributed to stream flow at T-2, but the stream  was losing water to the shallow ground water
   system at T-l. It is believed that an upland agricultural field caused increased dissolved P levels in
   shallow ground water at T-2, resulting in a transport of P from ground water to the stream that
   increased stream P levels. In addition, cattle contributed nutrients directly to the stream via
   excretion at the embedded stream crossing at T-2.

   Analysis of the benthic macroinvertebrate samples showed some apparent improvements relative
   to the control sites in riparian and instream habitat (sites T2-3 and T-l versus C-l). Some
   differences  in bottom substrate, bank stability, available cover, and scouring and deposition were
   observed in the downstream and upstream locations within the treatment basin that could
   potentially be considered slight improvements. Water quality data collected during the benthic
   macroinvertebrate sampling suggested the overall improvement to instream habitat was due to
   the decreased load of suspended sediment (Figure CS3-2). The fenced riparian buffer, despite
   being narrower than what was considered optimal, allowed vegetation to become fully
   established and bank stability to improve. It was particularly evident at site T2-3 where it became
   overgrown with vegetation and blocked the stream from view.

   The composite benthic index, which combines  all metrics, is called the "Macroinvertebrate
   Aggregated Index" (MAI). For both spring  and fall samples, the index showed some improvement
   for the treatment sites relative to control  sites, though, trends were mixed overall. For the
   treatment basin, sites Tl-3 and T2-3 showed no change with spring samples, while the outlet site,
   T-l, showed a slight 1 unit increase.  From  the pre-treatment to the post-treatment period, fall
   index scores changed in the control basin  by 1 unit, increasing at C-l and decreasing at Cl-2. Fall
   scores for the treatment basin also changed over this timeframe, increasing by 2 units for T-l and
   Tl-3, but decreasing by 1 unit at T2-3.

   Disaggregating the index into individual metrics allows evaluation of different components of the
   benthic macroinvertebrate assemblage. In this dataset, there are different responses by different
                                             4-25
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Monitoring and Evaluating Nonpoint Source Watershed Projects                                      Chapter 4
   metrics. No difference was seen for five of the 10 genus-level metrics, which included percent
   dominant taxa (generic level) (PDTG), EPTtaxa, percent EPTtaxa, percent shredders, and ratio of
   scrapers to filterers. Thus, treatment elicited no effect for 50 percent of the metrics. Two of the
   metrics (EPT/Chironomidae ratio, Hilsenhoff Biotic Index [genus level]; 20 percent) suggested
   some, or slight, effect of the streambank fencing on treatment sites relative to control; and,
   distinct effects were seen for the remaining three metrics: percent Chironomidae (Figure CS3-3),
   taxa richness, and percent Oligochaeta.

   Further evaluation of taxa lists for dominance, occurrence, and uniqueness of and by individual
   taxa can help illuminate differences, in particular, for those taxa that are known to be more
   pollution-tolerant or sensitive. Spring samples were numerically dominated by worms (Naididae,
   Tubificidae), scud (Amphipoda: Gammaridae), several different midges (Diptera: Chironomidae:
   i.e., Cricotopus, Orthocladius, Dicrotendipes, Micropsectra), and blackflies (Diptera: Simuliidae:
   Simulium). The fall samples illuminated a shift in the actual taxonomic composition of the site to a
   greater diversity (i.e., a larger number of taxa) and dominance by largely different taxa, including
   riffle beetles (Coleoptera: Elmidae: Dubiraphia, Stenelmis), net-spinning caddisflies (Trichoptera:
   Hydropsychidae: Hydropsyche, Cheumatopsyche), midges (Chironomus, Dicrotendipes,
   Polypedilum, Rheotanytarsus), and blackflies. The only taxa that retained any kind of dominance
   for this site across seasons were Dicrotendipes and blackflies. The overall differences are driven by
   seasonality of the system, simply showing a greater diversity during the fall season and not by any
   changes in stressor load.  Although the two outlet sites  (C-l, T-l) basically had the same taxa
   dominating sample data, each had a greater diversity than the upstream sites. Elevated
   taxonomic/biological diversity can be an indicator of greater diversity and complexity of habitat
   characteristics; lacking other types of stressor loads, this diversity is likely what is reflected at the
   outlet sites. The upstream sites in the treatment basin  (T2-3, Tl-3) consistently showed more
   diversity than Cl-2, but the overall PDTG means for Tl-3 and T2-3  increased slightly (<1 percent)
   and by 13 percent, respectively, from the pre-to post-treatment period. Because differences in
   assemblage makeup are largely explainable by factors other than what might be introduced by
   streambank fencing, such as seasonality, and expected  physical habitat characteristics, the
   authors concluded that the treatment did not seem to  improve benthic-macroinvertebrate
   community structure based on PDTG.

   Across the full dataset, the dominant family-level taxa in spring samples were Chironomidae,
   Gammaridae, Naididae, and Tubificidae, all recognized  as being semi-tolerant to organic
   enrichment. The dominant families in fall samples were Gammaridae, Tubificidae, Elmidae,
   Physidae, Baetidae, Chironomidae, and Simuliidae, all considered as being moderately to very
   tolerant of organic enrichment. This indicates that the more sensitive taxa were not able to
   become dominant members of the benthic-macroinvertebrate assemblage after the fences were
   installed in the treatment basin. Sensitive taxa may not have been present or only a few
   individuals were present during the post-treatment period because of 1) not enough time for the
   system to equilibrate to the new conditions, or 2) because these are spring-fed, first- to second-
   order limestone streams. Limestone streams typically support assemblages including mayflies
   (Ephemeroptera),  midges (Chironomidae), scud (Amphipoda: Gammaridae), and pillbugs/sowbugs
   (Isopoda: Asellidae), all of which were present in treatment and control basins. Some of the more
   stressor-sensitive taxa that were present in  small numbers included Promoresia (Elmidae),
   Oxyethira (Hydroptilidae), Antocha (Tipulidae), and two species of Chironomidae (Pagastia and
   Prodiamesa).
                                              4-26
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Monitoring and Evaluating Nonpoint Source Watershed Projects
                                                                     Chapter 4
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                 Figure CS3-3. Distribution of the percentage of Chironomidae to total number
                 of individuals for May and September sampling events at benthic-
                 macroinvertebrate sites for the pre- and post-treatment periods in the Big
                 Spring Run Basin, Lancaster County, PA. Light shaded boxes are pretreatment,
                 dark shaded are post-treatment, C represents Control, and T represents
                 Treatment.

   Overall, the authors conclude that streambank fencing had a positive influence on the taxonomic
   diversity of benthic-macroinvertebrates, both at genus and family levels. This positive influence is
   interpreted as primarily resulting from stabilization of the riparian zone, allowing growth of
   streamside vegetation to progress, and ultimately allowing better habitat to develop and support
   more taxa.

   Previous studies suggest that for optimal reduction of nutrient loads into nearby aquatic systems,
   the buffer size should be greater than the 1.5- to 3.6-m buffer used in this study. Because there
                                               4-27
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Monitoring and Evaluating Nonpoint Source Watershed Projects                                      Chapter 4
   was such a wide range of what would be considered an adequate buffer size, it was uncertain
   which nutrient types, if any, could be controlled or reduced with this approach. Study results
   show that while the fenced streambank buffer was relatively small, it still was substantially
   effective in improving surface and near-stream shallow ground water quality, and led to some
   improvement in instream biology. Small-scale stream  buffers and exclosure fencing both have
   limited effectiveness in controlling high nutrient input ultimately transported through subsurface
   flows; the most pronounced effects of exclosures are  in reducing suspended sediment inputs  and
   consequently leading to improved habitat. There may be some effectiveness in controlling
   excessive nutrient flows, but the benefits are likely minor in  comparison to the habitat effects.


   Literature
   Galeone, D.G., R.A. Brightbill, D.J. Low, and D.L. O'Brien. 2006. Effects of Streambank Fencing  of
        Pasture/and on Benthic Macroinvertebrates and the Quality of Surface Water and Shallow
        Ground Water in the Big Spring Run Basin of Mill Creek Watershed, Lancaster County,
        Pennsylvania, 1993-2001. Scientific Investigations Report 2006-5141. U. S. Geological
        Survey, Reston, VA.

   Galeone, D.G. and E.H. Koerkle. 1996. Study design and Preliminary Data Analysis for a
        Streambank Fencing Project in the Mill Creek Basin, Pennsylvania. Fact Sheet FS-193-96.
        U. S. Geological Survey, Lemoyne, PA.
                                              4-28
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Monitoring and Evaluating Nonpoint Source Watershed Projects
Chapter 4
                             Table 4-3. Waterbody stratification hierarchy
Population
Operational
Sampling Unit (SU)
Strata (or higher
stages) comprising
SUs
Habitat within SUs
Streams
Channel segment
(i.e., a length of river
channel into which no
tributaries flow)
Ecoregion
Watershed
Stream/river channel
(ordinal/areal)
Segment
Characteristic water
quality (natural
conditions) Hydraulic
conductivity
(homogeneous,
heterogeneous,
isotropic, anisotropic)
Macrohabitat
(pool/riffle; Shorezone
vegetation;
submerged aquatic
macrophytes)

Microhabitat
Lakes'^
Self-contained basin
Ecoregion
Size/surface area of
lake (km2 )
Lake hydrology
(retention time,
thermal stratification)
Characteristic water
quality (natural
conditions)


Depth zone (eulittoral
6 profundal)
Substrate/
microhabitat
Reservoirs'^
Self-contained basin
(hydrologically
isolated from other
basins)
Ecoregion
Size/surface area of
reservoir (km2 )
(watershed area/basin
surface area)
Hydrology (water level
fluctuation/
drawdown; retention
time stratification)
Characteristic water
quality (natural
conditions)

Longitudinal zone
(riverine; transitional;
lacustrine; tail waters
(can be more riverine
but always associated
with dams))
Depth
Substrate/
microhabitat
Estuaries6
Self-contained basin
Biogeographic
province
Watershed
Watershed area (km2 )
Zones (tidal basin,
depth, salinity)


Substrate/habitat

Wetlands'
Transect upland or
deep water boundaries
Wetland system type
(marine, estuarine,
riverine, lacustrine,
palustrine)
Watershed recharge,
discharge or both
Class (based on
vegetative type;
substrate and flooding
regime; hydroperiod)
Flooding regime water
chemistry soil type


Subsystem (subtidal,
intertidal, tidal, lower
perennial, upper
perennial, intermittent,
littoral, limnetic)
Substrate/
microhabitat
"Frisselletal. 1986; bGerritsenetal. 1996;cWetzel 1983;d Thornton etal. 1990;e Day etal. 1989;fCowardinetal. 1979
                                                    4-29
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Monitoring and Evaluating Nonpoint Source Watershed Projects                                      Chapter 4


4.4  Biological Assessment Protocols
Biological indicators are widely recognized as being critical for evaluating ecological conditions, helping
identify and prioritize problems, designing controls or other solutions, and in evaluating effectiveness of
management efforts. However, prior to being able to make management decisions using these indicators,
two things need to occur. First, the indicator must be calibrated; and, second, sampling and analysis must
be instituted in a routine and consistent manner to directly address management objectives. To obtain
valid assessment results, and to optimize defensibility of decisions based on them, it is imperative that
monitoring be founded on data of known quality (Flotemersch et al. 2006b, Stribling 2011). In this
section, our discussion focuses on wadeable streams, and we assume that the user has access to indicators
that have been calibrated and are applicable to their region and water body (-ies) of concern. Many states
have developed MMIs using one or more biological groups, but most typically benthic
macroinvertebrates, and sequentially less so, fish and periphyton (algae and diatoms). Carter and Resh
(2013) surveyed state agencies about different characteristics of their biological monitoring programs,
and, although there are differences in some of the specific techniques, there has also been considerable
convergence among methods during the past 10 to 15 years. Monitoring programs that have gone through
the index calibration process have worked or are working through technical issues associated with
customizing sampling techniques to water body type, prevailing climatic conditions, and programmatic
capacity; using field data to characterize environmental/ecological variability; defining mathematical
terms of the indicator (metrics and index make-up); and defining thresholds for judging degradation.

Part of customizing field sampling and laboratory analysis methods for a program involves understanding
the range of variability of field conditions and the data/assessments that arise from them. One approach
for developing such an understanding is to recognize that biological monitoring and assessment protocols
are made up of a series of methods (Flotemersch et al. 2006b, Stribling 2011) generally corresponding to
different steps of the overall process: field sampling, sample preparation, taxonomic identification,
enumeration, data entry, data reduction, and site assessment/interpretation. In this section, we present
descriptions of the background, purpose, application, and output of the methods along with relevant
procedures for documenting data quality associated with each.


4.4.1  Field Sampling
In the context of the field sampling approach being used for biological assessment, taking or observing
organisms from a defined sample location is intended to provide a representation of the biological
assemblage supported by that water body, whether benthic macroinvertebrates, fish, or periphyton. These
three assemblages are emphasized  because they are most commonly used in routine monitoring and
assessment programs in the US, and methods for them are relatively well-documented (Barbour et al.
1999, Moulton et al. 2002,  Stribling 2011, Carter and Resh  2013).
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4.4.1.1   Benthic macroinvertebrates
Benthic macroinvertebrate samples are taken from multiple habitat types, and composited in a single
sample container (Figure 4-3). If sampled from transects as per the USEPA national surveys (USEPA
2009), they are collected along 11 transects evenly distributed throughout the reach length, using a
D-frame net with 500-^m mesh openings (Klemm et al. 1998, Flotemersch et al. 2006b). An alternative to
transects is to estimate the proportion of different habitat types in a defined reach (e.g., 100m), and
distribute a fixed level of sampling effort proportional to their frequency of occurrence throughout the
reach (Barbour et al. 1999, 2006). Whether using transects or proportional distribution, organic and
inorganic sample material (leaf litter, small woody twigs, silt, and sand; also includes all invertebrate
specimens) are composited in one or more containers, preserved with 95% denatured ethanol, and
delivered to laboratories for processing (Figure 4-4). A composite sample over multiple habitats in a reach
is a common protocol feature of many monitoring program throughout the US (Carter and Resh 2013),
although some programs choose to keep samples and data from different habitat types segregated.
Figure 4-3. Removing a benthic macroinvertebrate sample
from a sieve bucket and placing the sample material in a 1-liter
container with approximately 95% ethanol preservative
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Monitoring and Evaluating Nonpoint Source Watershed Projects
Chapter 4
Figure 4-4. Labelling benthic macroinvertebrate sample containers and recording field data


4.4.1.2  Fish
Fish sampling is designed to provide a sample that is representative of the fish community inhabiting the
reach, and which assumed to reasonably represent species richness, guilds, relative abundance, size, and
anomalies. The goal is to collect fish community data that will allow the calculation of an IBI and
observed/expected (O/E) models. Electrofishing is the preferred method of sampling, involving the
operator and (ideally) two netters, and occurs in a downstream direction at all habitats along alternating
banks, over a length of 20 times the mean channel width at designated transects (USEPA 2009).
Collection of a minimum of 500 fish is the target number of specimens (USEPA 2009), and in the event
this is not attained, sampling will continue until 500 individuals are captured or the downstream extent of
the site is reached.


4.4.1.3  Periphyton
Periphyton collections are made  from shallow areas near each of the sampling locations on the 11 cross-
section transects established within the sampling reach and are collected at the same time as the benthic
macroinvertebrate samples (USEPA 2009). There is one composite sample of periphyton for each site,
from which separate types of laboratory  samples can be prepared, if necessary. The different  sample type
could include a) an ID/enumeration sample to determine taxonomic composition and  relative abundances,
b) a chlorophyll sample, c) a biomass sample (for ash-free dry mass [AFDM]), or d) an acid/alkaline
phosphatase activity [APA] sample). There are potentially other analysis types that could be performed,
thus requiring additional sample segregates.
                                              4-32
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Monitoring and Evaluating Nonpoint Source Watershed Projects                                      Chapter 4


4.4.1.4  Quality control measures
Other than a qualitative judgment that field personnel are adequately trained, have sufficient experience,
and have been successfully audited as having completely and accurately applied the correct SOP, the
quality of field sampling cannot be determined without sample processing. The consistency of field
sampling is a measure of data quality quantified by precision calculations using indicator values -
individual metrics, IBI scores, or predictive models - collected from adjacent stream reaches (i.  e., two
stream channel lengths where the second [B] begins at the endpoint of the first [A]). As a rule-of-thumb,
we recommend a site duplication rate of 10 percent, where duplicate locations are randomly selected from
the full sample lot, and fieldwork occurs as routine. Terms calculated from the duplicate sample results
include median relative percent difference (mRPD), 90 percent confidence intervals/minimum detectable
difference (CI90/DD90), and coefficient of variation (CV) (Flotemersch et al. 2006b,  Stribling et al.
2008a, Stribling 2011). Depending on programmatic application, natural variability of the landscape the
watershed is draining, density and distribution of potential stressor sources, number of field crews, and, of
course, budgetary resources, it can be useful to stratify distribution of duplicate reaches. This will allow
programmatic measurement quality objectives (MQO) to be established for objective  benchmarks for
acceptable quality of data. Typical MQO for field sampling precision (Stribling et al.  2008a, Stribling
2011) might be:
  «   mRPD<15,
  «   CI90<15 index points on a 100-point scale, and
  •   CV< 10% for a sampling event

Depending on programmatic  needs, values exceeding these MQO could highlight samples for more
detailed scrutiny to determine causes for the exceedances, and the need for corrective actions.


4.4.2 Sample processing/laboratory analysis
For biological monitoring and assessment programs, sample processing employs procedures for
organizing sample contents so that analysis is possible. For benthic macroinvertebrate and periphyton
samples, those procedures are laboratory-based; however, for fish, they are performed primarily in the
field (USEPA 2004, 2009).


4.4.2.1  Benthic macroinvertebrates
The three aspects of sample processing for benthic macroinvertebrate samples are a) sorting, which serves
to separate the organisms from other sample material, specifically, organic detritus inorganic  silt, and
other materials (Figure 4-5), b) subsampling, which isolates a representative sample fraction from the
whole, and c) taxonomic identification, which characterizes the (sub)sample by naming and counting
individuals in it.
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Figure 4-5. Examining, washing, and removing large components of sample material prior
to putting in sample container
4.4.2.1.1  Sorting and subsampling
The sorting/subsampling procedure is based on randomly selecting portions of the sample material spread
over a gridded Caton screen (Caton 1991, Barbour et al. 1999, Flotemersch et al. 2006b, Stribling 2011),
and fully removing (picking) all organisms from the selected fractions. The screen is divided into 30 grid
squares, each individual grid square measuring 6 cm x 6 cm, or 36 cm2 (note that it is not 6 cm2 as
indicated in Figure 6-4b of Flotemersch et al. [2006b]). Prior to beginning the sorting/subsampling
process, it is important that the sample is mixed thoroughly and distributed evenly across the screen to
reduce the effect of organism clumping that may have occurred in the sample container. Depending on the
density of organisms in the sample, multiple levels of sorting may be necessary, the purpose of which is
to minimize the likelihood that the entire sample to be identified comes from a very small number of
grids. Initially, four grids are randomly selected from the 6x5 array, removed from the screen, placed in
a sorting tray, and coarsely examined. If the density of organisms is high enough that there  are many more
than the target number in the four selected grids (i.e., greatly exceeding by twofold or more the 100-,
200-, 300-, 500-organisms, or more, depending on the project), that material is re-spread on a second
gridded screen and the process repeated (second level sort). This is repeated until it is apparent that the
density of specimens will require at least four grids to be sorted to attain the target number  (±20%). Once
re-spreading is no longer needed, all organisms are removed from the four grids using forceps. If the final
rough count is ±20 of the target subsample size, then subsampling is complete; if >20% less than the
target subsample size, then additional, single grids of material are moved from the tray, and picked in
entirety. This is repeated, one grid at a time, until within 20 percent of the target number. Following
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picking, the sort residue should be transferred to a separate container labeled with complete sample
information, and the words "SORT RESIDUE" clearly visible. Completely record the number of sort
levels and grids processed. The sorting and subsampling process should result in at least three containers:
a) clean (sub)sample, b) sort residue, and c) unsorted sample remains. Container 'a' is provided to the
taxonomist for identification and counting, 'b' is available for QC sort re-check, and 'c' is archived until
all QC checks are complete. In the event of certain QC failures, it may be necessary to process portions of
the unsorted remains.

Fixed count subsamples - Fixed organism counts vary among monitoring programs (Carter and Resh
2013), with 100, 200, 300 and 500 counts being most often used (Barbour et al. 1999, Cao and Hawkins
2005, Flotemersch et al. 2006a). Flotemersch et al. (2006a) concluded that a 500-organism count was
most appropriate for large/nonwadeable river systems, based on examination of the relative increase in
richness metric values (< 2%) between sequential 100-organism counts. However, they also suggested
that 300-organism count is sufficient for most study needs. Others have recommended higher fixed
counts, including a minimum of 600 for wadeable streams (Cao and Hawkins 2005). The subsample
count used for the USEPA national surveys is 500 organisms (USEPA 2004); many states use 200 or
300 counts.


4.4.2.1.2 Taxonomic identification
Genus level taxonomy is the principal hierarchical level used by most routine biological monitoring
programs for benthic macroinvertebrates (Carter and Resh 2013), although occasionally family level
taxonomy is used. For genus level to be attained, most direct observations can be accomplished with
dissecting stereomicroscopes with magnification ranges of 7-112x; however, midges (Chironomidae) and
worms (Oligochaeta) need to be slide-mounted and viewed through compound microscopes that have
magnification ranging 40-1500x, under oil. Slide-mounting specimens in these two groups  is usually
(though, not always) necessary to attain genus level nomenclature, and sometimes even more coarse level
for midges (i.e., less specific). Taxonomic classification is a major potential source of error in any kind of
biological monitoring data sets (Stribling et al.  2008b, Bortolus 2008) and the rates of error can be
managed by specifying both hierarchical targets and counting rules. Hierarchical targets define the level
of effort that should be applied to each specimen but may often not be possible for some specimens due to
poor slide mounts, damaged, or their being juvenile (early instars). Further, the requirement for some taxa
may be more coarse, such as genus-group, tribe, subfamily, or even family. In any case, the principal
responsibility of the taxonomist is to record and report the taxa in the  sample and the number of
individuals of each taxon. Consistency in the nomenclature used is more important than the actual keys
that are used, although, some programmatic SOPs may specify the technical literature. For example, the
identification manual "An Introduction to the Aquatic Insects of North America" (Merritt et al. 2008) is
useful for identifying the majority of aquatic insects in North America to genus level. However, because
many taxonomic groups are often (correctly) under perpetual revision and updates, the nomenclatural
foundation of many may have changed, thus requiring familiarity of the taxonomist with more current
primary taxonomic literature. Merritt et al. (2008) is not applicable to non-insect macroinvertebrate taxa
that are often captured in routine sampling, including Oligochaeta, Mollusca, Acari, Crustacea,
Platyhelminthes, and others; exhaustive lists of literature for all invertebrate groups are provided by
Klemm et al. (1990) and Thorp and Covich (2010). Identification staff may also need information on
accepted nomenclature, including validity, authorship, and spelling, all of which could be found in the
integrated taxonomic information system (ITIS; http://www.itis.gov/). Although it is a nomenclatural
clearinghouse, it should be recognized that it is not completely current for all taxa potentially requiring
independent confirmation.
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It should be noted that some volunteer monitoring programs, such as the Izaak Walton League (IWL) and
the Maryland DNR Stream Waders Program (MDNR), use simpler taxonomic procedures. The IWL uses
field identification of a small number of organisms that are of a limited number of kinds, like mayflies,
stoneflies, caddisflies,  beetles, and mollusks, and just note their presence or absence. The MDNR Stream
Waders program, including metrics and index, are based on family level data.


4.4.2.2  Fish (field taxonomic identification)
Identification and processing offish occur at the completion of each transect (USEPA 2009), where the
data recorded include species names, number of individuals of each, length, and BELT anomalies
(Deformities, Eroded fins, Lesions and Tumors). Taxonomic identification and processing should only be
completed on specimens >25 mm total length and by qualified staff. Common names of species should
follow those established under the American Fisheries Society's publication, "Common and Scientific
Names of Fishes from  the United States, Canada and Mexico" (Nelson et al. 2004). Species not positively
identified in the field should be separately retained for laboratory identification (up to 20 individuals per
species). For programs not  using the transect method of sample reach layout, electrofishing will cover all
habitat throughout the  reach. Further, fish sample vouchers are developed for a minimum of 10% of the
sites sampled (USEPA 2012).


4.4.2.3  Periphyton
Two activities making up sample processing for periphyton are further segregated into those for a) soft-
bodied algal forms and b) diatoms. Although methods for both are presented by USEPA (2012), diatom
procedures are based principally on those of the US Geological Survey National Water Quality
Assessment Program (USGS/NAWQA) (Charles et al. 2002). Microscopic diatoms encountered are
identified (to lowest possible taxon level), enumerated and recorded. Estimates of the biovolume of
dominant species are made using existing parameters, or those found in the literature, and used to
determine the biovolume of the sample. Detailed information on the different procedures, especially on
the analytical approaches for soft algae using the Sedgewick-Rafter and extended Palmer-Maloney count
techniques, can be found in USEPA (2012) and Charles et al. (2002).


4.4.2.4  Quality control measures/data quality documentation
Quality control (QC) for sample processing for these three taxonomic groups is, in some respects, similar,
but in others, different. Some of the similarities are that several aspects quality evaluations are based on
repeating processes; specifically, duplicating field samples, or repeating of sample processing activities
(sorting, identification, and counting). Differences arise out of the fact that there are not always analogous
methods for dealing with the different organism types. Specifically, fish are identified in the field,
whereas, benthic invertebrates and algae/diatoms are laboratory-identified. Logistical constraints prevent
whole-sample re-identification offish, whereas it is easily done for the other groups. And, subsampling is
not done with fish samples, where it is explicitly done for benthic invertebrates, and functionally done for
algal/diatom samples.

Sorting QC (benthic macroinvertebrates [only]). - Sorting QC is accomplished through rechecking the
sample sort residue from 10% of the samples, randomly selected, and calculating the term 'percent sorting
efficiency' (PSE) (Stribling 2011, Flotemersch et al. 2006b). This value reports the number of specimens
missed during primary sorting as a proportion of the original number of specimens found. A typical MQO
for this is PSE>90%, with the goal of minimizing the number of samples that fail. Individual programs
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must specify what is acceptable, but generally, the goal should be to have <10% of the samples fail. It is a
measure of bias associated with sample sorting.

Taxonomic QC. - As a measure of precision in taxonomic identifications, consistency is quantified by
independent re-identification of whole (sub)samples, where those samples are randomly selected (10%, as
a rule-of-thumb) from the full sample lot. Sample results from the QC taxonomist are directly compared
to those of the primary taxonomist, and differences quantified as 'percent taxonomic disagreement' (PTD)
for identifications, as 'percent difference in enumeration' (PDE) for counts, and as 'absolute difference in
percent taxonomic completeness'  (afejTTC]). Typical MQO for these are PTD<15%, PDE<5%, and
afePTC<5% (Stribling 2011).

If MQO thresholds are routinely or broadly exceeded, samples failing should be examined in more detail
to determine causes of the problem, and what corrective actions may be necessary.


4.4.3 Data reduction/indicator calculation
Once necessary corrective actions for sample processing and taxonomic identifications have been
implemented and effectiveness confirmed, data quality is known and acceptable, sample data are
converted into the primary terms to be used for analysis. As stated above, monitoring practitioners usually
have access to published MMI for application to sample data, as well as sometimes predictive models and
established decision analysis systems. Indicators most often take the  form of a multimetric Index of
Biological Integrity (IBI; Karr et al. 1986, Hughes et al. 1998, Barbour et al. 1999, Hill et al. 2000, 2003)
or a predictive observed/expected (O/E) model based on the River Invertebrate Prediction and
Classification System (RIVPACS; Clarke et al. 1996, 2003, Hawkins et al. 2000b, Hawkins 2006). The
Illinois Department of Natural Resources used the Macroinvertebrate Biotic Index (MBI) in their analysis
of restoration effectiveness in the Waukegan River (see Case Study 4).


4.4.3.1  Multimetric indexes
The purpose of any MMI is to summarize complex biological and environmental information into a form
and format that can be used for management decision-making (Karr and Dudley 1981, Karr 1991,
Angermeier and Karr 1994), and doing so in a manner that allows uncertainty associated with those
decisions to be known and communicated. Index calibration is the empirical process of determining
which measures are best suited for that purpose, specifically in terms of their capacity for detecting
biological changes in response to environmental variables of concern (pollutants, or stressors). For the
purpose of this guidance it is assumed that the calibration procedure (Hughes et al. 1998, Barbour et al.
1999, McCormick et al. 2001) has been completed, and that an MMI is available to monitoring
practitioners for application. The reader should be aware that no attempt is made to be comprehensive in
discussing either metric diversity or index formulation, or of reviewing supporting technical literature. As
such, only selected examples are used below to illustrate different aspects of MMI application.
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                                                   Chapter 4
   CASE STUDY 4: BIOLOGICAL AND PHYSICAL MONITORING OF WAUKEGAN
   RIVER RESTORATION EFFORTS, LAKE COUNTY, ILLINOIS
   The Waukegan River watershed is located on the
   western shore of Lake Michigan, about 56 km
   (35 mi) north of Chicago in Lake County (Figure CS4-
   1). It is approximately 20 km (12.4 mi) long and has
   a drainage area of 2,994 ha (7,397 ac). The river
   channel drops from an approximate 222 m (730 ft)
   headwaters elevation to around 177 m (580 ft)
   above sea level, before discharging  directly into
   Lake Michigan through Waukegan Harbor on its
   western shore. The Waukegan River watershed
   receives a mean annual precipitation of 834
   millimeters (mm) (32.8 in) and has a mean annual
   temperature of 8.8°C (47.8°F). Historical records
   (circa 1840) indicate substantial marshes in the
   area, and recent soils studies indicate that wetlands
   covered approximately 15 percent of the
   watershed.
                       Urbanized watershed
                       Severely degraded stream habitat;
                       channel instability/bank erosion;
                       high velocity runoff
                       Bank stabilization using LUNKERS
                       and riparian re-vegetation
                       Grade control using rock weirs and
                       artificial riffles
                       Benthic macroinvertebrate
                       assemblage monitoring
                       Effectiveness evaluation
   The north and south branches
   of the basin, including the
   mainstem to Waukegan
   Harbor, comprise
   approximately 20 channel km
   (12.5 mi), excluding Yeoman
   Creek from the north. The
   mean channel width ranges
   from 4.4 to 6.7 m (14.6-
   21.9 ft) and has a mean depth
   from 0.07-0.28m (0.23-
   0.92 ft) (White etal. 2003).
   There is more substantial
   shading from riparian
   vegetation in the North Branch
   subwatershed than in the
   south. The South Branch has a
   greater discharge than the
   North Branch, approximately
   0.1 cubic meters per second
   (cms) (3.4 cfs) versus 0.01 cms
   (0.4 cfs). Dominant substrate
   types range from sand to large
   cobble and boulder, with some
   bedrock. Within the project area, there was one control monitoring site (S2) and three sites
   where stream restoration was carried out (SI, Ml, N2) (Figure CS4-2).

                                                 ' Waukegan
Figure CS4-1. The Waukegan River watershed in northeastern Illinois
(White et al. 2010)
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       Figure CS4-2. Aerial map taken in 1998 of a portion of the Waukegan River watershed, showing
       sampling locations and restoration project areas (White et al. 2010)
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Monitoring and Evaluating Nonpoint Source Watershed Projects                                     Chapter 4
   As of 2005, major land uses in the Waukegan River watershed included approximately
   36.7 percent residential; 21.5 percent transportation; 12 percent commercial, retail, government
   and institutional; and about 20 percent open space, forest, grasslands, and beaches. The
   remaining land uses are associated with small amounts of disturbed lands (3.6 percent), industrial
   (2.8 percent), wetlands/water (2.0 percent), and communication/ utilities (1.7 percent) (Lake
   County SWMC 2008). Approximately 80 percent of the densely urbanized City of Waukegan (2014
   population ~89,000)  lies within the watershed.

   The pace of development and sprawl of Waukegan was substantial throughout the latter part of
   the  19th century, and until the 1970s and 1980s, when current stormwater regulations began to
   take effect. Not surprisingly, streams were heavily impacted by urbanization. Minimal
   management of the stormwater quantity and quality led to flashy stormwater runoff conditions
   and elevated pollutant loads. Additional water resource issues associated with urban
   development included combined sewer overflows (CSO), stream channel instability (accelerated
   vertical and lateral erosion processes), nutrient enrichment, and contamination by metals,
   pesticides/herbicides, Pharmaceuticals and personal care products (PPCPs), and endocrine
   disrupters (White et  al. 2003, 2010). Channel erosion processes were accelerated by flashy
   stormflows, contributing to degraded physical habitat and decreased capacity of the stream  to
   support the survival and reproduction  of stream biota.


   Monitoring and  Sampling Design
   The overall restoration goal in the North Branch and South Branch was to rehabilitate physical
   habitat and hydrologic conditions to support recovery of benthic macroinvertebrate and fish
   assemblages (White et al. 2010).  Project leaders chose to design and install biotechnical
   stabilization, a combination of stream bank physical stabilization and riparian re-vegetation,  to
   address the severe channel instability and erosion problems in Washington Park and Powell Park
   (Figure CS4-2). The objectives of these techniques are complementary. A stream channel
   experiencing severe lateral and vertical erosion (mass-wasting and down-cutting, respectively), by
   definition, is losing habitat suitable for stream biota. Damaged or missing riparian vegetation
   results in diminished root mass to hold soil together, lowered inputs of leaf litter and woody
   materials (food source and habitat structure), and less shading, which can lead to warmer water
   and increased photosynthetic activity and algal  growth. Combinations of LUNKERS1, a-jacks,
   stone, coconut fiber rolls, dogwoods, willows, and grasses were installed at selected locations on
   the  North Branch/Powell Park (1992-93) and on the South Branch/Washington Park (1995).

   There were four sample locations, two each on  the South Branch and the North Branch (Figure
   CS4-2). For the South Branch, station S2 was the upstream control reach, and SI was the
   downstream treatment reach. On the North Branch, the two sample locations (Nl and N2) were
   located to coincide with treatment reaches. Wooden LUNKERS were used as the principal
   rehabilitation feature at Nl, while recycled plastic lumber and concrete a-jacks were used for
   LUNKERS construction at N2. All four locations were sampled annually during spring, summer, and
   fall over a 13-year period (1994-2006).

   This case study focuses solely on  responses of benthic macroinvertebrates, although it should be
   recognized that fish were also evaluated for both branches. Benthic macroinvertebrates, physical
   habitat, and chemical water quality were sampled, measured, and characterized at each stream
   location. Three macroinvertebrate samples were taken at each location using a Hess sampler with
 little LJnderwater Neighborhood Keepers Encompassing Rheotaxic Salmonids' (Vetrano 1988)
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Monitoring and Evaluating Nonpoint Source Watershed Projects                                      Chapter 4
   a 500 micron mesh net. Sample material was preserved in 95 percent ethanol; organisms were
   sorted to segregate individuals from non-target material, and subsequently identified to genus
   level.

   Sampling data were used to calculate the Macroinvertebrate Biotic Index (MBI). The MBI is based
   on the Hilsenhoff Biotic Index (HBI; Hilsenhoff 1977, 1982) but  uses an 11-point scale, rather than
   the HBI 5-point scale. Lower MBI scores indicate better or less-degraded water quality. Physical
   habitat was characterized using the Potential Index of Biological Integrity (PIBI) which
   incorporates percent substrate particle sizes, magnitude of sediment deposition, pool substrate
   quality, and substrate stability, a series of hydraulic and morphometric measures, riparian
   features, and various aspects of instream cover (White et al. 2003). The PIBI was calculated from
   measurements and field observations made on each of 10 equal-length segments established by
   the 11-transect method. The purpose of the PIBI is to help illuminate which habitat features, if
   any, might be limiting survival, growth, and reproduction of stream biota.


   Results
   Overall, among all four locations, MBI  scores ranged from around 5 to  just below 10 (good to very
   poor), with an average around 7.2, or "fair" (see Figure CS4-3). On the  South Branch, station  S2
   exhibited the highest mean score for a single year, 7.5, indicating "fair" stream  condition, slightly
   better than "poor." MBI scores indicate worsening conditions over time at stations SI and Nl.
   There was virtually no change over the 13 years for stations S2 and N2, with average MBI scores
   in the "fair" and "poor" ranges.

   All sites were dominated by stressor tolerant taxa, with sample data comprised of 82-89 percent
   non-biting midges (Insecta: Diptera: Chironomidae), segmented worms (Annelida: Oligochaeta),
   and aquatic sowbugs (Crustacea: Isopoda: Asellidae). The dominance of these animals in the
   North and South branches clearly shows stressed or degraded conditions before, during, and after
   any kind of habitat restoration or other remedial activities. Mean taxa  richness (number of
   distinct taxa) over the sampling period was 10 for site SI, and 8 for the other three locations.
   Ninety-two percent of the samples fell in the "poor" or "very poor" categories.  There were very
   low numbers of stressor-sensitive EPTtaxa (mayflies [Ephemeroptera], stoneflies [Plecoptera],
   and caddisflies [Trichoptera]) throughout the monitoring period. This is generally indicative of
   elevated pollutant levels and greater degradation. Some improvement in physical habitat quality
   was observed at treatment stations SI and Nl, likely due to improvements in bank stability and
   decreases in overall proportions of percent fines, silt, and mud. The other treatment station, N2,
   which was bank-armored, remained relatively consistent over the full period of record, as did the
   non-treatment control, S2.

   Improvement in physical habitat quality and overall biological diversity  was achieved as a result of
   these restoration activities, but improvement in biodiversity, primarily relative to the fish
   assemblage (not discussed in this case study) was only temporary (White et al. 2010). The authors
   acknowledged that sustainable biological diversity in a damaged watershed will require more
   complete understanding of landscape and watershed processes, their degree of degradation,  and a
   comprehensive approach  to conservation that addresses the system in its entirety. In the case of
   the Waukegan River watershed, this calls for a systematic approach to correcting other sources of
   hydrologic and chemical water quality stressors associated with water and  sewer management
   operations, channel and flow alterations, and extensive aquifer drawdown. One result of this
   project was initiation of a comprehensive watershed plan, selection of a coordinator, development
   of stakeholder and technical planning committees, and creation of a long-term  action plan.
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  CO
  CO
      10
8 -


6 -


4 -


2 -
                South Branch Station S1
         1994
                2000
 2006
      10
       8 -
       6 -
       4 -
                North Branch Station N1
         1994
                2000
2006
                                                  10
                                                          8 -
                                                          6 -
                                                          4 -
                                                          2 -
                                                                   South Branch Station S2
                                                            1994
                                                                   2000
                            2006
                                                  10
                                                   8 -
                                                                   North Branch Station N2
1994
2000
2006
Figure CS4-3. MBI scores from monitoring stations in Waukegan River (White et al. 2010). Assessment classes
(narrative ratings) for stream condition based on MBI scores are: very poor, 9.0-11.0; poor, 7.6-8.9; fair, 6.0-7.5;
good, 5.0-5.9; very good, <5.0.


   Literature
   Hilsenhoff, W.L. 1977. Use of Arthropods to Evaluate Water Quality of Streams. Technical Bulletin
        100. Wisconsin Department of Natural  Resources. Madison, Wl.

   Hilsenhoff, W.L. 1982. Using a Biotic Index to Evaluate Water Quality in Streams. Technical
        Bulletin 132. Wisconsin Department of Natural Resources. Madison, Wl.

   Lake County SWMC. 2008. Waukegan River Watershed, Lake County, Illinois. Lake County
        Stormwater Management Commission, Lake County Department of Information
        and Technology, GIS & Mapping Division. Map. Accessed February 9, 2016.
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   Vetrano, D.H. 1988. Unit Construction for Trout Habitat Improvement Structures for Wisconsin
        Coulee Streams. Administrative Report No. 27. Wisconsin Department of Natural Resources,
        Madison, Wl.

   White, W.P., J.D. Beardsley, J.A. Rodsater, and LT. Duong. 2003. Biological and Physical
        Monitoring of Waukegan River Restoration Efforts in Biotechnical Bank Protection and
        Pool/Riffle Creation. National Watershed Monitoring Project. Annual Report. Prepared by:
        Watershed Science Section, Illinois State Water Survey, Illinois Department of Natural
        Resources. Prepared for: Illinois Environmental Protection Agency and U.S. Environmental
        Protection Agency (Region 5).

   White, W.P., J. Beardsley, and S. Tomkins. 2010. Waukegan River, Illinois national nonpoint source
        monitoring program project. NWQEP NOTES. The NCSU Water Quality Group Newsletter.
        No. 133. April 2010. ISSN 1062-9149. North Carolina State University Water Quality Group,
        Campus Box 7637, North Carolina State University, Raleigh, NC 27695-7637. Accessed
        February 9, 2016. http://www.ncsu.edu/waterquality/.
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4.4.3.1.1  Metric and index calculations
Metrics are mathematical terms calculated directly from sample data, with resulting values scored relative
to quantitative criteria. The Table 4-5 below presents an example of four different metric sets representing
site classes (or bioregions) within a particular US State. Each set of either 5 or 6 metrics forms the basis
of an MMI previously calibrated to wadeable streams of the class. Metric values result from direct
calculations on raw sample data, taxonomic identifications and counts (list of taxa and number of
individuals of each, by sample).
     Table 4-4. Metrics and associated scoring formulas for four site classes from an example
                              monitoring and assessment program
Metrics
Scoring formulas
Site class A
1.
2.
3.
4.
5.
Total taxa
Percent EPT individuals, as sensitive
Percent Coleoptera individuals, as sensitive
Beck's biotic index
Percent of taxa, as tolerant
100*(mefrfci/a/ue)/51.5
100*(mefrfci/a/ue)/39
100*(mefrfci/a/ue)/10.5
100*(mefrfci/a/ue)/31
100*(43-[mefrfci/a/ue])/40
Site class B
1.
2.
3.
4.
5.
6.
Total number of taxa
Number of EPT taxa
Percent individuals Cricotopus/Orthocladius + Chironomus, of total Chironomidae
Percent EPT individuals, as sensitive
Number of taxa, as shredders
Hilsenhoff Biotic Index
100*(mefrfci/a/ue)/51.5
100*(mefrfci/a/ue)/14
100*(45-[mefrfci/a/ue])/45
100*(mefrfci/a/ue)/39
100*(mefrfci/a/ue)/7
100*(8.5-[mefrvci/a/ue])/5
Site class C
1.
2.
3.
4.
5.
6.
Total taxa
Percent of taxa, as non-insects
Percent individuals Cricotopus/Orthocladius + Chironomus, of total Chironomidae
Percent of individuals, as filterers
Number of taxa, as sprawlers
Hilsenhoff Biotic Index
100*(mefrfci/a/ue)/51.5)
100*(46-[mefrfci/a/ue])/40
100*(24-[mefrfci/a/ue])/24
100*(mefrfci/a/ue)/70
100*(mefrfci/a/ue)/14
100*(8.5-[mefrvci/a/ue])/5
Site class D
1.
2.
3.
4.
5.
6
Number of Oligochaeta taxa
Percent EPT individuals, as sensitive
Percent individuals, as Crustacea and Mollusca
Percent individuals, as Odonata
Number of taxa, as collectors
Percent individuals, as swimmers
100*(6-(mefrfci/a/ue])/6
100*(mefrfci/a/ue)/15
100*(mefrfci/a/ue)/30
100*(16.5-[mefrvci/a/ue])/16.5
100*(20-[mefrfci/a/ue])/19.5
100*(12-[mefrfci/a/ue])/12
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Many metrics require assigning different characteristics or traits to taxa in the dataset prior to calculation.
These characteristics include functional feeding groups (FFGs), habit, and stressor tolerance values. Many
states use various literature resources to develop traits databases (e.g., Merritt et al. 2008, Barbour et al.
1999, Vieira et al. 2006, Carter and Resh 2013).

The formulas in the table resulted from the calibration process, and serve to convert the calculated metric
value to a normalized, unitless score on a 100-point scale. The multiple metric values are then combined
for each sample by simple averaging. Additionally, formulas are developed, in part, so that individual
metrics are scored depending on their direction of change in the presence of stressors.


4.4.3.1.2  Quality control measure
Metric calculations are typically performed in spreadsheets or relational databases with embedded
queries. To ensure that resulting calculations are correct and provide the intended metric values, a subset
of them should be recalculated by hand. A reliable approach is to calculate a) one metric across all
samples, followed by b) all metrics for one sample. When recalculated values differ from those values in
the output matrix, reasons  for the disagreement are determined and corrections are made. Reports on
performance include  the total number of reduced values as a percentage of the total, how many errors
were found in the queries,  and the corrective actions specifically documented.


4.4.3.2  Predictive models (observed/expected [O/E])
Predictive models are based on the premise that the taxa occurring in a minimally disturbed system can be
predicted based on multiple measures of the environmental setting and that if the predicted taxa are not
observed in an evaluation site, then disturbance can be suspected. The ratio of the number of observed
taxa to that expected  to occur in the absence of human-caused stress is an intuitive and ecologically
meaningful measure  of biological integrity. Low observed-to-expected ratios (O/E « 1.0) imply that test
sites are adversely affected by some environmental stressor. The models are commonly called RIVPACS
models (River Invertebrate Prediction And Classification System [Wright 1995]) based on
observed:expected taxa (Clarke et al. 1996, 2003, Hawkins et al. 2000b). Because they are based on taxa
in reference sites, the predictive models are not well suited to assemblages with naturally low diversity (as
in oligotrophic fish communities). The loss of reference taxa is difficult to detect when only few taxa are
expected.

The number of taxa expected at a site is calculated as the sum of individual probabilities of capture for all
taxa found in reference sites in the region of interest. All probabilities greater than a designated threshold
are summed to calculate the expected number of taxa (E), and this number is  compared to the reference
taxa observed (O) at  a site. Because these models predict the actual taxonomic composition of a site, they
also provide information about the presence or absence of specific taxa. If the sensitivities of taxa to
different stressors are known, this information can lead to derived indices and diagnoses of the stressors
most likely affecting  a site. In addition, taxa can be identified as increasers or decreasers with respect to
general environmental stress encountered in the model development data set.  A variation of the O/E
models measures the  Bray-Curtis compositional dissimilarity between an observed and expected
assemblage directly,  which detects stress-induced shifts in taxonomic composition that leave assemblage
richness unchanged (Van Sickle 2008).

The steps that go into building a predictive model include 1) classifying reference sites into biologically
similar groups, 2) creating discriminant functions models to estimate group membership of sites from
environmental data, 3) establishing taxon-specific probabilities of capture for individual sites,
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4) identifying taxa expected and comparing to those observed, 5) estimating model error, and 6) applying
the model in test sites. These steps can be automated to allow exploration of model performance for
multiple subsets of environmental predictor variables.


4.4.3.3  Quantitative decision analysis systems  (biological condition gradient [BCG])
The Biological Condition Gradient (BCG) was described by USEPA (2011) as "a conceptual model that
describes how biological attributes of aquatic ecosystems might change along a gradient of increasing
anthropogenic stress." The model can serve as a template for organizing field data (biological, chemical,
physical, landscape) at an ecoregional, basin, watershed,  or stream segment level. The BCG was
developed by EPA and other agencies to support tiered aquatic life uses in state water quality standards
and criteria. It was developed through a series of workshops, and described fully by Davies and Jackson
(2006). The BCG includes a narrative description of ecological condition that can be translated across
regions, assemblages, and assessment programs. The descriptions recognize six levels of quality in
ecological condition ranging from "1" (most desirable) where natural structural, functional, and
taxonomic integrity is preserved to "6" (least desirable) in which there are extreme changes in structure
and ecosystem function and wholesale changes in taxonomic composition.

The quantitative decision analysis systems approach explicitly uses the BCG as a scale for biological
assessment. It differs from the multimetric and predictive model approaches in that it is not dependent on
definition of reference sites (although that can be useful), and development relies on consensus of experts
instead of an individual or a few analysts. It is similar to  the multimetric approach in its reliance on
distinct site classes. It is similar to the predictive modeling approach in its examination of individual taxa
(though metrics are also incorporated in the models).

Calibrating a BCG to local conditions begins with the assembly and analysis of biological monitoring
data. Following data assembly, a calibration workshop is held in which experts familiar with local biotic
assemblages of the region review the data and the general descriptions of each of the BCG levels. The
expert panel then uses the data to define the ecological attributes of taxa, and to develop narrative
statements of BCG levels based on sample taxa lists. The expert panel is usually convened multiple times
to refine decisions, to react to interim results, and to assign BCG levels to new sites. The steps typically
taken during a calibration workshop include the following:
    1) An overview presentation of the BCG and the  process for calibration;
    2) A "warm-up" data exercise to further familiarize participants with the process;
    3) Assignment of taxa to BCG taxonomic attributes (based on known tolerance and rarity);
    4) Description of biota in undisturbed conditions  (best professional judgment [BPJ]; regardless of
       whether such conditions still exist in observed reference sites);
    5) Assignment of sites in the data set to BCG levels; and
    6) Elicitation of rules used by participants in assigning sites to levels.

Documentation of expert opinion in assigning attributes to taxa and BCG levels to sites is a critical part of
the process. Facilitators elicit from participants sets of operational rules for assigning levels to sites.  As
the panel assigns example sites to BCG levels, the members are polled on the critical information and
criteria they used to make their decisions. These form preliminary, narrative rules that explained how
panel members make decisions. Rule development requires discussion and documentation of BCG level
assignment decisions and the reasoning behind the decisions. During these discussions, records are kept
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on each participant's decision ("vote") for the site; the critical or most important information for the
decision; and any confounding or conflicting information and how this is resolved.

A decision model is then developed that encompasses the taxa attributes and quantitatively replicates the
rules used by the expert panel in assigning BCG levels to sites. The decision model is tested with
independent data sets as a validation step. A quantitative biological assessment program can then be
developed using the rule-based model for consistent decision-making in water quality management.

The decision analysis models can be based on mathematical fuzzy-set theory (citation) to replicate the
expert panel decisions. Such models explicitly use linguistic rules or logic statements, e.g., "If taxa
richness is high, then condition is good" for quantitative, computerized decisions. The models can usually
be calibrated to closely match panel decisions in most cases, where "closely matched" means the model
either exactly matched the panel, or selected the panel's minority decision as its level of greatest
membership. The decision analysis models can also be cross calibrated to other assessment tools, such as
the MMI. Models can be developed as spreadsheet tools to facilitate programmatic application.


4.4.4  Index scoring and site assessment
The site-specific MMI score, as calculated above in section 4.4.3, is compared to degradation thresholds
(Table 4-6) to determine whether biological degradation exists relative to minimally degraded reference
conditions (Barbour et al. 1999, Stoddard et al.  2006). The range of potential scores in Table 4-6 is
0 (most degraded) to 100 (least degraded). The  90 percent confidence intervals (CI90) are calculated
using sample repeats (see section 4.4.2.4). Defining the numeric values of degradation thresholds is an
integral phase of index calibration, and is affected by regional and climatic conditions, along with the
overall level and consistency of landscape alteration and available data to characterize the broad range of
degradation.
                      Table 4-5. Degradation thresholds to which MMI score
                            are compared for determination of status
Site class
A
B
C
D
Degradation threshold
52.3
65.7
66.0
55.9
The confidence interval (CI), also known as detectable difference (DD) (Stark 1993), is associated with
individual MMI scores and represents the magnitude of separation between two values before they can be
considered truly different (Stribling et al. 2008a). Reported values falling below the threshold are
considered degraded, those above are non-degraded, while site index values falling near a threshold may
require additional samples to determine final rating category (Stribling et al. 2008a, Zuellig et al. 2012).
Some programs, if not most, also subdivide value ranges above and below the degradation threshold to
allow communication of multiple levels of non-degradation and degradation,  e.g., very good, good, fair,
poor, or very poor.
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4.4.5 Reporting assessment results at multiple spatial scales
Depending on the monitoring design, it is possible to use assessment results for several purposes, some of
which may have been previously unanticipated. For example, a probability-based design provides
assessments that can be aggregated for assessments broader than the individual location from which a
sample was taken (Larsen 1997, Urquhart et al. 1998). Simultaneously, each sample from that kind of
design provides information useful for interpreting conditions at individual sample locations.
Assessments from a targeted design provide information about the sites sampled, and although they
cannot be used for broader scale assessments, can assist with confirming the effects of known stressors
and stressor sources.


4.4.5.1   Watershed or area-wide
For programs using a stratified random monitoring design, a simple inference model similar to that
described by Olsen and Peck (2008) and Van Sickle and Paulsen (2008), can be used to estimate the
number of degraded stream miles (D) for a watershed or area-wide region with the formula:

                                        D = (N/T}x L
where:
       TV is the number of sites rated by the MMI as degraded,

       T is the total number of sites assessed for the sampling unit (subwatershed or watershed group),
       and

       L is the total number of stream miles in the sampling unit.

Total stream channel miles (L) should be estimated with GIS using the National Hydrography Dataset
(NHD), or other stream data layer appropriate to the watershed or region of interest. Note that replicate
samples taken for QC purposes are not included in these calculations. Results can also be presented as
percent degradation (%D) by using the calculation:

                                       %D = (N/T)xlQQ

For the Lake Allatoona/Upper Etowah River watershed, site selection and monitoring was stratified by
the 53 HUC subwatersheds,  and cumulative assessments showed distinctive patterns of degradation
(Figure 4-6). More intensive development and imperviousness are closer to transportation corridors.

Trends in %D over time can be evaluated using test such as the Kendall tau test (Helsel and Hirsch 2002).
It should  be noted that for very small sample sizes (i.e., 3 or 4), all values would need to be consecutively
decreasing to reject a one-sided null hypothesis with ap equal to 0.167 and 0.042, respectively.
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Chapter 4
                                                               Degraded Watersheds

                                                               HUC-12
                                                               % Degraded Stream Miles
                                                                    Major Streams
                                                               I    lHUC-10
                                                                    HUC-12, not assessed
           2.5
                         10
                                  15
Figure 4-6. Percent degradation of subwatersheds as measured by biological monitoring and
assessment, Lake Allatoona/Upper Etowah River watershed (Millard et al. 2011)
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Monitoring and Evaluating Nonpoint Source Watershed Projects                                       Chapter 4


4.4.5.2  Stream- or site-specific
The MMI scores and status ratings are approaches useful for summarizing and communicating site
specific conditions; this is the application for which they are designed, and for which they are ideally
suited. However, a well-organized and functional database allows the index to be disaggregated where
individual metrics and even taxa can be evaluated by biologists to help determine those which are most
influencing an assessment. Presenting site-specific point assessments from the previous example (Figure
4-7) shows the specific distribution of the most- and least-degraded streams, and more detailed
examination can begin to reveal proximity of potential stressor sources (Figure 4-8). At this stage of
evaluating watershed-based stream assessments, if necessary, the assessor can turn to the USEPA stressor
identification process, also known as "The Causal Analysis/Diagnosis Decision Information System", or
CADDIS (http://www.epa.gov/caddis/). to assist in determining the most probable causes of biological
degradation. It is using this process, including evaluating the relative dominance of the various taxa that
taxon-specific environmental requirements, stressor tolerances, feeding types, and habits, which can lead
to more defensible decisions on stressor control actions, such as BMPs or stream/watershed restoration
activities. MMI confidence intervals can be computed and used for point comparisons in the same manner
as other water quality variables (see section 7.3).


4.4.5.3  Relative to specific sources
Monitoring objectives requiring documentation of instream biological condition relative to a specific and
known source of stressors require that sample data be drawn from one or more locations exposed to those
stressors. In particular, if the source is an area of specific land use, a type of BMP, or a point source,
confidence in the result will likely be enhanced by a thorough and quantitative description of the source.
However, it should be recognized that lack of a clear site-specific response to either measured or assumed
exposure to stressors does not mean that the biota are non-responsive. Neither does it mean that the BMP
is ineffective. The BMP can likely be proven effective at reducing the single or multiple stressors for
which it is intended and designed.
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Monitoring and Evaluating Nonpoint Source Watershed Projects
              Chapter 4
                                  1 Miles
    0   2.5
                        10
                                  15
Narrative Ratings
  0  Very Good
  •  Good
  O  Fair
  O  Poor
  •  Very Poor
    - Major Streams
 I    | County Lines
 I    lHUC-10
 I    | HUC-12
Figure 4-7. Distribution of stream biological assessments in the Lake Allatoona/Upper Etowah
River watershed, using a benthic MMI developed by the Georgia Environmental Protection
Division (Millard et al. 2011)
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Figure 4-8. More detailed examination of the Yellow Creek subwatershed, Lake Allatoona/Upper
Etowah River watershed, Georgia, reveals a sample location, rated biologically as "poor," is on a
stream flowing through a poultry production operation (Millard et al. 2011)
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Monitoring and Evaluating Nonpoint Source Watershed Projects
                           Chapter 5
5   Photo-Point Monitoring
     By S.A. Dressing and D.W. Meals


5.1   Introduction
Good photographs can yield much information, and photography can play important roles in watershed
projects because the technology is available to everyone, training is simple, and the cost is relatively low
(USEPA 2008, ERS 2010). In the assessment phase, photos can help identify problem areas both within
the water resource (e.g., algal blooms, streambank erosion) and within the drainage area (e.g., cattle in
streams, discharge pipes). These same photos can also be very helpful in generating interest in the project
because they can convey easily understood information to a wide audience. In addition, photos can be
used to document implementation of practices including contour strip-cropping, stream buffers, rain
gardens, and other practices where physical changes are observable. Finally, photos can be used in project
evaluation. For example, photos taken before and after implementation of some types of remedial efforts
(e.g., trash removal and prevention) provide an indicator of progress that can be communicated  easily to
most people.

To be useful, however, photographs must be taken in accordance with a protocol that ensures the
photographic database accurately represents watershed conditions and is suitable for meeting stated
objectives. This section provides an overview of ground-based photographic, or photo-point, monitoring,
including specific elements of an acceptable protocol and example applications.
5.2  Procedure
Photo-point monitoring requires careful planning to
ensure that meaningful information is provided to
assess condition or trends (Bauer and Burton 1993).
Monitoring design begins with a set of clear objectives,
and different objectives will generally require different
photo points (Hamilton, n.d.).

There are two basic methods of photo-point monitoring
- comparison photography and repeat photography -
but these methods can be used in combination
(i.e., comparison photography repeated overtime).
Method selection should generally precede other design
decisions but choices made in one step of monitoring
plan design can affect the options in other steps, so
flexibility is necessary. Selection of monitoring areas,
identification  of the specific features to photograph,
camera placement, and the timing and frequency of
photography are all typically determined after
monitoring objectives and basic method are addressed
(after Hamilton n.d.).
  Photo-Point Monitoring
Set objectives
Select method
Select monitoring areas
Establish, mark, and assign
identification numbers to photo and
camera points
Identify a witness site
Record site information and create a
site locator field book
Determine timing and frequency of
photographs
Define data analysis plans
Establish data management system
Take and document photos
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All photo and camera locations must be marked, monitoring site characteristics must be recorded, and a
field book or similar documentation should be created to assist those taking photographs at the sites over
time. This is critical if different people will be taking photographs throughout the course of a project.
Plans for analysis of the photos and use of any photo-derived information must be determined and
documented before photo-point monitoring begins. The data analysis plan will also help determine how
best to organize and file photos and metadata.

These various design steps are described in greater detail below.


5.2.1  Setting Objectives
There will most likely be different photo-point monitoring objectives for project assessment, planning,
implementation, and evaluation. Objectives for all project phases should be defined as early on in the
project as possible, however, to maximize the efficiency of the photo-point monitoring effort. It may be
possible, for example, to use photos from problem assessment or planning as pre-implementation photos
for tracking implementation.

Realistic objectives begin with an understanding of what is likely to be seen and measured with
photographs. Cameras exist that can take photos in both the visible and the non-visible spectrum
(e.g., infrared or ultraviolet). For example, aerial photography has been used successfully to identify
sediment sources at the watershed scale through correlation of photo density readings from the
transparencies of color-infrared photographs with suspended sediment measurements (Rosgen 1973
1976). In addition, Hively et al. (2009a 2009b) combined cost-share program enrollment data with
satellite imagery and on-farm sampling to evaluate cover crop N uptake on 136 fields within the
Choptank River watershed on Maryland's eastern shore. Thermal infrared (TIR) images acquired from
airborne platforms have been used in stream temperature monitoring and analysis programs,  detecting and
quantifying warm and cool water sources, calibrating stream temperature  models, and identifying thermal
processes (Faux et al. 2001). TIR imagery has also been used in the mapping of groundwater inflows and
the analysis of floodplain hydrology. While such applications are indeed useful, this guidance and the
example objectives that follow focus solely on ground-based photography in the visible spectrum.

An array of observable features listed in various guidance documents includes pasture condition, livestock
distribution in a meadow, ground cover, tree canopy and health, vegetation density, woody vegetation,
native vegetation area, wetland area, native plant richness, large trees, stream profile, streambank
stability, streambank cover, fallen woody material and in-stream habitat, farm water flow, gully erosion,
hill slope erosion, wind erosion, weed cover and species (Bauer and Burton 1993, ERS 2010, Hall 2001,
Shaff et al. 2007). In addition, Hall (2001) provides numerous examples of successes and failures to
measure changes in observable features with photo-point monitoring.

Examples of potential objectives for photo-point monitoring at various project stages include the
following.

Assessment
  " Document trash levels on beaches or in urban settings
  " Document stream features
  " Document algal blooms in waterbodies
  " Identify sources of sediment plumes
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Monitoring and Evaluating Nonpoint Source Watershed Projects                                      Chapter 5

  •   Document livestock activity near waterbodies
  •   Identify gullies and areas of streambank instability
  •   Identify areas in greatest need of urban runoff control measures
Planning
  •   Help locate areas were streambank protection and stream restoration are needed
  •   Document livestock operation needs to assist in budget development
  •   Provide evidence of watershed problems and potential solutions for public outreach
  •   Provide photos to assist the design of urban runoff control measures
Implementation
  •   Document tree growth in riparian zone over time
  •   Document implementation of rain gardens
  •   Document stream restoration activities
  •   Document and track changes in percent residue at representative agricultural sites across a
      watershed
Evaluation
  •   Document changes in streambank cover or stream profile as a result of stream restoration
  •   Demonstrate the effects of different grazing management systems on pasture condition
  •   Illustrate how a stream handles high-flow events before and after restoration
  •   Document changes in beach trash over time
The type and rigor of photo-point monitoring needed to meet these objectives varies. Alternative methods
are described below.

5.2.2  Selecting Methods
As defined by Hall (2001), ground-based photo monitoring involves "using photographs taken at a
specific site to monitor conditions or change," something that is accomplished by one of two methods:
comparison or repeat photography. Comparison photography typically involves the creation of a photo
guide from a set of standard photos taken to represent the expected range of an attribute (or condition) of
interest (e.g., utilization of grazing plants). Field measurements are taken to establish values for the
attribute of interest at levels represented by each of the photos in the guide. Figure 5-1 illustrates the
concept whereby the value (percentage of area covered with dots) is determined from field measurement
of the attribute of interest (dots/unit area in this conceptual example). The comparison photos in the guide
are then used in the field to perform on-site assessment.
                                               5-3
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Monitoring and Evaluating Nonpoint Source Watershed Projects
Chapter 5

: Photo: 1:
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^ ;:;:;:;:;:;:;:;:;;;




Photos
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Photo 4





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                                             I
Figure 5-1. Comparison photos

In repeat photography, photos are taken of the subject over time at the same location to document change
or monitor activity. Repeat photography has been used to document landscape change, including the
advance and retreat of glaciers (Key et al. 2002). This method has also been used extensively to document
progress in dam removal (USDA-FS 2007), riparian area protection (Bauer and Burton 1993), and stream
restoration projects (Bledsoe and Meyer 2005).

A third type of photography is opportunistic photography. As described by Shaff et al. (2007),
opportunistic photos are not taken from a permanently marked location, and they are not part of a repeat
photography effort. There is also no photo guide as is used in comparison photography. Examples of
subjects that can be addressed with  opportunistic photography include a site during construction or an
area after a significant natural or human-induced event.

Comparison photography is generally well suited to meeting assessment objectives in cases where
photography is an appropriate monitoring approach. Opportunistic photography also usually plays a role
in problem assessment.  Both methods can be used for qualitative purposes, and comparison photography
can be used in quantitative analyses to a limited degree (see "Qualitative" and "Quantitative" below).
Opportunistic photography is not designed for quantitative analyses, however. Other information sources
(e.g., livestock inventories, street maps, and permitted discharge reports) and monitoring data (e.g., water
chemistry, aquatic biology, and habitat) will be needed in combination with photos to meet assessment
objectives.

A combination of comparison and opportunistic photography can be helpful in achieving planning
objectives, coupled with information from other sources. Opportunistic photos, in particular, can be quite
helpful in communicating to the general public and stakeholders the need for restoration or BMPs to
achieve watershed objectives. Visual inventories can be helpful in estimating implementation costs but
should be used in  combination with more traditional approaches to assessing need.
                                               5-4
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Monitoring and Evaluating Nonpoint Source Watershed Projects                                      Chapter 5
Repeat photography is generally most useful for tracking restoration and implementation of BMPs.
Comparison photos can be used to assess such important indicators as the extent that conservation tillage
has resulted in increased percent residue. Opportunistic photos can help show how restored stream
reaches or urban runoff practices handle high-flow events.

While photo-point monitoring can be very helpful, it should be kept in mind that tracking implementation
of rain gardens, for example, does not require photos. Observers could simply record in a database that a
rain garden has been implemented at a specific address or global positioning system (GPS) location, but a
photograph might add valuable information about the rain garden (e.g., size, location, plant selection and
density) that could be explored at a later date if water quality data raise questions about rain garden
performance.

Watershed projects cannot rely on photographs as the sole source of information for problem assessment
or planning. Project implementation is nearly always tracked by means other than photo-point monitoring,
but the addition of photographs can be the best way to document the installation of structural practices
(e.g., lagoons, constructed wetlands) or the growth of vegetation associated with stream restoration or
grazing management. It is important to keep in mind that photo-point monitoring should always be
considered as a cost-effective tool for providing information in conjunction with other monitoring and
information gathering efforts. While there are examples where photo-point monitoring is relied on as the
primary monitoring method due to budgetary constraints, it is not recommended.

All three photo-point monitoring methods - comparison, repeat, and opportunistic - can support
qualitative analyses, and comparison and repeat photography can also be used in quantitative analyses.


5.2.2.1  Qualitative Monitoring
Photographic monitoring methods usually generate qualitative information (e.g., Shaff et al. 2007).
Creating a pictorial record of changing conditions, showing  major changes in shrub and tree populations,
visually representing physical measurements taken at a location, or recording particular events such as
floods are typical of the types of photo-point monitoring objectives stated for these projects (ERS 2010).
Those who have used photographic monitoring for watershed projects have generally used this method to
document implementation of practices, typically the growth of vegetation associated with
stream/streambank restoration or grazing management. These qualitative findings have been used most
frequently to corroborate findings from more quantitative monitoring methods.

Photos are recommended for long-term monitoring of grassland, shrubland, and savanna ecosystems but
simply as a qualitative indicator of large changes in vegetation structure and for visually documenting
changes measured with other methods (Herrick et al. 2005 2005a). Photos  should not be considered as a
substitute for quantitative data; it is very difficult to obtain reliable quantitative data from photos unless
conditions are controlled. Bledsoe and Meyer (2005) used photographs to compare changes from year to
year, document noteworthy morphologic adjustments, document features of interest at various locations
and times during the year, and analyze vegetation establishments as part of monitoring channel stability.


5.2.2.2  Quantitative Monitoring
Quantitative monitoring involves either measurement or counting. When measurement is desired it is
important to use meter boards (field rulers mounted vertically) or other size control boards to provide a
reference for measurement (Hall 2001 2002). Small frames (1 m2) have been used for closeup or plot
studies, while meter boards and Robel poles are often used for more distant studies. These standard
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Monitoring and Evaluating Nonpoint Source Watershed Projects
Chapter 5
references are captured within the photographs to provide a means of measuring features of interest.
Counts of items of interest (e.g., trees of varying heights) can be obtained through visual observation of
images. Another alternative for obtaining counts or percentages for quantitative analysis is to count digital
image pixels that fall within a specified color range (see Digital Image Analysis below).

Meter boards can also provide a consistent point for camera orientation and a point on which to focus the
camera (Hamilton n.d.). Figure 5-2 illustrates the use of a meter board and photo identification card (see
section 5.2.13). The following are methods described by Hall (2001) that incorporate varying degrees of
quantitative analysis. It should be noted that while these methods all  support some level of quantification,
documentation of precision and accuracy is generally lacking.
                                        '
                                 Photo Point Number
                                 Cam era Point ID:
                                 Photographer
Figure 5-2. Illustration of a photo identification card and a meter board


5.2.2.2.1 Photo Grid Analysis
Photo grid analysis involves placing a standardized grid over a photo and counting the number of
intersects between the grid lines and features of interest. When photo grid analysis is planned, it is very
important that the distance between the camera and meter board is constant (Hall 2001 2002). It is
recommended that the camera height is held constant, but it is only required to be constant if the grid is
used to track position (in addition to size) of features over time. The size control board should cover at
least 25 percent of the photo height, with the optimum range being 35 to 50 percent. The board, however,
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Monitoring and Evaluating Nonpoint Source Watershed Projects                                      Chapter 5
cannot obstruct the features of interest that will be measured. A level meter control board is preferred
because it will match up more easily with a superimposed grid. Vegetation around the front of the meter
board should be removed to expose the bottom measurement line to provide maximum precision in grid
adjustment.

Hall (2001 2002) notes that both grid precision and observer variability are major factors in determining
the ability to measure change. The percentage of photo height taken by the meter board is a very
important factor in the precision with which grids are fit. It should be noted that changes in technology
(cameras and software) may provide better results than found by Hall. For example, testing showed that a
meter board that covers 35 percent of the photo height was 1.3 times more precise than a board that
covered 25 percent of the photo height. Testing on observer variability also indicated that, on average, a
change >12 percent in intersects for all shrubs (a measurement for grid analysis) was needed to
demonstrate change at the 5 percent confidence level. Additional details and examples of photo grid
analysis are provided by Hall (2001 2002).


5.2.2.2.2 Transect Photo Sampling
Photo points can also be established along a transect to obtain more quantitative information (Hamilton
n.d.). Hall (2001) describes in detail five kinds of photo transects: (1)  1-ft2 frequency photographed with
or without a stereo attachment on the camera, (2) nested frequency using four plot sizes in a 0.5- by 0.5-m
frame, (3) 1-m2 plot frame photographed at an angle, (4) vertical photographs of tree canopy cover, and
(5) measurement of herbaceous stubble height using the Robel pole system.

Transect installation is straightforward, requiring skillsets and procedures similar to those for the
establishment of photo-point and camera sites (see sections 5.2.4 and 5.2.5). Equipment needs are similar
as well. Size control boards are required, and they can serve multiple purposes, including estimation of
height of grass and shrubs, orientation (for consistency) and focus (for greatest depth of field) of the
camera, and grid analysis (Hall 2001). Key features of the five kinds of photo transects are provided
below, but the reader should not select any of these methods until reviewing the detailed discussion of
each by Hall.


5.2.2.2.2.1  One-Square-Foot Sampling
This method uses a 1-ft2 plot placed every 5 ft along a 100-ft transect. The 20 plots are monitored to
document changes in species, species density, and frequency as a means to estimate change in vegetation
and soil surface conditions. Statistical analysis of data generated by this method is not possible.


5.2.2.2.2.2  Nested Frequency
This method uses a sample frame with four nested plot sizes to document change in species frequency
along five 100-ft transects of 20 plots each. Statistical analysis suggests significant change in frequency
(the number of times a species occurs in a given number of plots) at the 80-percent level of probability.


5.2.2.2.2.3  Nine-Square-Foot Transects
This plot system uses five 9-ft2 plots along a 100-ft transect to document changes in species frequency.
Photographs are taken of the plot frame at an oblique angle rather than from directly above. Interpretation
of change is based not on statistical analysis but on professional judgment and interpretation of the
photos.
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5.2.2.2.2.4 Tree Canopy
It is recommended that any transect placed in a forest setting should have tree cover sampled because of
its effects on the density and composition of ground vegetation. Tree canopies are photographed from
ground level by using a camera leveling board or other means to ensure that the camera is pointing
directly above. The method requires photographs of tree cover at the 0-, 25-, 50-, 75-, and 100-ft locations
on transects used for any of the three methods described above. Because photo grid analysis is used to
estimate tree cover, the same focal length must be used for all photos and the long axis of the camera
should be perpendicular to the transect.


5.2.2.2.2.5 Robel Pole
A Robel pole is a 4-ft pole with 1-in bands painted in alternating colors (USDA-CES et al. 1999).
Vegetation height is measured by photographing the pole from a specific distance and height above the
ground. This is accomplished by attaching a 4-m-long line between the 1-m mark on the Robel pole and
the top of a 1-m-tall line pole. The Robel pole is placed at the sample location and the line is stretched
out. The camera is set on top of the line pole and a photo is taken. By consistently using the 4-m line and
1-m camera height (4-to-l ratio), the same angle is obtained for all photos.


5.2.2.2.3  Digital Image Analysis
Many of the methods described by Hall (2001) were centered on film-based photography, and they often
require a substantial amount of measurement and analysis by hand. Newer methods such as digital image
analysis (DIA) use computers to analyze digital images, offering the potential advantages of improved
objectivity, accuracy, and precision. In one form of DIA, color images are converted to grayscale
(monochrome) images using an algorithm that converts each pixel to white or black based on the color
content of the original pixel. The algorithm in this case is designed to select those colors that represent the
feature to be counted. For example, Rasmussen et al. (2007) used  DIA to determine the proportion of
pixels in digital images that were green to estimate crop soil cover in weed harrowing research.

There are significant hurdles to overcome in applying DIA to photo-point monitoring for watershed
projects. Factors such as lighting, camera angle, size of the area photographed, and the growth stage of
plants should be evaluated to quantify their effects on the accuracy or precision of the method
(Rasmussen et al. 2007). It is also important to have a true value to compare against the DIA-based results
to assess the accuracy of the method (Richardson et al. 2001).

A significant contribution to DIA made by Rasmussen et al. (2007) was automated determination of the
gray-level threshold which defines the difference between vegetation (the subject of interest in their
study) and non-vegetation. This is especially important when lighting conditions vary in the field.  With
this capability, the researchers were able to develop an automated DIA procedure for converting each
digital image into a single leaf cover (proportion of pixels that are green) value for analysis. Their
research used the MATLAB Image Processing Toolbox (MathWorks 2012) but other options include
Mathematica (Wolfram 2012) and a wide range of image processing products developed for a large
number of applications.
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5.2.3  Selecting Areas to Monitor
The areas selected for photo-point monitoring must be appropriate for the stated objectives and consistent
with the data analysis plans (section 5.2.11). Depending on the monitoring objectives, suitable sampling
locations may be chosen to represent average or extreme conditions.

For problem assessment where opportunistic photography is used, site selection may be similar to that
employed in a synoptic survey for water quality monitoring. Photos may be taken by individuals walking
the stream to identify areas of streambank erosion or point source discharges. Photography of sources
could involve a windshield-survey approach where photos are taken on a pre-determined route. Each
opportunistic photo would need to be properly labeled as described in section 5.2.13.

When tracking project implementation (e.g., BMPs, restoration) or evaluating project success, it is most
important to select an area that is most likely to undergo the physical  transformations that can and must be
tracked in order to support these objectives. Hall (2001) notes that this task may be straightforward (e.g.,
measuring the impact of stream restoration on the segment restored) or somewhat more complicated (e.g.,
documenting the impacts of livestock grazing  on riparian vegetation). The latter case is more complicated
because it requires some knowledge of livestock distribution, areas sensitive to grazing, and grazing
patterns. Because it is likely that only a portion of the area of interest can be monitored, it is important to
determine up front whether or not the findings can be extrapolated to  areas not monitored. This is
particularly challenging for photo-point monitoring because statistical analysis of photo-based data is not
common. Attribution of sample findings to the broader area of interest would require the sample is
representative, there is a measurable variable from the photos, the distribution for that variable is known,
and an estimate of the standard deviation is  available.

Some may wish to use photo-point monitoring to track BMP-related information in support of a
traditional biological or chemical monitoring program. For example, if total suspended sediment
concentration or loads are monitored in a predominantly agricultural watershed, it may be useful to track
percent residue as an indicator of the extent to which reduced tillage practices have been implemented
across the watershed. This could be accomplished in a number of ways including photo-point monitoring
of a set of randomly selected  field sites. Both comparison  (to determine percent residue) and repeat (to
track changes in percent residue  overtime) photography would be used in this application (see section
5.2.2). Again, attribution of sample findings to the broader area would require that the samples are
representative, the distribution of percent residue is known, and an estimate of the standard deviation is
available.


5.2.4  Identifying Photo Points
Photo points are defined somewhat differently in various guidance manuals, which can lead to confusion
when flipping back and forth between manuals. This document adopts the terminology used by Hall
(2001), in which the photo point is essentially what you point the camera at when you take the
photograph, and the camera point is a permanently marked location for the camera (Figure 5-3). Photo
points have also been defined as permanent or semi-permanent sites set up from where you take a series
of photographs over time (ERS 2010). Despite the different definitions and intermingling of various
concepts within these definitions, photo-point monitoring manuals ultimately address the area to be
photographed, the location from which the photos are taken, and the camera direction and settings to
identify what will be captured in the photos. In simple terms, the  photo point is what you point the camera
at when you take the photograph.
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The area captured in each photo will depend on the monitoring objectives and is controlled by camera
settings and the distance between the camera location and the subject. Hall (2001) describes three general
types of photos, each of which has an associated scale:
  •  Landscape - distant scenes with areas generally greater than 10 ha
  •  General - specific topics monitored on areas 0.25 to 10 ha
  •  Closeup - specific topics on areas under 0.25 ha
Figure 5-3. Photo illustrating photo points (A and B) and camera points (1 and 2). Photos of A and
B are taken from cameras located at 1 and 2.

Landscape photography generally requires a long-term commitment during which repeat photos are taken
as infrequently as every 20 years or so (Hall 2001). This timeframe is greater than typically encountered
in watershed projects. General and closeup monitoring will be more appropriate for most watershed-scale
projects. Hamilton (n.d.) states that general photography can be used to document an entire scene,
whereas topic (closeup) photography narrows the target down to specific elements or subjects in the
landscape.

Scale is also incorporated within the definitions of photo types found in other guidance documents. For
example, one scheme refers to spot, trayback (small truck with short, flat tray in back rather than a typical
pickup box), and landscape photographs which generally correspond to Hall's closeup, general, and
landscape photos (ERS 2010). Shaff et al. (2007) describes feature, landscape, and opportunistic photos.
Landscape photos cover a broader area than feature photos, while opportunistic photos (see section 5.2.2)
vary in scale but are generally at the  feature or finer scale. The authors also provide guidelines on the type
of photography and features to photograph for various restoration activities associated with habitat
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improvement projects, road projects, water management projects, wetlands, and fish passage
improvement.

Be sure to consider the following when establishing photo points:
  •   The general or specific features that must be photographed to meet the monitoring objectives.
  •   How representative the photo points are of conditions in the study area.
  •   Whether the number and type of photo points are sufficient for tracking change.
  •   Whether changes will be visible at the desired scale.
  •   Whether the site is accessible and lighting and sight lines are adequate during the entire monitoring
      period.


5.2.5 Establishing Camera Points
As noted in section 5.2.4, camera points are permanently marked locations for the camera. Hamilton
(n.d.) suggests selecting camera points from which multiple photo points can be photographed. The same
photo point can also be photographed from multiple camera points, for example, if there is a need to
examine the subject matter at different scales or from different angles. If the sizes of objects will be
compared in photos taken from multiple camera points, the distance from each camera point to the  photo
point must be the same. In addition, to avoid shadowing of the photo point, camera points should be
located north of photo points when they are close together.

Hall (2001) performed field testing of camera point setups (e.g., distance from photo point and the
vertical and horizontal positioning of the camera) to determine the effects of various camera positions and
settings on the ability to perform reliable repeat photography. Results of this testing clearly showed the
following:
  •   Distance from the camera to the meter board (or subject) affects both the size and location of
      objects photographed.
  •   The vertical and horizontal position  of the camera affects the location but not the size of objects
      photographed.
  •   Focal length is not a critical issue  because images can be enlarged or reduced to  a constant area of
      coverage. Resolution can be lost, however, if images are enlarged or cropped too much, so it is best
      that the same or similar focal length be used for all photos.

Depending on the study objectives, therefore, camera point setup should provide a constant distance from
the camera to the photo point (for size and location considerations), and consistent height and left-right
orientation of the camera (for location).  It  should be noted that in Hall's testing, camera position was
shifted both upward and sideways by 40 cm (16 in) from an initial position centered at 1.4 m (55 in)
above the ground. Smaller shifts would result in lesser changes in object location.

Figure 5-3 illustrates the location of photo and camera points. Both camera points 1 and 2 would need
consistent camera positions if object locations were to be tracked overtime. Meter boards can be used to
guide camera position when taking photos, with the camera siting always on the top, bottom, or other
specific marking on the meter board.
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A recommended standard equipment list for establishing photo-point monitoring areas can be found in
section 5.3.


5.2.6 Marking and Identifying Photo and Camera Points
Every photo and camera point should be geolocated, photographed, and permanently marked so that those
returning to take photos can find the sites with little waste of time. Capturing prominent features such as a
ridge line in the photos can help others identify the location and the photo points (Bauer and Burton
1993). Labor is usually the  greatest cost associated with monitoring efforts (see chapter 9), and doing
whatever it takes to minimize the time needed to find photo-monitoring sites is cost effective. If
volunteers perform the monitoring, marking of photo and camera points is essential to efficiently finding
the locations so they can spend more time taking and documenting photos and less time  searching for
sites.

The best material to mark sites depends on the circumstances, but metal fenceposts work well in many
cases (Hamilton n.d.). If metal fenceposts are unsuitable due to appearance or other considerations, steel
survey stakes driven into the ground may be appropriate provided that metal detecting equipment is
available (Hall 2001). If steel stakes are used, they can be covered with plastic pipe for safety, and all
stakes can be painted in bright colors to improve visibility (Larsen 2006). Each photo and camera point
should be given a unique identification number.

It is very important that the distance between camera points and photo points is measured and
documented (Hamilton n.d.). Site location can be facilitated by use of a GPS but marking of photo and
camera points will still be necessary in many cases, given that the best resolution for GPS systems is
currently about 3-5 meters. Identifiers for opportunistic photos and temporary photo and camera points
used for problem assessment and planning should at least include the purpose, address or GPS
coordinates, camera direction, date photos were taken, narrative description of what was observed, and
photographer name to provide sufficient information to interpret the information obtained and revisit the
site if necessary.


5.2.7 Identifying  a Witness Site
A witness site is an object that can be easily identified when returning to the monitoring area (Hamilton
n.d., Hall 2001). It may  be a large rock, a structure, or other feature that is easily identifiable from the
road or path to the photo and camera points. It is important to measure and document the distance and
direction from the witness site to the camera points, photo points, or both. If possible, it is also helpful to
attach a permanent identification tag to the witness site with the distance and direction to the photo and/or
camera points inscribed on the tag (Hamilton n.d.). Newer photo-monitoring guidance recommends the
use of GPS devices to facilitate finding the photo and camera points (ERS 2010, Shaff et al. 2007). In all
cases, however, it is helpful to have photographs of the site and a description of landmarks to help locate
and identify important spots within the monitoring area.


5.2.8 Recording Important Site  Information
Information about any monitoring site, whether it be chemical, biological, physical, or photographic
(permanent or temporary), should be recorded to help future staff understand the reasons for selecting the
site and to help in the interpretation of data collected from the site. Maps, aerial photographs, and
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standardized forms can be used to record date, observer name(s), location, site description, objectives,
identification numbers, and locations of the witness site, photo points, and camera points, including
distances and directions between points. It is important to indicate whether directions are magnetic or true
degrees (Hamilton n.d.), a topic addressed in detail by the U.S. Search and Rescue Task Force
(USSARTF n.d.). Standardized forms for all  aspects of photo-point monitoring can be found in existing
documents (Hall 2001 2002, Shaff et al. 2007).


5.2.9  Determining Timing and Frequency of Photographs
Monitoring frequency should be based primarily on the monitoring objectives, planned data analyses,
features to be photographed, and expectations regarding detectable change in those features. Photo-point
monitoring for problem assessment and planning can be a one-time activity or may involve multiple
photographs taken at various times during the year to characterize seasonal, flow-related, or other
significant variability. Efforts to track project implementation or evaluate project success will usually
involve multiple years, with the frequency and timing of photos based on an understanding of seasonal
and other variability.

Land managers are encouraged to photograph native vegetation at least once per year at the end of the
growing season, or twice per year to show seasonal differences (ERS 2010). For restoration projects, the
frequency options are generally seasonal, annual, or biennial (Shaff et al. 2007). In addition, photos taken
during the high-flow and low-flow seasons should be compared to give some indication of the causes
affecting streambank condition. Regardless of the frequency selected, annual changes should be assessed
using photos taken at the same time of year.

Although photo-point monitoring for watershed projects is usually qualitative rather than quantitative, the
concept of MDC (see section 3.4.2) can still be applied when determining the frequency and duration of
photography.  In essence, MDC is based on sample variance and the number of independent samples taken
over time. Kinney and Clary (1998) used repeat photography to track cattle density (animals/ha) on
various vegetation-soil categories in a riparian meadow and used analysis of variance to test for
differences in cattle distribution across vegetation-soil categories. Such time-series data could be analyzed
to estimate variance (i.e., variability) in the number of cattle in each photograph. This data could then be
used in an MDC analysis to estimate  how often photographs would need to be taken to detect a significant
change in cattle density at a given level of confidence. It is important to note that the authors found
autocorrelation in their data due to frequency of photography, something that would have to be addressed
in the MDC analysis (see section 3.4.2).

In an assessment of photo grid analysis precision, it was found that variability  among different observers
was about 12  percent, indicating that a change in mean intersects of that much would be needed to
indicate that the change was real at the 5 percent level of confidence (Hall 2001). Monitoring, therefore,
would need to continue until a 12 percent change or more was expected.

Absent a rigorous database to support MDC analysis, it is recommended that a qualitative assessment of
time needed to see measurable change is performed. Guidelines that can be used to estimate the number
of years photo-monitoring should continue to document measurable change include plant growth rates for
restoration activities, typical timeframes for construction of urban runoff controls, and historical patterns
for adoption of agricultural BMPs.
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5.2.10  Creating a Field Book
Hamilton (n.d.) recommends creation of a field book to help others find the monitoring location, witness
site, and photo and camera points. Field books should also include copies of the original photo-point
photographs, and other important site information recorded as described under section 5.2.8. Advances in
GPS, portable computer, and cell phone technology, however, may reduce the need for a physical field
book, but a printed version should be created as a backup.


5.2.11  Defining Data Analysis Plans
It is essential to establish plans for analysis before taking the photos. As described in section 5.2.1 and
5.2.2, photo-point monitoring objectives can range from highly qualitative to  quantitative, and data
analysis plans need to be worked out in advance to ensure that information collected through photo-point
monitoring will be sufficient to achieve these objectives.

Although statistical analysis of photo-based data for watershed projects is uncommon, examples exist that
could be  applied to watershed projects. For example, quantitative analysis of differences in grazing
patterns in various areas of a riparian meadow was performed by Kinney and Clary (1998) using analysis
of variance. Photos were analyzed to count the number of cattle within each of five vegetation-soil
categories that were delineated within the study area and superimposed on individual photographs.
Through this method, researchers created a database with counts that were converted to a density measure
that was associated with both year and class variables (e.g., vegetation-soil category,  pasture number).

In another example where statistical analysis was applied to photo-derived data, digital image analysis
was compared against subjective analysis (SA) and line-intersect analysis (LIA) in determining the
percentage  of turf cover on study plots (Richardson et al. 2001). For DIA, the percentage of green pixels
in images of turfgrass taken from a digital camera mounted on a monopod was calculated to determine the
turf coverage percentage in each of the images. The DIA approach was shown to be very accurate through
calibration with turf plugs of known cover, and DIA also performed far better than either SA or LIA in
determining the percent cover of study plots. The variance for DIA was only 0.65, while the variances for
LIA and  SA were 13.18 and 99.12, respectively.

As described in section 5.2.2, both the photo grid analysis and nested frequency methods support
statistical analysis (Hall 2001). For example, demonstration of regression analysis of grid intersects from
annual photography over a 20-year period appeared to be useful.

If these or other monitoring approaches that support statistical analysis are planned, it is essential that the
statistics  to be performed are identified, the data needs to support the statistical analyses are documented,
and plans are developed at the beginning of a project to obtain the needed information from photo-point
monitoring. Because statistical analysis of photo-derived data is uncommon for watershed projects,  it is
essential  that a statistician is involved in the design of the monitoring effort.


5.2.12  Establishing a Data  Management System
Data management systems are described in detail in section 3.9. The basic requirements and safeguards
associated with a data management system for water quality data also apply to photo-point monitoring
data sets. These include an organized and readily accessible filing system, quality assurance and quality
control procedures, working interfaces between data files and data analysis software, and backup systems.
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It is recommended that backup archives are kept at a location separate from the original data (Hamilton
n.d.).

As with water quality monitoring data records, information on monitoring objectives, designs, and
locations must also be recorded and associated with the photos taken at each site. All information
recorded on forms should be included in the database and linked to photos as appropriate.

If necessary, hard copies of photos can be stored in manila folders in filing cabinets or above-floor boxes
and should be labeled clearly with locational information, date, time, and camera and photo point
identifiers (Bechtel 2005, Larsen 2006, Shaff et al. 2007). Digital images and files will need to be stored
in a computer database housed on a computer or computer network, and it is recommended that file
names provide the same information contained in the labels on the paper photos (Bechtel 2005,  Shaff et
al. 2007). Software such as GPS Photo Link can be used to process the GPS information onto the images
(Larsen 2006). Digital information should be backed up on CDs or other "permanent" storage devices,
and networks should be backed up nightly (Bechtel 2005). Photo-point monitoring will usually be
performed far less frequently than storm-event monitoring, for example, but the file sizes associated with
photographs may create data storage challenges that should be considered early on in the project.

Whether photos are used for qualitative or quantitative analyses, it is important that standard procedures
are established and followed. For example, photos used in a river continuity assessment in New
Hampshire were taken in accordance with a standard operating procedure that was incorporated within a
quality assurance project plan (Bechtel 2005). The QAPP identified equipment needs and the roles and
duties of team members, provided general instructions, and gave details  on all important aspects of
selecting sites and taking the photos. In addition, volunteers were trained in photo documentation, and
standardized forms were provided to ensure consistency.


5.2.13 Taking and Documenting Photographs
Whether photo points are temporary or permanent, opportunistic or part of a trend assessment, certain
guidelines should be followed to ensure that the photos support the monitoring objectives. It should be
clear from the following recommendations, some of which are slightly at odds with each other, that
photography is part art, part science (Bechtel 2005, ERS 2010, Shaff et al. 2007):
  "   Closeup photos should be taken from the north facing south to minimize  shadows.
  •   Both medium and longer distance photos should be taken with the sun behind the photographer.
  •   Recommendations on the best times for taking photos vary, with some choosing early in the
      morning, late in the afternoon, or on slightly overcast days to reduce shadows and glare, and others
      wanting clear days between 9 a.m. and 3 p.m.. Photos taken before 9 a.m. and after 3 p.m. can
      result in increased shadowing and a different color cast that could conceal some features.
  •   Some recommend camera settings that give the greatest depth of field, while others simply
      recommend using the camera's auto settings.
  "   Report the true compass bearing (corrected for declination) if possible.
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Additional guidelines apply when the monitoring plan involves repeat photography. For example,
consistency is essential for trend assessment, and the following information taken from a variety of
sources should be recorded with each photograph to ensure such consistency (Bechtel 2005, ERS 2010,
Hall 2001, Hamilton n.d., Larsen 2006, Shaff et al. 2007):
  * When shooting repeat photography it is helpful to compare the view through the camera with a
     copy of the original photo to create comparable photos. Camera settings should be the same as
     those documented when the original photo was taken.
  • Document the type of camera and lens used, digital resolution, tripod and camera height, lens focal
     length or degree of zoom, light conditions, compass direction of the photo, and the distance from
     the camera to the one-meter board or center of the photo area.
  • Document whether the camera is held horizontally or vertically.
  « Record the date, location, compass bearing, and management history since the last photo was taken
     (e.g., description of observable progress in achieving restoration or BMP goals).
  • Describe the scene or subject and record that information.
  « Hold the camera at eye level, positioning it so the one-meter board is centered in the middle of the
     photo. Try to include some skyline in the photo to help establish the scale of the area. Photo
     identification cards should be placed within the camera's field of view for each  photograph to
     embed relevant information into the picture. Figure 5-2 illustrates one approach to positioning of
     the 1-m board and photo-identification card. The recommended content for each card is illustrated
     in Figure 5-4. Some of this information (e.g., date and time) can be embedded using digital camera
     options, and these options are likely to improve over time.
  • Blue paper should be used for photo identification cards. Alternative approaches may include
     laminated cards or small chalk boards.
  * Framing of the photo should ensure that the photo identification card does not obscure features of
     interest.
  « The angle from which the photo is taken should be consistent. When taking photos at a height of
     about 3 m from a trayback, tripod, or step ladder, a downward angle of 15 degrees is recommended
     to illustrate ground condition and features, (e.g., the amount of feed available in a pasture).
Date: / /
Time:
Site Name:
Photo Point Number:
Camera Point ID:
Photographer:



Figure 5-4. Photo identification card

Logistical considerations for repeat photography include the following:
  «   Photo-documentation teams should consist of two people for both safety and logistical concerns
      (Bechtel 2005, Herrick 2005).
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  •   Once at the site, it is estimated that it will take about 3 min per photo from a single camera point
      (Herrick 2005).
  «   Landowner permission may be needed for some monitoring locations, and it is advisable to check
      on the legality of taking photos of private property in your jurisdiction before monitoring begins.
      There may also be gates for which keys or combinations are needed to gain access to the photo
      points. It is important that landowners be notified before photos are taken and that keys or
      combinations for gates are in hand.

A recommended standard equipment list for photo-taking events can be found in section 5.3. Larsen
(2006) recommends using GPS  Photo Link, a software program that "links" digital photos to the GPS
coordinates. This software program is now marketed as GeoJot+ Core (GeoSpatial Experts 2016). A geo-
location feature is available on some current digital camera models. There are a wide range of GPS
receivers now available, with most enabling the user to take precise position coordinate readings and
record details about each position in an attribute table that can be downloaded to a computer (ERS 2010).
In addition, GIS software usually supports display of digital images, and there are numerous options for
property mapping software that can be found on the Internet (ERS 2010).


5.3  Equipment Needs
Methods described by Hall (2001 2002) are still largely relevant today but equipment has changed
considerably in the past decade. Most cameras in use today are digital, with resolutions far exceeding the
2 megapixel cameras described by Hall. Storage cards are larger and faster as well, and batteries last far
longer than they did just five years ago. The many improvements in camera technology have increased
the capabilities of photo-point monitoring by increasing the amount and quality of information contained
in each photo, increasing the number of photos that can be taken and stored under a single battery charge,
improving the options for time-lapse and programmed photography, and greatly enhancing the
capabilities for photo interpretation and analysis with computer  software.

Because camera technology will continue to improve,  it is recommended that an initial step in designing a
photo-point monitoring effort should be to survey currently available cameras and associated hardware
and software to assess the possibilities for photographic data collection and analysis, the potential for
unattended time-lapse  photography (e.g., how long will batteries last at various resolutions and frequency
of taking photos), the ability to retrieve photos from a remote location through a computer link or to
rapidly upload images directly from the camera to a remote website, and the cost of various options.
Coordination with others (e.g., USDA) may be an excellent way to obtain access to integrated technology
for photo-point monitoring. For example, software such as GPS Photo Link1 has been used by NRCS to
link photos to GPS coordinates and create data files that include the photos, coordinates, and other
descriptive information (GeoSpatial Experts 2004). Technology should not drive study objectives but it is
common sense to assess the extent to which available technology can be used to meet or augment study
objectives. With labor the major cost in many monitoring efforts, there may be attractive options for using
more technology and less  labor to keep costs down.

The following items should also be considered in standard equipment lists for site establishment and
subsequent photo-taking visits (Bechtel 2005, Hamilton n.d., Herrick et al. 2005a, Larsen 2006):
1 Now marketed as GeoJot+ Core (Geospatial Experts 2016).
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Site Establishment
  •   Camera (and extra batteries)
  «   GPS unit or map of monitoring areas
  •   Clipboard, data forms (site description/location, camera location and photo points), and pencils OR
      field computer with data entry software (extra battery for field computer if used)
  •   Compass
  «   Level (for permanently mounted meter boards)
  *   Hammer or post driver
  «   Keys and gate combinations (if needed)
  «   Measuring tape
  •   Rebar (3 ft)  or other states for marking transect ends (if used)
  «   Shovel
  •   Whiteboard (and marker), chalkboard (and chalk), or photo-point ID cards
  *   Fenceposts
  «   Stakes or posts made of wood, fiberglass, plastic, rebar, or steel (point markers)
  •   Meter board
  «   Spray paint
  «   PVC pole (1.5 m or 5 ft long) or tripod for mounting camera at fixed height
Each Photo-Taking Visit
  •   Camera (and extra batteries)
  «   Compass
  «   Level
  *   Timepiece
  «   GPS unit or map of monitoring areas
  •   Site locator field book or field computer with copies of original photos and site information (extra
      battery for field computer if used)
  •   Clipboard, data forms (site description/location, camera location and photo points), and pencils or
      field computer with data entry software (e.g., GPS-photo ID software)
  «   Whiteboard, chalkboard, or photo-point ID cards
  *   Thick marking pen
  •   PVC pole (1.5 m or 5 ft long) or tripod for mounting camera at fixed height
  «   Keys and gate combinations (if needed)
  •   Measuring tape
  •   Metal detector (if needed for stake location)
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  •  Ruler (optional - for scale on close-ups)
  •  Spray paint


5.4   Applications of Photo-Point Monitoring

5.4.1  Comparison Photos
Comparison photography has been used in a number of applications associated with grazing. In one
example cited by Hall (2001), the height and weight of grasses and forbs were measured, and a height-
weight curve was developed and used to estimate percent utilization based on height measurements
(Kinney and Clary 1994). The utilization level of an individual plant was determined by matching its
residual stubble to a photo in the guide and then assigning the percent utilization value for that photo to
the plant. Average utilization in an area was estimated from a number of individual plants (e.g., 50 to
100). It should be noted that the quality of estimates developed with this method depends substantially on
the level of detail in the photo guide. It may be necessary to develop seasonal or species-specific guides
depending on the level of accuracy and precision needed for the study. The authors concluded that about
25 random plant height measurements should give mean plant height estimates within 5 percent of the
mean at 95 percent confidence.

Comparison photos have also been used to provide a quick approximation of percent residue under
various conservation tillage practices (Eck and Brown 2004, Hickman and Schoenberger 1989, Shelton et
al. 1995). Percent cover can usually be estimated within 10 to 20 percent of the actual cover when using
the photo-comparison method. When using this method to estimate percent residue it is important to find
a representative area of the field, look straight down at the residue if it is flat or at an angle if it is standing
residue, and compare the observed residue cover with photos of known cover. Interpolation between
photos  may be necessary, and it is recommended that the results of three or more observations from
different representative locations on the field be averaged for a better estimate.

The Queensland BioCondition Assessment Framework specifies a quantitative approach to photo-point
monitoring to assess terrestrial biodiversity, incorporating a 100-m vegetation transect and spot (close-up)
and landscape photos taken in accordance with a detailed protocol (Eyre et al.  2015). Despite the attention
to detail regarding the taking of photographs, no analysis of the photographs is described, and photos are
only recommended, not required. The related method for establishing reference sites for biocondition
assessment states only that spot photos can be useful to capture the variability  in ground cover within
sample locations  (Eyre et al. 2011).


5.4.2  Repeat Photography
Repeat photography has been used for a range of purposes in a large number of NFS projects including
wetland restoration, streambank restoration, and fencing (OEPA n.d., Oregon DEQ 2002, Shaff et al.
2007).  The Jordan Cove, CT, Section 319 National Nonpoint Source Monitoring Program (NNPSMP)
project took weekly photos as homes were constructed and documented all development changes in the
suburban lot. Weekly observation of construction activities allowed documentation of water quantity
effects  such as storage of water in cellar excavations and rainfall ponding on pavement (Clausen 2011).
The Morro Bay Section 319 NNPSMP project in California documented implementation of BMPs with
photo-point monitoring (CCRWQCB 2012b). In the Maino Ranch study area of the Morro Bay project,
photo-point monitoring failed to document changes in stream channels as a result of fencing and other
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Monitoring and Evaluating Nonpoint Source Watershed Projects                                      Chapter 5
practices designed to control cattle movement through pastures (CCRWQCB and CPSU 2003). This
result agreed, however, with the findings from the monitoring of stream channel stability and stream
profiles from fall 1993 through spring 2001.

Photo-monitoring of pre- and post-construction conditions is used to document the success of all erosion
control projects on rural roads in Santa Cruz County, California (CCRWQCB 2012a). A report on Section
319 projects funded in NM from 1998 to 2008 showed that 11 of 127 projects used photo-point
monitoring for project evaluation, and many others used photos to assist in problem documentation
(NMED 2009). Of the 11, nine photographed vegetation to track progress associated with range/grazing
management and/or riparian restoration, one tracked road reclamation, and the other used photo-point
monitoring to document improvement from trail reconstruction.

Photo-point monitoring at Chinamans Beach, Australia, was used to gain understanding of the movement
and accumulation of wrack (piles of seaweed) on the beach (MMC n.d.). Photos collected two times per
week over a 12-week period helped determine the need for and best approach to beach raking.
Supplemental information on tides, weather, and activities in the area was used to help interpret the
photos but all observations were qualitative.

Photo-documentation was a major component of assessment monitoring for the South Fork Palouse River
riparian area restoration project (PCEI 2005). Permanent photo monitoring stations were established
along the restoration site to document both vegetation establishment success and streambank stability.
Using the methods of Hall (2001), bank stability was evaluated with photos taken twice per year (in
March following high-flows and in July under base-flow conditions) at three photo points located along
the restored site. Permanent meter stakes installed at the top of the bank at each location served as visual
reference points for photo monitoring and as references to measure  erosion. Vegetation establishment
success (changes in growth and production) was also tracked through photo monitoring, with photos
taken during the first week of August and then yearly for 10 years following restoration.

The NRCS has published guidance on photo-point monitoring as a qualitative method for documenting
short-term and long-term effects of a prescribed grazing plan (Larsen 2006). In support of this guidance,
the Nebraska NRCS developed a field office guide to demonstrate the use of GPS Photo Link2, a software
program that "links" digital photos to the GPS coordinates (GeoSpatial Experts 2004).

Kinney and Clary (1998) used time-lapse photography to demonstrate differences in time spent by cattle
on several pastures within a riparian meadow. Cattle location was classified by five broad plant
community-soil groups. Photographs were taken at 20-min intervals during daylight hours, a frequency at
which auto-correlation was observed. Information obtained from the photos was reduced to number of
cattle per unit area, and analysis of variance was performed on number of animals per ha per plant-soil
site per photograph, with pasture and year used as explanatory variables that would account for
differences in animal stocking densities. The authors were able to show statistically significant differences
in cattle densities among site categories overall and for three different animal positions (standing head
down, standing head up, and lying down).

Photo-documentation is very popular among volunteer monitoring groups. For example, the SOLVE
Green Team in Oregon uses photo point monitoring to track progress at watershed restoration sites
(SOLVE 2011). The Missouri Stream Team uses photo-point monitoring to supplement water quality and
other stream monitoring activities (MST n.d.).
2 Now marketed as GeoJot+ Core (GeoSpatial Experts 2016).
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                             Chapter 5
5.5  Advantages, Limitations, and Opportunities
Photo-point monitoring can potentially be used for a variety of purposes, including problem assessment
and planning, tracking BMP implementation, providing supporting information for traditional water
quality monitoring, discovering unexpected events, serving as surrogates for water quality parameters,
and serving as direct measures of water quality conditions (Figure 5-5).
                  Assessment and Planning
                  •  Document conditions
                  •  Identify sediment sources
                  •  Document treatment needs
  BMP Implementation
•  Presence/Absence
•  Plant growth
•  Percent residue
                                                               Supporting Information
                                                               • Snow cover
                                                               • Grazing
                                                                  The Unexpected
                                                                 Manure spreading
                                                                 Stream bank failure
                      Direct Measures
                 •  Algal blooms
                 •  Flow (requires calibration)
  Surrogate Measures
• Percent shade
• Plant growth
Figure 5-5. Various potential applications of photo-point monitoring

5.5.1  Advantages
Every monitoring option has advantages and limitations, and Hamilton (n.d.) identified the following
strengths of photo-point monitoring:
  •  Uses readily available equipment.
  •  Is an effective communication tool for public education.
  •  Is a method of providing landscape context for a study area.
  •  Is a standardized evaluation procedure for comparing multiple locations.
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Monitoring and Evaluating Nonpoint Source Watershed Projects                                      Chapter 5


  "  Is a method to document rates of change.

In addition to these observations, photo-point monitoring is less expensive than most other watershed
project monitoring options.

5.5.2  Limitations
Some weaknesses of photo-point monitoring were also identified by Hamilton (n.d.):
  •  Only limited quantitative data can be obtained.
  "  Bias in photo point placement may occur.
  "  It may be difficult to use in dense vegetation.
  "  Photo points can be lost or obscured over time.

An  additional limitation of photo-point monitoring for watershed projects is that, in most cases, it cannot
be used to evaluate progress in achieving water  quality objectives. Further, statistical approaches to using
photo-derived data remain to be developed for use by those who apply photo-point monitoring
techniques.


5.5.3  Opportunities
Recognizing the inherent advantages and limitations of photo-point monitoring, there are many
opportunities to use this tool for watershed projects.  Several of these opportunities have been realized,
while others are suggested only for consideration, with full understanding that any method must be tested
and evaluated before being adopted.

Photo-point monitoring can be very helpful in assessing watershed problems. For example, it was used in
a volunteer-led river continuity assessment of the Ashuelot River water in New Hampshire (Bechtel
2005). Photos were taken at each dam site (at the downstream end) and at both the upstream and
downstream ends of stream crossings. The QA officer used the photographs to ensure that information
recorded regarding bridge and culvert type made sense. Photos were also used as part of the permanent
inventory record.

Photo-point monitoring for western grazing lands has been found to be an easy and inexpensive way to
provide an excellent visual representation of conditions at a given point in time. These photographs were
considered only as supplementary data, however, not sufficient alone to evaluate objectives (Bauer and
Burton 1993). Photographs could be used to indicate a trend in woody vegetation, streambank stability,
and streambank cover, but the authors noted that vegetation "expression" as seen in photographs was not
the  same as vegetation "succession" needed for  stream ecosystem health.

At the farm-scale, researchers at the University of Wisconsin-Platteville have applied photo-point
monitoring to farm-scale research. Photos have been used for a variety of applications as seen in the
sidebar (Busch and Mentz, 2012).

As  an example of new applications of photo-point monitoring, it is feasible that photo-point monitoring
could be used to track flow provided that a stage-discharge relationship is first established. While this
may at first seem to offer no advantage over visual observation of a staff gage, tracking stage with
photographs could offer the advantages of 24-hour surveillance and safety during high-flow events.
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Monitoring and Evaluating Nonpoint Source Watershed Projects                                      Chapter 5
Cameras would need to be positioned in secure locations, however, and remote transmission of photos
may be required.

The greatest opportunity for photo-point monitoring at the watershed scale, however, may be an
improvement in the quantification of variables of interest and statistical analysis of photo-derived data.
All monitoring is limited by sample size and representativeness but interpretation of water chemistry
monitoring data, for example, is supported by a long history of statistical analysis. Photo-point monitoring
for watershed projects has almost no history of statistical analysis. Numeric data are needed for statistical
analysis. The primary challenge for those who want to pursue low-cost photo-point monitoring options
for project evaluation is to develop more quantitative data and put that data through statistical analyses to
create a record of achievement and potential.
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Monitoring and Evaluating Nonpoint Source Watershed Projects
                                                                                   Chapter 5
                                                           Figure 1. Sample bottles
           Photographic Data Collected at UW-Platteville Pioneer Farm

Researchers at the University of Wisconsin-Platteville have applied photo-point monitoring to
farm-scale research. Photographs are used to identify areas of concern, record field conditions
within research project areas, monitor the locations of grazing cattle, record unusual or atypical
events, and support QA/QC efforts in the surface-water runoff monitoring program.
Photographs can be especially useful to convey information to off-site researchers.

Time-lapse photos are taken on a 24-hr interval at
surface-water gauging stations to create a record of
field conditions within  monitored areas. These
photographs are useful in determining soil cover,
plant canopy, snow cover, and crop growth
throughout the year- especially at times when runoff
events occur. Moreover, photographs of surface
water runoff sample bottles are taken after collection
and prior to lab analysis (Figure 1). While bottle
photos provide only qualitative information, such  as
relative sample color, this information, along with
time-lapse photos can help confirm results when
laboratory test results  are in question. Photos of the
bottle tops are used as part of the chain of custody
record and project QA/QC, providing an accurate
record of samples shipped for analysis.

Daily time-lapse photos have also been used both to
identify paddocks where cattle are grazing in riparian
corridors, and to record pasture vegetation height
and density. In studies where the location of grazing
cattle needs to be recorded daily, landscape
photographs can identify the paddocks in which
cattle are grazing on a daily basis (Figure 2). Plot
photos of pasture vegetation have been used to
create a visual record  of pasture condition and grass
height for runoff studies as well (Figure 3).

Photographs are often taken to record extreme
events and unusual field observations. For example,
photographs have been taken of high-flow events
where water depth was greater than the flume height
and runoff water flowed over the wing walls holding
the flume (Figure 4). Information from these photos
can be used to confirm recorded maximum stage
readings, and estimate discharge by providing
information that can be used to calculate cross-
section flow area that  occurs above the flume.
                                                           Figure 2. Grazing cattle
                                                           Figure 3. Pasture vegetation
                                                           Figure 4. Flume
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5.6  References
Bauer, S.B. and T.A. Burton. 1993. Monitoring Protocols to Evaluate Water Quality Effects of Grazing
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Bechtel, D.A. 2005. River Continuity Assessment of the Ashuelot River Watershed Quality Assurance
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       February 10,2016.
       http://des.nh.gov/organization/divisions/water/wmb/was/qapp/documents/ashuelot.pdf.

Bledsoe, B.P., and J.E. Meyer. 2005. Monitoring of the Little Snake River and Tributaries Year 5 - Final
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       _Report.pdf.

Busch, D. and R. Mentz. 2012. Photographic Data Collected at UW-Platteville Pioneer Farm. University
       of Wisconsin-Platteville, Platteville, WI.

CCRWQCB (Central Coast Regional Water Quality Control Board). 2012a. Water Quality Success
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CCRWQCB (Central Coast Regional Water Quality Control Board). 2012b. Water Quality Success
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       Practices Study. Central Coast Regional Water Quality Control Board, San Luis Obispo, CA.
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       http: //www .waterboards. ca. go v/centralcoast/about_us/morrobay. shtml.

CCRWQCB (Central Coast Regional Water Quality Control Board) and CPSU (California Polytechnic
       State University).  2003. Morro Bay National Monitoring Program: Nonpoint Source Pollution
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Clausen, John C., University of Connecticut, Department of Natural Resources and the Environment.
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Eck, K.J. and D.E. Brown. 2004. Estimating Corn and Soybean Residue Cover. AY-269-W. Purdue
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ERS (Environment and Resource Sciences). 2010. Land Manager's Monitoring Guide -Photopoint
       Monitoring. State  of Queensland Department of Environment and Resource Management,
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       Environment and Resource Sciences, Brisbane. Accessed February 10, 2016.
       http: //www. granitenet. com. au/assets/file s/Landcare/rabbits_photopoint indicator aug2010 .pdf.

Eyre, T.J., A.L. Kelly, and V.J. Neldner. 2011. Method for the Establishment and Survey of Reference
       Sites for Biocondition Version 2.0. State of Queensland Department of Environment and
       Resource Management, Biodiversity and Ecological Sciences Unit, Brisbane. Accessed February
       10, 2016. https://www.qld.gov.au/environment/assets/documents/plants-
       animals/biodiversity/reference-sites-biocondition.pdf.

Eyre, T.J., A.L. Kelly, V.J. Neldner, B.A. Wilson, D.J. Ferguson, M.J. Laidlaw, and A.J. Franks. 2015.
       Biocondition: a Condition Assessment Framework for Terrestrial Biodiversity in Queensland.
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       Management, Biodiversity and Ecosystem Sciences, Brisbane. Accessed February 10, 2016.
       https://www. qld.gov.au/environment/assets/documents/plants-animals/biodiversity/biocondition-
       assessment-manual .pdf.

Faux, R.N., P. Maus, H. Lachowski, C.E. Torgersen, and M.S. Boyd. 2001. New Approaches for
       Monitoring Stream Temperature: Airborne Thermal Infrared Remote Sensing. U.S. Department
       of Agriculture, Forest Service, Remote Application Center, Integrating & Monitoring Steering
       Committee,  San Dimas, CA. Accessed February 10, 2016.
       http://www.fs .fed.us/eng/techdev/IM/rsac reports/TIR.pdf.

GeoSpatial Experts.  2004. Introduction to GPS-Photo Link. U.S. Department of Agriculture, Natural
       Resources Conservation Service, NE. Accessed February 10, 2016. GPS-
       Photo  Link  step by step .ppt

GeoSpatial Experts.  2016. Geotagging and Photo Mapping Software. Geospatial Experts, Inc., Thornton,
       CO. Accessed February 10, 2016. http://www.geospatialexperts.com/geotagging.php.

Hall, Frederick C. 2001. Ground-Based Photographic Monitoring. General Technical Report PNW-GTR-
       503. U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station,
       Portland, OR. Accessed February 11, 2016. http://www.fs.fed.us/pnw/publications/pnw gtr503/.

Hall, Frederick C. 2002. Photo Point Monitoring Handbook. General Technical Report PNW-GTR-526.
       U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station, Portland,
       OR. Accessed February 10, 2016. http://www.fs.fed.us/pnw/pubs/gtr526/.

Hamilton, R.M. n.d.  Photo Point Monitoring, aWeed Manager's Guide to Remote Sensing and GIS —
       Mapping & Monitoring. U.S. Department of Agriculture, Forest Service, Remote Sensing
       Applications Center, Salt Lake City, UT. Accessed February 10, 2016.
       http: //www. fs .fed .us/eng/rsac/invasivespecie s/documents/Photopoint monitoring .pdf .

Herrick, J.E., J.W. Van Zee, K.M. Havstad, L.M. Burkett, and W.G. Whitford. 2005. Monitoring Manual
       for Grassland, Shrubland and Savanna Ecosystems - Volume I: Quick Start. U.S. Department of
       Agriculture, Agricultural Research Service Jornada Experimental Range, Las Cruces, NM.
       Accessed February 10, 2016.
       http: //www .ntc.blm.gov/krc/viewresource .php ?courseID=2 81 &programAreaId= 148.

Herrick, J.E., J.W. Van Zee, K.M. Havstad, L.M. Burkett, and W.G. Whitford. 2005a. Monitoring
       Manual for Grassland, Shrubland and Savanna Ecosystems - Volume II: Design, Supplementary
                                              5-26
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       Methods And Interpretation, U.S. Department of Agriculture, Agricultural Research Service
       Jornada Experimental Range, Las Cruces, NM. Accessed February 10, 2016.
       http: //www .ntc.blm.gov/krc/viewresource .php ?courseID=2 81 &programAreaId= 148.

Hickman, J.S. and D.L. Schoenberger. 1989. Wheat Residue. Bulletin L-781. Kansas State University,
       Cooperative Extension Service, Manhattan, KS. Accessed February 10, 2016.
       http://www.ksre.ksu.edu/bookstore/pubs/L781 .pdf.

Hively, W.D., M. Lang, G.W. McCarty, J. Keppler, A. Sadeghi, and L.L. McConnell. 2009a. Using
       satellite remote sensing to estimate winter cover crop nutrient uptake efficiency. Journal of Soil
       and Water Conservation 64(5):303-313.

Hively, W.D., G.W.  McCarty, and J. Keppler. 2009b. Federal-state partnership yields success in remote
       sensing analysis of conservation practice effectiveness: results from the Choptank River
       Conservation Effects Assessment Project. Journal of Soil and Water Conservation 64(5): 154A.

Key, C. H., D. B. Fagre, and R. K. Menicke. 2002.  Glacier Retreat in Glacier National Park, Montana. In
       Satellite Image Atlas of Glaciers of the  World, Glaciers of North America - Glaciers of the
       Western United States. U.S. Geological Survey Professional Paper 1386-J, R.M. Krimmel, pp.
       J365-J381. Accessed February 10, 2016.
       http://nrmsc.usgs.gov/files/norock/products/GCC/SatelliteAtlas  Key 02 .pdf.

Kinney, J.W. and W.P. Clary. 1994. A. Photographic Utilization Guide for Key Riparian Graminoids.
       General Technical Report INT-GTR-308. U.S. Department of Agriculture, Forest Service,
       Intermountain  Research Station, Ogden, UT. Accessed February 10, 2016.
       http://www.fs.fed.us/rm/pubs int/int  gtr308.html.

Kinney, J.W. and W.P. Clary. 1998. Time-Lapse Photography to Monitor Riparian Meadow Use. USDA
       Forest Service  Research Note RMRS-RN-5. U.S. Department of Agriculture, Forest Service,
       Rocky Mountain Research Station, Boise, ID. Accessed February 10, 2016.
       http://www.fs.fed.us/rm/pubs/rmrs_rn005.pdf.

Larsen, D. 2006. Minimum Standards for Nebraska NRCS Photo-Point Monitoring. Range and Pasture
       Technical Note No. 16. U.S. Department of Agriculture, Natural  Resources Conservation Service.
       Accessed February 10, 2016.
       http://efotg.sc.egov.usda.gov/references/public/NE/NE TECH NOTE 16.pdf.

MathWorks. 2012. MATLAB Image Processing Toolbox. MathWorks, Natick, MA. Accessed February
       10, 2016. http://www.mathworks.com/products/image/.

MMC (Mosman Municipal Council), n.d. Chinamans Beach Photo Point Monitoring Program
       Spring/Summer 2004 & Autumn/Winter 2005. Mosman Municipal Council, Mosman, Australia.

MST (Missouri  Stream Team), n.d. Stream Team Photo Point Monitoring. Missouri Stream Team,
       Jefferson City, MO. Accessed February 10, 2016.
       http://www.mostreamteam.org/Documents/STActivities/ppmonitoring.pdf.

NMED (New Mexico Environment Department). 2009. Watershed Protection Section Clean Water Act
       §319(H) Projects 1998 - 2008. New Mexico Environment Department, Surface Water Quality
       Bureau.
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OEPA (Ohio Environmental Protection Agency), n.d. Ohio Section 319 Success Story: Wetland
       Restoration in the Sandusky Watershed.  Ohio Environmental Protection Agency, Columbus, OH.
       Accessed February 11, 2016.
       http://www.epa.ohio.gov/portals/35/nps/319DOCSAVetlands%20Restoration%20in%20Northern
       %20Sanduskv%20Watershed.pdf.

Oregon DEQ (Department of Environmental Quality). 2002. Oregon 2002 Nonpoint Source Pollution
       Program Annual Report. Oregon Department of Environmental Quality, Water Quality Division,
       Portland, OR. Accessed February 11, 2016.
       http: //www .deq. state. or .us/wq/nonpoint/docs/annualrpts/rpt02 .pdf.

PCEI (Palouse-Clearwater Environmental Institute). 2005. South ForkPalouse River -Lower Watershed
       Restoration Project Final Report. Palouse-Clearwater Environmental Institute, Moscow, ID.

Rasmussen J., M. N0rremark, and B.M. Bibby. 2007. Assessment of leaf cover and crop soil cover in
       weed harrowing research using digital images. Weed Research 47, 299-310.

Richardson, M.D., D.E. Karcher, and L.C. Purcell. 2001. Quantifying turfgrass cover using digital image
       analysis. Crop Science 41:1884-1888.

Rosgen, David L.,  1973. The Use of Color Infrared Photography for the Determination of Sediment
       Production. In Fluvial Process and Sedimentation: Proceedings of Hydrology Symposium,
       Canadian National Research Council, Edmonton, Alberta,  May 8-9, 1973, pp. 381-402.

Rosgen, David L.,  1976. The Use of Color Infrared Photography for the Determination of Suspended
       Sediment Concentrations and Source Areas. In Proceedings of the 3rd Inter-Agency Sediment
       Conference,  Water Resources Council, Denver, Colorado,  March 22-25, 1976, Chapter 7, pp. 30-
       42. Accessed February 11, 2016.
       http://www.wildlandhydrologv.com/assets/The Use  of Color  Infrared Photography-FISC.pdf.

Shaff, C., J. Reiher, and J. Campbell. 2007. OWEB Guide to Photo Point Monitoring. Oregon Watershed
       Enhancement Board, Salem, OR. Accessed February 11, 2016.
       http://www.Oregon.gov/OWEB/docs/pubs/PhotoPoint_Monitoring_Doc_July2007.pdf?ga=t.

Shelton, D.P., P.J.  Jasa, J.A. Smith, and R Kanable.  1995. G95-1132 Estimating Percent Residue Cover.
       Paper 784. Historical Materials from University of Nebraska-Lincoln Extension. Accessed
       February 11, 2016. http://digitalcommons.unl.edu/extensionhist/784/.

SOLVE. 2011. Adventures in Monitoring. SOLVE Green Team, Oregon. Accessed February 11, 2016.
        http://solvgreenteam.wordpress.com/tag/photo-point-monitoring/.

USDA-CES (U.S.  Department of Agriculture, Cooperative Extension Service), USDA-NRCS (U.S.
       Department of Agriculture, Natural Resources Conservation Service), and USDOI-BLM (U.S.
       Department of Interior-Bureau of Land Management). 1999. Utilization Studies and Residual
       Measurements. Interagency Technical Reference 1734-3. U.S. Department of Agriculture,
       Cooperative Extensive Service and Natural Resources Conservation Service and U.S. Department
       of the Interior, Bureau of Land Management, Denver, CO. Accessed February 11, 2016.
       http://www.blm.gov/nstc/library/pdf/utilstudies.pdf.
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USDA-FS (U.S. Department of Agriculture, Forest Service). 2007. Removal of Marmot Dam. U.S.
       Department of Agriculture, Forest Service, Mt. Hood National Forest, Sandy, OR. Accessed
       February 11, 2016. http://www.fs.usda.gov/detail/mthood/home/?cid=STELPRDB5200643 and
       http: //or .water .usgs. gov/proj s_dir/marmot/index.html.

USEPA (U.S. Environmental Protection Agency). 2008. Handbook for Developing Watershed Plans to
       Restore and Protect Our Waters. EPA 841-B-08-002. U.S. Environmental Protection Agency,
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       runoff-nonpoint-source-pollution/handbook-developing-watershed-plans-restore-and-protect.

USSARTF (United States Search and Rescue Task Force), n.d. Compass Basics, United States Search and
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Wolfram. 20\2.Mathematica. Wolfram Research, Champaign, IL. Accessed February 11, 2016.
       http://www.wolfram.com/mathematica/?source=nav.
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Monitoring and Evaluating Nonpoint Source Watershed Projects                                    Chapter 6
6  Monitoring Challenges  and Opportunities
    By D.W. Meals and S.A. Dressing

Monitoring is the foundation of water quality management and provides essential information about the
resource. Carefully done, monitoring can answer important questions and contribute to a successful NFS
watershed project.  However, monitoring can also be challenging and offer numerous pitfalls.

Sections 6.1 and 6.2 of this chapter highlight some of the problems that can hinder watershed monitoring
efforts from the planning stage through execution. Opportunities to enhance and expand the impact and
utility of monitoring data are discussed in sections 6.3 and 6.4.


6.1   Monitoring  Pitfalls
Too many watershed monitoring projects have reported little or no improvement in water quality after
extensive implementation of BMPs in the watershed. Reasons for this outcome are numerous and varied
and may include:
  •   Mistakes in understanding of pollution sources
  •   Improper selection of BMPs
  •   Poor experimental design
  •   Uncooperative weather
  •   Lag time between treatment and response

There are numerous ways that a monitoring effort can fail to achieve its objectives. Reid (2001) examined
30 U.S. monitoring programs and classified reasons for failure into design flaws and procedural problems.
Design flaws are errors or shortcomings inherent in the monitoring plan that prevent monitoring from
obtaining appropriate data, answering fundamental questions, or otherwise achieving its goals. Serious
design flaws can doom a monitoring project from the start and no amount of hard work or added
resources can salvage it. Procedural problems are problems in execution of a program that can cause even
the best design to fail. Unlike design problems, procedural problems can be overcome by applying
additional resources, more personnel, better training, or good management.

A list  of the top reasons for monitoring failure drawn from Reid (2001) and experience with numerous
NPS monitoring projects includes both design and procedural problems.


6.1.1 Design  Fla ws
  •   Inadequate  problem identification/analysis. In some cases, the source of NPS problems is
      unclear. For  example, E. coll bacteria can come from livestock, domestic pets, septic systems, or
      wildlife. Without accurate identification of the pollutant source (E.  coll  in this case), monitoring is
      unlikely to be able to document a response to treatment effectively.
  •   Fundamental misunderstanding of the system. Effective monitoring  of pollutant load or delivery
      requires an understanding of how the pollutant moves through the watershed. Monitoring in the
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Monitoring and Evaluating Nonpoint Source Watershed Projects                                      Chapter 6
     wrong place or on the wrong pathway will doom a program to failure. If nitrate-N moves mainly
     through ground water, for example, monitoring of surface runoff or streamflow is unlikely to yield
     good results. Similarly, if most suspended sediment at a watershed outlet comes from stream
     channels and banks, edge-of-field monitoring will not be effective.
     Inability of the monitoring plan to measure what is needed. If a sampling station is mis-located
     - upstream of a critical tributary inflow, for example - samples taken cannot record the pollutant
     load delivered in that inflow.
     Insufficient study duration. Significant lag time between land treatment and water quality
     response is common (see section 6.2, below). No matter how well-executed, a three-year
     monitoring program cannot document a response to BMPs if the response takes ten years to become
     evident because of legacy pollutants or slow watershed processes.
     Statistically weak design. As discussed in section 2.4, monitoring design must be carefully
     selected to achieve program objectives, be they load measurement, change in pollutant
     concentration, or response to land treatment, notably in the context of weather-driven variability
     characteristic of NFS pollution. A statistically weak design - such as a single watershed before and
     after or side-by-side watersheds - cannot control for weather variations and is unlikely to be able to
     attribute  observed changes in water quality to a specific cause.
6.1.2 Procedural Problems
  "  Lack of training or enthusiasm of field staff. If a field technician is unable or unwilling to collect
     essential data because of lack of knowledge or initiative, critical data may be lost. In extreme cases,
     individuals can compromise a data record by cutting corners as illustrated in Figure 6-1. A simple
     time plot of recently obtained laboratory results revealed a pattern that indicated a sampling
     irregularity, thus triggering an investigation into the cause before further damage could be done.
  "  Failure to collect collateral information.  Often, collateral information is required to properly
     interpret monitoring data. Information on stream stage, for example,  may be essential to understand
     if a water sample was collected on the rising or falling limb of the hydrograph. Failure to record
     stage at the time of sample collection will greatly reduce the meaning of the sample result.
  "  Bad or misunderstood technology. Modern field or laboratory instruments make it easy to collect
     a great deal of monitoring data. However, if a field instrument is deployed for long periods without
     maintenance or calibration, or if a laboratory instrument is not calibrated and tested regularly, the
     resulting bad data will seriously impair a monitoring program.
  "  Failure to evaluate data regularly. As noted in section 3.10.2 and illustrated in Figure 6-2, it is
     essential to examine monitoring data frequently to catch problems early. Two dramatic changes in
     the apparent pattern of TKN concentration  were caused by laboratory actions. Replacement of a
     defective probe in a lab instrument changed the range and sensitivity of the analytical results  (point
     labeled #1). Later a change in lab method significantly raised the detection limit (point labeled #2).
     These two phenomena required rejection of almost a year of TKN data, but if the problems had not
     been noted in a data review, serious bias would have been introduced into the monitoring results for
     a seven-year monitoring effort (Meals 2001).
  "  Protocol changes. Whether in field or laboratory settings, consistent operating procedures are
     essential to generating consistent monitoring data. Although long-term monitoring programs should
     strive for consistency in methods and procedures, sometimes it is necessary to replace  or upgrade
                                               6-2
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Monitoring and Evaluating Nonpoint Source Watershed Projects
                         Chapter 6
      instruments or change analytical methods. Without careful documentation and extensive
      comparative analysis, changes in monitoring or analytical procedures can introduce spurious
      changes in resulting data, changes that do not reflect conditions in the water resource.
      Personnel change. Complex monitoring activities - such as those involving GIS or sophisticated
      laboratory instruments - require a high level of expertise and/or training. Frequent personnel
      changes can result in loss of such expertise, with a consequent loss of data or of data accuracy,
      especially if transitions are not managed properly.
      Lack of institutional integration. Most watershed monitoring projects involve multiple
      participants, with responsibility for different activities sometimes spread across several institutions.
      If the different departments or agencies do not share information or talk to each other regularly,
      critical information may be overlooked and the monitoring program may suffer.
    •U      LiU
                                                       +++
     1*1     .11
                  Llfl      .11
                                      Wfl     .11     .LI
Daily samples were
manufactured from a single
large sample taken once per
week. A volunteer's
violation of sampling
protocol was detected after
samples were analyzed and
data were plotted to reveal a
suspicious pattern.
Figure 6-1. Detection of violation of sampling protocol (R.P. Richards, Heidelberg University,
Tiffin, OH)
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Chapter 6
Figure 6-2. Effects of changing (1) a defective probe and (2) a laboratory method detection limit
(Meals 2001)

Because design flaws may doom a monitoring project from the start, it is essential to follow the steps in
designing a monitoring program discussed in chapters 2 and 3. Procedural problems can be addressed
with additional resources, training, and good management during the course of a monitoring program, but
such corrections require constant vigilance to identify the problems before they cause too much damage.


6.2  Lag Time Issues in Watershed Projects
One important reason NFS watershed projects may fail to meet expectations for water quality
improvement is lag time. Lag time can be thought of as the time elapsed between installation or adoption
of management measures at the level projected to reduce NFS pollution and the first measurable
improvement in water quality in the target waterbody. Even in cases where a program of management
measures is well-designed and fully implemented, water quality monitoring efforts (even those designed
to be "long-term") may not show definitive results if the monitoring period and sampling frequency are
not sufficient to address the lag between treatment and response. Lag time issues have been explored in
detail in a recent review (Meals et al. 2010).

Project management, watershed processes, and components of the monitoring program itself influence the
lag between treatment and response (Figure 6-3). Any or all of these may come into play in a watershed
project.
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                                                       Chapter 6
        Project Management
            Components
                       System Components
      Time required for
      practice(s) to
      produce desired
      effect
Time required for
effect to be
delivered to
water resource
Time required for
water body to
respond to effect
 LAG
TIME
                                                                 Measurement
                                                                 Components
Figure 6-3. Lag time conceptual model



6.2.1  Project Management Components

The time required for planning and implementation of a NFS watershed project often causes the public to
perceive a delay between the decision to act and results of that decision. A project may be funded and
announced today, but it will be some time before that project will be fully planned and implementation
begins. It might even take years, considering the essential time required to identify NFS pollution sources
and critical areas, design management measures, engage landowner participation, and integrate new
practices into cropping and land management cycles. Although such planning delays are not part of the
physical process of lag time, stakeholders will often perceive them as part of the wait for results.


6.2.1.1  Time Required for an Installed or Adopted Practice to Produce an  Effect

BMPs are installed in watersheds to provide a wide range of effects to protect or restore the physical,
chemical, and biological condition of waterbodies, including:

  •  Change hydrology

  •  Reduce dissolved pollutant concentration or load

  •  Reduce particulate/adsorbed pollutant concentration or load

  •  Improve vegetative habitat

  •  Improve physical habitat
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The time required for a BMP to be fully installed and become operational influences how quickly it can
produce an effect. Some NFS control measures may become functional quickly. Installation of livestock
exclusion fencing along several Vermont streams over a three-month period resulted in significant
nutrient concentration and load reductions and reductions of fecal bacteria counts in the first post-
treatment year as the fences immediately prevented manure deposition in the stream (Meals 2001).
However, other NFS management measures, especially vegetative practices where plant communities
need time to become established, may take years to become fully effective. For example, in a
Pennsylvania study of a newly-constructed riparian forest buffer, the influence of tree growth on nitrate-
N removal from groundwater did not become apparent until about ten years after tree planting (Newbold
etal. 2009).

Lag time between BMP implementation and reduction of pollutant losses at the edge-of-field  scale varies
by the pollutant type and the behavior of the pollution source. Erosion controls such as cover crops,
contour farming, and conservation tillage tend to have a fairly rapid effect on soil loss from a  crop field as
these practices quickly mitigate the forces contributing to detachment and transport of soil particles
(Nearing et al. 1990). However, the response time of runoff P to nutrient management is likely to be much
slower. It may take years to "mine" excess P out of the soil through  crop removal to the point where
dissolved P in runoff is effectively reduced (Zhang et al. 2004, Sharpley et al. 2007).


6.2.1.2  Time Required for the Effect to be Delivered to the Water Resource
Practice effects initially occur at or near the practice location, yet managers and stakeholders usually want
and expect the impact of these effects to appear promptly in the water resource of interest in the
watershed. The time required to deliver an effect to a water resource depends on a number of factors,
including:
  •  The route for delivering the effect
     •   Directly in (e.g., streambed restoration)  or immediately adjacent to (e.g., shade) the water
         resource
     •   Overland flow (particulate pollutants)
     •   Overland and subsurface flow (dissolved pollutants)
     •   Infiltration groundwater and groundwater flow (e.g., nitrate)
  "  The path distance
  •  The path travel rate
     •   Fast (e.g., ditches and artificial drainage outlets to surface waters)
     •   Moderate (e.g., overland and subsurface flow in porous soils)
     •   Slow (e.g., infiltration in absence of macropores and groundwater flow)
     •   Very slow (e.g., transport in a regional aquifer)
  "  Hydrologic patterns during the study period
     •   Wet periods generally increase volume and rate of transport
     •   Dry periods generally decrease volume and rate of transport
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Once in a stream, dissolved pollutants like N and P can move rapidly downstream with flowing water to
reach a receiving body relatively quickly. However, sediment and attached pollutants (e.g., P and some
synthetic chemicals) can take years to move downstream as particles are repeatedly deposited,
resuspended, and redeposited within the drainage network by episodic high flow events. This process can
delay sediment and P transport (when attached P constitutes a large fraction of the P load) from
headwaters to outlet by years or even decades. Substantial lag time could occur between reductions of
sediment and P delivery into the headwaters and measurement of those reductions at the watershed outlet.

Pollutants delivered predominantly in groundwater such as nitrate-N generally move at the rate of
groundwater flow, typically much more slowly than the rate of surface water flow. For example, about
40% of all N reaching the Chesapeake Bay travels through groundwater before reaching the bay. Phillips
and Lindsey (2003) estimated that N loads associated with groundwater in the Chesapeake Bay
Watershed would have a median lag time often years for water quality improvements to become evident.
Groundwater nitrate concentrations in upland areas of Iowa were still influenced by the legacy of past
agricultural management conducted more than 25 years earlier (Tomer and Burkart 2003).


6.2.1.3  Time Required for the Waterbody to Respond to the Effect
The speed with which a waterbody responds to the effect(s) produced by and delivered from management
measures in the watershed introduces another increment of lag time. For example, hydraulic residence
time (or the inverse, flushing rate) is an important determinant of how quickly a waterbody may respond
to changes in nutrient loading. Residence times in selected North American waterbodies range from
0.6 year for Chesapeake Bay to 3.3 years for Lake Champlain to 191 years for Lake Superior to more than
650 years for Lake Tahoe. Simply on the basis of dilution,  it will likely take considerably longer for water
column nutrient concentrations to respond to a decrease in  nutrient loading in Lake Superior than in
Chesapeake Bay.

Apparent lag time in water quality response may also depend on the indicator evaluated or the impairment
involved, especially if the focus is on biological water quality. A relatively short lag time might be
expected between reductions of E. coll bacteria inputs and  reduction in bacteria levels in the receiving
waters because the bacteria generally do  not persist as long in the aquatic environment as do heavy metals
or synthetic organic chemicals. Such response has been demonstrated in estuarine systems where bacterial
contamination of shellfish beds has been reduced or eliminated through improved waste management on
the land in less than a year (BBNEP 2008). Improved sewage treatment in Washington, B.C. led to sharp
reductions in point source P and N loading to the Potomac  River Estuary in the early 1970s (Jaworski
1990). The tidal freshwater region of the estuary responded significantly over the next 5 years with
decreased algal biomass, higher water column dissolved oxygen levels, and increased water clarity.

In contrast, lake response  to changes in incoming P load is often delayed by recycling of P stored in
aquatic sediments. When P loads to Shagawa Lake (MN) were reduced by 80% through tertiary
wastewater treatment, residence time models predicted  new equilibrium P concentrations within
1.5 years, but high in-lake P  levels continued to be maintained by recycling of P from lake sediments
(Larsen et al. 1979). Even more than 20 years after the reduction of the external loading, sediment
feedback of P continued to influence the  trophic  state of the lake (Seo and Canale 1999). Similarly,
St. Albans Bay (VT) in Lake  Champlain failed to respond rapidly to reductions in P load from its
watershed. From 1980 through 1991, a combination of wastewater treatment upgrades and intensive
implementation of dairy waste management BMPs through the Rural Clean Water Program brought about
a reduction of P loads to this eutrophic bay. However, water quality in the bay did not improve
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significantly. This pattern was attributed to internal loading from sediments highly enriched in P from
decades of point and NFS inputs (Meals 1992). Although researchers at that time believed that the
sediment P would begin to decline over time as the internal supply was depleted, subsequent monitoring
has shown that P levels have not declined over the years as expected (LCBP 2008). Recent research has
confirmed that a substantial reservoir of P continues to exist in the sediments that can be transferred into
the water under certain chemical conditions and nourish algae blooms for many years to come (Druschel
et al. 2005). In effect, this internal loading has become a significant  source of P,  one that cannot be
addressed by management measures on the land.

Macroinvertebrate or fish response to improved water quality and habitat conditions in stream systems
requires time for the organisms to migrate into the system and occupy newly improved habitat.
Significant lag times have been observed in the response of benthic invertebrates and fish to management
measures implemented on land, including in the Middle Fork Holston River project (Virginia), where IBI
scores and Ephemeroptera-Plecoptera-Trichoptera (EPT) scores did not improve, even though the project
accomplished substantial reduction in the sediment, N, and P loadings (VADCR 1997). The lack of
increase in the biological indicator scores indicates a system lag time between the actual BMP
implementation and positive changes in the biological community structure. This lag could depend in part
on the amount of ecological connectivity with neighboring healthier aquatic systems that could provide
sources of appropriate organisms to repopulate the restored habitats. In several Vermont streams, the
benthic invertebrate community improved within 3 years in response to reductions of sediment, nutrient,
and organic matter inputs from the land (Meals 2001). However, despite observed improvement in stream
physical habitat and water temperature, no improvements in the fish community  were documented. The
project attributed this at least partially to a lag time in community response exceeding the monitoring
period.


6.2.2 Effects Measurement  Components of Lag Time
Watershed project managers are routinely pressed for results by a wide range of  stakeholders. The
fundamental temporal components of lag time control how long it will take for a response to occur, but
the effectiveness of measuring the response may cause a further delay in recognizing it. The design of the
monitoring program is a major determinant of our ability to discern a response against the background of
the variability of natural systems.

In the context of lag time, sampling frequency with respect to background variability is a key determinant
of how long it will take to document change. In a given system, taking n samples per year provides a
certain statistical power to detect a trend. If the number of samples per year is reduced, statistical power is
reduced (the magnitude by which is influenced by the degree of autocorrelation), and it may take  longer
to document a significant trend or to state with confidence that a concentration has dropped below a water
quality standard. Simply stated, taking fewer samples a year is likely to introduce an additional
"statistical" lag time before a change can be effectively documented.


6.2.2.1   The  Magnitude of Lag Time
The magnitude of lag time is difficult to predict in specific cases and generalizations are difficult to make.
A few examples, summarized in Table 6-1, illustrate some possible time frames  for several categories of
lag times.
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   Table 6-1. Examples of lag times reported in response to environmental impact or treatment
Parameter(s)
Sediment
Sediment
Chloride
NOs-N
NOs-N
NOs-N
NOs-N
NOs-N
NOs-N
Soil test P
Soil test P
Soil and runoff P
P
P
P, N, £ co//
Fecal bacteria
Fecal bacteria
Macro! nvertebrates
Macro! nvertebrates
Fish
Fish
Scale
Large watershed
Large watershed
Large aquifer
Small watershed
River basin
Large watershed
Small watershed
Small watershed
Small watershed
Field
Field
Plot/field
Lake
Lake
Small watershed
Estuary
Estuary
Small watershed
Small watershed
First order stream
Small watershed
Impact/Treatment
Extreme storm events
Cropland erosion control
Road salt
N fertilizer rates
N fertilizer rates
Nutrient management
Nutrient management
Prairie restoration
Riparian forest buffer
P fertilizer rates
P fertilizer rates
Poultry litter management
WWTP upgrade
WWTP upgrade/agricultural
BMPs
Livestock exclusion
Waste management
Waste management
Livestock exclusion
Mine waste treatment
Habitat restoration
Acid mine drainage treatment
Response
lag
8-25 yr
19 yr
>50yr
>30yr
>50yr
>5yr
15-39yr
10 yr
10 yr
8-14 yr
10-1 4 yr
>5yr
>20yr
>20yr
<1 yr
< 1yr
1yr
Syr
10 yr
2yr
3-9 yr
Reference
Marutani et al. 1999
Newson 2007
Besteretal. 2006
Tomer and Burkart 2003
Bratton et al. 2004
STAC 2005
Galeone 2005
Schilling and Spooner 2006
Newboldetal. 2009
McCollum 1991
Giroux and Royer 2007
Sharpleyetal. 2007
Larsenetal. 1979
LCBP 2008
Meals 2001
BBNEP2008
Spooner etal. 2011
Meals 2001
Chadwicketal. 1986
Whitney and Hafele 2006
Cravotta et al. 2009
6.2.3 How to Deal with Lag Time
In most situations, some lag time between implementation of BMPs and water quality response is
inevitable. Although the exact duration of the lag can rarely be predicted, in many cases the lag time will
exceed the length of typical monitoring periods, making it problematic to document a water quality
response. Several possible approaches are proposed to deal with this challenge.


6.2.3.1  Recognize Lag Time  and Adjust Expectations
It usually takes time for a waterbody to become impaired and it will take time to accomplish the clean-up.
Failure to meet quick-fix expectations may cause frustration, pessimism, and a reluctance to pursue
further action. It is up to scientists, investigators, and project managers to do a better job explaining to all
stakeholders in realistic terms that current water quality impairments usually result from historically poor
land management and that immediate solutions should not be expected.
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6.2.3.1.1 Characterize the Watershed
Before designing a NFS management program and an associated monitoring program, investigate
important watershed characteristics likely to influence lag time. Determining the time of travel for
groundwater movement is an obvious example. Watershed characterization is an important step in the
project planning process (USEPA 2008) and such characterization should especially address important
aspects of the hydrologic and geologic setting, as well as documentation of NFS pollution sources and the
nature of the water quality impairment, all of which can influence observed lag time in system response.


6.2.3.1.2 Consider Lag Time Issues in Selection, Siting,  and Monitoring of Best
          Management Practices
First and foremost, proper BMP selection must be based on solving the problem and ensuring that
landowners have the capability and willingness to implement and maintain the BMPs. Lag time can be an
important factor in the final design of BMP  systems by ensuring that when down-gradient BMPs are
installed, they are  ready to handle the anticipated runoff or pollutant load from up-gradient sources. In
addition, when projects include targeted BMP monitoring to document interim water quality
improvements, recognition of lag time may require an adjustment of the approach to targeting the
management program. When designing a program for projects that include BMP-specific monitoring,
potential BMPs should be evaluated to determine which practices might provide the most rapid
improvement in water quality, given watershed characteristics. For example, practices such as barnyard
runoff management that affect direct delivery of nutrients into surface runoff and streamflow may yield
more rapid reductions in nutrient loading to  the receiving water than practices that reduce nutrient
leaching to groundwater, when groundwater time of travel is measured in years. Fencing livestock out of
streams may give an immediate water quality improvement, compared to waiting for riparian forest
buffers to grow. Such considerations, combined with application of other criteria such as cost
effectiveness, can help determine priorities for BMP implementation in a watershed project.

Lag time should also  be considered in locating management practices within a watershed.  Managers
should consider the need to demonstrate results to the public, which may be easier at small scales, along
with the need to achieve water quality targets and consequently wider benefits at the large watershed
scale. Where sediment and sediment-bound  pollutants from cropland erosion are primary concerns,
implementing practices that target the largest sediment sources closest to the receiving water may provide
a more rapid water quality benefit than erosion controls in the upper reaches of the watershed. Where
groundwater transport is a key determinant of response, application of a groundwater travel time model
before application of management changes could help managers understand when to anticipate a water
quality response and communicate this issue to the public. At best, the model will support targeting the
application of an initial round of management measures to land areas where the effects are expected to be
transmitted to receiving waters quickly. An example of this can be found in Walnut Creek, Iowa
(Schilling and Wolter 2007).

It is important to point out that factoring lag time into BMP selection and targeting is not to say that long-
term management improvements like riparian forest buffer restoration should be discounted  or that upland
sediment sources should be ignored. Rather, it is suggested that planners and managers may  want to
consider implementing BMPs and treating sources likely to exhibit short lag times first to increase the
probability of demonstrating some water quality improvement as quickly as possible. "Quick-fix"
practices with minimum lag time must be complemented by other needed practices to ultimately yield
permanent reductions in pollutant loads.
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6.2.3.1.3 Monitor Small Watersheds Close to Sources
In cases where documentation of the effects of a management program on water quality is a critical goal,
lag time can sometimes be minimized by focusing monitoring on small watersheds, close to pollution
sources. Lag times introduced by transport phenomena (e.g., groundwater travel, sediment flux through
stream networks) will likely be shorter in small watersheds than in larger basins. In the extreme, this
principle implies monitoring at the edge of field or above/below a limited treated area, but small
watersheds (e.g., < 1500 ha) can also yield good results. In the NNPSMP, projects monitoring BMP
programs in small watersheds (e.g., the Morro Bay Watershed Project in California, the Jordan Cove
Project in Connecticut, the Pequea/Mill Creek Watershed Project in Pennsylvania, and the Lake
Champlain Basin Watersheds Project in Vermont) were more successful in documenting improvements in
water quality in response to change than were projects that took place in large watersheds (e.g., the
Lightwood Knot Creek Project in Alabama and the Sny Magill Watershed Project in Iowa) in the 7- to
10-year time frame of the NNPSMP (Spooner et al. 2011).

Monitoring programs can be designed to get a better handle on lag time issues. Monitoring indicators at
all points along the pathway from  source to response or conducting periodic synoptic surveys over the
course of a project will identify changes as they occur and document  progress toward the end response.
Supplementing a stream monitoring program with special studies can help project managers understand
watershed processes, predict potential lag times, and help explain delays in water quality improvement to
stakeholders. In the Walnut Creek (IA) watershed, no changes in stream suspended sediment loads were
documented, despite extensive conversion of row  crop land to prairie and reductions in field erosion
predicted by RUSLE (Revised Universal Soil Loss Equation). This was explained largely by a 22-mile
stream survey showing that streambank erosion contributed more than 50% of Walnut Creek sediment
export (Spooner et al. 2011).


6.2.3.1.4 Select Indicators Carefully
Some water quality variables can be expected to change more quickly than others in response to
management changes. As documented in the Jordan Cove (CT)  NNPSMP Project (1996-2005), peak
storm flows from a developing watershed can be reduced quickly through application of stormwater
infiltration practices (Clausen 2007). NNPSMP projects in California, North Carolina, Pennsylvania, and
Vermont demonstrated rapid reductions in nutrients and bacteria by reducing direct deposition of
livestock waste in surface waters through fencing  livestock out of streams (Spooner et al. 2011).

Improvements in stream biota, however, often come beyond the time  frame of many watershed-scale
monitoring efforts, but a number of NNPSMP projects have documented success with biological
monitoring. As noted in section 6.2.1, Meals (2001) found that the benthic invertebrate community in
Vermont streams improved within 3 years in response to livestock exclusion practices, but improvements
in the fish community were not documented. Whitney and Hafele  (2006) noted improvements in the fish
community within two years of a habitat restoration effort, and Cravotta et al. (2009) documented the
gradual return offish to streams within a few years after treatment to  neutralize acid mine drainage.

Despite these successes, many other watershed-scale projects have failed to document improvements by
monitoring macroinvertebrates and fish. This may simply argue for a more sustained monitoring effort to
document a biological response to land treatment.  Failing that, however, selection of indicators that have
relatively short lag times where possible will make it easier (and quicker) to demonstrate success. Simple
numbers of macroinvertebrates, for example, may respond before more complex community indices show
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change. See chapter 4 for additional details and illustrative case studies on biological monitoring
approaches.


6.2.3.1.5 Design Monitoring Programs to Detect Change Effectively
Monitor at locations and at a frequency sufficient to detect change with reasonable sensitivity. Assess
background variability before the project begins and conduct a minimum detectable change analysis as
described in section 3.4.2 to determine a sampling frequency sufficient to document the anticipated
magnitude of change with statistical confidence (Spooner et al. 1987, Richards and Grabow 2003).
Although lag time will still be a factor in actual system response, a paired-watershed design (Clausen and
Spooner 1993, King et al. 2008), where data from an untreated watershed are used to control for weather
and other sources of variability, is one of the most effective ways to document water quality changes in
response to improvements in land management. If a monitoring program is intended to detect trends,
evaluate statistical power to determine the best sampling frequency for the project. See Meals et al. (2011)
and section 7.8.2.4 for additional information on trend analysis.

Target monitoring to the effects expected from the BMPs implemented, in the sequence that those effects
are anticipated. For example, when the ultimate goal is habitat/biota restoration in an urban stream, if
BMPs are implemented first that will  alter peak stormflows, design the monitoring program to track
changes in hydrology. After the needed hydrologic restoration is achieved, monitoring can be redirected
to track expected changes in channel morphology. Once changes in channel morphology are documented,
monitoring can then focus on assessment of habitat and biological community response. Response of
stream hydrology is likely to be quicker than restoration of stream biota and would therefore be a
valuable—and more prompt—indicator of progress.


6.3  Integrating  Monitoring and Modeling
Monitoring and modeling are the primary tools for assessment of NFS watershed projects. By providing
essential data about the resource, water quality monitoring has long been the foundation of water quality
management. Monitoring can, however, be expensive and technically challenging and requires careful
design and execution to achieve objectives. Modeling, on the other hand, is indispensable in evaluating
alternative scenarios and in forecasting water quality over time. Modeling is also technically demanding,
and application of a model in the absence of observed data can contribute to legitimate skepticism and
uncertainty about model results that can compromise the utility of modeling for watershed management.
To meet the demands of future watershed programs, it is essential that we integrate the strengths of both
tools.


6.3.1  The Roles of Monitoring and Modeling
Both monitoring and modeling have distinctive roles to play in watershed projects. In many cases these
roles are complementary, but in some cases one tool is used as a substitute for the other for various
reasons including budgetary constraints.


6.3.1.1  Monitoring
Monitoring plays many key roles in watershed projects:
  •   Identify and document water quality problems and impairments
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Monitoring and Evaluating Nonpoint Source Watershed Projects                                       Chapter 6

  •   Assess compliance with water quality standards and other regulations
  •   Establish baseline conditions
  "   Provide credibility to project planning
  •   Provide data to support modeling
  "   Document water quality change
  "   Assess program or project effectiveness
  •   Provide information for adaptive management
  •   Inform stakeholders
  •   Contribute to behavior change by documenting actual watershed conditions
Monitoring can provide fundamental knowledge about the generation, fate, and transport of NPS
pollutants. Monitoring data provide hard evidence of water quality impairment and represent the best
evidence of water quality restoration. When successful, monitoring can effectively document water
quality response to land treatment, e.g., reductions in nutrient and sediment loads resulting from livestock
exclusion in Vermont (Meals 2004) and reductions in nitrate loading to streams from prairie restoration in
Iowa (Schilling and Spooner 2006).
Water quality monitoring also presents important challenges in watershed projects. Over the past decades,
many projects have failed to  show water quality response through monitoring. Such failure can be
attributed to shortcomings in both design (e.g., failure to measure what is needed, inadequate sampling
frequency) and execution (e.g., failure to evaluate data regularly, inadequate staff training, poor
institutional integration) (Reid 2001). As noted throughout this guidance, monitoring must be conducted
under appropriate objectives  with a statistical design that can meet those objectives. Monitoring must be
conducted at a frequency adequate to meet objectives (e.g., to document change) and for an adequate
duration (e.g., to overcome lag time). Water quality monitoring must be executed effectively, with careful
attention to procedural issues like collection of collateral information, regular data evaluation, and
institutional coordination.

6.3.1.2  Modeling
Modeling also plays a number of critical roles in watershed projects:
  •   Provide initial estimates of flow and pollutant loads
  •   Link sources to impacts and evaluate relative magnitudes of sources
  •   Identify critical areas for management
  •   Predict pollutant reductions and waterbody response to management actions
  "   Support informed choices among alternative actions
  •   Analyze cost-effectiveness of alternatives
  "   Address issues of lag time in system response to treatment
  "   Guide monitoring  design
  •   Help build knowledge  of natural processes and response to treatment
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Monitoring and Evaluating Nonpoint Source Watershed Projects                                      Chapter 6
  •  Provide opportunities for collaborative learning and stakeholder involvement

Modeling can forecast future response to alternatives too numerous or time-consuming to monitor
effectively. Modeling provides the means to assemble, express, and test the current state of knowledge
and point the way for future investigations.  Model applications for watershed evaluation range from the
simple to the very complex. An Oklahoma project used SIMPLE (Spatially Integrated Models for
Phosphorus Loading and Erosion) to identify high-risk P sources in the Peacheater Creek watershed to
design a land treatment plan (Storm et al. 1996). A recent Vermont project used SWAT (Soil and Water
Assessment Tool) to identify critical source  areas for NPS P in a large agricultural watershed (Winchell et
al. 2011). National CEAP Cropland Studies in the  Upper Mississippi River Basin (USDA-NRCS 2012),
the Chesapeake Bay region (USDA-NRCS 201 la), and the Great Lakes system (USDA-NRCS 201 Ib)
used SWAT and other models to quantify the effects of conservation practices currently present on the
landscape in the regions and to project potential benefits that could be gained by implementation of
additional conservation treatment in under-treated  agricultural acres.

Modeling also presents significant challenges in watershed projects. Some data are always required - for
model parameterization, calibration, and validation - and inadequate supporting data can significantly
degrade model performance. Technical and financial resources are required for modeling that may be
difficult to assemble and sustain. Modeling  may be impaired by inappropriate or outdated information
(e.g., soil surveys, use of Curve Numbers), or by lack of fundamental understanding of how
agroecosystems or urban stormwater processes function.  The credibility of model application may be
threatened by lack of appropriate algorithms for simulating conservation or urban stormwater
management practices and by failure to adequately analyze uncertainties associated with model results.
Model results nearly always require analysis and interpretation to be useful; failure to provide such
support can lead to justifiable skepticism about model results. The Chesapeake Bay model, for example,
has been criticized for overstating environmental achievements in contradiction to monitoring data (GAO
2005, Powledge 2005). Disputes or misunderstandings over pollutant loads simulated by the SPARROW
model in the Mississippi River Basin have generated economic and political conflict over source
identification and choices of alternatives for remediation (Robertson et al. 2009).


6.3.2  Using Monitoring and Modeling Together
Clearly, monitoring and modeling are not mutually exclusive and can be better integrated in watershed
protection and restoration projects. Each tool has its own strengths and weaknesses and neither can by
itself provide all the information needed for water  quality decision-making or program accountability.
Integration of monitoring and modeling should address these elements:

Use the strengths of both tools.
  •  Monitoring is the best tool for project evaluation, but modeling simulations and extrapolations can
      play an important role in projecting whether project success is likely.
  "  Modeling can provide guidance on where and how the on-the-ground monitoring is best conducted.
  •  Modeling is better than monitoring for comparing numerous scenarios and extrapolating effects into
      the future.
  "  Data collected through monitoring are essential for calibration and validation of models, and for
      establishing credibility for modeling-derived information.
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  •  The validity of model application and the type of questions that are addressed must be corroborated
      by watershed stakeholders.
  «  Models are underutilized for collaborative learning purposes. Their use within collaborative
      frameworks must be promoted to incorporate feedback from stakeholders while demonstrating how
      decisions at the field-scale affect the environment.

Begin with project objectives and design the monitoring-modelingprogram to do what can be done well
to meet those objectives.
  •  Begin with a clear set of objectives. Determine if the objectives need to be quantitative (e.g., reduce
      N load by 40%), if they need to incorporate time frames and scales for which accountability is
      needed (e.g., reduced N load at a tributary mouth or at each HUC-12), and if there is a need to
      attribute changes to activities on the land (e.g., in response to implementing specific management
      measures at a specified level).
  •  Establish a clear set of evaluation objectives. Define the specific questions to be answered with
      monitoring (measure N load reductions with a minimum detectable change of 20%) and with
      modeling (measure and project N load reductions within ±15% of actual loads). Incorporate the
      needed time frames and scales within the objectives, and ensure that monitoring and modeling
      objectives are complementary. For example, the monitoring objective might be to measure N load
      reductions with a minimum detectable change of 20% in select smaller watersheds within 10 years
      and assess with an MDC of 30% long-term N load trends at mouths of larger watersheds and the
      state line. The evaluation objective for modeling might be to estimate and project N load reductions
      within 15% of actual loads in select smaller watersheds within 10 years and estimate and project
      within 15% of actual long-term N load trends at mouths of larger watersheds and the state line.
      Address uncertainty at the outset and include uncertainty in all monitoring and modeling reporting.
  •  Select a model based on project needs - models selected solely by cost or convenience before
      setting objectives are unlikely to be satisfactory.
  «  Create a monitoring program that will collect the number and frequency of samples that are
      required to provide useful information - monitoring designs based solely on budget may yield data
      that cannot serve project objectives.

Select the appropriate designs.
  «  Establish the monitoring design(s). Address overall experimental design (e.g., long-term trend,
      upstream-downstream) and specify the elements of monitoring scale, sample type, station locations,
      sampling frequency, collection and analysis methods, land use/land treatment monitoring, and data
      management (see chapters 2 and 3).
  «  Select the modeling approach. Determine which model(s)  to use, input data requirements and
      availability, model testing locations and procedures, and procedures for output analysis. Make
      certain that adequate technical skill and support are available for the selected approach.

Pay attention to source data.
  «  Availability of data at consistent scales and of known quality is essential to an integrated
      monitoring-modeling effort.
  «  Spatially- and temporally-explicit land treatment and agricultural management data are necessary
      for both water quality monitoring and watershed modeling.
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   "  Identify common needs of monitoring and modeling. Share precipitation, land use, land treatment,
      and other data. Use monitored flow and water quality data to calibrate and validate the model(s).

Evaluate the suitability of both monitoring data/programs and proposed model(s) for the project in the
project planning stage, before a project is funded and underway.
   *  Evaluate existing and planned monitoring data for quality, consistency, and suitability for project
      purposes.
   "  Evaluate candidate watershed models for applicability to watershed characteristics, technical
      competence, and resources necessary to apply and support modeling in the project.
   "  Verify that important watershed characteristics (e.g., claypan soils) and conservation and
      stormwater management practice  functions can be adequately represented in the selected model.

Integrate  data analysis and reporting.
   "  Combine systems for discharge calculations, loads calculated from monitoring data, and land
      use/land treatment data.
   •  Link monitoring data to a GIS framework used for modeling.
   "  Provide for compatibility between monitoring data and model(s) to permit efficient use of
      monitoring data for model calibration and validation.
   "  Facilitate analysis of small-scale monitoring and modeling to develop input parameters for large-
      scale model application(s).

Include a documentation plan for both monitoring and modeling.
   "  Use a formal Quality  Assurance Project Plan (QAPP) to guide  and document all aspects of the
      monitoring and modeling efforts.
   *  Lay out the purpose of model application and the justification for the selection of a particular
      model.
   "  Document the model  name and version and the source of the model.
   "  Identify and document model assumptions.
   "  Document data requirements and  sources of data sets to be used.
   "  Provide estimates of the uncertainty associated with modeling  and monitoring results, particularly
      when they are used to quantify the environmental benefits of practices.

Develop a communication strategy. Control expectations from the beginning by addressing monitoring
and modeling uncertainty explicitly. Avoid overly optimistic projections.

Be aware of potential differences in precision and accuracy of modeling results vs. monitoring data.
Monitoring data may be used to identify trends or changes in water quality (see sections 7.7 and 7.8);
such trends are identified in the context  of statistical confidence, based largely on the characteristics of
the monitoring program (see MDC, section 3.4.2). Model predictions, however, may show changes in
water quality without the benefit of statistical trend analysis and thus suggest very small trends that
cannot be  verified by monitoring data. Monitoring data may, for example, support a MDC of 20% for
                                               6-16
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Monitoring and Evaluating Nonpoint Source Watershed Projects                                      Chapter 6
phosphorus concentration, while a model may predict a 7% reduction. This situation is not necessarily
contradictory, but calls for a bit of realistic caution in application and interpretation of model results.

Finally, in practical terms, project water quality monitoring and watershed modeling activities must be
closely coordinated so that information from each effort can be collected, shared, and combined at
appropriate times to meet project goals. Preliminary model runs to identify critical subwatersheds, for
example, can also be used to help select monitoring station locations. Similarly, water quality data that are
analyzed in atimely fashion as described in section 3.10.2 are more likely to be available at the right time
for model calibration and validation.


6.4  Supporting BMP and Other Databases

6.4.1  General Considerations
Monitoring is often performed to develop a belter understanding of BMP effectiveness, characterize
reference conditions over broad geographic areas, determine effluent characteristics, or address other
purposes not directly related to problem assessment or watershed project evaluation. In some cases this
monitoring can be done in conjunction with problem assessment or project evaluation to maximize the
return on resources expended, but this monitoring is often done separately.

The basic steps presented in chapters 2 and 3 should also be applied to development of monitoring plans
in support of BMP and other databases. Some of the specifics may not apply, however, such as watershed
characterization or monitoring of meteorological  variables in cases where urban stormwater BMPs are
assessed in a laboratory setting. Pollutant transport mechanisms and pollutant source activities may be of
little interest in monitoring designed to establish reference conditions. Still, the focus on objectives must
be the driving force behind all monitoring design.

For new databases, decisions need to be made regarding the types and quality of data that will be
included. Development of a QAPP (see chapter 8) is an important first step in defining data needs and
data quality expectations for the database.

When monitoring to support existing databases, it is essential that data requirements are reviewed and
understood before the monitoring plan is developed to ensure that suitable data will be collected. For
example, those managing the International Stormwater BMP Database have developed guidance with
recommended  BMP monitoring protocols that are directly related to requirements of the database,  and
have established a recommended protocol for evaluating BMP performance (Geosyntec and WWE 2009).
This database is described in section 6.4.2.

Databases may have specific requirements for monitoring designs (e.g., above/below), sampling type
(grab or composite), sampling frequencies, specific variables (e.g., EPA Method 365.4 for total P), and
other monitoring details, as well as requirements  for reporting information on the study conditions and
features. For example, it may be required that designs for BMPs are reported in accordance with industry
standards, or that a specific level of detail be reported for soils or crops. All of these requirements need to
be reviewed and understood before monitoring begins.

Data format, approaches to data analysis, and data transmittal requirements may also be specified.
Questions and  issues associated with these requirements need to be addressed up front to prevent
problems later.
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The single most important step to take when monitoring in support of database development is for those
performing the monitoring to communicate with those managing the databases to ensure that monitoring,
data analysis, reporting, and data management requirements are understood and that the proposed
monitoring plan is suitable before monitoring begins.


6.4.2 International Urban Stormwater BMP Database
The International Stormwater BMP Database (www.bmpdatabase.org/) is a database of over 530 BMP
studies, performance analysis results, tools for use in BMP performance studies, monitoring guidance,
and other study-related publications. The overall purpose of the project is to provide scientifically sound
information to improve the design, selection, and performance of BMPs. Data obtained from BMP studies
are expected to help create a better understanding  of factors influencing BMP performance.

The database is focused on field studies of post-construction, permanent BMPs (International Stormwater
BMP Database 2013). Data entry requirements are specified in a user's guide (WWW and Geosyntec
2010).  Options for BMPs include structural BMPs, non-structural BMPs, low-impact development sites,
and composite BMPs. Monitoring results may include precipitation, flow, water quality, and settling
velocity.

Guidance is provided on approaches to determining  BMP performance using concentrations, loads, and
volume reductions (Geosyntec and WWW 2009).  Comparison of the average value of the Event Mean
Concentrations (EMC) or storm loads for the outlet as compared to the inlet is emphasized. Examining
the cumulative distribution of each of the outlet and inlet storm EMCs allows for more detailed
examination of the efficiency at different inlet loadings. This approach, the Effluent Probability Method
(Strecker et al. 2003, Erickson et al. 2010), is described in more detail in section 7.7.2.

The database structure and contents may be downloaded from the project website and used solely for the
following purposes (International Stormwater BMP  Database 2013):
  •  Research and analysis related to BMP performance and costs, characterization of urban runoff,
     characterization of receiving water impacts, and characterization of the ability of BMPs to meet
     water quality goals or criteria.
  •  Use of database structure and/or data entry spreadsheets to track performance data for regional,
     state, watershed or local purposes or for subsequent upload to the International Stormwater BMP
     Database.


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Chadwick, J.W., S.P. Canton, and R.L. Dent. 1986. Recovery of benthic invertebrate communities in
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       Bay Basin. Final Report to Lake Champlain Basin Program. Accessed March 15, 2016.
       http://www.lcbp.org/techreportPDF/63B Missisquoi CSA.pdf.

WWE (Wright Water Engineers, Inc.) and Geosyntec. 2010. International Stormwater Best Management
       Practices (BMP) Database User's Guide for BMP Data Entry Spreadsheets - Release Version
       3.2. Prepared for Water Environment Research Foundation, Federal Highway Administration,
       Environmental and Water Resources Institute of the American Society of Civil Engineers, U.S.
       Environmental Protection Agency, and American Public Works Association, by Wright Water
       Engineers, Inc. and Geosyntec Consultants, Denver, CO. Accessed February 12, 2016.
       http://www.bmpdatabase.org/Docs/2010%20BMP%20Database%20User's%20Guide.pdf.

Zhang, T.Q., A.F. MacKenzie, B.C. Liang, and C.F. Drury. 2004. Soil test phosphorus  and phosphorus
       fractions with long-term phosphorus addition and depletion. Soil Science Society of America
       Journal 68:519-528.
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Monitoring and Evaluating Nonpoint Source Watershed Projects                                      Chapter 7
7   Data Analysis
     By J. Spooner, J.B. Harcum, D.W. Meals, S.A. Dressing, and R.P. Richards


7.1  Introduction
This chapter of the guidance examines options for planning and analyzing data collected in nonpoint
source watershed studies. The emphasis of this chapter is on projects at the watershed or subwatershed
level, although evaluation of individual BMPs is also addressed. These analysis approaches complement
the watershed project design considerations discussed in section 2.4 of this guidance.

Specifically, this chapter discusses the following topics:
  •  Exploratory data analysis
  •  Data transformations that might be necessary to prepare data for valid statistical analysis
  •  Methods to deal with extreme values, censored data, and missing data
  •  Data analysis methods for water quality problem assessment
  •  Data analysis methods for project planning
  •  Data analysis methods for assessing BMP or watershed project effectiveness
  •  Techniques for load estimation

The reader may wish to refer to chapter 4 (Data Analysis) of the 1997 guidance (USEPA 1997b) which
was written largely to provide a primer on statistical methods for analysis of data generated by nonpoint
source watershed projects. The 1997 guidance addresses various topics on statistical analysis in
considerable detail, including estimation and hypothesis testing, characteristics of environmental data, and
basic descriptive statistics. In addition, the  1997 guidance compares parametric and nonparametric tests,
recommends appropriate methods for routine analyses, and provides numerous examples of the
application of various statistical tests. Additional resources for data analysis approaches are also available
in various Tech Notes and other publications (see References).


7.2  Overview of Statistical Methods
A wide range of parametric and nonparametric methods exists for analyzing environmental data. In some
cases, graphical methods will be suitable to meet analysis objectives; more rigorous statistical analysis
approaches may be best otherwise. This section provides  a brief overview and summary of key features of
these various methods. Readers should consult the 1997 guidance (USEPA 1997b) and additional sources
(e.g., statistics textbooks and software packages) for greater detail.

Recommended statistical methods are summarized in Table 7-1 through Table 7-6 based on watershed
project phase or need because experience indicates that this type of grouping will be practical for many
involved in such efforts. Methods in these tables are recommended, but the tables do not include all
possible alternative approaches. Additional discussion and illustrative examples follow in sections 7.3
through 7.8. Because of its importance to many watershed projects, especially those addressing TMDLs,
                                               7-1
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Monitoring and Evaluating Nonpoint Source Watershed Projects
Chapter 7
pollutant load estimation is addressed separately in section 7.9. While most of the methods described in
this chapter are more commonly applied to water chemistry, flow, and precipitation data, many can also
be applied to biological data as well. Recommended approaches for analyzing biological data are
described in detail in chapter 4, and some examples are also provided in this chapter.


7.2.1 Exploratory Data Analysis and Data Transformations
It is often necessary to work with a mix of information and data during the initial stages of watershed
projects. A major first task involves gathering and organizing available information and data, followed by
an initial examination of the data to help identify water quality problems, pollutants, sources, and
pathways. Exploratory data  analysis techniques are well suited to this project phase, and should also be
applied as a first step to all data subsequently collected by the project. Exploratory data analysis is also a
critical first step in beginning to analyze water quality data from watershed projects that are underway,
before undertaking more complex analysis.

Exploratory data analysis provides basic information about the data record, including the data distribution
and an assessment of missing and extreme values. The presence of autocorrelation and seasonal cycles
should also be evaluated. EDA can also be useful to examine clusters in the data or relationships between
variables and/or sample locations.

Table 7-1 summarizes exploratory data analysis methods by analytical objective. The type of method
(parametric,  nonparametric, graphical), basic data requirements (e.g., distribution, independence), and
major cautions and concerns are also included in the table.
            Table 7-1. Exploratory data analysis methods (see discussion, section 7.3)
Analytical Objective
Describe behavior of
variable(s)
Evaluate distribution and
assumptions of
independence and
constant variance
Identify extreme values
and anomalies
Recommended Method
Univariate statistics (e.g.,
range, mean, median,
interquartile range, variance)
Plots (histogram, probability,
lag-n autocorrelation,
cumulative distribution
functions); skewness, kurtosis;
Durbin-Watson statistic to
detect presence of
autoregressive lag 1 pattern;
Shapiro-Wilk test;
Kolmogorov-Smirnov test
Plots (e.g., time series,
boxplots)
Compute frequency or
proportion of observations
exceeding threshold value;
cumulative frequency or
duration plots
Method
Type*
P, N
P, N, G
G, P, N
Data
Requirements
Minimal
Minimal to
moderate
Minimal
Major Cautions and Concerns
Mean is sensitive to extreme values;
median may be preferred measure of
central tendency.
Data transformations to satisfy likely
statistical testing assumptions should
be examined.
Autocorrelation functions (ACF) which
examine auto correlation at each lag
require equal time-space data and
appropriate software.
Outliers should not be deleted if error
cannot be confirmed.
                                               7-2
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Monitoring and Evaluating Nonpoint Source Watershed Projects
Chapter 7
Analytical Objective
Observe seasonal or
other cycles
Find clusters or
groupings
Preliminary comparison
of two or more locations
or time periods
Examine relationships
between variables
Recommended Method
Plots (time series, seasonal
boxplots)
Examination of autocorrelation
pattern
Cluster analysis, principal
components analysis,
canonical correspondence
analysis, discriminant function
analysis
Boxplots
Correlation, regression
Spearman's rho or rank
correlation coefficient
Bivariate scatterplots
LOWESS smoother
Method
Type*
G

P, N, G
G
P
N
G
Data
Requirements
Minimal


Minimal
Data must be
normally
distributed to
apply parametric
analysis
Can be used
when both
independent and
dependent
variables are
ordinal or when
one variable is
ordinal and the
other is
continuous
Minimal
Major Cautions and Concerns
More intensive techniques are
generally required to confirm and
quantify trends.
Use software that can generate
autocorrelation function (ACF) graphs
(see section 7.3.6).
Factors determining groupings may be
difficult to discern or interpret.
Visual comparisons should be
confirmed by numerical tests.
Graphical analysis should be used to
confirm and understand numerical
correlation coefficient. Correlation does
not guarantee causation.
Visual comparisons should be
confirmed by numerical tests.
*Key to Method Type: G = Graphical, N = Nonparametric, P = Parametric

Table 7-2 summarizes methods that can be applied to adjust (e.g., transform) data based on the
requirements of methods (e.g., normal distribution required for parametric analyses) to be used in the next
phase of data analysis. This table also identifies methods that can be used to address problems caused by
unexpected events, including washed out monitoring equipment, floods, droughts, ice, failed BMP
implementation plans, and equipment and laboratory errors.
                                                 7-3
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Monitoring and Evaluating Nonpoint Source Watershed Projects
Chapter 7
    Table 7-2. Methods for adjusting data for subsequent analysis (see discussion, section 7.3)
Analytical
Objective
Obtain a
normal
distribution (for
parametric
approaches)
Accommodate
extreme values
Manage
missing data
Recommended Method
logio and loge (In) are most
commonly used transformations in
water resources
arc-sine square root transformation
If distribution assumptions cannot
be met, adopt methods resistant to
errors in results caused by
deviations from the assumption of
normality
Use methods resistant to errors in
results caused by extreme values
such as: nonparametric trend tests
or frequency analyses
Data stratification (e.g., by seasons,
base flow, storm, and floods)
Use covariate/ explanatory variable
such as flow to help 'explain' the
influence of extreme values
Utilize log transformed data to
minimize skewness caused by the
extreme values
Data aggregation to create uniform
time intervals by averaging or using
the median value
Estimate missing values based
upon regression relationship from
other sites or events
Method
Type*
P
P
N
N, G


P
P

Data Requirements
Original data values must
be positive and non-zero.
Used for proportions
Minimal
Moderate
Moderate

Minimal
Minimal
Regression relationship
with data from similar
basin (e.g., flow).
Sometimes it may also be
appropriate to use the
flow/concentration
relationship at the same
station to estimate missing
concentration data
Major Cautions and Concerns
Other transformations (e.g., Box-
Cox) may be required to achieve
normal distribution. Very small
numbers and legitimate zero
values may require a different
transformation (e.g., logio(value
+ n). Transformations will not
correct issues of independence.
Back-transformations may be
difficult to interpret.

Nonparametric procedures may
still have other assumptions that
must be met for usage.
If distributional assumptions can
be met, then parametric tools
tend to be more powerful.
If the data are missing due to
right censoring (too high to
measure), techniques discussed
in section 7.4 should be
considered.



Missing values are ignored in
most nonparametric and
parametric tests; however, some
tests require equal spacing of
observations. Data aggregation
to accommodate missing data or
changes in data frequency must
be done with care.
Only use when the data meet
the assumptions for regression
analysis and the sample size is
large enough that the regression
relationship is reliable.
                                                7-4
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Monitoring and Evaluating Nonpoint Source Watershed Projects
Chapter 7
Analytical
Objective
Adjust for
autocorrelation
Adjust for
seasonality or
other cycles
Recommended Method
Aggregation of data to less frequent
observations
Use of parametric time series
analysis techniques available in
many statistical software tests
Adjust the standard error for the
trend (difference or slope) to
accommodate for the reduced
effective degrees of freedom
Use non-parametric trend tests that
adjust for seasonality
Add explanatory variables that
'explain' the season affect
Use time series models that
incorporate a lag term(s) to
incorporate for seasonal cycles into
statistical models
Method
Type*
N
P




Data Requirements
Minimal
Generally equally time-
space data observations
Need to calculate the
autocorrelation coefficient
at lag 1 for this adjustment
(see section 7.3.6)
Generally the month of
year is needed for the
input data set
e.g., add data columns
representing seasonal
components for seasonal
cycle (e.g., sin/cos terms)
or monthly indicator
variables

Major Cautions and Concerns
Aggregation must be consistent
(e.g., monthly mean of n daily
observations), not mix of
different sample frequencies.
Software may correct for both
autocorrelation and seasonality.




*Key to Method Type: G = Graphical, N = Nonparametric, P = Parametric
7.2.2  Dealing with Censored Data

Censored values are usually associated with limitations of measurement or sample analysis, and are
commonly reported as results below or above measurement capacity of the available analytical
equipment. Table 7-3 summarizes techniques to use when dealing with censored data.

            Table 7-3. Methods to deal with censored data (see discussion, section 7.4)
Analytical
Objective
Accommodate
censored data (i.e.,
values less than
detection or reporting
limits)
Recommended Method
Use parametric (e.g.,
maximum likelihood
estimation (MLE) and
robust regression on
order statistics (ROS)) or
nonparametric procedures
designed to
accommodate censored
data.
Method
Type*
P, N, G
Data Requirements
Knowledge about analytical
detection limits, practical
quantitation limits, and data
reporting conventions is
required to interpret the
meaning of censored data.
Major Cautions and
Concerns
Although common, substitution
of half the detection limit is not
recommended as more robust
tools are readily available.
*Key to Method Type: G = Graphical, N = Nonparametric, P = Parametric
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Monitoring and Evaluating Nonpoint Source Watershed Projects
Chapter 7
7.2.3 Data Analysis for Water Quality Problem Assessment
Problem assessment is generally considered the first phase of a watershed project. Data analysis at this
stage typically involves using historical data to assess whether water quality standards are being met or
whether designated beneficial uses of waters are threatened, and the causes (e.g., pollutants) and sources
of identified problems. More refined problem assessment will include determination of pollutant
pathways and critical areas needing restoration or BMPs. Methods to support these types of analyses are
summarized in Table 7-4.

      Table 7-4. Data analysis methods for problem assessment (see discussion, section 7.5)
Analytical
Objective
Summarize
existing
conditions
Assess
compliance with
water quality
standards
Identify major
pollutant sources
Define critical
areas
Recommended Method
Univariate statistics (e.g., mean,
median, range, variance, interquartile
range) for different sampling
locations, time series analysis for
long-term trends and seasonal ity, and
regression analysis comparing
pollutant concentrations or loads to
hydraulic variables
Boxplots and/or time series plots for
different sampling locations
Identification of extreme values with
boxplots or time series plots;
calculation of means (arithmetic or
geometric) over specific time
period(s)
Frequency or probability plots,
duration curves
Correlation or regression analysis or
Kendall's Tau for monotonic
association of water quality
constituent(s) vs. subwatershed
characteristic(s) (e.g., total P
concentration vs. manured acres)
Compare boxplots or bivariate
scatterplots from monitored
subwatersheds with distinctive land
use and/or management; ANCOVA
analysis
t-Test, ANOVA, Kruskall-Wallis,
cluster analysis to identify significant
differences in pollutant
concentration/load among multiple
sampling points
Method
Type*
P,N
G
P
G
P, N, G
G, P
P, N
Data Requirements
Minimal to moderate
Minimal to moderate
Concurrent data
from monitored
subwatersheds:
subwatershed land
use and/or
management data
Concurrent data
from monitored
subwatersheds:para
metric or
nonparametric
analysis can be used
depending on data
distribution
Major Cautions and Concerns
To compare locations within or
across watersheds, data from
different locations must be
consistent and comparable
(e.g., synoptic survey, multiple
sampling stations).
Criteria for determining
impairment vary (e.g., single
observation exceedance vs.
geometric mean over n
observations); both monitoring
program and data analysis
must be tailored to regulatory
requirements.
Correlation does not guarantee
causation; consider transport
and other pollutant delivery
mechanisms.
Conditions determining
pollutant generation (e.g., storm
event, season, management
schedule) must be considered
in drawing conclusions about
critical areas. Modeling may be
useful.
*Key to Method Type: G = Graphical, N = Nonparametric, P = Parametric
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Monitoring and Evaluating Nonpoint Source Watershed Projects
Chapter 7
7.2.4 Project Planning Data Analysis
Project planning involves both land treatment and monitoring design. Decisions regarding project
duration, BMP and restoration needs and scheduling, and implementation tracking and monitoring should
all be supported by information and appropriate analysis. The quality of information available will vary
from project to project. In many cases, the analysis and decisions will have to rely on historical data
(perhaps collected for other purposes) or on data from other sites in the region. The methods summarized
in Table 7-5 are recommended to assist with various aspects of project planning.

        Table 7-5. Data analysis methods for project  planning (see discussion, section 7.6)
Analytical
Objective
Determine pollutant
reductions needed to
meet water quality
objectives
Estimate BMP
treatment needs
Estimate minimum
detectable change
(MDC)
Locate monitoring
stations
Recommended Method
Massbalance/TMDL
Receiving waterbody
relationships
Load-duration curves
Reference watershed
Compare estimated
pollutant reduction
efficiencies of planned
BM Ps with reductions
needed
MDC calculation (Spooner
etal.2011a)

Identify major pollutant
sources, critical areas as in
Table 7.5 if data are
available
Target land areas of
particular land
use/management and/or
expected treatment
implementation
Method
Type*
P, G
P
P
P
G
Data Requirements

Appropriate local or
published values on BMP
pollutant reduction
efficiencies
Mean and variance of
water quality variable(s)
of interest; parameters of
planned monitoring
program (e.g., sampling
frequency)
Concurrent data from
subwatersheds (e.g.,
from a synoptic survey)
Land use and
management data,
estimates of treatment
adoption
Major Cautions and Concerns

Published efficiencies do not
generally account for interactions
in multiple-BMP systems or
pollutant transport or delivery
issues beyond edge of field/BMP
site. Modeling may be a better
approach.
If MDC is larger than anticipated
response to treatment, may need
to re-evaluate extent of planned
land treatment and/or duration of
water quality monitoring.
If data are unavailable from
subject watershed, data from
elsewhere must be used.
Conditions determining pollutant
generation (e.g., storm event,
season, management schedule)
must be considered.
Station location depends on many
other factors, including project
objectives, monitoring design, and
site requirements.
*Key to Method Type: G = Graphical, N = Nonparametric, P = Parametric
7.2.5 BMP and Project Effectiveness Data Analysis
Table 7-6 includes recommended methods for assessing the effectiveness of BMPs and watershed
projects. In general, the analytical objective of both kinds of efforts is to document change in pollutant
concentrations or loads or both in response to BMP implementation. These methods are linked to
monitoring designs that are described in section 2.4. Methods for assessing BMP and project
effectiveness using biological data are presented in chapter 4.
                                               7-7
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Monitoring and Evaluating Nonpoint Source Watershed Projects
Chapter 7
     Table 7-6. Data analysis methods for assessing BMP or watershed project effectiveness
                             (see discussion, sections 7.7 and 7.8)
Analytical
Objective
BMP efficiency
Watershed project
effectiveness
Monitoring Design
Used
Plot
Input/output
Paired watershed
Above/below-
Before/after
Single Watershed
Monotonic
Trend
Recommended
Method
ANOVA
Kruskal-Wallis
Paired t-Test,
Wilcoxon, or Mann-
Whitney tests of input
vs. output EMCs
(Event Mean
Concentrations) or
loads
Effluent probability
ANCOVA, paired t-
Test, Wilcoxon Rank
Sum, Mann-Whitney
t-Test of input vs.
output EMCs or loads,
ANCOVA, Wilcoxon
Rank Sum, Mann-
Whitney
Linear regression on
time
Multiple linear
regression on time
and covariates
Linear regression on
time, covariates, and
periodic functions
Mann-Kendall
Mann-Kendall on
residuals from
regression on
covariates
Seasonal Kendall
Method
Type*
P
N
P, N
N
P, N
P, N
P
N
Data Requirements
Data must meet
assumptions for
parametric statistics to
apply; otherwise use
nonparametric test
Data must meet
assumptions for
parametric statistics to
apply; otherwise use
nonparametric test
Data from control and
treatment watersheds
must exhibit significant
linear relationship.
Conditions (e.g.,
precipitation,
discharge) must be in
similar range during
calibration and
treatment periods.
Data must meet
assumptions for
parametric statistics to
apply; otherwise use
nonparametric test
Numerous techniques
are available,
depending on
objectives, available
data on covariates,
seasonal ity
Numerous techniques
are available,
depending on
objectives, available
data on covariates,
seasonal ity
Major Cautions and
Concerns
Plot data may not easily
extrapolate to field or
watershed scale.
Representing change in
load or concentrations as
a percent reduction may
not be representative for
low input concentrations
or loads.
Quality of relationship
between control and
treatment watersheds
determines level of
change that can be
detected. Addition of
covariates to paired
regression model may
improve ability to
document response to
treatment.
Change in pollutant
concentration or load
measured at the below
station may be difficult to
detect if concentrations or
loads at the above station
are high.
Trend analysis is most
effective with data
sampled consistently at
fixed locations and fixed
time intervals for period
sufficient to overlap
seasonal or management
cycles that do not
represent real trends.
Covariates such as
stream flow, season, etc.
are essential to assist
with isolating trends due
to BMPs.
                                               7-8
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Monitoring and Evaluating Nonpoint Source Watershed Projects
Chapter 7
Analytical
Objective

Monitoring Design
Used
Single Watershed
Step Trend
Multiple watersheds
Linking land
treatment to water
quality changes
Recommended
Method
t-Test before and after
step, Wilcoxon Rank
Sum, Mann-Whitney
t-Test or Wilcoxon
Rank-Sum test
ANOVA or Kruskal-
Wallis test
Regression analysis
Boxplots of results
from watershed
groupings (e.g.,
treated/untreated)
Correlation, regression
of pollutant
concentration or load
on land treatment
metric(s)
Method
Type*
P, N
P, N
G
P, N
Data Requirements
Data must meet
assumptions for
parametric statistics to
apply; otherwise use
nonparametric test
Data must meet
assumptions for
parametric statistics to
apply; otherwise use
nonparametric test
Minimal
Requires quantitative
monitoring data on
land treatment. Use of
explanatory variables
(e.g., precipitation,
animal populations)
may strengthen
analysis.
Major Cautions and
Concerns
Selection of step change
point in time must be
made a priori and related
to watershed activities,
e.g., onset of treatment.
Covariates such as
stream flow, season, etc.
are essential to assist
with isolating trends due
to BMPs.
Watersheds need to fall
into 2 groups (e.g.,
treated and untreated) for
t-Test or Wilcoxon Rank-
Sum test.
For more than two groups
use ANOVA or Kruskal-
Wallis.
Visual comparisons
should be confirmed by
numerical tests.
Water quality and land
treatment data must be
collected on comparable
spatial and temporal
scales. Monitored
pollutants must match
pollutants addressed by
implemented BMPs.
*Key to Method Type: G = Graphical, N = Nonparametric, P = Parametric
7.2.6  Practice Datasets
This chapter presents a wide range of parametric and nonparametric methods, including several
illustrative examples. Because practice is the best way to learn how to apply these methods, example
datasets and eight problems are provided to allow readers to test their skills. Using their own statistics
software, readers are encouraged to apply the tests indicated in Table 7-7 to the example datasets listed in
the fourth column. The objective and statistical tests are listed in the second and third columns of the
table. The specific problems and the answers are given in the files identified in the last column.
                                                7-9
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Monitoring and Evaluating Nonpoint Source Watershed Projects
Chapter 7
                                   Table 7-7. Practice datasets
Problem
Number
1
2
3
4
5
6
7
8
Objective
Test for conformance to normal
distribution
Characterize data
Compare two groups
Compare input/output for a
BMP
Compare three groups
Examine relationships between
variables/stations
Assess change due to
treatment in paired-watershed
design
Calculate MDC for a single
station
Test
Graphical, skewness, kurtosis,
Shapiro-Wilk, Kolmogorov
Descriptive statistics
t-Test
Wilcoxon/Kruskal-Wallace
Paired t-Test
Wilcoxon Rank Sum Test
ANOVA
Kruskal-Wallace
Correlation
Simple linear regression
ANCOVA
Minimum detectable change
Dataset in
Sampledata.xlsx
1
1
1
2
1
1
1
3
Problem and Answer
File
normality.pdf
description.pdf
2groups.pdf
pairedtests.pdf
3groups.pdf
correlationregress.pdf
pairedancova.pdf
mdc.pdf
All files are available at: https://www.epa.gov/polluted-runoff-nonpoint-source-pollution/monitoring-and-evaluating-nonpoint-source-watershed
7.3  Exploratory Data Analysis  (EDA) and Data Adjustment
After a monitoring program is up and running, it is never too soon to begin to evaluate the data. Basic
data evaluation should not wait until the end of the project or when a report is due; regular examination of
the data should be part of ongoing project activities. A carefully designed monitoring program will have
the right kind of data, collected at appropriate times and locations to achieve the objectives, and a plan for
analyzing the data.

Describing and summarizing the data in a way that conveys their important characteristics is one purpose
of EDA. When deciding how to analyze any data set, it is essential to consider the characteristics of the
data themselves. Evaluation of characteristics like non-normal distribution and autocorrelation will help
determine the appropriate statistical analysis. Some common characteristics of water quantity and quality
data (Helsel and Hirsch 2002) include:
   •  A lower bound of zero - no negative values are possible.
   •  Presence of outliers, extreme low or high values that occur infrequently, but usually somewhere in
     the data set (outliers on the high side are common).
   •  Skewed distribution, due to outliers or influential data.
   •  Non-normal  distribution.
   •  Censored data - concentration data reported below some detection limit or above a certain value.
   •  Strong seasonal patterns.
   •  Autocorrelation - consecutive observations strongly correlated with each other.
                                               7-10
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Monitoring and Evaluating Nonpoint Source Watershed Projects                                       Chapter 7


  •  Dependence on other uncontrolled or unmeasured variables - values strongly co-vary with such
      variables as streamflow, precipitation, or sediment grain size.

As such, the overall goal of data exploration is to uncover the underlying structure of a data set and set the
stage for more detailed analysis, including hypothesis testing. Specific objectives for data exploration
might include:
  •  To find potential problems with data quality such as data entry error, lab or collection errors
  *  To find extreme values and potential anomalies
  •  To describe the behavior of one or more variables
  •  To test distribution and assumptions of independence and constant variance
  •  To see cycles and trends
  •  To find clusters or groupings
  •  To make preliminary comparisons of two or more locations or time periods
  •  To examine relationships between variables

At the start, check the data for conformity with original plans and QA/QC procedures. Use the approved
project Quality Assurance Project Plan (QAPP) as a guide; see section 8.3 for details on preparing a
QAPP. A key part of EDA is to verify the data entered in the data sets are valid and not anomalies due to
data entry, lab, or collection errors.

Understanding how the data behave with respect to such features as distribution(s), cycles, clusters,
seasonality, and autocorrelation assists with selecting the appropriate statistical tests to evaluate
achievement of project goals. Data analysis to address project goals will involve more thorough statistical
analysis that will be guided by understanding of the data set through EDA.

A secondary reason for doing exploratory data analysis is to start to make sense of the data actually
collected. The purpose of EDA is to get a feel for the data, develop ideas about what it can tell, and how
to draw some preliminary conclusions. EDA is similar to detective work - sifting through all the facts,
looking for clues, and putting the pieces together to find suggestions of meaning in the data.

This process of data exploration differs from traditional hypothesis testing. Testing of hypotheses always
requires some  initial assumption or prediction about the data, such as "The BMP will reduce phosphorus
loads." Although formulating and testing hypotheses is the foundation of good data analysis, the first pass
through of the data should not be too narrowly focused on testing a single idea. Hypothesis testing is
discussed in section 7.6.1, which focuses on data analysis for project planning. EDA is an approach to
data analysis that postpones the usual assumptions about what kind of model the data follow in favor of
the more direct approach of allowing the data themselves to reveal their underlying structure. EDA uses a
variety of techniques, both numerical and graphical, to open-mindedly search for new, perhaps
unexpected, insights into the data. Approaches to EDA for aquatic system biological data have been
described by EPA as part of the Causal Analysis/Diagnosis - Decision Information System (CADDIS)
(USEPA2010).

Data exploration is a necessary first step in analyzing monitoring data. Unless initial exploration reveals
indications of patterns and relationships, there is unlikely to be something for further analysis to confirm.
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J. W. Tukey (1977), the founder of exploratory data analysis, said, "EDA can never be the whole story,
but nothing else can serve as the ... first step."

For more information, refer to Tech Notes 1: Monitoring Data Exploring Your Data,  The First Step
(Meals and Dressing 2005).


7.3.1  Steps in Data Exploration
Data exploration is a process of probing more deeply into the dataset, while being careful to stay
organized and avoid errors. Here are some typical steps in the process of EDA (modified from Jambu
1991), although not all of them may apply to every  situation.
    1.   Data management. In the process of working with the data, files will be created. These files
        should be updated, checked, and validated at regular intervals. The importance of data screening
        and validation cannot be overemphasized. This should always be done before embarking on
        specific analyses, plotting, or other procedures. Be as sure as possible that the data are free from
        entry errors, typos, and other mistakes before proceeding.
   2.   One-dimensional analysis. The first step in really exploring the data is often to simply describe
        or summarize the information one variable  at a time, independent of other variables. This can be
        done using basic statistics on range, central tendency, and variability, or with simple graphs like
        histograms, pie charts, or time plots. This kind of information is always useful to put data in
        context, even though more intensive statistical analysis will be pursued later.
   3.   Two-dimensional analysis. Relationships between two variables are often of great interest,
        especially if there is a meaningful connection suspected (such as between suspended sediment
        and phosphorus) or cause and effect process (such as between rainfall and streamflow).
        Relationships between two sampling locations (such as treatment and control watersheds) or
        between two time periods (like spring snowmelt and summer) are often of interest. Graphical
        techniques like scatter plots and numerical techniques like correlation are often used for this
        purpose.

Because graphs summarize data in ways that describe essential information more quickly and completely
than  do  tables of numbers, graphics are important diagnostic tools for exploring the data. There is no
single statistical tool that is as powerful as a well-chosen graph (Chambers  et al. 1983). Enormous
amounts of quantitative information can be conveyed by graphs and the human eye-brain system is
capable  of quickly summarizing information, simultaneously appreciating overall patterns and minute
details. Graphs will also be essential in ultimately conveying project results to others. With computers and
software available today, there are  no real constraints to graphing data as part of EDA. Graphical display
options  are described in section 4.3 of the 1997 guidance (USEPA 1997b).

There are more advanced steps in data exploration including analysis of multiple variables and cluster
analysis (section 7.3.8). Also, see chapter 4 of the 1997 guidance (USEPA  1997b) for background on
some of these methods.

The project goals and the type of monitoring should guide exploration. If monitoring occurs at a single
point while upstream BMPs are implemented gradually, trends may be of the greatest interest. If sampling
for phosphorus above and below a  land treatment area, a comparison of phosphorus concentrations at the
two stations might be necessary. For an erosion problem, a relationship between streamflow and
suspended solids concentrations before and after land treatment might be of interest.
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The following sections present some specific techniques for exploring data.


7.3.2 Describe Key Variable Characteristics
In most cases, the data should be examined to summarize key characteristics and to determine if the data
satisfy statistical assumptions required for parametric statistical analyses. Data that do not meet
parametric statistical assumptions should be transformed or nonparametric tests should be used. Key
characteristics that are meaningful include central tendency, variability, and distribution.


7.3.2.1  Central Tendency
  «   The mean is computed as the sum of all values divided by the number of values. The mean is
      probably the most common data summary technique in use; however, an extreme value (either high
      or low) has much greater influence on the mean than does a more 'typical' value. Because of this
      sensitivity to extremes, the mean may not be the best summary of the central tendency of the data.
  «   The median, or 50th percentile, is the central value of the distribution when the data are ranked in
      numerical order. The median is the data value for which half of the observations are higher and half
      are lower. Because it is determined by the  order of observations, the median is only slightly
      affected by the magnitude of a single extreme value. When a summary value is desired that is not
      strongly influenced by a few extremes, the median is preferable to the mean.

Both the mean and median should be calculated for comparison.


7.3.2.2  Variability
  •   The sample variance, and its square root the standard deviation, are the most common measures
      of the spread (dispersion) of a set of data. These statistics are computed using the squares of the
      difference between each data point and the mean, so that outliers influence their magnitudes
      dramatically. In data sets with major outliers, the variance and standard deviation may suggest a
      much greater spread than exists for the majority of the  data. This is a good reason to supplement
      numerical statistics with graphical analysis.
  •   The coefficient of variation (CV), defined as the standard  deviation divided by the mean, is a
      relative measure of the variability (spread) of the data.  The  CV is sometimes expressed as a percent,
      with larger values indicating higher variability around the mean. Comparing the CV of two data
      groups can suggest their relative variability.
  •   The interquartile range (IQR) is defined  as the 75th percentile minus the 25th percentile. Because it
      measures  the range of the central 50 percent of the data, it is not influenced at all by the 25 percent
      of the data on either end and is relatively insensitive to outliers.
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7.3.2.3  Skewness
Water resources data are usually skewed, meaning that the data values are not symmetric around the mean
or median, as extreme values extend out farther in one direction. Streamflow data, for example, are
typically right-skewed because of occasional high-flow events (Figure 7-1). When data are skewed, the
mean is not equal to the median, but is pulled toward the long tail of the distribution by the effects of the
extreme values. The standard deviation is also inflated by the extreme values. Because highly skewed
data restrict the ability to use hypothesis tests that assume the data have a normal distribution, it is useful
to evaluate the skewness of the data. The coefficient of skewness (g) is a common measure of skewness;
a right-skewed distribution has a positive g and a left-skewed distribution has a negative g. There are
multiple measures of skewness with varying possible ranges. Interpretation of skewness values calculated
by Excel, for example, is aided by estimating the standard error of skewness with the following
simplified1 equation for large (<5 percent difference from true value for n>30) samples (Elliott 2012):
                                    Standard Error =
where n is the sample size. For n=24, the standard error of skewness is 0.5 using the simplified equation.
A skewness value of more than twice this amount (i.e., less than -1 or greater than 1 in this case) indicates
a skewed distribution, but a value between -1 and 1 is not proof that the data are normally distributed.
Other tests such as goodness-of-fit tests (below) must also be performed to determine if the distribution is
normal.
       160
                            Streamflow (ft3/sec)
Figure 7-1. Right-skewed distribution
 The true standard error of skewness is calculated as: rn(jl  1-),
'(n-2)(n
                                                                   3)
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7.3.2.4  Data Distribution
Many common statistical techniques for hypothesis-testing (parametric tests) require, among other
characteristics, that the data be normally distributed. It is common practice to apply tests such as the
Shapiro-Wilk test or the Kolmogorov-Smirnov (KS) test to evaluate the normality of the data; both of
these tests are  commonly available in statistical software. The probability plot correlation coefficient
(PPCC) can also be used to test for normality. PPCC is essentially a correlation coefficient between the
data values and their normal score (i.e., data on probability paper) and the interpretation of the PPCC is
similar to that  for the correlation coefficient r. This procedure is outlined by Helsel and Hirsch (2002) in
section 4.4 and in Appendix Table B.3 which gives critical values for accepting/rejecting the normal
assumption.

Histograms are familiar graphs, where bars are drawn whose height represents the number or fraction of
observations falling into one of several categories or intervals (see Figure 7-1). Histograms are useful for
depicting the shape or symmetry of a data set, especially whether the data appear to be skewed. However,
histogram appearance depends strongly on the number of categories selected for the plot. For this reason,
histograms are most useful to show data that have natural categories or groupings, such as fish numbers
by species, but are more problematic for data measured on a continuous scale such as streamflow or
phosphorus concentration.

Quantile plots  (also called cumulative frequency plots) show the percentiles of the data distribution. Many
statistics packages calculate and plot frequency distributions; instructions for manually constructing a
quantile plot can be found in Helsel and Hirsch (2002) and other statistics textbooks. Quantile plots show
many important data characteristics, such as the median or the percent of observations less than or greater
than some critical threshold or frequency. With experience, an analyst can discern information about the
spread and skewness of the data. Figure 7-2 shows a quantile plot of E. coll bacteria in a stream; the
frequency of violation of the Vermont water quality standard can be easily seen (the standard was
exceeded -65  percent of the time). Flow and load duration curves (see section 7.9.3) are useful tools  for
visualizing the distribution of streamflows or pollutant loads across a full range of conditions.
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                                                     Chapter 7
   o
   c
   0)
   o-
   0)
   SJ
   i
   3
   E
   3
   O
VTWQS77E. coli/100 ml
                       10.0
                                  100.0
                                            1000.0
                                                       10000.0
                                                                  100000.0
                                                                             1000000.0
                                  E. co//count (#/100 ml)
Figure 7-2. Quantile plot or cumulative frequency plot of E. co//data,  Berry Brook, 1996
(Meals 2001)

A boxplot presents a schematic of essential data characteristics in a simple and direct way: central
tendency (median), spread (interquartile range), skewness (relative size of the box halves), and the
presence of outliers are all indicated in a simple picture. There are many variations and styles of boxplots,
but the standard boxplot (Figure 7-3) consists of a rectangle spanning the 25th and 75th percentiles, split by
a line representing the median. Whiskers extend vertically to encompass the range of most of the data
(e.g., the 5th and 95th percentiles), and outliers beyond this range are shown by dots or other symbols. The
definition of whiskers and outliers may differ among graphing programs; standard definitions can be
found in statistics textbooks (e.g., Cleveland 1993; Helsel and Hirsch 2002). When boxplots are
presented, the definitions of the rectangle, whiskers, and outlier symbols should be clearly specified.


o
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O
CL
1—
1

1.8 -,
1.6-
1.4-
1.2-
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0.8-
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Figure 7-3. Boxplot of weekly TP concentration, Samsonville Brook, 1995 (Meals 2001)
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7.3.2.5  Transformations to Handle Non-normal Data with Parametric Statistical Tests
Evaluations conducted thus far may suggest that the data do not conform to a normal distribution. In cases
where it is desirable or convenient to use statistical tools that require normally distributed data sets or
have a constant variance, transformation may reduce skewness and result in a data set that is more
normally distributed. Transformation is simply defined as applying the same mathematical operation to all
records in the dataset. Helsel and Hirsch (2002) provide a summary of common transformations.
Statistical software packages will often come with Box-Cox transformation tools that allow the analyst to
identify the best transformation for achieving normality, although logarithmic (e.g., logio or loge)
transformation is certainly the most common strategy (Box and Cox 1964). Regardless of which
transformation is used, the data analyst should verify that the transformation results in a dataset that
satisfies applicable assumptions.

Subsequent analysis of log-transformed data must be done with care, as quantities such as mean and
variance calculated on the transformed scale are often biased when transformed back to the original scale.
The geometric mean (the mean of the log-transformed data back-transformed to the arithmetic scale), for
example, differs from the mean of the untransformed distribution. Furthermore, results of statistical
analysis may be more difficult to understand or interpret when expressed on the transformed scale.
Typically, when analysis is performed on the log transformed data, the final statistical results are
converted to express the results as a percentage change (see Spooner et al. 201 la for additional details on
this approach).

Do not assume that a transformation will solve all the problems with the data distribution. Always test the
characteristics of the transformed data set again. Violations of the assumption of a normal distribution can
lead to incorrect conclusions about the data when parametric tests are used in subsequent hypothesis
testing. With that said, some parametric trend tests are robust to some deviation from normality. From a
practical standpoint it is best to be consistent. For example, if a log transformation is merited for TP
concentrations at most locations in a particular data set, then log transforming all TP for all site locations
is a practical course of action.

If transformed data cannot satisfy the assumptions of parametric statistical analysis, consider
nonparametric techniques for data analysis. With  regard to hypothesis testing, there are a host of
nonparametric tests that are robust against non-normality. These tests are often based on the  ranks of the
data and the influence of a few extreme values is reduced. However, keep in mind that while the
normality assumption is relaxed, nonparametric tests have other assumptions (constant variance and
independence of data observations) that must be met for their results to be valid. If distributional
assumptions can be met, then parametric tools tend to be more powerful. Many nonparametric procedures
are described in section 4.11.3 in the 1997 guidance and recommended in Table 7-1 through Table 7-6.


7.3.3 Examination for Extreme, Outlier, Missing, or Anomalous  Values

7.3.3.1   Extremes and Outliers
Extreme values are frequently encountered in NPS monitoring efforts and include the exceptionally high
and low flow values associated with floods and droughts, respectively. Suspended sediment
concentrations may be exceptionally high during spring runoff when cropland fields are bare or when
streambank slumping occurs. Very low pesticide levels may be observed with increasing time elapsed
since application on cropland. In some cases, the extremes may be more important for water quality than
are typical conditions. For example, the extreme values in some lake variables (e.g., Secchi disc readings,
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turbidity, and pH), the duration of the extreme values, and the season may be the dominant influence on
the extent to which lakes support designated beneficial uses. In streams, it is often the extreme low
dissolved oxygen condition that determines the character of the biological community the stream can
support. Extreme concentrations in toxic contaminants such as pesticides may also be more important
than the mean values with respect to acute toxicity to aquatic biota. Nevertheless, extreme concentrations
can have an inordinate effect on some statistical analyses, and the analyst must consider these issues when
selecting data analysis tools.

On the other hand, outliers can result from measurement or recording errors and this should be the first
thing checked (e.g., check lab and field logs). If no error can be found, an outlier should never be rejected
just because it appears unusual or extreme. All samples considered valid after exploratory analysis
contain information that should be  considered when analyzing monitoring data. Different subsets of the
same dataset may reveal varying aspects of the condition of the water resource. For example, extreme
conditions may be most important when considering violations of water quality standards or load
allocations from a watershed. Annual or monthly loads may not completely illuminate the severity of a
problem, whereas high loads during extreme flow conditions may account for most of the pollutant load.
It is commonly observed that the majority of annual pollutant export occurs during a small proportion of
the time. Identifying these extremes and understanding the conditions under which they occur may be a
key to understanding and interpreting watershed monitoring results.

One approach for identifying and summarizing extreme values is to describe the situation by computing
the frequency or proportion of observations exceeding some threshold value (e.g., a water quality
criterion). Cumulative frequency or duration plots are also useful to visualize the influence of extreme
values on a dataset. In addition, determine whether most or all of the extreme values can be attributed to
certain conditions in the watershed  (e.g., spring runoff, cropland tillage). In these cases, it might be more
useful to stratify the dataset by season or management condition. In this way, monitoring results can be
analyzed by season, and values that were "extreme" in the dataset as a whole may be more easily
interpreted in their respective season(s).

Histograms can be useful to illustrate exceedances of standards, targets, and goals by setting categories or
classes that are outside the standard or target. Quartile plots and boxplots are  also useful tools to evaluate
the presence of extreme values.

Boxplots can be a useful visual tool for highlighting extreme values in environmental data. They show
both the spread and the range of the data. Important values visualized by boxplots include the mean (or
the median), and standard  error limits (or 25th and 75th percentiles). Values falling outside these 'limits'
depict values that are from the tails of the data distribution.

Plotting the data in sequence with date as the horizontal axis are time series plots. Figure 7-4 shows a time
series plot of weekly phosphorus concentration data from three stream stations. It is clear that around the
middle of the year, something occurred that led to dramatic spikes in P concentration at Station 2, a
phenomenon demanding further investigation. Field investigation revealed concentrated overland flow
from a new CAFO upstream.

To analyze data sets with extreme values, consider using non-parametric trend tests. If documenting the
number or occurrence of extreme values is an objective (e.g., for evaluation of violations of water quality
standards or pesticide spikes), frequency analyses are useful. Stratifying the data by seasons or flow
conditions (e.g., base flow, storm flows, and flooding) may be helpful in evaluating conditions and trends
within each flow regime. Using flow as an explanatory variable/covariate in trend analysis may be helpful
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to explain the influence/importance of the extreme values. Using the log transformation often minimizes
the skewness caused by the extreme values and enables the use of parametric trend techniques. If the data
are missing due to right censoring (too high to measure), techniques discussed in section 7.4 should be
considered.
        O)O)O)O)O)O)O)O)O)O)O)O)
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        O)O)O)O)O)O)O)O)O)O)O)O)
       -Sta. 1
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Figure 7-4. Time plot of weekly TP concentration, Godin Brook, 1999 (Meals 2001)


7.3.3.2  Anomalous Values
Plotting the data can also reveal data errors or anomalies. Figure 7-5 shows a time series plot of total
Kjeldahl nitrogen (TKN) data collected from three Vermont streams. Something happened around May,
1996 that caused a major shift in TKN concentrations in all three streams. In addition, it is clear that after
October, no values less than 0.5 mg/L were recorded. In this case, this shift was not the  result of some
occurrence in the watersheds, but an artifact of a faulty laboratory instrument, followed  by the
establishment of a lower detection limit of 0.50 mg/L.  Discovery of this fault, while it invalidated a
considerable amount of prior data, led to correction of the problem in the lab and saved the project major
headaches down the road.
       -Seriesl
 -Series2 '•'•'•'•'Ser\es3
Figure 7-5. Time plot of TKN data from three stream stations, 1995-1996 (Meals 2001)
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7.3.3.3  Missing Data
The reality of any watershed project monitoring program is that samples will be missed, equipment will
fail or be overwhelmed, droughts and floods will occur, and sample analysis limitations will be exposed,
resulting in missing and extreme (both high and low) values. However, if data are missing because of
extreme conditions (e.g., streamflow was too high to obtain  a measurement or water was so low a sample
could not be drawn), then missing data may also represent extreme conditions.

The presence of a few missing values in a data series is not generally a major cause for concern, although
some parametric tests (e.g., trend analyses that include autocorrelation errors using time series) require
equal spacing of observations2. One way to cope with extensive missing data is to aggregate data to a
longer, uniform time interval by averaging or using the median value of a group of data points. Daily
observations, for example, could be aggregated to weekly means or medians. Such an operation would
have an additional potential benefit of reducing autocorrelation (see section 7.3.6). A downside to this
approach, however, is a reduced significance level due to fewer degrees of freedom. Do not aggregate
data when there is a systematic change in sampling.  For example, if the early data were collected as
monthly observations and the more recent data were collected as quarterly data, it is not correct to
aggregate the monthly data to quarterly averages and then perform analyses. This is because the averaging
calculation changes the variability of that portion of the record in comparison to the remainder of the
record, resulting in a violation of "identically distributed" assumption of most (including nonparametric)
hypothesis tests. In these cases, the analyst will need to subsample from the more intensely monitored
data set to best mimic the sampling from the less sampled portion of the data.

For loading analyses that require flow data, it is expected that the missing flow data due to equipment
failure could be estimated by evaluating regression relationships with flow from nearby basins. On the
other hand, flows that exceed the weir capacity or reach a stage so high that the technician cannot access
the site are exceptional events. Certainly one approach to addressing this data gap is to apply the
previously mentioned regression relationship with a nearby station. Another approach might be to treat
these observations as "greater than the maximum flow" and apply methods appropriate for censored data
described in section 7.4.


7.3.4  Examination for Frequencies
For categorical data such as watershed area in different land uses or number of aquatic macroinvertebrates
in certain taxonomic groups, data can be effectively  summarized as frequencies in histograms or pie
charts. Figure 7-6 shows a pie chart of the percent composition of orders of macroinvertebrates in a
Vermont stream, clearly  indicating that dipterans dominate the community.
2 Some statistical software such PROC AUTOREG in SAS yield valid trend results with autocorrelated data with
missing data points, as long as the input record contains equal spaced time intervals (e.g., weekly).
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                       4%
            19%
            11%
                              57%
    n Coleoptera
    ETrichoptera
ffl Diptera
GJPIecoptera
I Ephemeroptera
lOligochaeta
Figure 7-6. Percent composition of the orders of macroinvertebrates,
Godin Brook, 2000 (Meals 2001)
7.3.5 Examination for Seasonality or Other Cycles
Monitoring data often consist of a series of observations in time, e.g., weekly samples over a year. One of
the first, and the most useful, things to do with any time series data is to plot it. Plotting time series data
can provide insight into seasonal patterns, trends, changes, and unexpected events more quickly and
easily than tables of numbers.

Figure 7-7 shows a time series plot of E. coli counts in a Vermont stream. The extreme range of the
counts (five orders of magnitude) and the pronounced seasonal cycle are readily apparent, with the lowest
counts occurring during the winter. It is easy to see the times of year when the stream violates the water
quality standard for bacteria.
                                     Tlme
Figure 7-7. Time series plot of weekly E. coli counts, Godin Brook, 1995-1999 (Meals 2001).
Red line indicates Vermont WQS of 77 £. co///100 ml.
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7.3.6 Autocorrelation

Because many hypothesis-testing statistical techniques require that residuals from the statistical tests be
independent, it is useful to check the data set for autocorrelation during EDA. Typically, if the data points
exhibit autocorrelation, so will the residuals from a statistical test which does not correct for
autocorrelation.

Time series data collected through monitoring of water resources often exhibit autocorrelation (also called
serial correlation or dependent observations) where the value of an observation is closely related to a
previous observation (usually the one immediately before it). Autocorrelation in water quality
observations is usually positive in that high values are followed by high values and low values are
followed by low values. For example, streamflow data often show autocorrelation, as numerous high wet-
weather flows tend to occur in sequence, while low values follow low values during dry periods.
                                  Terms Used in this Section

  Lag: the difference in time steps by which one observation comes after another. The lag value is the
  number of time steps.

  Autocorrelation: the correlation between lagged values in a time series (data collected over equal
  intervals of time, can also be spatial distances)

  Correlation Coefficients, p\: a set of correlations for each lag. The autocorrelation coefficient for lag 1
  is the correlation between each data in a time series and its previous (lag 1) observation. The
  autocorrelation coefficient, pj, for lag j is the correlation between each datumin a time series and the
  observation that lags by j time  steps.

  Autoregressive: situation where past values (or nearby values for spatial analyses) have an effect on
  current values. For example, when most of the correlation between the lag variables is between each
  current value and the immediately preceding value, it is a first-order auto regressive process denoted
  as AR(1). AR(2) is second order, where previous two values effect the current value, etc.
  Autoregressive, order 1, AR(1) is common for weekly and monthly  water quality samples.

  Moving Average: an averaging of a fixed number of consecutive observations, with or without weights.
  Moving average models are denoted MA(1),  MA(2), ...MA(q) to indicate the order or maximum lag for
  consecutive observations that  are averaged.

  ARIMA (autoregressive integrated moving average) models: time series models that include both
  auto regressive terms and/or moving average terms

  Autocorrelation  Function (ACF): the set of correlations (e.g., autocorrelation coefficients) between
  each value in a  series of values (e.g., xt) and the lagged values within the same series (e.g., XM, xt-2,
  etc.). Alternatively stated, this  is the pattern of correlation coefficients vs. lag value. This is generally
  depicted as a graph of each lag and its autocorrelation coefficient with a standard error bar to help
  determine the statistical significance of each  of the correlation coefficients for each lag. The
  pattern/shape of the ACF, along with the PACF, is used to assist in determining if the data follow an
  AR, MA, or ARIMA pattern, and by what order (lag). For example, a seasonal AR(1) series has a large
  p-i, with subsequent pj's trailing off,  and a strong seasonal lag correlation.

  Partial Autocorrelation  Function (PACF): the  correlation between two variables, taking into account the
  relationships of other variables to these two variables. The PACF for an AR(1) series drops to  0 after
  lag 1).
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Autocorrelation usually results in a reduction of the effective sample size (degrees of freedom). It
therefore affects statistical trend analyses and their interpretations. As the magnitude of autocorrelation
increases, the effective sample size decreases, and the true standard error is therefore greater than if
autocorrelation is incorrectly ignored. Adjustment for autocorrelation is needed so that the power of
detecting a difference or trend is not incorrectly inflated. For data sets with high autocorrelation, a larger
sample size (e.g., longer monitoring duration) than would be necessary in the absence of autocorrelation
may be required to correctly detect significant changes or trends.

Autocorrelation is often significant in very frequent data collection, such as that done with recording
sensors (e.g., temperature, turbidity). Daily, weekly, and monthly samples also exhibit autocorrelation,
but usually to a lesser extent. The time interval between independent samples differs with the water
resource and variable. The magnitude of autocorrelation in surface water quality concentrations is usually
quite large for samples collected more frequently than monthly (Loftis and Ward 1980a and 1980b,
Lettenmaier 1976, Lettenmaier 1978, Whitfield and Woods  1984). Loftis and Ward (1980a and 1980b)
verified that some surface water quality samples collected less frequently than once a month may be
considered independent if the seasonal variation is removed, although Whitfield (1983) found significant
autocorrelation between stream discharge samples taken as much as 60 days  apart. Compared to surface
water data series, ground water data series tend to retain significant autocorrelation, even with longer
sample intervals. Similarly, a ground water data series tends to have greater autocorrelation when
compared to surface water data series taken at the same time intervals. This may be due to slower water
movement and mixing in ground water as compared to surface waters.

There are  numerical techniques to test for autocorrelation, but a simple graphical method can suggest
whether data have significant autocorrelation:  the lag plot. A lag plot is a graph where each data point is
plotted against its predecessor in the time series, i.e., the value for day two and the value for day one are
plotted as an x, y pair, then day three, day two, and so on. Different time lags can be examined. A "lag-1"
plot uses each data value paired with its immediate predecessor (t2, tl), a "lag-2" plot uses each data
value paired with the value observed two steps previously (t3, tl), and so on. Random (independent) data
should not exhibit any identifiable structure or pattern in the lag plot. Non-random structure in the lag plot
indicates that the underlying data are not random and that autocorrelation may exist. Figure 7-8 shows a
lag-1 plot of weekly streamflow data, suggesting  that autocorrelation needs to be addressed.
    100
                      1             10
                  Streamflow (time t-1) (ft3/sec)
100
Figure 7-8. Lag-one plot of streamflow observations, Samsonville Brook, 1994 (Meals 2001)
                                                7-23
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Monitoring and Evaluating Nonpoint Source Watershed Projects                                       Chapter 7
Autocorrelation can be expressed numerically by calculating the correlation between observations
separated by j lag time periods. The autocorrelation corresponding to the jth lag is the correlation between
the observation at a given time and the observation taken j observation periods earlier. It is denoted by PJ
or p(j). For example, for the first lag (j=l), pi represents the autocorrelation of data points one time period
removed. The time period is a function of the sample frequency and corresponds to the length of time
between samples (e.g., daily, weekly, monthly). The range of values for PJ is -1 to +1, where +1 represents
a perfect positive autocorrelation and -1 is a perfect negative correlation. The sample estimate of
autocorrelation is given by 15 (in practice, PJ is often used to depict the sample autocorrelation
coefficients).

Time series generally exhibit patterns indicated by the pattern of autocorrelation coefficients at various
lags. These patterns reveal key characteristics about the data that should be incorporated into subsequent
trend analyses. For weekly and less frequent water quality sample collection, the autoregressive, lag 1 or
AR(1) data structure is usually appropriate. In this case, most of the autocorrelation can be explained by
the correlation between each observation and its previous observation. Moving Average (MA) data
structures occur when an observation is only related to the observations up to the  lag value (q) and not
observations before3. Rarely is a MA structure alone useful with water quality samples. However, for
some daily or more frequent sampling, a combination of AR and MA data structures become appropriate,
known as ARIMA (AutoRegressive Integrated Moving-Average) models.

One  common test for autocorrelation is the Durbin-Watson (DW) test. The DW test is appropriately used
when the data exhibit first order (lag 1) autoregressive (AR(1)) behavior. AR(1) is common with water
quality data collected weekly, biweekly, or monthly. Daily or samples collected more frequently usually
exhibit ARIMA autocorrelation structures. Even so, the DW test can be useful to  indicate the presence of
autocorrelation with such samples as well. The DW test may also be used to test for independence
(i.e., the absence of autocorrelation) in the residuals from regression models.

Many statistical software packages offer tools for examining autocorrelation. For example, the
Autocorrelation Function (ACF) is the set of all the lag j autocorrelations and is usually depicted as a plot
of each  lag autocorrelation versus the lag number (Figure 7-9 from  Minitab (2016) and Figure  7-10 from
JMP (SAS Institute 2016b)) for the same data set. Visual inspection of the ACF is useful to detect the
presence of autocorrelation and define the structure of the autocorrelation. Typically, the lag
autocorrelation confidence limits (approximately two-standard deviation errors) are also shown on the
ACF graphs. This helps analysts determine if the autocorrelation coefficient at lag j is significant.
Seasonal patterns show up as cycles in the ACF. As a point of comparison, Figure 7-11  shows a time
series plot of independent data (i.e., zero correlation) together with  its ACF graph.

Another useful graph is the Partial Autocorrelation Function (PACF) which is included as the last chart in
Figure 7-9 and in the last column of Figure 7-10. The PACF is the partial amount of R-square
(i.e., correlation) gained due to the additional lag term added to the  right  hand side of the model (Box and
Jenkins 1976). Patterns of the PACF that show dramatic decrease to non-significant values after a lag j,
indicate an autoregressive series of order (lag) j. For a qth order moving average model, MA(q), the
theoretical ACF function drops off to 0 after lag  q with an exponentially  decaying PACF value between
lag 0 and lag q.
3 j and q both refer to the number of lags, j for AR and q for MA.


                                               7-24
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Monitoring and Evaluating Nonpoint Source Watershed Projects
                                                             Chapter 7
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autocorrelation function (PACF) graph of Log(10) weekly flow from the Corsica River National
Nonpoint Source Monitoring Program Project generated by Minitab. The steps are: Stat > Time
Series > Autocorrelation (or Partial Autocorrelation). Identify the time series variable and enter
number of lags. Select options for storing ACF, PACF, t statistics, and  Ljung-Box Q statistics as
desired. Press ok.
                                                7-25
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Monitoring and Evaluating Nonpoint Source Watershed Projects
                                                     Chapter 7
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Click "Analyze" tab, select "Modeling" followed by "Time Series." Select Y time series
(LFLOW) and X time series (Date).
                                                        7-26
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Monitoring and Evaluating Nonpoint Source Watershed Projects
                                                                  Chapter 7
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with respect to time)
                                                    7-27
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Monitoring and Evaluating Nonpoint Source Watershed Projects                                       Chapter 7
An autoregressive error pattern of order 1, AR(1) means that an observation is correlated with the
previous observation. And, because each previous observation is related to the observation prior to it,
each observation is related to all past values, but the highest correlation is with the most recent
observation. A theoretical4 AR(1) time series structure is identified by an ACF pattern that trails off
bounded by an exponential decay after the first lag and the PACF dropping to 0 after lag 1 (or lag j for
higher order AR series).

The patterns for both ACF and PACF in Figure  7-9A and Figure 7-9B are typical of a water quality data
set with AR(1) and a strong seasonal pattern (some might argue for an AR(2) in this case which speaks to
the fact that interpretation of the patterns is required, with analysts often relying on the preponderance of
evidence across monitoring sites). The lag autocorrelations for weekly flow data from the Corsica River
(MD) NNPSMP project in these figures do show some significant autocorrelation coefficients. The PJ
falling outside of the red/blue lines are significant at the 95 percent confidence level. Significant
autocorrelation for lag  1,  as well as a strong seasonal autocorrelation pattern is evident.

Readers should consult statistics textbooks and software packages for greater detail on this and other
methods to test for autocorrelation.


7.3.6.1  Methods  to Handle Autocorrelation
Autocorrelation in analysis of time series data can sometimes be reduced by aggregating data over
different time periods,  such as weekly means rather than daily values. Use of weekly means preserves
much of the original  information of a daily data series, but separates data points far enough in time so that
autocorrelation is reduced. When aggregating data, it is important to use a consistent procedure, e.g.,
using the weekly mean of 7 daily values for each week in the year, rather than mixing weekly means for
some weeks with single grab samples for other weeks. Aggregation has disadvantages including: reducing
the degrees of freedom and potential  power of a statistical test and dampening out the potentially
important high or low data.

Several statistical packages can incorporate a time series error term in the statistical model to address
autocorrelation. For example, PROC  AUTOREG in SAS (SAS Institute 2016d) can be used for linear
regression when the error terms are autoregressive. Similar tools are available in Minitab's time series
tools (i.e., Stat > Time  Series) or R's statistics package.

Alternatively,  if the data exhibit AR(1), which is typical for water quality data collected weekly,
biweekly, or monthly, an adjustment  can be made to the standard error of the trend (step or slope) terms.
The correction factor was derived by Matalas and Langbein (1962) and simplified with a large sample
size approximation by  Fuller (1976):5
                          Sid. d6V.correcj-e(i    Sid. d6V.uncorrecj-e(i
4 Patterns from water quality sampling data will resemble theoretical patterns but will usually deviate in some way,
requiring that the analyst develop a feel for interpreting such graphics.
                                                                   ll+p   2 p(l-p")
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sample size
The exact formula is given by    std. dev.corrected = std. dev.uncorrected  \-^ - -  ^ _  2   where n is the
                                                7-28
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Monitoring and Evaluating Nonpoint Source Watershed Projects                                       Chapter 7
       Where p = autocorrelation coefficient at lag 1
              Std. dev = the standard deviation of the trend term (e.g., standard error of the difference
       between mean values between two time periods or standard error of the slope of a linear
       regression).

See Spooner et al. (201 la) for additional details on this approach.


7.3.6.2  Methods to Handle Autocorrelation Caused by Seasonality
When the data exhibit seasonal cycles, incorporation of explanatory variables can be added to parametric
methods to allow for adjustment of seasons. Four common approaches are used. One is to add 1 or
2 cycles by using sine and cosine terms to a linear regression model, for example, as described in
Tech Notes 6: Statistical Analysis for Monotonic Trends (Meals et al. 2011). This approach assumes that
the sine or cosine terms realistically simulate annual or semiannual seasonal cycles.

A second approach is to incorporate seasonality into the time series model. An ARIMA time series model
could be used that incorporates a time series model with seasonal lag value ("differencing value" or "d"6
in an ARIMA model, ARIMA(p,d,q)) corresponding to the length of the seasonal cycle. For example, an
annual cycle will appear as a strong positive autocorrelation at lag 12 when the data series consists of
monthly values or at lag 4 for quarterly values. As noted above, readers should consult statistics textbooks
and software packages for greater detail on ARIMA models.

A third approach is to simply add monthly (or other seasonal) indicators to each observation in the dataset
and incorporate these indicator variables  in a regression model. The number of indicator variables needed is
S-17. For example S-\ would be 11 when the cycle is annual, but where the same months behave similarly
over the years. Each indicator variable (Xi through Xn) is assigned a value of 0 or 1, as indicated below:

       Xi = "1" for "January" but "0" otherwise
       X2 = "1" for "February" but "0" otherwise

       Xn = "1" for November" but "0" otherwise
       Note: December values would all be depicted by "0" values for Xi-Xn

After the indicator  variables are added to the dataset, regress Yt on the indicator variables and other
independent variables (e.g., time).

A fourth approach to address seasonality is to use non-parametric tests that can handle monthly
seasonality. The  Seasonal Wilcoxon Rank Sum Test or Seasonal Mann-Whitney Rank Sum Test
compares two or more groupings (e.g., seasonal t-test or analysis of variance). The Seasonal Kendall Test
incorporates seasonal components when testing monotonic trends. Both parametric and non-parametric
trend tests are featured in section 7.8.2.4. There is also a variant of the Kendall tau test (seasonal Kendall
tau test with serial correlation correction (Hirsch and Slack 1984)) that can handle seasonality while also
adjusting for autocorrelation.
6 Differencing is a term used in time series analyses, where d is the order of differencing which creates a new time
series, Wt, whose values at time t is the difference between x(t) and x(t+d). Wt then becomes the series used in the
time series analysis.
7 Where S would represent the number of time periods (e.g., months, seasons).
                                               7-29
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Monitoring and Evaluating Nonpoint Source Watershed Projects
Chapter 7
7.3.7 Examination of Two or More Locations or Time Periods
Comparison of two or more variables with EDA can mean comparing different data sets, such as stream
nitrogen concentrations above and below a feedlot or phosphorus concentrations from a control and a
treatment watershed, or comparing data from the same site over two different time periods, such as
phosphorus loads from calibration vs. treatment periods.

The characteristics that make boxplots useful for summarizing and inspecting a single data set make them
even more useful for comparing multiple data groups representing multiple sites or time periods. The
essential characteristics of numerous groups of data can be shown in a compact form. Boxplots of
multiple data groups can help answer several important questions, such as:
  •   Is a factor (location, period) significant?
  •   Does the median appear to differ between groups?
  •   Does apparent variability differ between groups?
  •   Are there outliers? Where?

Boxplots are helpful in determining whether central values, spread, symmetry and outliers differ among
groups. If the main boxes of two groups, for example, do not substantially overlap on the vertical  scale,
there may be a reason to suspect that the two groups differ significantly (note that such difference should
be tested using quantitative statistical techniques). Interpretation of boxplots can help formulate
hypotheses about differences between groups. Figure 7-12 shows a boxplot of total suspended solids
concentrations in three Vermont streams. The plot suggests that TSS concentrations may tend to be
slightly lower at Station 3 compared to the other two stations; however, because the boxes overlap, it is
unlikely that any comparison of medians would result in statistically significant differences.

Inferences about differences between locations or time periods resulting from graphical evaluation of the
data must be confirmed by more rigorous hypothesis testing analyses (see sections 7.7 and 7.8).


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                                              7-30
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Monitoring and Evaluating Nonpoint Source Watershed Projects
Chapter 7
7.3.8  Examine Relationships between Variables
Looking at how variables relate to each other is a way to begin to consider causality, i.e., is the behavior
of one variable the result of action by another.  Such ideas can suggest sets of variables to evaluate
together. For example, if variable B (e.g., suspended sediment concentration) goes down as variable A
(e.g., acres of reduced tillage) goes up, has the BMP program improved water quality? Examination of
correlations between different variables observed simultaneously (e.g., SSC and total P or turbidity and
SSC) can suggest relationships that might change with BMP programs or indicate where one variable
could serve as a surrogate for another. Graphical analysis (e.g, scatterplots of variable A vs. variable B)
can suggest meaningful correlations that would need to be confirmed with more rigorous statistical tests.

The two-dimensional scatterplot is one of the most familiar graphical methods for data exploration. It
consists of a scatter of points representing the value of one variable plotted against the value of another
variable from the same point in time. Scatterplots illustrate the relationship between two variables. They
can help reveal if there appears to be any association at all between two variables, whether the
relationship is linear, whether different groups of data lie in separate regions of the  scatterplot, and
whether variability is constant over the full range of data.

Figure 7-13 shows a scatterplot of phosphorus export in a control and a treatment watershed in Vermont.
Note that the data are plotted on a log scale to obtain a linear relationship. There is a strong positive
association between P in the two streams. This simple scatterplot indicates that it is probably worth
proceeding with more rigorous statistical analysis to evaluate calibration between the two watersheds in a
paired-watershed design. As with this example, it is common that the relationship between variables is
exponential. In such cases, the log transformation allows the relationship to be expressed linearly and
evaluated using linear regression.

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Figure 7-13. Scatterplot of weekly TP export from control
and treatment watersheds, calibration period (Meals 2001)

Figure 7-14 shows another scatterplot examining the relationship between streamflow and E. coll counts
in another Vermont stream. In a nonpoint source situation, a positive association between streamflow and
bacteria counts may be expected, as runoff during high flow events might wash bacteria from the land to
the  stream. In this case, however, it does not require application of advanced statistics to conclude from
Figure 7-14 that there is no such association (in fact the correlation coefficient r is close to zero).
However, recall that EDA involves an open-minded exploration of many possibilities. In Figure 7-15, the
                                               7-31
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Monitoring and Evaluating Nonpoint Source Watershed Projects
                                       Chapter 7
data points have been distinguished by season. The open circles represent data collected in the summer
period and there still appears to be no association between streamflow and E. coll counts. The solid
circles, representing winter data, now appear to show some positive correlation (r = 0.45) between
streamflow and bacteria counts, with high bacteria counts associated with high flows. This picture
suggests that something different is happening in winter compared to summer with respect to streamflow
and E. coll in this watershed, a subject for further investigation.
      1000000
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        1000 -
         100
          10
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                                          1000
Figure 7-14. Scatterplot of E. colivs.
streamflow, Godin Brook, 1995-1998, all data
combined (Meals 2001)
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Figure 7-15. Scatterplot of E. coli vs.
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solid circles = winter, open circles = summer
(Meals 2001)
In looking for correlations in scatterplots, choose the variables carefully. A common mistake is the
comparison of variables that are already related by measurement or calculation. An example of such
spurious correlation is the comparison of streamflow with load. Because load is calculated as
concentration multiplied by flow, a Scatterplot of flow vs. load has a built-in correlation that means very
little, even though it looks good in a Scatterplot. Also remember that correlation does not guarantee
causation - just because two variables are correlated does not mean that the variation in one is caused by
variation in the other.

There are many numerical techniques available to examine and test the relationship between two or more
variables. In EDA, the simplest technique is correlation, which measures the strength of an association
between two variables. The most common measure of correlation is Pearson's r, also called the linear
correlation coefficient. If the data lie exactly on a straight line with positive slope, r will equal 1; if the
data are perfectly random, r will equal 0. For Pearson's r, both variables should be normally distributed
and continuous (Statistics Solutions 2016). The test  also assumes a straight-line relationship between the
variables and constant variance (homoscedasticity). Pearson's r is sensitive to outliers.

Other measures of correlation that are less sensitive to outliers include the nonparametric Kendall's tau
and Spearman's rho (Spearman's rank correlation coefficient). Spearman's rho makes no assumptions
about the distribution of the data and is an appropriate test when the variables are at least ordinal and the
variables are monotonically related (Statistics Solutions 2016). With ordinal variables, the ordering of
values is known but the differences between them are not quantified (e.g., Excellent, Good, Fair, Poor).
                                               7-32
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Monitoring and Evaluating Nonpoint Source Watershed Projects                                       Chapter 7
Measures of correlation are easily calculated by most statistical software packages and are described in
chapter 4 of the 1997 guidance (USEPA 1997b). It must be cautioned that whenever a numerical
correlation is calculated, the data should also be plotted in a scatterplot and examined visually as
described above. Many different patterns can result in the same correlation coefficient. Never compute a
correlation coefficient and assume that the data follow a simple linear pattern.

There are several methods of simultaneously evaluating variables that are likely related to each other.
Cluster analyses group variables and/or observations into similar categories usually based on an
agglomerative hierarchical algorithm which is the most common clustering pattern used in water quality
analyses. In this clustering procedure, each observation begins as an individual "cluster." The similarities
or distances between these clusters are measured using one of several options, including Euclidian
distance and correlation coefficients. The closest two clusters are then merged into a new cluster.
Distances are calculated again using the updated set of clusters, and the process repeated until only one
cluster remains. The result of this analysis is a sequence of groupings that can be represented in a cluster
tree or dendrogram. The analyst can then perform a visual analysis to infer potential groupings and
relationships among variables. It is important to note that cluster analysis does not consider
multicollinearity between the variables. Cluster analysis  conducted as part of EDA might be used to
explore and define site or time groupings that would be useful to explore in later analysis.

Other multivariate techniques that can be applied in subsequent analysis include principal components
analysis, canonical correlation, and discriminant analysis (SAS Institute 1985). These methods are
discussed further in section 7.5.2.5.


7.3.9 Next Steps
Data exploration results (knowledge of how data are distributed, their characteristics, and their
relationships) will help illustrate any needs to adjust the data to enable the appropriate subsequent
statistical tests. In addition, hypotheses can be refined to facilitate  more advanced statistical techniques.
section 7.4 describes methods for accounting for censored data. Sections 7.5 through 7.9 present various
advanced procedures for analyzing data for a range of purposes. Section 7.10 presents a list of tools and
other resources for data analysis.


7.4  Dealing with Censored Data

7.4.1  Types of Censoring
Monitoring programs such as those analyzing for pesticides, metals, or other constituents often present at
very low concentrations may report lab results where concentration is below the detection limit of the
analysis. Bacteriological tests may report very high results as "too numerous to count" (TNTC). Such data
- typically reported as "<" or ">" (left- and right-censored, respectively) some value - are referred to as
"censored" data.

Censored values are usually associated with limitations of measurement or sample analysis, and are
commonly reported as results below or above measurement capacity of the available analytical
equipment. Results that are indistinguishable from a blank sample are normally reported as less than the
detection limit (DL). The true values of these left-censored observations are considered to lie between
zero and the DL. Depending on the laboratory, some results greater than the DL may be identified as less
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Monitoring and Evaluating Nonpoint Source Watershed Projects                                      Chapter 7
than the quantitation limit (QL) or reported as a single value and given a data qualifier to indicate the
value is less than the QL. Typically, results reported as less than the QL indicate that the analyte was
detected (i.e., greater than the detection limit), but at a low enough concentration where the precision was
deemed too low to reliably report a single value. These interval-censored observations are considered to
lie between the DL and QL.

Left- and interval-censored observations are less commonly encountered when working with sediment
and nutrients because they are usually present at levels above their QLs. However; left censoring is
common when toxics and pesticides are being analyzed.

An example of right-censoring includes microbiological analyses with misestimated dilution resulting in
TNTC (too numerous to count) and exceedance of flow gage limits during floods. Right-censoring may
also be encountered when lakes and estuaries are monitored for light penetration via Secchi depth and the
result is reported as visible on bottom, i.e., the Secchi disk is observable on the bottom.

Helsel (2012) provides a seminal discussion of varying reporting limits and concerns with some data
censoring practices. This guidance recommends that detection limits and quantitation limits be stored with
the measurements and each result be clearly qualified to indicate its relation to the DL or QL as
appropriate.


7.4.2  Methods for Handling Censored Data
There is no single ideal method for managing censored data in statistical analyses. When comparing
various methods, this guidance recommends that analysts use methods that minimize bias and error.
Extensive research in water resources  as well as other fields of science such as survival analysis
(e.g., how long does a cancer patient live after treatment) has considered numerous techniques. One
deficiency over the last 20 years has been the lack of readily available tools for widespread use, making
many of these tools out of reach  for general use. Efforts continue to improve upon the availability of these
tools. The most notable is a compilation of methods and recommendations developed by Helsel (2012)
with additional information provided at Practical Stats. Much of the remaining discussion in this section
is derived from HelsePs book (Helsel  2012) and the reader is encouraged to review his book for a more
in-depth discussion.


7.4.2.1  Past Methods
With improved tool access, past  methods for accommodating censored observations can be avoided. The
most notable past method is simple substitution. This involves the replacement of censored observations
with zero, !/2DL, or DL. Although simple substitution is commonly used (and even recommended) in
some state and federal government reports as well as some refereed journal articles, there is no real
theoretical justification for this procedure.  Substitution may perform poorly compared to other more
statistically robust procedures, especially where censored data represent a high proportion of the entire
dataset. More egregiously, some reports have simply deleted observations less than the detection limit.
Some past researchers have recommended simply  reporting the  actual measured  concentrations even if
the concentrations are below the DL (Gilliom et al. 1984). This  approach has not gained traction as
laboratories are reluctant to implement such a practice, although Porter et al. (1988) suggested that an
estimate of the observation error could be reported to better qualify the measurement. While simple
substitution might be convenient for initial exploratory analyses using spreadsheet tools, more robust
procedures are available and are  recommended.
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Monitoring and Evaluating Nonpoint Source Watershed Projects                                      Chapter 7


7.4.2.2  Using Probability Distribution Theory to Estimate the Summary Statistics
In environmental sciences, two common methods considered for estimating summary statistics from
censored data sets include maximum likelihood estimation (MLE) and robust regression on order
statistics (ROS). Both methods ultimately rely on a distributional assumption and both methods allow for
multiple detection limits and estimation of confidence intervals. The reader is referred to Helsel (2012)
for a more detailed discussion.

MLE uses the uncensored observations, the proportion of censored observations, and a distributional
assumption to compute estimates of summary statistics. A lognormal distribution is commonly assumed
with water quality data; however, commercial software will usually allow a variety of assumptions to be
considered.

The robust ROS procedure (Helsel and Cohn 1988) relies on fitting a regression line to a normal
probability plot of the uncensored observations and is applicable for multiple censoring levels. If the
uncensored data do not fit a normal distribution, the analyst can transform the uncensored data with
lognormal or other appropriate transformation.  The process of selecting the best transformation is similar
to that if all data were uncensored and diagnostics are typically available in current statistical software.
The regression is then used to impute values for the censored data.  The imputed and uncensored data are
then, if necessary, transformed back to their original data scale, allowing summary statistics to be
estimated using standard techniques. Confidence intervals for the mean and standard error estimates can
be computed using bootstrapping (e.g., Helsel 2012). In summary for the mean, a random sample (with
replacement) is selected from the site data. These data are passed through the robust ROS procedure
described above, and a resulting mean is computed. The process of selecting a random sample,
implementing the robust ROS procedure and computing a resulting mean is repeated, say, 1,000 times.
Confidence limits are then empirically selected from this set of 1,000 means (e.g., the 5th and 95th
percentile of these 1,000 means would be the 90 percent confidence interval on the mean).

The MLE tool can be applied to  less-thans and  TNTC in the same data set. Helsel (2012) provides
recommendations for which method to use based on the number of observations and degree of censoring.
Notably, no method works well when the degree of censoring exceeds  80 percent. In the situations where
the censoring level exceeds 80 percent, Helsel (2012) recommends reporting information on the percent
of observations above a meaningful threshold and no further summary statistics. For all summary
statistics with censored data, this guidance recommends reporting the maximum detection limit, number
of observations, and number of censored observations with all summary statistics.


7.4.2.3  Hypothesis Testing with Censored Data
There are a variety of nonparametric hypothesis tests that can be directly used with raw data sets that have
censored observations and generally  rely on the rank (or order) of the data. These tests include the Mann-
Whitney test (two random  samples), Wilcoxon (paired samples), and Kruskal-Wallis (several random
samples), and Kendall and Seasonal Kendall tau (monotonic trends). In these tests, censored observations
are treated as tied values, no different from cases where ties might occur between uncensored
observations. Consider the ordered data set of <1, <1, 1.5, 4, 8, 9, 10, and 10. The two censored
observations (of <1) are less than all the other observations, but are treated as tied to each other. The
handling of the two "<7 's" is no different than the two 70's which  are both greater than all the other
values, but tied with each other.  One deficiency of these tests is that they are limited to a single detection
limit (e.g., the tests do not have a method to compare "<1" and "<2"). To apply the above nonparametric
tests with data sets that have multiple detection limits, the analyst will need to re-censor the data to the
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Monitoring and Evaluating Nonpoint Source Watershed Projects                                      Chapter 7
highest detection limit. Note: do not use the previously described ROS procedure to impute values for
censored data and then apply one of the nonparametric tests described in this paragraph (or parametric
tests), as erroneous results might be computed because the rank of the imputed values were calculated
based upon the order of data set entry, which is not related to any true ranking of the actual water quality
values.

An alternative approach is to apply MLE regression tools that are designed for multiply censored
dependent variables. Similar to simple regression or multiple regression, relationships between singly- or
multiply-censored dependent variables can be established with independent variables. Indicator variables
can be used to set up groupings to expand the MLE regression tool for comparing two or more groups or
seasonal/explanatory adjustments as well.


7.5  Data Analysis  for Problem Assessment

7.5.1  Problem Assessment- Important Considerations
One of the most critical steps in controlling NFS pollution is to correctly identify and document the
existence of a water quality problem. The water quality problem may be defined either as a threat to or
impairment of the designated use of a water resource. Impairments are generally defined and identified as
violations of water quality standards (WQS). Water quality standards define the goals for a waterbody by
designating its uses, setting criteria to protect those uses, and establishing provisions such as
antidegradation policies to protect waterbodies from pollutants. A WQS consists of four basic elements:

    1.   A designated use of the water body. States and Tribes specify appropriate water uses to be
        achieved and protected, taking into consideration the use and value of the waterbody for public
        water supply, for protection offish, shellfish, and wildlife, and for recreational, agricultural,
        industrial, and navigational purposes. In designating uses for a water body, States and Tribes
        consider the suitability of a water body for the uses based on the physical, chemical, and
        biological characteristics of the water body, its geographical setting and  scenic qualities, and
        economic considerations.
    2.   Water quality criteria. Water quality criteria are science-based numeric pollutant concentrations
        or narrative requirements that, if met, will protect the designated use(s) of the water body. Criteria
        may be based on physical, chemical, or biological characteristics. Numeric criteria may, for
        example, establish limits for concentrations of toxic pollutants to protect human health or aquatic
        life. Narrative criteria stating that a water body must be "free from" toxic contaminants can serve
        as a basis for limiting the toxicity of waste discharges to aquatic life.
    3.   An antidegradation policy. Water quality standards include an antidegradation policy that
        maintains and protects existing uses and water quality conditions necessary to support such uses,
        maintains and protects high quality waters where existing conditions are better than necessary to
        protect designated uses, and maintains and protects water quality in outstanding national resource
        waters. Except for certain temporary changes, water quality cannot be lowered in such waters.
    4.   General policies. States and Tribes may adopt policies and provisions regarding implementation
        of water quality standards, such as mixing zones, variances, and low-flow policies. Such policies
        are subject to EPA review and approval.
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Monitoring and Evaluating Nonpoint Source Watershed Projects                                      Chapter 7
Water quality monitoring to support problem assessment is usually focused on documenting violations of
WQS in time (e.g., frequency of exceedance) and space (e.g., geographic extent of exceedance). Water
quality data for such purposes may be collected by an ongoing monitoring program (e.g., a state ambient
monitoring program) or by a reconnaissance study designed to provide a preliminary, low-cost overview
of water quality conditions in the area of interest (see section 2.4.2.1). The  EPA ATTAINS database is the
repository for information from state integrated reporting (IR) on water quality conditions under sections
305(b), 303(d), and 314 of the Clean Water Act, and the Reach Address Database contains state IR
geospatial data. ATTAINS includes state-reported information on support of designated uses in assessed
waters, identified causes and sources of impairment, identified impaired waters, and TMDL status.

A detailed discussion of monitoring designs has been presented in chapter 2 of the 1997 guidance
(USEPA 1997b). Some designs appropriate for problem assessment have been discussed in section 2.4 of
this guidance. In general, monitoring designs appropriate for collecting  data to support NFS problem
assessment include:
  •   Synoptic surveys designed to determine the magnitude and geographic extent of WQS violations,
      often used to identify pollutant source areas within a watershed;
  •   Above/below monitoring, wherein a potential pollutant source area  is bracketed between upstream
      and downstream sampling points to assess the impact of the source area on pollutant levels;  and
  "   Trend monitoring designed to collect long-term time-series data at one or more watershed
      sampling points that are useful in determining the frequency and magnitude of exceedance of WQS.

Both above/below (if pre- and post BMP data is collected) and trend monitoring designs can also be
applied to other monitoring objectives such as project effectiveness evaluation using permanent
monitoring stations equipped with automatic sampling equipment and continuous flow measurement
devices.

Grab samples with instantaneous flow measurements for a few sampling events may be sufficient for
initial problem assessment and source identification, but monitoring data for problem assessment should
include both baseflow and stormwater monitoring necessary to fully characterize the system. Storm
sampling is useful for documenting the delivery of pollutants by runoff and overland flow, critical
considerations for waters impacted by NFS. Combined with hydrologic data, basic climatic information
can be used to evaluate the seasons or times of the year when pollutant levels are highest or lowest and
when high flow events, drought, or other factors affect water quality. Note  that concentration data  alone
without concurrent flow or stage data are often of limited utility.

Biological monitoring is used widely in water quality assessments and EPA provides information and
links to resources addressing various aspects of the application of aquatic life criteria in water quality
assessments. Chapter 4 of this guidance is devoted to biological monitoring. The discussion below,
however, emphasizes the use and application of statistical analysis to chemical and physical monitoring
data for which there is a greater body of literature. See chapter 7 of Handbook for Developing Watershed
Plans to Restore  and Protect Our Waters (USEPA 2008) for a broad discussion of approaches to
assessing water quality problems and identifying causes and sources of those problems using a wide range
of information sources.
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Monitoring and Evaluating Nonpoint Source Watershed Projects                                      Chapter 7


7.5.2 Data Analysis Approaches

7.5.2.1   Summarize Existing Conditions
In a single stream or subwatershed, one monitoring location may be sufficient for problem assessment.
More often, sampling at two or more locations is necessary to evaluate existing conditions of the
watershed. Concurrently, sampling at two or more locations can aid in identification of subwatersheds
that merit further evaluation for pollution reductions or water resource protections.

When data from different locations in a watershed or different sampling time periods are consistent and
comparable (e.g., from a synoptic survey or from multiple watershed stations in the same monitoring
regime), a first step is to summarize existing conditions using univariate statistics - mean, median, range,
variance, interquartile range - for different sampling locations. If differences over time or flow conditions
are evident, it may be useful to group the data into separate baseflow and wet-weather strata or by season.
If enough samples have been collected (i.e., at least three), existing water quality can be compared across
multiple sites. Visual comparisons between sites can be depicted graphically using boxplots. Figure 7-16
shows a set of boxplots for one year of weekly conductivity data from three small watershed trend
stations in Vermont (Meals 2001). Conductivity at site WS1 appears to be substantially lower than that
observed at the other two stations; conductivity at WS2 tended to be somewhat higher than that observed
at WS3, with more frequent high extreme values. Mean or median values can be compared between two
sites using the unpaired Student's t-Test or a nonparametric equivalent such as the Wilcoxon Rank Sum
Test (also known as the Mann-Whitney Rank Sum Test). More than two sites can be compared using
Analysis of Variance or the Kruskal-Wallis k Sample Test. Adjustments for seasons or hydrologic
explanatory variables should be considered by employing appropriate statistical tests such as Analysis of
Covariance or the Seasonal Wilcoxon Rank Sum Test (also known as the Mann Whitney Rank Sum Test).
If the data between two sites are paired, differences can be tested using the paired Student's t-Test or the
Wilcoxon Signed Rank Sum Test. Paired tests are generally more powerful and should be used when
enabled by collecting samples at the same time period at two sites.
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Monitoring and Evaluating Nonpoint Source Watershed Projects
                                             Chapter 7
          250
          200 -
          150 -
          100
          50 -
                                                           I
                           WS1
WS2
WS3
Figure 7-16. Boxplots of conductivity at three Vermont monitoring stations, October 1999 -
September 2000 (Meals 2001)

Time series plots can visually reveal relationships overtime and between locations. Figure 7-7 from section
7.3, for example, shows very clearly the seasonal cycle in E. coli counts in a Vermont stream, and
Figure 7-4 reveals a different behavior at Station 2 compared to other stations regarding P concentrations.
Time series statistical analyses can reveal autocorrelation and seasonality (see section 7.3.6).

Regression analysis between variables of primary interest (e.g., pollutant concentration/loads) and
explanatory variables such as stream discharge can assist in documenting hydraulic relationships at a
single monitoring location or between subwatersheds. Establishing relationships among variables can be
very helpful in project planning as well. Scientists involved in the Upper Grande Ronde (OR) NNMP
project, for example, explored relationships between fish and  environmental factors via multivariate
analysis and found that management and restoration activities that focus on reducing the maximum annual
stream temperature would be the most effective in creating stream conditions that support salmonids
(Drake 1999).


7.5.2.2  Assess Compliance with  Water Quality Standards
Water quality data can be evaluated for violation of water quality standards (WQS). Note that specific
requirements for documenting impairment in a regulatory sense may vary by circumstance. For some
states and for some pollutants, a single observation exceeding a WQS may be sufficient to  designate
impairment. In other cases, determination of impairment must be based on violation of a WQS over a
defined period of time or number of observations. A WQS for bacteria to support shellfishing may, for
example, be based on a geometric mean of a number of different samples collected over a 30-day period,
rather than on a single sample. Sanitary surveys in North Carolina, for example, include a shoreline
survey to identify potential pollutant sources, a hydrographic and meteorological survey, and a
                                               7-39
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Monitoring and Evaluating Nonpoint Source Watershed Projects
Chapter 7
bacteriological survey (NCDENR 2016). Both the monitoring program and data analysis must be tailored
to the regulatory requirements that apply to the watershed under study.

A data series should be plotted and the pattern evaluated for exceedance of WQS; plots of a time series at
a single station or boxplot of multiple stations can be examined. Figure 7-17 shows how a time series plot
can illustrate both the frequency and magnitude of violations of WQS. The dashed line represents the
water quality criterion for chronic exposure; all of the observations exceed that level. The red line marks
the acute criterion and shows that several observations exceeded that concentration. Moreover, most of
the excursions above the acute criterion occurred around April, suggesting a seasonal aspect to the
impairment. This kind of pattern may support inferences about pollutant source activity.

One way to evaluate the frequency or probability of violating WQS is to use probability plots or duration
curves. Figure 7-18 shows a cumulative frequency plot of three years of E. coll data from a Vermont
agricultural watershed (Meals 2001). In this case, it can be seen that compliance with the Vermont WQS of
77 cfu/100 ml E. coll occurred about 36 percent of the time and the stream was therefore considered
impaired for E. coll about 64 percent of the time. If the USEPA criterion of 235 cfu/100 ml were applied,
the stream would be in compliance with that criterion about 48 percent of the time and impaired about
52 percent of the time.
Observed Aluminum Vs. Water Quality Standards
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Figure 7-17. Example time series plot of observed aluminum concentrations compared to water
quality criteria
                                               7-40
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Monitoring and Evaluating Nonpoint Source Watershed Projects
                                                       Chapter 7
                 WS 2  Mean Weekly EC Counts 1995 - 1998
                      10
100
1000    10000    100000  1000000
                          Single-sample E coli Count (cfu/100 ml)
Figure 7-18. Cumulative frequency plot of three years of E. coli data from a Vermont stream
(adapted from Meals 2001). Red lines represent frequency of observations at or below the VT WQS
of 77 cfu/100 ml and the frequency of observations at or below the EPA criterion of 235 cfu/100 ml.


7.5.2.3  Identify Major Pollutant Sources
Cost-effective treatment of watersheds to address the pollutants and other causes of water quality
problems requires knowledge of the sources contributing to the problems. Commonly used approaches to
identifying and characterizing sources use both water quality and land-based information at varying levels
of detail and quality (USEPA 2008). This  section describes methods for analyzing water quality and
associated monitoring data to characterize and aid in the prioritization of pollutant sources as part of the
watershed planning process. See section 4.4.5 for an example of using biological monitoring in the Lake
Allatoona/Upper Etowah River (GA) watershed.

Data from a synoptic survey or from regular monitoring of several subwatersheds combined with data on
land use, management, or other land-based characteristics can inform understanding of major pollutant
sources in a watershed. Correlation or regression analysis can be  applied to explore relationships between
pollutant concentrations and subwatershed characteristics, e.g., total P (TP) concentrations vs. manured
cropland or suspended sediment concentration vs. cropland in cover crops. Annual mean or median values
for pollutant concentrations could be compared to annual data on land use/management activities because
concentrations will vary widely between individual events against land characteristics that are relatively
constant within a single year or crop season. However, this simplification will not reveal seasonal and
hydrologic variability in water quality or responses to short term  land use changes such as animal
numbers or fertilization. Where suitable knowledge of land use or land management is available, it may
be more useful to provide water quality summary data for different periods that reflect distinctly different
land use/management conditions (e.g.,  after spring manure applications vs. remainder of the year) during
the monitoring period.
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Monitoring and Evaluating Nonpoint Source Watershed Projects                                      Chapter 7
Boxplots or bivariate scatterplots can be compared between monitoring sites that reflect distinctive land
use or management, thereby suggesting important pollutant source activities. If sufficient data from
different subwatersheds or sampling stations exist, analysis of variance (ANOVA), or the nonparametric
Kruskal-Wallis k Sample test can be used to test for significant differences in pollutant concentrations
between sites and then compare these findings to differences in land use between the drainage areas
sampled (graphical or tabular summaries). Analysis  of covariance (ANCOVA) should be considered in
cases where data are sufficient to test for differences among sites or seasons with adjustment for
covariates such as precipitation or flow. See sections 4.6 and 4.8 of the 1997 guidance (USEPA 1997b)
for a discussion of ANOVA and ANCOVA.

If flow data are available with concentration data, load estimates can be calculated to compare the
magnitudes of pollutant sources (see section 7.9 for  load estimation methods). The spatial and temporal
resolution possible for load estimates will be determined by the number and location of sampling sites
and the time frame and frequency of sampling events, respectively. Source-specific or subwatershed loads
will generally be more helpful than loads at the watershed outlet, and in many cases seasonal loads or a
classification of event vs. baseflow loads will be very helpful in the watershed project planning phase (see
section 7.6).

It should be noted that correlation does not guarantee causation. Specifics of pollutant  source activity and
transport/delivery mechanisms must be considered to focus in on causation. Time of travel studies for
various points in the watershed, for example, can be helpful in belter characterizing the relationship
between various sources or subwatersheds and downstream water quality. USGS describes methods for
measuring time of travel (Kilpatrick and Wilson 1989).


7.5.2.4  Define Critical Areas
Data collected in the problem assessment phase can be used to help define critical source areas for
pollutants, knowledge that is key to understanding the watershed, prioritizing land treatment, and evaluating
project effectiveness. With  concurrent data from monitored subwatersheds or tributaries  (e.g., from a
synoptic survey), statistical tests such as the Student's t Test or ANOVA can be used to identify  significant
differences in pollutant concentration or load among multiple sampling points. Such data can be displayed
graphically in a map to show watershed regions that may be major contributors of pollutants. Figure 7-19,
for example, shows a map of NO2+NOs-N concentrations from an April, 2003 synoptic survey in the
Corsica River (MD) watershed (Primrose 2003). Nitrate/nitrite concentrations were found to be excessive in
four subwatersheds, high in sixteen, and moderately elevated in seventeen others. Benchmarks for
determining excessive/high/moderate or similar categories can be based on numeric water quality criteria or
reference watershed values. If flow data were also available, it would be possible to estimate loads and
compare subwatersheds on the basis of absolute (e.g., kg TP) or areal (e.g., kg TP/ha) loads. Figure 4-3 of
section 4.4.5 illustrates how biological monitoring data from the Lake Allatoona/Upper Etowah River (GA)
watershed were used for site-specific assessments of biological condition.
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Monitoring and Evaluating Nonpoint Source Watershed Projects
Chapter 7
  Corsica WRAS'-Mutrient Synqptie^ApFil,
  NO2+NO3 CtJhjfc. {rfecj/L) yCf"
Figure 7-19. Map of synoptic sampling results from 41 stations in the Corsica River Watershed
(Maryland) for NCh+NOs-N concentration (Primrose 2003). Pink and red shaded subwatersheds
represent drainage areas contributing high (3-5 mg/L) and excessive (>5 mg/L) NCh+NOs-N
concentrations, respectively.

Assessment of critical areas using a small set of water quality data has some limitations. Conditions
determining pollutant generation (e.g., storm event, season, management schedules) must be considered in
drawing conclusions about critical areas. Data collected during the active crop growth season may show a
very different situation from data collected in winter, although for source identification purposes, it may be
preferable to sample during the most critical times of year. The data mapped in Figure 7-19, for example,
were collected in April, during or immediately following the spring planting and fertilizer application season
when N losses from recently applied fertilizers might be expected to be high. Secondly, the spatial
resolution of source area identification is limited by the resolution of the sampling network. Detailed site
evaluation and/or modeling may be required to identify critical source areas on a finer scale.

Another problem with using only a small set of water quality samples to determine critical areas is that
some sources are by default removed from consideration. For example, the role of streambanks and
stream channels in delivering sediment and sediment-bound pollutants such as P is often only partially
understood at the beginning of watershed projects. The Sycamore Creek (MI) NNMP project, for
example, focused on no-till and continuous cover to reduce sediment loads, but later concluded that the
stream channel stabilization implemented in one subwatershed must have been at least as important as no-
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Monitoring and Evaluating Nonpoint Source Watershed Projects                                       Chapter 7
till in reducing suspended solids loads (Suppnick 1999). Solutions to sedimentation problems in Lake
Pittsfield (IL) progressed from an initial emphasis on no-till, terraces, and waterways (1979-1985), to
numerous water and sediment control basins and a single large sedimentation basin (1992-1996), and then
to stream restoration using stone weirs and streambank vegetation (1998) when it was learned that
massive bank erosion was increasing sediment yield (Roseboom et al. 1999).  See section 4.4.5 for a
detailed example of using biological monitoring in the Lake Allatoona/Upper Etowah River (GA)
watershed.


7.5.2.5  Additional Approaches
In most cases, projects in the planning phase have limited information with which to perform statistical
analyses, particularly advanced procedures. Where such data exist, however, multivariate statistical
procedures such as factor analysis, principal component analysis, canonical correlation analysis, and
cluster and discriminant analysis can be used to define (and perhaps subsequently adjust for) complex
relationships among variables such as precipitation, flow, season, land use, or agricultural activities that
influence NFS problems. Spatial and temporal patterns can be revealed with these techniques. Scatterplots
of ordination scores can be a useful method to summarize multivariate datasets and visualize spatial and
temporal patterns.

Ordination techniques can also be powerful during the EDA phase when looking for patterns and
structure in the data. The upper Grande Ronde basin project, for example, used correlation and canonical
correspondence analysis to determine which environmental variables are largely  responsible for
differences in fish assemblages between reference and impaired sites (Drake 1999). Figure 7-20 shows a
correspondence analysis plot showing intermediate/impaired sites and reference sites ordinating on the
left and right side of the origin (Drake 1999). Scatterplots such as Figure 7-20 can be a useful way to
summarize multivariate datasets and visualize these spatial and temporal patterns. With such variables
identified, the next step was applying principal component analysis to determine  if these variables could
be used to track stream improvements over time. These statistical procedures  are discussed briefly below.
The reader is referred to statistics textbooks and other resources for additional information. Further, it is
recommended that these procedures are performed by or in consultation with  a trained statistician.
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Monitoring and Evaluating Nonpoint Source Watershed Projects
Chapter 7
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.2 -08 -0.4 0 0.4 0.8 1.2 1.6
CA AXIS I
    DARKL: Dark Canyon Creek - lower site  MCCL1 & 2: McCoy Creek - Lower site 1 & 2
    DARKU; Dark Canyon Creek- Upper site  MCCM: McCoy Creek - Middle site
    LIML; Limber Jim Creek- Lower site     MCCR: McCoy Creek - Restored reach
    LIMU: Limber Jim Creek- Upper site     MEADL: Meadow Creek - Lower site
    LOOK: Lookout Creek                  MEADS: Meadow Creek at Starkey

Figure 7-20. Correspondence analysis biplot of Grande Ronde fish data (Drake, 1999)

Principal component analysis (PCA) is a multivariate technique for examining linear relationships among
several quantitative variables, particularly when the variables are correlated to each other. PCA can be
used to determine the relative importance of each independent variable and determine the relationship
among several variables. Given a data set with p numeric variables, p principal components or factors can
be computed. Each principal component (or factor) is a synthesized variable that is a linear combination
of the original variables (SAS Institute 1985). The first principal component explains the most variance in
the original data, while the second principal component is uncorrelated with (i.e., orthogonal to or
statistically independent from)  the first principal component and explains the next greatest proportion of
the remaining variance. This process is continued until there  are p  statistically independent principal
components that explain as much of the variance as possible. The results of PCA can often be enhanced
through factor analysis, which is a procedure that can be used to identify a small number of factors that
explain the relationships among the original variables. One important aspect of factor analysis is the
ability to transform the factors  (i.e., reconfigure  the linear combinations of original variables) from PCA
so that they make more sense scientifically. The SAS procedures PROC PRINCOMP and PROC
FACTOR can be used for these analyses (SAS Institute 2010).

Principal component analyses and factor analysis can be used in regression analysis to reduce the number
of variables or degree of freedoms (d.f) by using a subset of the principal components (factors) that
explain the majority of the variance of the data set instead of using all of the original variables. This
essentially reduces the  degrees  of freedom used, but incorporates most of the information from each of
                                              7-45
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Monitoring and Evaluating Nonpoint Source Watershed Projects                                       Chapter 7
the explanatory variables, hence increasing the validity and power of the regression analysis. Using PCA
to incorporate many explanatory variables into a regression model is superior to other techniques that
arbitrarily drop explanatory (X) variables; those may incorrectly drop the more important variables due to
multicollinearity between the X's. In principle, PCA and factor analysis could be beneficial to projects in
a number of other ways, including helping investigators focus problem assessments on the most important
indicators and stressors, aiding in the selection of water quality and land use/treatment variables to be
used in the monitoring program, and guiding BMPs toward the most important pollutant sources.

Canonical correlation analysis (CCA8) is a technique for analyzing the relationship between two sets of
multiple variables (e.g., a set of nutrient variables and a set of biomass-related variables). This
multivariate approach examines said relationship "by finding a small number of linear combinations from
each set of variables that have the highest possible between-set correlations" (SAS Institute 1985). These
linear combinations of variables from each set are synthetic variables called 'canonical variables' and the
coefficients of the linear combinations (which are similar to Pearson r) are referred to as the 'canonical
weights'  (SAS Institute 1985). The first canonical correlation is the correlation between the canonical
variables from each set that maximizes the correlation value in accounting for as much as possible of the
variance in the variable sets. The second canonical correlation is between a second set of canonical
variables, is uncorrelated with the first canonical variables, and produces the second highest correlation
coefficient. Additional correlations are established until all variance is explained or the maximum number
of canonical correlations has been used (i.e., the number of variables in the smaller set). As such, the
canonical variables are  similar to principal components in summarizing total variation (SAS Institute
1985).

In simple terms, CCA can be used in problem  assessment to look for relationships  between sets of
grouped variables to help better understand existing water quality problems or the  relationships between
land use/management variables (e.g., imperviousness, acreage receiving manure) and pollution variables
(e.g., discharge, pollutant concentrations) to help guide decisions on BMP selection and placement. There
are several output statistics (e.g., significance, correlations, coefficients) in CCA, and the reader is
referred to statistical textbooks and other sources for additional details. It should be noted, however, that
while many correlations may be output from a specific analysis, only the strongest correlations should be
considered for interpretation.

Discriminant analysis is used to assess relationships between a categorical (grouping) variable
(e.g., presence or absence of a fish species) and multiple quantitative (predictor) variables (e.g., pH,
temperature, D.O.). The category options (e.g., present or absent) are assigned a priori—normally
verification of the a priori grouping is performed during discriminant function analysis. Discriminant
analysis can be  used to  verify the observational groupings defined by each cluster (see section 7.3.8) or
other defined grouping  based on the values of the quantitative variables. This type  of analysis is referred
to as ' classificatory discriminant analysis' and is  probably the most common application of discriminant
analysis in water quality research. The SAS procedures DISCRIM (parametric) and NEIGHBOR
(nonparametric) can be used to perform classificatory discriminant analyses (SAS  Institute 1985).

Discriminant analyses can also be used to define a subset of quantitative variables that best describes the
differences among the groups; see, for example, the SAS procedure STEPDISC (SAS Institute 1985).
Canonical discriminant analysis is equivalent to canonical analysis described above except that a set of
quantitative variables is related to a set of classification variables (SAS Institute 1985). Principal
 : Canonical correspondence analysis is also often abbreviated as CCA.


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Monitoring and Evaluating Nonpoint Source Watershed Projects                                      Chapter 7
component analysis is used as an intermediate step in the calculation of the canonical variables. The SAS
procedure CANDISC can be used to perform canonical discriminant analyses (SAS Institute 1985).

Cluster and discriminant analyses can be used to understand and adjust for relationships among water
variables. For example, spatial heterogeneity and homogeneity can be revealed. This may be necessary to
study the transport of a pollutant in a system or to remove the spatial component in order to detect
changes overtime.

In many cases, watershed projects use simulation models to help with problem assessment and planning.
Water quality models that include land use/land treatment and are calibrated using water quality data from
the watershed or similar watershed(s) can also assist with identification of critical pollutant sources.  The
reader is referred to USEPA's watershed project planning guide (USEPA 2008) and TMDL modeling
website for additional information on water quality models.


7.6  Data Analysis for Project Planning
Existing data or data collected specifically in support of a developing watershed project may play
important roles in project planning, including determination of land treatment needs and design of a water
quality monitoring program. These and other aspects of watershed planning are addressed in detail in
Handbook for Developing Watershed Plans to Restore and Protect Our Waters (USEPA 2008).


7.6.1  Estimation and Hypothesis Testing
Project planning - including setting clear project goals - should result in the articulation of hypotheses
that can be tested using appropriate statistical tests. The hypothesis must be  stated in quantitative terms
that can be adequately addressed by statistical analyses and must be directly related to the stated water
quality monitoring goals.

The null hypothesis (H0) is a specific hypothesis about a population that is being tested by analyzing the
collected sample data. In water quality studies, the null hypothesis is generally a statement of no change,
no trend over time or space, or no relationship(s). In contrast, the alternative hypothesis (Ha or Hi) is
generally the opposite of the null, e.g., a statistically significant change, a trend over time or space, a
relationship between 2 or more variables.

The general approach to hypothesis testing is to:
    1.  State the null and alternative hypotheses. For example:
     •   Ho - There is no statistically significant trend over 10 years in TP at the subwatershed stream
         outlet
     •   Ha- There is a statistically significant trend over 10 years in TP at the subwatershed stream
         outlet
    2.  Determine a parameter (e.g., mean, median,  slope/trend over time) that would provide a point
       estimate to test if the sample data follow a distribution that would be expected if the null
       hypothesis was true, or more importantly, to test if there is evidence that the data come from an
       alternative population.
    3.  Design a sampling plan that would collect data to test if there is statistical evidence to reject the
       null hypothesis and accept the  alternative hypothesis.
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Monitoring and Evaluating Nonpoint Source Watershed Projects                                       Chapter 7


    4.  Analyze the sample data to calculate the sample point estimate and its confidence interval based
       upon the collected data variability.
    5.  Compare the confidence interval to the point estimate under the null hypothesis to determine if
       there is statistical evidence to reject the null and accept the alternative hypothesis (e.g., statistical
       evidence that a trend has occurred overtime).

It should be noted that if the null hypothesis is not rejected, it is inappropriate to state that the null
hypothesis is accepted. Instead, failure to reject the null or failure to  detect significant differences or
trends is the proper way  to state such results. Failure to reject the null could be due to high sample
variability, low sample size, or no real differences or trends. The chance of documenting a true difference
or trend with statistical significance is improved by increasing sample frequency and longevity, and by
using a monitoring design that will isolate the change/trend, while accounting  for some of the high
variability in data values observed in natural water quality systems. Effective monitoring designs are
described in chapters 2-4.

There are two types of errors in hypothesis testing:
    1.  Type I: The null hypothesis (H0) is rejected when H0 is really true.
    2.  Type II: The  null hypothesis (H0) is not rejected when H0 is really false.

The probability of making a Type  I error is equal to the significance  level (a).  The probability of a Type II
error is (3. The power of  a test (1- (3) is the probability of correctly rejecting Ho when Ho is false. While
the  significance level is often taken for granted to be 0.05, a different value might be more appropriate for
some NFS studies.


7.6.2 Determine Pollutant Reductions Needed
To set goals for a watershed project, it is important to estimate the pollutant reduction required to meet
water quality objectives, usually to meet WQS. There are several approaches to developing such
estimates:
  " Mass balance/TMDL. In a TMDL setting, a load reduction goal is established based on a mass
     balance approach.  Monitoring data are used to estimate the pollutant load a waterbody can receive
     while complying with WQS. The pollutant load reduction goal for a watershed project becomes the
     difference between the current load and the TMDL which is defined by:

                                   TMDL = WLA + LA + MOS

       Where WLA is the Waste Load Allocation (the allowable point source load);
               LA is the Load Allocation (the allowable nonpoint source load); and
               MOS is  the Margin of Safety to account for uncertainty in the other estimates.

               Note that the LA term (NFS load) is often estimated by difference and  is not subdivided
               by source type. The pollutant load reduction goal for a watershed project focused on
               agricultural sources, for example, will not necessarily address the full difference between
               current load and LA because there may be other significant nonpoint sources in the
               watershed such as urban and residential nonpoint sources. TMDLs are  frequently based
               on modeling analysis, but also use available water quality data to the extent possible.
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Monitoring and Evaluating Nonpoint Source Watershed Projects                                      Chapter 7
     Detailed information on TMDL analysis is available through USEPA (2013). See Case Study 5 for
     an illustration of how water quality data can be used in the development of a watershed-scale mass
     balance. The accuracy of this approach, however, depends on the quality and representativeness of
     the data used in the analysis. In Case Study 5, for example, because internal P loading is being
     computed based on estimates of the other terms, underestimation of external P loading will lead to
     an equal overestimate of internal P loading, thus confounding interpretation of the effects of alum
     application. For this and other reasons, the adaptive management approach is a cornerstone of
     TMDL implementation. As additional data are collected, mass balances should be revisited.

     Receiving waterbody relationships. Numerous tools exist to evaluate the impacts of pollutant
     loads on waterbodies that may be helpful in estimating pollutant load reduction goals. In lakes, for
     example, there are many analytical procedures and modeling tools to relate phosphorus load to lake
     eutrophication,  including the "Vollenweider models" (Vollenweider 1976, Vollenweider and
     Kerekes 1982) and BATHTUB (Walker 1999). Such tools may be used to "back-calculate"
     permissible phosphorus loads to lakes. Other receiving water models may be  used for similar
     purposes in other types of waterbodies, e.g., OUAL2K. CONCEPTS, and WASP. All of these
     models can employ available monitoring data to both establish model parameter values and to
     conduct calibration and validation. Additional information on models useful in this kind of analysis
     can be found in the USEPA TMDL Modeling Toolbox. Many of these models need to be calibrated
     with water quality collected from the study watershed or similar watershed(s).
     Load duration curves. A flow or load duration curve is a cumulative frequency plot of mean daily
     flows or daily loads at a monitoring station (e.g., a watershed  trend station or tributary outlet) over a
     period of record, with values plotted from their highest value to  lowest without regard to
     chronological order (see section 7.9.3). For each flow or load  value, the curve displays the
     corresponding percent of time (0 to 100) that the value was met  or exceeded over the specified
     period - the flow or load duration interval. Extremely high values are rarely exceeded and have low
     flow duration interval values; very low values are often exceeded and have high flow duration
     interval values.  An estimate of the pollutant reductions needed is obtained by comparing a load
     duration curve developed from monitored loading data against a similar curve with loads estimated
     as the product of monitored flows and the pollutant concentration established in a WQS. Detailed
     information on the application of load duration curves to pollutant load reduction estimates can be
     found in An Approach for Using Load Duration Curves in the Development of TMDLs (USEPA
     2007).
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Monitoring and Evaluating Nonpoint Source Watershed Projects
                      Chapter 7
   CASE STUDY 5: MASS-BALANCE APPROACH USED FOR ESTIMATING
   PHOSPHORUS LOADS

   Grand Lake St. Marys (GLSM) is located in the Grand Lake
   St. Marys watershed in western Ohio (Figure CS5-1). GLSM
   is a large (5,000  ha), man-made, shallow (mean depth: 1.6
   m) lake originally constructed as a "feeder reservoir" for
   the Miami-Erie Canal (Moorman et al. 2008; ODNR 2013;
   Tetra Tech, Inc. 2013). Over 90 percent of the watershed is
   in cropland with associated livestock operations.
   Cyanobacteria blooms in GLSM result both from external
   and internal phosphorus loading (Tetra Tech, Inc.  2013).
     Western Ohio

Treated a large, shallow
lake with aluminum sulfate
to reduce internal
phosphorus loads

Used the mass-balance
approach to estimate
internal phosphorus loads
pre- and post-treatment
   The lake was treated with aluminum sulfate (alum) in June
   2011 (23.6 mg AI/L, 49.6 g/m2) and in April 2012 (21.5 mg
   AI/L, 45.2 g/m2) to reduce internal phosphorus loads. The
   combined treatments totaled approximately 70 percent of
   the recommended treatment for the lake (recommended treatment was 86 mg AI/L, 120 g/m2).
   Monitoring data from 2012 were compared against monitoring data collected between 2010 and
   2011 to analyze the results of the treatments (Tetra Tech, Inc. 2013). While the assessment also
   included analysis of algal biomass and aluminum in the water column and sediments, this
   summary focuses on  total phosphorus (TP).
              Figure CS5-1. Grand Lake St. Marys watershed
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Monitoring and Evaluating Nonpoint Source Watershed Projects
                                                                                 Chapter 7
   Monitoring and Sampling
   Data from eleven water column
   monitoring sites were used in the
   assessment, with the five lake sites
   (shown in Figure CS5-2) sampled every
   two weeks after alum treatment.
   Samples at these five sites were always
   collected at 0.5 m from the surface,
   while some sampling events also
   included samples at the bottom of the
   water column. Samples were analyzed
   forTP, soluble reactive phosphorus,
   alkalinity, and chlorophyll. The Ohio
   Environmental Protection Agency
   (OEPA) also conducted routine sampling
   of tributaries, with  sample analysis
   including TP (Tetra  Tech, Inc. 2013).
                                         USGS Flow Station
                                         Sampling Stations
                                         WWTP
                                         Places
                                        Figure CS5-2. Tributary and lake sampling stations
Mass-Balance Approach
The mass-balance approach helped
estimate internal TP loading before and
after alum treatment. This approach consisted of five basic steps: (1) Estimating the water budget
for GLSM; (2) Developing a basic P budget for the same time period as the water budget (May
2010 through May 2011 prior to any alum addition); (3) Predicting GLSM mean TP concentrations
using a P mass balance model for which input values are based on available monitoring data for
inflows and outflows; (4) Comparing estimated GLSM mean TP concentrations with measured TP
concentrations; and (5) Adjusting the rates of P sedimentation and release of P into the water
column (internal loading) to match predicted with measured TP concentrations in  GLSM (Tetra
Tech, Inc. 2013).
   Water budget
   A water budget for GLSM was determined at a two-week time step. Change in lake storage was
   determined using the following equation:
           Change in GLSM lake storage = Inflow (creek and WWTP inputs) + Precipitation -
           Outflow (water treatment plant withdrawal, groundwater loss, outlets) - Evaporation
           + Groundwater

   The only tributary for which flow data were collected continuously was Chickasaw Creek where
   USGS has a gaging station (see Figure CS5-2). Wastewater treatment plant (WWTP) flow volumes
   were obtained from WWTP records and removed from the creek flow volumes so that loads from
   the four WWTPs in the watershed to GLSM could be calculated separately. Flow volumes from
   ungaged tributaries and areas draining directly to the lake were estimated by multiplying the
   adjusted Chickasaw Creek flow (minus WWTP) by the ratio between the other contributing
   drainage and Chickasaw Creek drainage areas. If creeks were observed to be dry, the flow was
   assumed to be zero for that period (Tetra Tech, Inc. 2013).
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Monitoring and Evaluating Nonpoint Source Watershed Projects                                     Chapter 7
   Precipitation records were obtained from a nearby weather station and multiplied by the surface
   area of the lake to get a volume of direct inflow from precipitation. Monthly mean pan
   evaporation rates were taken from the Hydrologic Atlas for Ohio (Harstine 1991; after Farnsworth
   and Thompson 1982).

   Groundwater inflow was negligible and the rate for groundwater loss was assumed based on
   productivity of the underlying aquifer. This rate was adjusted such that there was more loss or
   recharge during the drier months when there was no outflow. Daily WWTP withdrawals were
   obtained from plant records. GLSM has two spillways, neither of which is continuously gaged.
   Lake level data were used to determine when losses would occur over the spillways and two
   instantaneous flow measurements were used to check estimated flows over the west spillway
   which is the major outflow. Outflow over the east spillway was assumed to be 10 percent of the
   west spillway outflow based on communication with local experts (Tetra Tech, Inc. 2013).

   Total Phosphorus mass-balance model
   A TP mass balance model was developed using the same two-week time step as used for the
   water budget (Perkins et al.  1997; Tetra Tech, Inc. 2013). Mass was estimated for two-week
   periods by multiplying the estimated flow volume and mean TP concentration. The principal use
   of the mass-balance model was to estimate changes in internal P loading for GLSM based on input
   of measured and estimated values for other terms  in the model. Model calibration was based on
   matching predicted with measured lake TP concentration (Tetra Tech, Inc. 2013).

   The following model was used to predict whole lake TP concentrations:
           dTP/dt = Wext + Wint - Ws - Wout,

   where Wext is external loading, Wint is internal loading, Ws is loss to sediments, and Wout is loss
   through the lake outlet. Predicted whole-lake TP concentrations were compared to observed
   whole lake mean TP concentrations determined from monitoring at the five lake sites (Figure
   CS5-2).

   Tributary TP concentrations  were based on samples collected by OEPA during its routine
   monitoring. An average of all tributary TP concentrations was used for the ungaged portion of the
   basin. The TP concentration  in direct precipitation was assumed to be 20 u.g/L based on an
   average areal loading rate at Lake Erie from 1996 to 2002 (Dolan and McGunagle 2005).
   Concentration data for WWTPs were obtained from OEPA where available, and a concentration of
   2 mg/L based on an OEPA analysis was assumed otherwise.

   Assuming complete mixing, all but one outflow TP concentration was set equal to the whole lake
   average TP concentration predicted by the model. The actual measured TP concentration of the
   outflow, 210 u.g/L, was used in the model  for a single, very large storm event. Sedimentation rates
   (loss of TP to sediments) and sediment release rates (internal loading) of TP were adjusted in the
   model to reflect alum applications and to  improve the relationship between predicted and
   measured lakeTP concentrations (Tetra Tech, Inc. 2013).


   Results
   The phosphorus mass balance model was used to determine whole-lake mean TP concentrations
   based on external loading, internal loading, TP sedimentation, and TP loss through outflows.
   Whole-lake mean TP concentrations predicted by the 2012 model were compared to observed
   concentrations as collected and analyzed by OEPA. Sedimentation rates were adjusted to fit the
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Monitoring and Evaluating Nonpoint Source Watershed Projects
                                   Chapter 7
   predicted to measured TP concentrations in the lake (Figure CS5-3). With the 2012 model thus
   calibrated,  results were compared with those from 2010 and 2011 to determine if changes in
   internal TP loading had occurred as a result of alum treatments (Tetra Tech, Inc. 2013).
              300
           ._. 250

           ~ 200
           _o
           I 150
            01
            u
            §100
               50
    —•—Predicted (Pink-Calibrated 2010; Green-Summer 2011) -m-Predicted (2012)*  Observed

   Figure CS5-3. GLSM predicted vs. observed TP concentrations from May 2010 through October
   2012 (Adjustments made to internal loading estimates to match predicted November 2011 -
   October 2012 values to observed TP concentrations)

   Table CS5-1 shows that gross summer internal TP loading to GLSM declined steadily from 2010 to
   2012. The mass-balance modeling showed that average summer internal loading rate decreased
   from 4.0 mg/m2 per day before alum treatment to 1.8 mg/m2 per day after the two alum
   treatments, even though the combined 2011 and 2012 treatments totaled only 70 percent of the
   recommended treatment for the lake (Tetra Tech, Inc. 2013).

   Table CS5-1. Comparison of internal TP loading in GLSM (2010-2012)
                                                   2010
                 2011
                 2012
   Total Gross Summer Internal TP Load (kg)
26,470
16,487
11,374
   Average Summer Internal Loading Rate (SRR)
   (mg/m2-day)
     4.0
     2.4
     1.8
   (Tetra Tech, Inc. 2013)
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Monitoring and Evaluating Nonpoint Source Watershed Projects                                    Chapter 7
   References
   Dolan, D.M. and K.P. McGunagle. 2005. Lake Erie total phosphorus loading analysis and update:
        1996-2002. Journal of Great Lakes Research 31 (Suppl. 2):ll-22.
        .
        Accessed November 2013.

   Farnsworth, R.K. and E.S. Thompson. 1982. Mean Monthly, Seasonal, and Annual Pan Evaporation
        for the United States. NOAA Technical Report NWS 34. National Oceanic and Atmospheric
        Administration, National Weather Service, 82 pp.
         Accessed November
        2013.

   Harstine, L.J. 1991. Hydrologic Atlas for Ohio: Average Annual Precipitation, Temperature,
        Streamflow, and Water Loss for a 50-Year Period, 1931-1980. Ohio Department of Natural
        Resource Division of Water, Ground Water Resources Section. Water Inventory Report No.
        28.

   Moorman, J., T.Hone, T.Sudman Jr., T.Dirksen, J. lies, and K.R. Islam. 2008. Agricultural impacts on
        lake and stream water quality in Grand Lake St. Marys, Western Ohio. Water, Air, and Soil
        Pollution. 193:309-322.

   ODNR. 2013. Grand Lake St. Marys State Park. Ohio Department of Natural Resources.
        . Accessed November 2013.

   Perkins, W.W., E.B. Welch, J. Frodge, and T.  Hubbard. 1997. A zero degree of freedom total
        phosphorus model: Application to Lake Sammamish, Washington, Lake and Reserv. Manage.
        13:131-141.

   Tetra Tech, Inc. 2013. Preliminary Assessment of Effectiveness of the 2012 Alum Application-
        Grand Lake St. Marys.
        . Accessed November 2013.
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Monitoring and Evaluating Nonpoint Source Watershed Projects                                      Chapter 7
7.6.3 Estimate Land Treatment Needs
A watershed project must set land treatment goals based on estimates of pollutant reductions needed and
the BMPs available to accomplish those reductions. Where aquatic habitat improvement is needed, the
project's plan must also be based on an assessment of the change in habitat parameters (e.g., water
temperature, cobble embeddedness, flow characteristics as well as pollutant loadings) needed to support
aquatic life. Various approaches to determining land and in-stream treatment needs to restore and protect
aquatic habitat have been documented (e.g., OWEB 1999, Rosgen 1997). Obviously, the BMPs selected
must be those capable of addressing the pollutants and sources identified in the planning process.  Setting
goals for the level and extent of BMP implementation is necessary, but is an inexact science, partially
because of the largely voluntary (and hence poorly predictable) nature of land treatment programs, and
partially because it is  difficult to predict water quality response to BMP implementation at the watershed
level. See USEPA (2008) for a comprehensive discussion of watershed project planning.

Where local data on BMP performance exist (e.g., a documented 45 percent reduction in suspended
sediment load through a water and sediment control structure or a 25 percent reduction in runoff
phosphorus concentration from fields in conservation tillage), they can be applied to estimate pollutant
reductions anticipated from different levels of implementation. Where locally-validated data do not exist,
there is ample information in published literature (e.g.,  Simpson and Weammert 2009, USDA-NRCS
2012). Planners should use caution when applying performance data from other studies  due  to potential
local site differences.

It should be noted that published BMP efficiencies do not generally account for interactions in multiple
practice systems or address pollutant transport or delivery processes beyond the edge of field or BMP site
scale. Modeling, e.g. the Soil Water Assessment Tool (SWAT), may be a better method for  estimating
treatment needs because some models account for routing of BMP effects through a watershed. Simple
pollutant load estimation tools such as USEPA's STEPL (Spreadsheet Tool for Estimating Pollutant
Load) can be used to provide general estimates of load reductions achievable via various BMP
implementation options, but STEPL, for example, addresses a limited set of pollutants and simulates a
limited set of BMPs.


7.6.4 Estimate Minimum  Detectable Change
One critical step in watershed project planning is to use the data that have already been collected to
evaluate the Minimum Detectable Change (MDC), the smallest monitored change in a pollutant
concentration or load over a given period of time required to be considered statistically significant.
Understanding of the  MDC will assist in planning both land treatment and water quality monitoring
design and will support predictions of project success. See section 3.4.2 for details.

The basic concept in the calculation of MDC is simple: variability in water quality measurements is
examined to estimate  the magnitude of changes in water quality needed to detect significant differences
over time. The MDC is  a function of pollutant variability, sampling frequency, length of monitoring time,
explanatory variables or covariates (e.g., season, meteorological, and hydrologic variables) used in the
analyses which 'adjust' or 'explain' some of the variability in the measured data, magnitude and structure
of the autocorrelation, and statistical techniques and the significance level used to analyze the  data. In
general, MDC decreases with an increase in the number of samples and/or duration of sampling in a
monitoring program.
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Monitoring and Evaluating Nonpoint Source Watershed Projects                                      Chapter 7
The MDC for a system can be estimated from data collected within the same system during the planning
or the pre-BMP project phase or from data collected in a similar system, such as an adjacent watershed.

As noted above, MDC is influenced by the statistical trend test selected. For the MDC estimate to be
valid, the required assumptions must be met. Independent and identically distributed residuals are
requirements for both parametric and nonparametric trend tests. Normality is an additional assumption
placed on most parametric trend tests. However, parametric tests for step or linear trends are fairly robust
and therefore do not require 'ideally' normal data to provide valid results.

The standard error on the trend estimate, and therefore, the MDC estimate, will be minimized if the form
of the expected water quality trend is correctly represented in the statistical trend  model. For example, if
BMP implementation occurs in a short period of time after a pre-BMP period, a trend model using a step
change would be appropriate. MDC in this case is an extension of the  Least Significant Difference (LSD)
concept (Snedecor and Cochran 1989). If the BMPs are implemented over a longer  period of time, a
linear or ramp trend would be more appropriate. Calculation of the MDC is discussed in detail in Spooner
et al. (201 la) and illustrated in section 3.4.2. The reader is advised to consult that publication to calculate
and apply the MDC analysis.

MDC provides an excellent feedback to whether the planned BMPs (type and location, acres served) will
result in an amount of change in pollutant concentration or loads that can be statistically documented.
Results of the MDC analysis can also be applied to the design of a long-term monitoring program
(e.g., sampling frequency, monitoring duration). Decisions about data analysis such as the use of
covariates to reduce effective variability and thereby reduce MDC can be made, or MDC calculations can
be used to better understand the potential and limitations of an ongoing monitoring  effort. Note that the
MDC technique is applicable to water quality monitoring data collected under a range of monitoring
designs including single fixed stations and paired watersheds.  MDC analysis can  be performed on
datasets that include either pre- and post-implementation data  or just limited pre-implementation data that
watershed projects have in the planning phase.


7.6.5 Locate Monitoring Stations
The general location of monitoring stations is described for each monitoring design in section 2.4.
Analysis of pre-project data, in conjunction with monitoring objectives, can provide insight into optimum
location of monitoring stations to be used in watershed project effectiveness evaluation. Section 3.3
provides a discussion on how site characteristics, access, and logistics influence decisions on locating
monitoring stations. Spatial analysis of land use  and management data, including understanding of
relationships between land use and management patterns and water quality (see section 7.5.2.3) can be
used to inform monitoring site selection. Inferences on critical source  areas (section 7.5.2.4) should also
be used to guide station location. Subwatersheds showing very high and very low NCh+NOs-N
concentrations in Figure  7-19, for example, might be selected  for monitoring as treatment and control
watersheds, respectively.


7.7  Data Analysis for Assessing Individual BMP Effectiveness
The availability of BMPs that perform a known water quality function is fundamental to NPS watershed
projects. Many practices  have a long history (e.g., buffers,  conservation tillage for erosion control,
grassed waterways) and their efficiency in reducing NPS pollutants is well-documented by research,
although highly variable  depending on site, management, and  other factors. The performance of other
                                              7-56
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Monitoring and Evaluating Nonpoint Source Watershed Projects                                       Chapter 7
BMPs, such as novel practices or practices not common locally, may not be fully understood. In such
cases, and in cases where specific assurance that BMPs will perform adequately in local circumstances is
required, the effectiveness of individual BMPs may be assessed through monitoring.

Common monitoring designs for assessing BMP effectiveness include:
  "   Plot studies
  "   Input/output at the BMP practice scale
  "   Above/below at the site scale
  "   Paired watershed at the edge-of-field scale

Data analysis for above/below and paired-watershed BMP monitoring is  essentially the same as for these
designs at the watershed project level (see section 7.8). This section will  focus on discussion of data
analysis for plot studies and for BMP input/output studies.


7.7.1 Analysis  of Plot Study Data
Controlled, replicated plot or field studies are effective for testing specific practices of undocumented
effectiveness or evaluating the effectiveness of a BMP program or system at a farm or watershed scale
(USEPA 1997b). To  some extent, plots represent microcosms of an area where a full-scale BMP might be
applied, where inputs, management, and outputs can be controlled and measured to a degree that would
be extremely challenging at full  scale. Most importantly, because plots are small (often less than 100 m2),
it is possible to  test different levels of treatment and replicate treatments in the same experiment, thus
potentially capturing enough variability to have some statistical confidence in the outcome.

As discussed in section 2.4.2.2, there  are a variety of plot study designs, including factorial experiments,
Latin Squares, and complete and incomplete block designs. Approaches to analyzing data from these
various options  differ to some degree, but most follow three basic steps:
  "   Test to see if there are significant differences among the treatments
  "   Test to find which treatments are significantly different
  •   Determine the magnitude of differences

Statistical approaches discussed in this section focus on one- and two-factor designs (generally
Randomized Complete Block, RGB). Readers should consult statistics textbooks and other resources for
information on procedures to analyze data from the more complicated designs such as Latin Squares and
incomplete block designs.

Data from simple plot studies are usually analyzed using ANOVA (parametric) or the Kruskal-Wallis test
(nonparametric). These procedures allow the determination of significant differences in group means for
pollutant concentration or load coming from plots. When a plot study is conducted for a single
precipitation/runoff event (either natural or simulated rainfall),  the groups tested would be the replicate
plots for each type or level of treatment, plus control plots. For a plot study conducted over a series of
events, the groups tested could be data from replicate groups within individual events or mean
concentration or total load over the entire series of events, depending on the study design. Note that the
ANOVA and Kruskal-Wallis procedures only document that one or more group means differ significantly
from the other groups. To determine which of the group means are significantly different, use a multiple
                                               7-57
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Monitoring and Evaluating Nonpoint Source Watershed Projects                                      Chapter 7
comparison test such as Tukey's or the Least Significant Difference tests (Snedecor and Cochran 1989,
USEPA 1997b). Applications of the Least Significant Difference and Tukey's tests are illustrated in
section 4.6.1 (pages 4-55 to 4-56) and 4.6.4 (pages 4-63 to 4-64), respectively, of the 1997 guidance
(USEPA 1997b).

The ANOVA procedure can also be used where there is more than one factor or explanatory variable
(e.g., plot, slope), whereas the Kruskal-Wallis test handles only one factor. The Friedman nonparametric
test is recommended for more than one factor. Application of these tests is described and illustrated in
section 4.6 (pages 4-52 to 4-64) of the 1997 guidance (USEPA 1997b).

One-factor comparisons using ANOVA assume random samples, independent observations, and normal
distributions for each group, as well as the same variance across groups. Group sample sizes can differ,
however. An illustrative example application of the Kruskal-Wallis test for one-factor comparisons is
included in the 1997 guidance (USEPA 1997b), pages 4-56 to 4-58.

Two-factor comparisons using ANOVA depend on whether the factors interact.  An example of an
interaction is the relationship between crop yield and precipitation, both of which can independently
influence soil nitrate levels; greater yields remove more nitrate from the soil profile and greater
precipitation moves more nitrate through the soil profile. Yield, however, is also influenced by
precipitation (e.g., drought or excessively wet soil conditions), so there is an interaction between the two
factors. The plot study analysis from Vermont (see Example 7.7-1) illustrates consideration of
interactions.

Both the scope of inferences that can be made and the F statistic calculation differ for fixed effect models
(e.g., rainfall simulation studies in which rainfall rates are not  randomly selected) versus models using
randomly selected or combinations of randomly selected and fixed factors. Readers are recommended to
section 4.6.2 (pages 4-58 to 4-61) of the 1997 guidance (USEPA 1997b) for an illustrative example and a
discussion of these and other important considerations when applying ANOVA to two-factor
comparisons. If the data are log-transformed prior to ANOVA, the treatment effects are then interpreted
as multiplicative (rather than additive) in the original units. An alternative approach is to rank-transform
the data prior to ANOVA, resulting in a comparison of the medians of the data in the original units (see
pages 4-61 of the 1997 guidance for details).

Once a statistically significant difference has been demonstrated and the different group means have been
identified, it is possible to explore the magnitude of such differences. Methods for two random samples,
two paired samples, or a single sample versus a reference (e.g., criterion for a WQS) are described  in
section 4.5.3 (pages 4-51 to 4-52) of the 1997 guidance. It is important to take the extra step of
determining confidence intervals for difference estimates.

In addition to using statistical tests to document differences among treatment groups, plot data can  be
evaluated by direct comparison of event mean concentration (EMC) or event load (or areal load) among
treatments. For plot studies evaluating practice performance over a series of events, a cumulative export
plot (where the sum of cumulative mean export from each group is plotted sequentially over the study)
will illustrate the behavior of treatment groups in different events. It must be cautioned that data and
quantitative inferences about practice performance from plots  are usually very difficult to extrapolate to
field or watershed scale because physical processes like runoff velocity are not well-represented in very
small areas.
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Example 7.7-1. Plot Study Analysis:  Bacteria Runoff from Manure Application in Vermont
Objective
Evaluate several practical methods for controlling
£. co/; in runoff from manure application sites.
Specific objectives included: (1) determine the
effect of manure storage time on £. co/;  losses in
runoff from hay and corn land receiving  liquid dairy
manure; (2) determine the effect of manure
incorporation on £. co/; losses from corn land
receiving  manure; (3) determine the effect of
vegetation height on £. co/; losses in runoff from
hay land;  and (4) determine the effect of delay
between manure application and rainfall on £. co/;
losses in runoff from hay land and  corn land.

Monitoring Design
Two runoff experiments were conducted at
separate hay land and corn land sites. For each
experiment, 40 1.5- by 3-m plots were created,
representing a factorial design of 3 replicates for
each treatment combination, 3 manure ages,
2 vegetation heights (for hay) or
incorporated/unincorporated (for corn), 2 delay to
rain durations, resulting in 3x3x2x2 (36)
treatments, plus three control plots (no manure
applied), and one extra plot reserved as a backup.
Specific treatments were assigned to  plots
randomly. A rainfall simulator was  used  to
generate  runoff from the test plots  by  continuously
and uniformly applying water at an intensity
resembling natural rainfall. For each experiment,
the first hour or first 19 L of runoff was collected
from each plot.

   ANOVA table for hay land runoff experiment. Results show
   significant manure age, delay to rain, and two interactions
Analysis of Variance
Source
Model
Error
Total
df
7
26
33
Sum of
Squares
34.8385
3.4846
38.32311
Mean of
Squares
4.9769
0.1340

F Ratio
37.135


P
<0.001


Effects Tests
Source
Manure Age
Vegetation Height
Delay to Rain
Manure Ag x
Vegetation Height
Vegetation Height
x Delay to Rain
df
2
1
1
2
1
Sum of
Squares
31.1188
0.0673
0.602
0.7427
1.2076






F Ratio
116.096
0.502
4.494
2.771
9.011
P
<0.001
0.485
0.044
0.081
0.006
 A.T

 i6-:
    6-
  i   -
  ,!!  2 -
            a       Manure Age

                         b
B. -
I 6-
I 5-
!<-!
1 3-
           0-day        30-day
                  Treatment Factor

                        Delay
                                    90-day
                 I
              1-day                3-day
                   Treatment Factor

 Levels of £. co// in hay land plot runoff by two treatment factors.
 Error bars represent+1 standard deviation; bars labeled with
 different letter(s) differ significantly (P < 0.1).
Data Analysis
Statistical analysis of £. co/; data was conducted
on logio transformed data to satisfy the
assumptions of normality and equal variances. All
statistical tests were performed using JMP
software at an a of 0.1. The effect of treatment on
levels of £. co/; in runoff was evaluated by multi-
factor analysis of variance (ANOVA). After an
initial pass that included all treatment factors and
all possible interactions, nonsignificant (P > 0.1)
interactions were removed from the model and a
final reduced-model ANOVA was conducted.
Interpretations of treatment effects were based on
the reduced model.
                                                                             Source: Meals and Braun 2006
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Monitoring and Evaluating Nonpoint Source Watershed Projects                                      Chapter 7


7.7.2 Analysis of BMP Input/Output Data
For some BMPs, such as agricultural water and sediment control basins or stormwater treatment devices,
it is possible to assess practice effectiveness by directly monitoring input and output pollutant
concentration and load. In either an agricultural or an urban setting, inflow and outflow variables such as
flow volume, peak flow, EMC, or pollutant loads, are measured and the effectiveness of the BMP is
calculated by comparing input vs. output.

Paired input and output data can be compared by testing for significant differences in group means using
the parametric paired Student's t or the nonparametric Wilcoxon Rank Sum test. Comparison of random
observations from two samples (e.g., input and output from a large constructed wetland for which it is not
possible to collect paired samples due to uncertain or variable flow pathways or time of travel) can also be
made with a t-test if equal variance is confirmed (e.g., F test); the Mann-Whitney test is the nonparametric
alternative in this case. These tests are described and illustrated in detail in chapter 4 (pages 4-34 to 4-52)
of the 1997 guidance  (USEPA 1997b).

Once a statistically significant difference is confirmed, BMP efficiency can be reported in a number of
ways, including:
   «   Efficiency ratio (percent reduction in flow,  EMC,  or load),
   •   Summation of loads (percent reduction in sum of all monitored loads)
   •   Regression of loads (reduction efficiency is expressed  as the slope of a regression line for input
      load vs. output  load)
   «   Efficiency of individual storm load reductions across all monitored events
   •   Percent removal relative to a water quality criterion

All of these methods are described and illustrated by Geosyntec and WWE (2009). It is recommended
that more than one method is used wherever possible because the results may differ. For example, results
from the summation of loads and efficiency ratio (e.g., EMC) methods may not agree because of
differences in how the water budgets are represented (Erickson et al. 2010b).

The EMC is the total  event load divided by the total runoff volume. It should be noted that, for large
practices such as some constructed wetlands, the influent EMC (EMCi) must be adjusted to account for
rain that falls directly onto the practice (Erickson  et al. 2010a). Long-term performance can be determined
by calculating the average EMCs (AvgEMC) for both influent (input or AvgEMCi) and effluent (output
or AvgEMCo) and using these values to calculate the percent reduction in concentration (Erickson et al.
2010b). The simple equation becomes:


                                                     (AvgEMCj - AvgEMC0\
                  Long - Term Efficiency =  100 x —	
                                                     V       AvgEMC,       I

An alternative approach that can add statistical power is to pair the input and output EMCs for each storm
and calculate the average of the differences as an estimate of pollutant reduction efficiency. A paired li-
test can then be  used to determine both the statistical significance of and confidence interval for the
reduction. See section 4.2.1  (pages 4-11  to 4-14) of the 1997 guidance (USEPA 1997b) for additional
information and an illustrative example of EMC calculations.
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Monitoring and Evaluating Nonpoint Source Watershed Projects
Chapter 7
The percent reduction in the sum of all monitored loads is calculated using the summed loads for both the
input (Li) and output (Lo):
                              Percent Reduction = 100 x
                                                           (LI -
Similar to the alternative proposed for EMCs, the average differences between paired input and output
loads can also be used as an estimate of pollutant reduction efficiency.

Erickson et al. (201 Ob) illustrate a method for determining the uncertainty of long-term performance
estimates that are based on either the EMC or summation of load method they describe. Required input is
the number of storm events, the standard deviation of the performance data, and a Student's t value.

Using data from Erickson et al. (201 Ob), Figure 7-21 illustrates regression of effluent against influent
event loads. It should be noted that in this example the y-intercept was not constrained to the  origin as
recommended9 by Geosyntec and WWE (2009). The slope of the line indicates that effluent concentration
is 37 percent of influent concentration above the baseline level (y intercept) of 0.01 kg TP. In other
words, the BMP reduces the load by 63 percent (100-37), a number that agrees well with the  57.5 percent
removal rate calculated by summation of loads (Erickson et al. 2010b). Regression analysis is illustrated
and described at CADDIS Volume 4: Data Analysis.
Regression of Effluent vs. Influent Load
0~)t.
n 7
3
•D
ra
5 n IR
Q_
"S
i~ n 1
^ U.I
01
3
it
LU n OR

y = 0.3731x + 0.0109
R2 = 0.6012 ^
+ + ^
^^ *
^^
* ^^
^
0 0.1 0.2 0.3 0.4 0.5 0.6
Influent Total P Load (kg)
Figure 7-21. Regression of output versus input load (data from Erickson et al. 201 Ob)
9 While specified in the definition of the regression of loads method, Geosyntec and WWE (2009) includes a
comment suggesting that such a constraint "is questionable and in some cases could significantly misrepresent the
data."
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Monitoring and Evaluating Nonpoint Source Watershed Projects
                                         Chapter 7
BMP efficiency evaluated by input/output
monitoring is frequently reported as simply
percent removal of a pollutant. In most cases,
this is an inadequate basis for assessing BMP
performance. Percent removal is primarily a
function of input quality, and BMPs with a
high apparent removal percentage may still
have unacceptably high concentrations or
loads in their output. Some BMPs with long
retention times (e.g., constructed wetlands)
show long-term performance that is not
evident in comparing  paired input-output
samples because material from one event is
not discharged until a subsequent event (i.e.,
the samples are not paired or matched).
Finally, a simple percent removal calculation
can be dominated by outliers that distort an
average performance indicator.

For these and other reasons, USEPA and
ASCE have recommended the Effluent
Probability Method for evaluating
input/output data from a BMP (Geosyntec and
WWE 2009). In this procedure, a statistically
significant difference  between input and
output EMC or load is verified (e.g., by
Student's t Test). Then, a normal probability plot is constructed of input and output data that allows
comparison of BMP performance over the full range of monitored conditions. For example, Figure 7-22
shows an effluent probability plot for chemical oxygen demand (COD) from an urban wet detention pond
evaluation The plot shows that COD was poorly removed at low concentrations (<20 mg/L), but that
removal increased substantially for higher concentrations.

The Effluent Probability Method is essentially a cumulative distribution function for the EMCs of the
inflows and outflows. The cumulative distribution function depicts the probability of values being below
a given EMC value or the EMC values that a percentage (e.g., 50 percent) of the data falls above.

The magnitude of the difference in EMC (or loads) from the inflow and outflows can be examined across
the range of EMC values. The Kolmogorov-Smirnov test is based on cumulative distribution functions
and can be used to determine if the two empirical distributions are significantly different (Snedecor and
Cochran  1989).
  Constructing an Effluent Probability Plot
The cumulative distribution function for the EMCs
for the outflows and inflows can be created from
the following steps:
  •   Calculate the EMC for each storm's outflows.
  •   Rank all EMCs for all storms from smallest to
      largest.
  •   Assign a 0 to 1 'probability' to the data based
      upon their ranked order. For example, if 10
      storms were monitored, the ranked values
      would receive a 'probably ranking' value of
      0.1, 0.2, ... 1.0 for the lowest to highest EMC
      values.
  •   Plot the 'probability ranking' values on the
      Y-scale and the EMCs on the X-scale. The
      Y-scale should be plotted on a probability
      scale. Alternatively, the Y-axis could be
      expressed as the number of standard
      deviations (e.g., +/- 3). Because the EMCs
      are likely to follow a log-normal distribution,
      the X-axis should be a log scale.
  •   Repeat the procedure for the inflows and plot
      on the same graph.
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Monitoring and Evaluating Nonpoint Source Watershed Projects
                         Chapter 7
    100%
     80%
     60%
     40%
     20%
                                  Effluent Probability Plot
                         10C
101
102
                                        TSS (mg/L)
Figure 7-22. Effluent probability plot for input/output monitoring of a wet detention pond
                       Percent Removal vs Criterion = 100 x
 (C/ - C0)
 (C, - Cc)
Percent removal relative to a water quality criterion provides an indication of how well a BMP is
performing compared to limits or expectations established for the local waterbody. Use of this method is
recommended for specific event analysis, but not for a series of events (Geosyntec and WWE 2009).
Calculation requires values for the criterion (Cc), input (Ci), and  output (Co), all expressed in the same
units (concentration in this case):

For example, in a watershed with a target total N concentration of 0.75 mg/L, storm inlet and outlet
concentrations of 3.6 mg/L N and 1.6 mg/L N, respectively, would yield a relative  percent removal of
70 percent.

The reader is referred to Urban Stormwater BMP Performance Monitoring (Geosyntec and WWE 2009)
for additional information on evaluating urban storm water BMP  performance through monitoring.


7.7.3 Analysis of BMP Above/Below Data
As noted earlier, BMP performance can be assessed using an above/below-before/after monitoring design,
as long as the added area monitored by the downstream station is either entirely or predominantly
influenced by the BMP. In such cases, analysis of monitoring data is done by the same approach as
described in section 7.8.2.2. An example of this kind of above/below-before/after analysis of a single
BMP can be found in the Otter Creek (WI) NNMP project, which assessed the effects of barnyard runoff
control (see Example  7.7-2). This example illustrates application of the Hodges-Lehmann estimator
described in section 4.5.3 of the 1997 guidance (USEPA 1997b).
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Monitoring and Evaluating Nonpoint Source Watershed Projects
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 Example 7.7-2. Above/Below-Before/After Analysis: Barnyard Runoff BMPs in Wisconsin

 Monitoring Design
 Sampling stations upstream and downstream of two investigated dairy barnyards were established in
 1994/1995. At the upstream sampling stations, stream stage and precipitation were continuously
 monitored, and discrete water samples were collected automatically; at the downstream stations, only
 water quality samples were collected. Over the course of the study,  11-15 storm runoff periods were
 sampled at each of the sites. Continuous streamflow and instantaneous concentration data were used to
 estimate pollutant loads for individual storm-runoff periods.

 Pre-BMP Analysis
 A critical aspect of obtaining useful conclusions for this study was the ability to document that
 downstream loads were significantly greater than upstream loads before the BMP systems were
 implemented. Results of t-Tests showed that, for the pre-BMP period at both creeks, downstream loads
 of total P, ammonia, BOD, and fecal coliform bacteria were significantly greater than upstream loads. At
 Otter Creek, pre-BMP downstream loads of total suspended solids also were significantly greater than
 those upstream. These significant differences indicated that each barnyard was an important contributor
 to the instream pollutant loads for the storm-runoff periods monitored.

 Effects of Treatment
 The difference between upstream and downstream constituent loads was computed for each pre- and
 post-BMP storm-runoff period. These differences were considered to be the load contributed by each
 barnyard. The bar graphs indicate that both barnyard BMP systems  have reduced loads in the stream for
 each constituent. Each bar represents the median of all the differences between upstream and
 downstream constituent loads for both  pre- and post-BMP storm-runoff periods. Although these medians
 could have  been used to determine the percentage reduction achieved by each barnyard BMP system, it
 was decided that use of the Hodges-Lehmann estimator would be a more accurate approach (Helsel and
 Hirsch 2002). The Hodges-Lehman
 estimator is the median  of all possible
 pairwise differences between pre- and
 post-BMP barnyard loads. This median
 difference was then divided by the  pre-
 BMP median  barnyard load for each
 constituent. The result was a percentage
 load reduction for each constituent.
 The barnyard BMP system at Otter
 Creek reduced loads of total suspended
 solids by 85 percent, total P by 85
 percent, ammonia by 94 percent, BOD
 by 83 percent, and microbial loads of
 fecal coliform bacteria by 81 percent; the
 respective loads at Halfway Prairie
 Creek have been reduced by 47, 87, 95,
 92, and 9 percent.
                OTTER CREEK
 N  .'la;  :1J
                                   Fecal Colflorm *
* Par-carnage raduclicn ia computed by dry id ing the H edges- La hm ann estimator for pra-and poal-
 BMP barnyard loads bythepra-BMP median barnyard bad.
" Fecal Coliform microbial load in 1011 coloniaa.
                                                               Source: Stuntebeck and Bannerman 1998
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7.7.4 Analysis of BMP Paired-Watershed Data
Some BMPs - especially agricultural BMPs that involve treatment of an entire field such as conservation
tillage, cover crops, or nutrient management - can be evaluated using a paired-watershed design. In this
case, monitoring takes place at the edge of field-sized watersheds, wherein one entire monitored field is
designated to receive the BMP treatment. Automated samplers are required to collect storm event runoff.
In the paired-watershed design, monitoring occurs during a calibration period in which both fields or
subwatersheds have identical management. Then, after their pollutant responses to the same rainfall
events are correlated, a treatment period occurs in which one of the subwatersheds receives the BMP
treatment and the other remains in the 'controlled' management. Analysis of covariance (ANCOVA) is
used to analyze the monitoring data from this type of study.  See section 7.8.2.1 for details.


7.8  Data Analysis for Assessing Project Effectiveness

7.8.1 Recommended Watershed Monitoring Designs
Assessing the effectiveness of a watershed project where multiple BMPs are implemented in a land
treatment program across a broad watershed area is a complex task with many sources of variability and
uncertainty. Attributing changes in water quality documented through monitoring to land treatment, rather
than to other causes such as drought or extreme weather, is another significant challenge. Monitoring
designs (see chapter 2) recommended for assessing watershed project effectiveness are:
  •  Paired-watershed (link to section 2.4.2.3)
  •  Above/below-before/after (link to section 2.4.2.6)
  •  Nested-watershed (link to 2.4.2.3)
  •  Single watershed trend (link to section 2.4.2.5)

While not generally recommended because of cost and logistical constraints (see section 2.4.2.8), data
analysis for multiple-watershed studies is also discussed here. These designs vary in their ability to
evaluate watershed project effectiveness while controlling for sources of change other than land
treatment; the designs also vary in the appropriate approach  to data analysis. The paired-watershed design
is generally considered to be the best design for this purpose because it strives for a controlled experiment
to evaluate BMP effectiveness at a watershed scale, accounting for year-to-year variability in weather and
streamflow through the use of a control watershed.  Several common watershed project designs are
excluded from the above list because they are not generally capable of reliably documenting water quality
change and attributing the change to land treatment. Single watershed before/after and side-by-side
watersheds, for example, cannot be recommended for watershed project effectiveness monitoring because
they cannot be used directly to separate the effects of the BMPs from those of climate  or watershed
differences (e.g., soils, slope, land management) which may be the actual causes of the observed
differences (see section 3.4). The single watershed before/after design can, however, be useful in
comparing pollutant loads over time to determine if TMDL goals have been achieved (see section 7.9).

None  of these designs will perform effectively, however, if all the requirements of the design are not met. In
some  cases, failure to meet a single criterion (e.g., unexpected treatment in the control watershed of a paired
design, or changing analytical procedures during a long-term  single-station study) may doom the effort.
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Monitoring and Evaluating Nonpoint Source Watershed Projects
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Each of these designs is discussed in chapter 2; information relevant to data analysis procedures are
provided in this section.


7.8.2 Recommended Statistical Approaches
The following sections recommend statistical approaches to analysis of data from recommended
watershed monitoring designs. Additional details on specific statistical tests can be found in chapter 4
(Data Analysis) of the 1997 guidance (USEPA 1997b).
                                                    Additional Information on ANCOVA
                                                    •   USEPA. 1997b. Monitoring Guidance for
                                                       Determining the Effectiveness of
                                                       Nonpoint Source Controls Chapter 4;
                                                    •   Clausen and Spooner. 1993. Paired
                                                       Watershed Study Design. 841-F-93-009;
                                                    •   Grabow et al. 1999. Detecting Water
                                                       Quality Changes Before and After BMP
                                                       Implementation: Use of SAS for
                                                       Statistical Analysis: and

                                                    •   Grabow et al. 1998. Detecting Water
                                                       Quality Changes Before and After BMP
                                                       Implementation: Use of a Spreadsheet
                                                       for Statistical Analysis of Paired
                                                       Watershed, Upstream/Downstream and
                                                       Before/After Monitoring Designs.
7.8.2.1   Paired Watershed
As described in chapter 2, the most effective
practical design for evaluating watershed-level
BMP effectiveness through monitoring is the
paired-watershed design due to the presence of an
experimental control for year-to-year hydrologic
variability (Clausen and Spooner 1993). The
paired-watershed design has been discussed in
section 2.4.2.3. The basic design involves two
watersheds (a control, where no BMPs are to be
implemented, and a treatment watershed where
land treatment will be applied) and two periods (a
pre-treatment or calibration period, and a treatment
period). Analysis of paired data (i.e., frequently
collected chemical or physical data) from treatment
vs. control areas should show a statistically
significant correlation and result in a strong linear
regression model (usually using log-transformed
data) that changes from the  pre-treatment to post-
treatment period. In the case of biological monitoring (e.g., sampling twice per year), relationships
between treatment and control watersheds should change in a more qualitative manner from pre- to post-
treatment periods. For example, treatment and control watersheds may both be of "poor" quality in the
pre-treatment (or pre-BMP) period, whereas the treatment watershed improves to "good" quality while
the control watershed remains at "poor" quality during the post-treatment period. Additional
considerations for paired-watershed designs with more than one treatment watershed are discussed at the
end of this section.

See section 4.8 of the 1997 guidance (USEPA 1997b) for details and an example including a method for
determining if enough calibration data has been collected to warrant advancing to the BMP treatment
period. Failure to establish a statistically valid pre-treatment correlation will doom the evaluation design.


7.8.2.1.1 Analysis of Covariance (ANCOVA) Procedure - Paired-Watershed Analysis
The Analysis  of Covariance (ANCOVA) procedure is used to analyze data from a paired-watershed study
(Clausen and  Spooner 1993, Wilm 1949, Clifford et al. 1986, Meals 2001). ANCOVA combines the
features of ANOVA with regression (Snedecor and Cochran 1989) and is an appropriate statistical
technique to use in analysis of watershed designs that compare pre- and post-BMP periods using
treatment and control watershed measurements. When applied to the analysis of paired-watershed data,
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ANCOVA is used both (a) to compare pre- and post-BMP regression equations between water quality
measurement values (e.g., sediment concentration) for the treatment and control watersheds and (b) to test
for differences in the average value (e.g., of sediment concentration) for the treatment watershed between
the two time periods after adjusting measured values for covariates such as flow. Covariates are added to
the analysis to decrease the residual error and give a more precise comparison between covariate-adjusted
mean values.

There are three basic steps to performing ANCOVA:
    1.  Obtain paired observations
    2.  Select the proper form of linear model
    3.  Calculate the adjusted means (LS-means) and their confidence intervals

Paired observations could represent observations collected on the same date, the same time period for
composite samples, or from the same storm event. Weekly flow-weighted composite samples taken at the
outlet of both control and study watersheds would satisfy this requirement.

The second step is to select the proper form of the model. There are two basic statistical models here for
paired-watershed studies:
   «   The change in treatment watershed concentration with change in control watershed concentration
      (i.e., the slope of the linear relationship between paired samples) remains constant through both the
      calibration and treatment periods.
   •   The slope of the relationship changes from calibration to treatment period.

ANCOVA for paired-watershed studies is illustrated by Figure 7-23 where pollutant concentration (or
load) pairs are plotted with the treatment basin values on the Y-axis and the control basin values on the
X-axis. The slopes of the pollutant concentrations plotted for both periods are tested to determine if they
are significantly different (see B in Figure 7-23) or if the same slope can be assumed (see A in Figure 7-
23). A change in slope and/or mean value indicates that pollutant concentrations for the treatment
watershed exhibited different patterns, or magnitude, after BMPs were applied as compared to the
calibration period. For example, in both A and B of Figure 7-23 the same concentration in the control
watershed corresponds to a lower concentration in the treatment watershed in the post- (treatment) versus
the pre-BMP (calibration) period, indicating beneficial effects from the BMPs. In the case of B, both the
mean and the slope are reduced in the treatment period. The adjusted mean concentrations (LS-means)  for
the calibration and treatment periods are  also compared for differences as described above under
"ANCOVA Procedure."

The best statistical model for a particular dataset is determined with a test for homogeneity of slopes
(i.e., same or different slopes) using the 'full analysis of covariance model' that allows for separate
regression lines (i.e., different slopes and intercepts, Figure 7-23B) for the calibration and treatment
periods (i.e., the groups) for the regression of the treatment watershed variable (Y) on the control
watershed variable (X):
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Monitoring and Evaluating Nonpoint Source Watershed Projects                                       Chapter 7
               = b0i + / bi; (X^) + 6jj     ("Full statistical model" for different slopes)
Where:
Yij = the jth observation for Y in period i (e.g., pollutant concentration or load from treatment
        watershed)
boi = the intercept (B0) for period i
bii = the regression coefficient (Bi) of Y on X for period i
Xij = the jth observation for X in period i (e.g., pollutant concentration or load from control
        watershed paired with same sample time as Y;J)
k = number of time periods (with 'calibration'  and 'treatment' periods, k=2)
eij = the residuals or experimental error for the j* observation for Y in period i. Note: if the data
        are weekly, biweekly, or monthly, this error series is likely autocorrelated with
        Autoregressive, Lag 1 or AR(1) and depicted as Vij or Vt. A statistical model that allows
        for this autocorrelated error structure should be used (e.g., PROC AUTOREG in SAS
        software (SAS  Institute 2016d) or use  a correction for the standard error on the test of
        LS-means (See section 7.3.6)

The F-Test for the homogeneity of slopes is used to see if the best model requires separate slopes for each
period or the same (pooled) slope (Clausen and Spooner 1993). The best model will have the lowest
residual sum of squares (SSE). The F-statistic for testing the homogeneity of slopes is:
                                           [(SSER - SSEF)1
                               F statistic =  -—-^	—— /MSEF
                                               (k-1)
Where:
SSER = Residual sum of squares for the reduced model with a common (pooled) slope (see
       below)
SSEF = Residual sum of squares for the full model which allows for separate slopes for the
       calibration and treatment periods
k = number of groups (calibration + treatment periods = 2 in this case)
MSEF = Mean square error from the full model

This F-statistic is compared to an F distribution with (k-1) and (N-2k) degrees of freedom (d.f.), where k
is the number of groups and N is the total sample size  (i.e., the total number of paired samples used in the
analysis). See Example 7.8-1 below for examples of how to test if the slopes are different using an
'interaction' term in the statistical software programs.

If there is no evidence for separate slopes, then a "reduced model" with the same slopes assumed for each
group (based on pooled data) should be used (see Figure 7-23 A). If the interaction term is significant,
then the "full model" is the correct model and the  significance of the difference  between all possible pairs
can be obtained (see Figure 7-23B).
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                      Chapter 7
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Figure 7-23. Conceptualized regression plots for paired-watershed data. The red line indicates the
comparison of the treatment watershed from the calibration vs. treatment periods evaluated at the
LSMEANS value of 2.5 (the mean of all sampled values in the control watershed over the entire
sampling duration (both treatment and calibration period).
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     Example 7.8-1. Software Examples for the Statistical Analyses using Analysis of
                  Covariance (ANCOVA) for the Paired-Watershed Study

Statistical software packages may vary in how they address ANCOVA. A few examples are given below.
NOTE: We will provide a sample dataset (e.g., Walnut Creek, IA) and results for this example so users
can test their own techniques and software.

A. SAS Software, assuming no autocorrelation
The SAS (SAS Institute 2010) program statements that generate a covariance model with unique slopes
for each group ("full model", different slopes) are:
       PROC GLM; CLASS  PERIOD;
       MODEL Y = X PERIOD PERIOD*X/ SOLUTION;
       LSMEANS PERIOD/PDIFF;
Where the user inputs the variable names used for their project data for:
       Y = Name of variable which contains the treatment watershed values (e.g., concentration/load)
       X = Name of variable which contains the control watershed values (e.g., concentration/load)
       PERIOD = calibration or treatment period
       PERIOD*X = the "interaction" term that allows for different slopes for each PERIOD
The other terms  are part of the SAS program software syntax. SOLUTION is optional but generates the
regression equation for each PERIOD. The LSMEANS SAS statement generates the LS-means for each
PERIOD. The PDIFF option produces significance tests to compare the LS-means for each PERIOD for
statistically significant differences.

If there is no evidence for separate slopes (i.e., the PERIOD*X interaction term in the SAS output is not
significant), then a "reduced model" with the same slopes assumed for each group (based on pooled
data) should be used. If the interaction term is significant, then the "full model" is the correct model  and
the significance of the difference between all possible pairs can be obtained from the PDIFF option in the
LSMEANS statement above.

The SAS program statements that generate a covariance model with common slope but unique
intercepts for each period ("reduced model") are:
       PROC GLM; CLASS  PERIOD;
       MODEL Y = PERIOD X/ SOLUTION SS1 SS3;
       LSMEANS PERIOD /PDIFF;
NOTE regarding data setup:
The input data set has columns for each of the variables: Y, X, PERIOD,  and DATE. Although DATE is
not used in this software example, it is useful to match the values  in each row for Y, X, and PERIOD to
the correct sample collection date so that the Y and X values are correctly paired up. For the PROC
GLM software procedure, PERIOD can be "0" and "1" or "Pre" and "Post" or any other numeric or
character value desired. But, be aware that internal to SAS, "0" and "1" values will be generated based
upon the alphabetical order- something to consider when interpreting the solutions for the regression
line equations for each time period.
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 Example 7.8-1. Continued

 B. SAS Software, data set with autoregressive, lag 1, AR(1) autocorrelation
 The SAS (SAS Institute 2010) program statements that generate a covariance model with unique slopes
 for each group ("full model", different slopes) and accommodate an AR(1) error structure are:
        PROC AUTOREG;
        MODEL Y = X PER PER_INTER/NLAG=1 DWPROB;
 Where the user inputs the variable names used for their project data for:
        Y =    Name of variable which contains the treatment watershed values (e.g.,
        concentration/load)
        X =    Name of variable which contains the control watershed values (e.g., concentration/load)
        PER = calibration or treatment period ("0" for pre-BMP period values; "1" for post-BMP values)
        PERJNTER = the "interaction" term that allows for different slopes for each period. This is a
        numeric variable whose values are created by multiplying the values of X and PER for each
        observation
 The other terms are part of the SAS program software syntax.  NLAG=1 indicates a lag 1 error structure
 (PROC AUTOREG assumes  an autoregressive error structure).
 If there is no evidence for separate slopes (i.e., the PERJNTER interaction term in the SAS  output is not
 significant), then a "reduced model" with the same slopes assumed for each group (based on pooled
 data) should be used. If the interaction term is significant, then the "full model" is the correct model.
 The SAS program statements that generate a covariance model with common slope but unique
 intercepts for each period ("reduced model") are:
        PROC AUTOREG;
        MODEL Y = X PER /NLAG=1 DWPROB;
 NOTE regarding data setup:
 The data setup is similar to the PROC GLM software example  in A above, except there is no CLASS
 option in PROC AUTOREG. Numeric input variables needs to  be created for all  input variables (e.g., 0
 and  1 for pre- and post- BMP periods). Since this model includes is a time series error structure, the data
 must be sorted by date order and have equal spaced time intervals. PROC AUTOREG can correctly
 handle missing values. In such cases, a data record for the date should be  included, but with missing
 values (indicated by a "." for the missing data input values.
When the reduced model with common slopes is used, the following equation (Snedecor and Cochran
(1989) should be used to describe the linear regression for each time period, i, which would have the same
slope, but be allowed to have different intercepts:

                YJJ = boi + b1(Xij) + 6jj       ("Reduced model" for same slopes)

Where:

Yij = the jth observation for Y in period i (e.g., treatment watershed concentration or load)
boi = the intercept for period i
bi = the regression coefficient of Y on X pooled over all periods
Xij = the jth observation for X in period i (e.g., control watershed concentration or load)
eij = the residual or experimental error for the j* observation for Y in period i (Vt for autocorrelated
       error series)

Note that this version of the covariance model forces the slope of the regression of Y on X to be the same
for each group, but allows the intercept to be unique (i.e., the regression lines representing each group are
parallel).
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 Example 7.8-1. Continued

 C. JMP Software, data set with no autocorrelation
 Steps: Analyze => Fit Model => Select "V Variable, Add variables to the Model Effects ("X" and
 "PERIOD, highlight PERIOD and X variables in Select Colum and then select 'Cross' in Model Effects to
 include interaction term=>Run
 NOTE regarding data setup:
 The input data set has columns for each of the variables: Y, X, PERIOD, and DATE. Although DATE is
 not used in this software example, it is useful to match the values in each row for Y, X, and PERIOD to
 the correct sample collection date so that the Y and X values are correctly paired up. For the  PROC
 GLM software procedure, PERIOD can be "0" and "1" or "Pre" and "Post" or any other numeric or
 character value desired. But, be aware that internal to SAS, "0" and "1" values will be generated based
 upon the alphabetical order- something to consider when interpreting the solutions for the regression
 line equations for each time period.
 Note: if data has autocorrelated, autoregression, order 1 or AR(1) error series, the standard error on the
 differences between the LS-means can be adjusted and then the corrected significant differences can be
 determined by:


                             std. dev.corrected=std. dev.uncorrected


                             Where p = autocorrelation coefficient at lag 1
                                     Std. dev = standard error on the differences of the LS-means


 D. MiniTab Software, data set with no autocorrelation
 Steps: Stat > ANOVA > General Linear Model. In the responses, model, and random factors dialogue
 boxes, enter "Y", "X PERIOD X*PERIOD", and "PERIOD", respectively. The user can choose whether to
 use adjusted or sequential sum of squares under the options button and pain/vise comparisons can be
 chosen from the comparisons button.  Pressing OK button runs the general linear model.

 Reference: Minitab (2016)
Lastly, calculation of the adjusted means and their confidence intervals can be performed. After the
correct model is determined ("Full" or "Reduced" model), then the adjusted LS-means10 which correct for
the bias in X between periods can be calculated. The LS-mean of each period (i.e., calibration and
treatment periods in this case) is the period mean for Y adjusted to an overall common value of X. In
other words, the LS-means are the calibration and treatment period regression values for the treated
watershed evaluated at the mean of all the control watershed values over both time periods (e.g., mean of
all the X values). Operationally, inserting the mean of all X values into the regression equations for the
calibration and treatment periods will yield the LS-mean values for each period, respectively. An F-test of
the adjusted LS-means then determines if there is sufficient evidence to conclude that the adjusted LS-
mean for the treatment period is different from the adjusted LS-mean for the calibration period. The SAS
program performs this F-test on the "Period" variable in Example 7.8-1.
10 LS-means (least square means) are used in ANCOVA as a better comparison of average values between periods
as compared to arithmetic means. LS-means are estimated values that are evaluated at the average value of the
specified covariate(s) such as the control watershed values in the paired-watershed study design.


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Caution must be used when interpreting the results for the comparisons of adjusted means when
individual slopes are used. When the slopes are not parallel, the comparisons of adjusted means may not
be the most meaningful question. One may be more interested in the behavior over the entire range of X.
In this case a graphical presentation may be most appropriate.

For samples collected daily, weekly, biweekly, or monthly, autocorrelation may be significant. In these
cases, autocorrelation can be addressed by using a software regression program that incorporates the
autocorrelation in the error term, for example PROC AUTOREG by SAS (SAS Institute 2016d); see
Example 7.8-1.


7.8.2.1.2 Multivariate ANCOVA-Paired  Watershed with Explanatory Variables
Note that the above analysis employed a basic univariate ANCOVA model that included only data on the
pollutant variable of interest (e.g., concentration or loads) from the control and treatment watersheds. The
New York NNPSMP project demonstrated the successful use of a multivariate ANCOVA technique that
included hydrologic variables (e.g., instantaneous peak flow  rate, event flow volume, and  average event
flow rate) in the model (Bishop et al. 2005). The project found that including the flow covariates
explained 80 to  90 percent of observed variability in annual and seasonal event P loads, an improvement
of 16 to 50 percent versus a simpler univariate model. In addition, inclusion of covariates reduced the
minimum detectable treatment effect by 11 to  53 percent versus the univariate model, a result that
indicates potential cost savings through reduced sample  size  requirements. It is important to note that the
inclusion of additional covariates (i.e., those in addition to the variable of interest in the control
watershed) is prefaced upon the assumption that they are not affected by BMP implementation. In this
example, testing indicated no influence of BMPs on farm runoff volume, event peak flow, or average
event flow.

In the case of a paired-watershed study, explanatory variables (covariates) would be added to the
statistical model. The full model which allows for different slopes for each time period and covariate  is:

                                     k             d+1
                         V  — h   J- N h  fy   } 4-   \h  fy  ^ -I- P
                         Mj   U0i ~ / t uli V. lijy     / ,  ci v^cijy ~  ij
                                    i=l            c=2

Where:

Yij = the jth observation for Y in period i (e.g.,  pollutant concentration or load from treatment
        watershed)
boi = the intercept (bo) for period i
hi; = the regression coefficient (bi) of Y on Xi for period i
bd = the regression coefficient (bc) for covariate Xc for period i
k = number of time periods (with 'calibration' and 'treatment' periods, k=2)
XHJ = the jth observation for Xi in period i (Xi is the pollutant concentration or load from control
        watershed paired with same sample time as Y;J)
d = number of explanatory variables in addition to the control watershed variable. For example, if
        only flow was used as a covariate, d=l and the explanatory variable for flow would be
        X2.
Xcy = the jth observation for Xc covariate in period i
ejj = the residuals or experimental error for the jth observation for Y in period i (Vij for
        autocorrelated error structure)
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As discussed above, a test for the homogeneity of slopes (by including interaction terms) would be
performed to see if a full or reduced model is the best choice, followed by calculation of adjusted means
and their confidence intervals to see if a significant difference exists between the two periods.

While the focus above has been on a basic paired-watershed study design consisting of two watersheds
(control and treatment) and two periods (calibration and treatment), ANCOVA is a powerful tool that can
also be applied to paired-watershed studies with multiple control and treatment watersheds and more than
two periods, as well  as to above-below studies that have two or more time periods.


7.8.2.1.3 Multiple Paired Watersheds
Both the Jordan Cove (CT) and Lake Champ lain Basin (VT) NNMP projects included three watersheds in
their paired-watershed designs. The Jordan Cove project included a previously developed drainage area as
a control, and two newly developed drainage areas, one following traditional subdivision requirements
and another using low-impact development BMPs (Clausen 2007). The Vermont project employed a
three-way paired design including one control watershed and two treatment watersheds receiving similar
BMP systems at different intensities (Meals 2001). For both studies, the two treatment watersheds were
separately compared versus the control watershed using ANCOVA.

Changes versus the control watershed for the Jordan Cove project were represented by the percent change
in flow, concentration, and export (Clausen 2007). These calculations were made by comparing mean
predicted values (P) from the calibration regression equations to observed values (O) using the equation:


                                               (0-P)
                                  %Change =	 x 100

Meals (2001) performed a series of analyses to examine the results of the Lake Champlain Basin study.
Where full ANCOVA models were used, the calibration and treatment period regression lines intersected,
suggesting, for example, that TP concentrations in one of the treatment watersheds decreased in the high
range, but not in the lower range (Figure 7-24). The importance of this observation is that the higher range
is where active runoff conditions occur, indicating that the BMPs may have been performing as expected.

Calculations similar to those performed for the Jordan Cove project were performed to estimate the
magnitude of change (i.e., %Change), but two additional analyses were carried out to estimate this  change
from different perspectives:
  « Breakpoint analysis for intersecting or crossed regression lines, and
  • Assessment of predicted-without-treatment versus observed-with-treatment.
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                                     Total Phosphorus Concentration
                                 WS 1 vs. WS 3  Calibration vs. Treatment
      10
 a.
       1 -
  I   0.1

     0.01
               -: Calibration
               * Treatment
                    O
        0.01
     10
                                     0.1                          1
                                      WS 3 Mean Weekly [TP] (mg/L)
Figure 7-24. Example of intersecting regression lines (Meals 2001)

For the former analysis, the point where the regression lines crossed (the "breakpoint") was used in
conjunction with the cumulative frequency of the breakpoint value in the control watershed to derive the
proportion of time or conditions at which concentration or load reductions did or did not occur in the
treatment watershed (Meals 2001). For example, the breakpoint in Figure 7-24 occurs at 0.055 mg/L in
the control watershed (WS 3), a value for which the cumulative frequency for the entire project period
was 0.32, or 32 percent. This is interpreted to mean that TP levels in the treatment watershed (WS 1) were
not reduced 32 percent of the time when the concentration in the control watershed was less than 0.055
mg/L. Conversely, TP levels were reduced 68 percent of the time when control watershed concentrations
exceeded 0.055 mg/L. This compares with an ANCOVA result that TP concentrations were reduced 15
percent in the treatment watershed.

The latter analysis was intended to assess the net treatment response regarding pollutant export over the
full range of project conditions  (Meals 2001). In this analysis, all weekly values for the treatment period
in the control watershed were input to the calibration period regression for each treatment watershed to
estimate what the pollutant export would have been for the hydrologic conditions of the treatment period
under pre-treatment management, a what-if scenario. In other words, it is an estimate of the difference
between measured loads for the treatment period and what those loads would have been if the BMPs had
not been implemented.


7.8.2.1.4   Multiple Time Periods within a Paired-Watershed Study
Small watershed projects will generally have a period before BMP implementation, a period during BMP
implementation, and a period after BMP implementation. The implementation and post-implementation
periods are  often lumped into the same period for data analysis, but this can complicate  interpretation of
results if the BMPs are not fully functional throughout the post-BMP period. Where feasible, it may be
most appropriate to separate true implementation, and in some cases maturation of living BMPs, from
post-implementation, to establish a better test of BMP or project effectiveness. There is  also a very real
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possibility that BMP implementation will occur in phases, creating the potential for more than two or
three periods of interest. For example, in the Waukegan River NNMP project, the state Water Survey
designed biotechnical and other practices to resist high velocity runoff while increasing riparian habitat
for stream fisheries within the stream channel (White et al. 2011). However, as the project progressed it
became clear that insufficient pool depth and the  lack of pools and riffles were important impairments yet
to be addressed. As a result, pool-and-riffle sequences were later added to the  restoration program,
creating a two-phase implementation effort. Still, however, project scientists concluded that there is a
remaining need to address sewage and stormwater management problems and take steps to increase
implementation of alternative conservation practices that infiltrate and treat stormwater. Were the
monitoring program to be continued, these could be considered additional BMP implementation phases.

Taken to the extreme, each year could also be considered its own period or group and the groups tested
for differences, but this is not recommended11. In some cases, BMPs may have different effects
depending on the season of the year, so including a seasonal covariate(s) may  be appropriate. The New
York NNMP project identified four seasons that reflect seasonal variation in both source  activities and
hydrologic runoff processes (Bishop et al. 2005). ANCOVA was performed separately on both seasonal
and full-year datasets. Despite the wide range of possibilities, time periods for the types of projects
envisioned by this guidance will largely be drawn from the following set of options:
  «   pre-BMP or calibration,
  •   BMP implementation (may be subdivided by growth stage if it involves vegetative BMPs), and
  •   post-BMP implementation (which may include BMP implementation as well).

Where multiple phases of BMPs are to be implemented, however, there could  be a separate pre-BMP
implementation and post-BMP implementation for each phase. It is important  to identify  and plan for
these phases at the beginning of the monitoring project. Adjustments may be warranted later, however,
because the implementation of BMPs may be more gradual or sporadic than anticipated during the
planning phases of a study, and some BMPs, like forested buffers, may take longer than expected to reach
critical growth stages.

For example, in a 15-year project monitoring the  effectiveness of a riparian forest buffer in an agricultural
watershed, it was expected that it would take several years for the planted seedlings to have a measureable
influence on water quality (Newbold et al. 2009). To account for this, the calibration period was taken to
be the first five years (1992-1996) of monitoring, a period during which the seedlings became established
but remained too small to affect stream nutrient concentrations. Regression analysis was used to detect
gradual change and one-way ANOVA was performed on the differences between paired samples, with
year treated as the main effect.


7.8.2.1.5  Other Statistical Approaches for Paired- Watershed Analyses
Paired watersheds can also be analyzed with other statistical techniques. For example, some  authors have
used the differences between sample pairs taken at each watershed for each sampling date (Carpenter et
al. 1989; Bernstein and Zalinski 1983; MacKenzie et al. 1987; and Palmer and MacKenzie 1985) for input
into t-test or intervention analysis. Hornbeck et al. (1970), Hibbert (1969), and Meals (1987) calculated a
11 It is feasible that a 2-year study could include one year each of pre-BMP and post-BMP monitoring, but this
would be highly unusual and not, in fact, recommended. A similar situation would be a 3-year study with a pre-
BMP, BMP-implementation, and post-BMP year.
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linear regression equation relating the observations from the two watersheds for the calibration period.
Observations from the treated watershed in the treatment period were compared to predicted values from
the calibration period regression. If the deviations exceeded the 95 percent confidence intervals placed
about the calibration regression, the treatment was thought to be significant (Hornbeck et al. 1970).


7.8.2.2  Above/Below - Before/After
An above/below-before/after watershed design monitors a water resource (e.g., a stream) above and
below the drainage area in which land treatment is applied for multiple years before and after BMP
implementation (see section 2.4.2.6). Consistency of sampling regime at both stations overtime is
essential. Hydrologic explanatory variables (e.g., covariates) such as stream flow must also be monitored
to permit correction for changes in these conditions.


7.8.2.2.1  Comparing Means and Differences between Means
Two principal approaches can be taken to statistical analysis of data from this monitoring design. Both
approaches are illustrated by the projects in Examples 7.8-2-7.8-5. In the first approach, mean upstream
and downstream pollutant concentrations and/or loads can be compared (e.g., with the Student's t or
Wilcoxon Rank Sum tests) prior to the application of BMPs to evaluate statistically significant
differences between group means. The purpose of this analysis is to confirm and quantify the pre-
treatment ("before") pollutant contribution of the untreated downstream area. This analysis is then
repeated for the "after" data to document the changes in pollutant contribution of the treated downstream
area. Differences between upstream and downstream conditions from the before to the after condition can
be evaluated simply by examining the percent reductions in concentration or load or by conducting a
group means test of the differences between upstream and downstream concentrations or loads from the
before to the after period. A significant decrease in this upstream/downstream difference in the "after"
period, for example, would suggest a significant effect of treatment. In addition to quantitative statistical
tests, it is also possible to visualize differences between above/below and before/after using comparative
boxplots, bar graphs, or other graphical techniques (see section 7.3.2).

A more statistically powerful approach would be to use the paired Student's t-test to test the differences
between the downstream and upstream sample values in  the pre-BMP period. In the post-BMP period, a
Student's t-test can be applied to the average downstream-upstream differences in the pre- vs. post-BMP
periods. Other explanatory variables can be added (e.g., stream discharge) by using an ANCOVA
statistical approach.

Differences between above and below stations were examined as part of the analyses performed for the
Otter Creek (WI) watershed project (Stuntebeck 1995). This project also incorporated innovative
sampling procedures to maximize the potential for distinguishing between upstream and downstream
water quality, including programming water quality samplers to be activated by precipitation so that time-
integrated samples were collected initially before stage-triggered samples were collected. This allowed
sampling of barnyard runoff in the stream before stage increased, thereby isolating runoff from sources
upstream. It also allowed sampling during small storms where barnyard runoff occurred in the absence of
substantial upstream contributions. In addition, investigators collected concurrent samples from both the
above and below sites via computer linkage to aid data interpretation. Paired Student's t-tests were used
to determine that the pre-BMP average of the differences between downstream and upstream event-mean
concentrations was different from zero at the 95 percent confidence level. An MDC analysis revealed that
the average downstream post-BMP event-mean concentrations of TP would need to decrease by at least
                                               7-77
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Monitoring and Evaluating Nonpoint Source Watershed Projects                                     Chapter 7
50 percent for the change to be considered statistically significant at the 95 percent confidence level. In
the final analysis, the Hodges-Lehmann estimator was used to determine that the barnyard BMP system at
Otter Creek reduced loads of suspended solids by 85 percent, TP by 85 percent, ammonia by 94 percent,
BOD by 83 percent, and microbial loads of fecal coliform bacteria by 81 percent (Stuntebeck and
Bannerman 1998; See Example 7.7-2). The nonparametric Hodges-Lehmann estimator is the median of
all possible pairwise differences between pre- and post-BMP barnyard loads (see section 4.5.3 of the
1997 guidance (USEPA 1997b) for a discussion of the Hodges-Lehmann estimator). This median
difference was divided by the pre-BMP median load for each constituent to determine percentage load
reductions.


7.8.2.2.2 ANCOVA

A second approach for analysis of the above/below-before/after design involves the application of
ANCOVA. The statistical analysis approach is the same as with the paired-watershed study (see section
7.8.2.1) In this case, a significant linear regression relationship for a water quality variable (e.g., weekly
mean total P concentration, weekly suspended sediment load) between the upstream and downstream
stations is obtained during the "before" period. The upstream station is considered to be the "control"
watershed. This regression relationship is then compared to a similar relationship during the "after"
period and significant difference between the two regression models indicates the effect of treatment.
Note that the analysis can include explanatory variables (e.g., covariates) like precipitation or flow in a
multiple regression model that may explain more of the variability in the water quality variable than a
simpler model.
       Example 7.8-2. Above/Below-Before/After Design - Long Creek, NC NNPSMP

A number of successful projects have used multiple approaches to analyzing their data. For example,
data from an above/below-before/after study of livestock exclusion as part of the Long Creek (NC)
NNPSMP project were first log-transformed and then analyzed using t-tests, two-way ANOVA, and
ANCOVA (Line et al. 2000). While the specific questions addressed by each method differ somewhat,
the results all supported the conclusion that livestock exclusion and establishment of riparian vegetation
reduced mean weekly loads of TSS, TKN, and TP.
  Example 7.8-3. Above/Below-Before/After Design (biological data) - Waukegan River, IL
                                          NNPSMP

 The Waukegan River (IL) NNPSMP project illustrates the application of the above/below design for
 biological monitoring. In this project, the South Branch was divided into an upstream untreated reference
 site designated as station S2 and a severely eroding downstream treated area designated as station S1
 (Spooner et al. 2011 b). At each location fish, macoinvertebrates, and habitat were sampled during the
 spring, summer, and fall  seasons. Sampling was also conducted at stations N1 and N2 on the North
 Branch for reference. Qualitative analysis of biological data collected through 2006 indicated that the
 number of fish species and abundance in the South Branch had improved after the construction of
 lunkers and rock grade control structures. The IBI rose sharply from a limited aquatic resource into the
 moderate category after  construction. Sites on both the South and North Branches where lunkers and
 Newbury Weirs were applied averaged higher IBI scores and fish population with more fish species than
 the untreated control at S2 or the N2 bank armored site from 1996 through 2006.
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Monitoring and Evaluating Nonpoint Source Watershed Projects                                     Chapter 7
 Example 7.8-4. Above/Below-Before/After Design with Flow as an Explanatory Variable -
                      Pequea and Mill Creek Watershed, PA NNPSMP

A Pennsylvania study of the effects of streambank fencing on surface-water quality, near-stream ground
water, and benthic macroinvertebrates employed both a paired-watershed and above/below-before/after
design (Galeone et al. 2006). Data for this Section 319 NNMPMS project were collected from 1993 to
2001, with the calibration period from October 1993 through mid-July 1997. Streambank fencing was
installed from May 1997 through July 1997. The above/below-before/after design featured two sites
above fence installation (T-3 and T-4) and two sites located to show the effects of fencing (T-1 and T-2);
T1 and T2 were paired with  T3 and T4, respectively, for data analysis. Both low-flow and storm-flow
samples were collected and analyzed for nutrients, suspended sediment, and fecal streptococcus  (only
low-flow samples). Explanatory data collected during the study included precipitation, inorganic and
organic nutrient applications, and the number of cows.

Figure 7-25 illustrates the major data preparation steps and statistical procedures used by the project to
analyze the chemical/physical data. Low-flow, storm-flow, pre-treatment, and post-treatment data were
separated as a preliminary step.  Concentrations were flow adjusted using a LOcally WEighted
Scatterplot Smoothing (LOWESS) procedure (Helsel and Hirsch 2002). Statistical tests were performed
on both original and flow-weighted data to determine if factoring  out the variability caused by flow
affected the results.

After the above steps were completed, the project applied the nonparametric rank-sum test (see Mann-
Whitney test and Wilcoxon Rank Sum test on pages 4-50 of the  1997 guidance, USEPA 1997b) to
determine if data for any one site significantly changed from the  pre-treatment to the post-treatment
period. In  addition, the nonparametric Kruskal-Wallis test (see pages 4-56 of the 1997 guidance) was
carried out to determine if there were significant differences between any of the sites, considering  pre-
treatment  and post-treatment data separately. Where significant differences were found, the Tukey
multiple-comparison test (see Multiple Comparisons on pages 4-63 of the 1997 guidance) was used to
identify which sites were significantly different. The nonparametric signed-rank test (see Wilcoxon
Signed Ranks test on pages 4-42 of the 1997 guidance) was used to determine if there were significant
differences (i.e., not zero) between paired observations (e.g., matched  samples from above/below sites).
Finally, ANCOVA (see section 4.8 of the 1997 guidance and section 7.8.2.1 for detailed discussions of
the ANCOVA procedure) was applied to determine the effects of streambank fencing using  a  procedure
highlighted by Grabow et al. (1999). ANCOVA was performed on concentrations and loads  for both low-
flow  and storm-flow samples. Loads were analyzed in two ways, as actual measured loads  and as
weighted loads  adjusted with a factor determined by dividing  the annual mean discharge for each water
year by the mean discharge for the entire period for each station.

The  procedures used by Galeone et al. (2006) demonstrated improvements relative to control or
untreated  sites in surface-water quality (nutrients and suspended sediment) during the post-treatment
period at T-1, but T-2 showed reductions only in suspended sediment. N species at T-1 were  reduced by
18 percent (dissolved nitrate) to 36 percent (dissolved ammonia); yields of total P dropped by 14
percent. Conversely, T-2 had increases in N species of 10 percent (dissolved ammonia) to 43 percent
(total ammonia plus organic N), and a 51-percent increase in total P load. The average reduction in
suspended-sediment load for the treated sites was about 40 percent. Two factors were evident at T-2
that helped to overshadow any positive effects of fencing on nutrient yields. One was the increased
concentration of dissolved P in shallow ground water (also monitored).  In addition, cattle excretions at
the low-cost, in-stream cattle crossings appeared to increase concentrations of dissolved ammonia plus
organic N  and dissolved P. See chapter 3 Case Study #1 for a discussion of how the benthic
macroinvertebrate data  were analyzed.
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Monitoring and Evaluating Nonpoint Source Watershed Projects
                                             Chapter 7
            Data
       /  Data*   /
          Separate
          Low-Flow
         and Storm-
         Flow Data*
          Separate
          Pre- and
           Post-
         Treatment
           Data*
\
f
Calculate Flow-
Weighted
Concentrations
using LOWESS*
\
Calculate
(
Weighted
Loads using Annual
Mean
Discharge/Period
Mean Discharge
Factor*
            Data Analysis
Nonparametric Rank
     Sum Test*
- Were post- and pre-
   treatment data
different at the site?
        _y
 Kruskal-Wallis Test
    -Were there
differences between
     the sites?
                                   Wlcoxon Signed-
                                      Rank Test
                                  -Was the difference
                                    between paired
                                    observations for
                                   above and below
                                   sites different from
                                        zero?
                                       ANCOVA
                                    -What were the
                                 effects of streambank
                                    fencing? Which
                                      factors are
                                      significant?
     "Performed separately for each monitoring site.
      Tukey
 Multi-Comparison
      Tests
-If differences were
  found between
groups, which were
    significant?
Figure 7-25. Basic data preparation and analysis procedure for above/below-before/after
study in Pennsylvania (Galeone et al. 2006)
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Monitoring and Evaluating Nonpoint Source Watershed Projects                                      Chapter 7
 Example 7.8-5. Above/Below-Before/After Design with Upstream Concentration and Flow
                   as Explanatory Variables - Walnut Creek, IA NNPSMP

 In some cases, projects are forced to develop alternative plans for data analysis due to unforeseen
 circumstances they cannot control. The Walnut Creek (IA) NNMP project, for example, began as a ten-
 year paired-watershed study that also included an above/below-before/after design and three
 subwatershed single-station designs within each of the paired watersheds (Schilling and Spooner2006).
 The primary purpose of the project was to evaluate the response of stream nitrate concentrations to
 conversion of row crops to native prairie.  The normal approach of analyzing project data (both for the
 paired-watershed and above/below-before/after designs) using ANCOVAwas compromised by two
 facts: prairie conversion began before the calibration period was completed, and conversion to prairie
 was gradual instead of rapid. Based  on the guidelines and experiences of others (Spooner et al. 1987,
 Grabow et al. 1998 and 1999), multiple linear regression analysis on all ten monitoring sites was
 selected as an alternative approach to  project evaluation (see Example 7.8-7 for the general form of
 equation used). Treatment in this case was modeled as time with covariates such as upstream
 concentration used to factor out hydrologic variability. For the downstream site in the treatment
 watershed, a model using month (forseasonality), upstream nitrate concentration, and downstream
 nitrate concentration in the control watershed provided the best fit to the data. For all other sites, month
 and the log of baseflow discharge from the same or  a different site were used as covariates in the  best-fit
 regression model. All tests resulted in detection of significant trends in nitrate concentrations, with  the
 downstream treatment site trend indicating nitrate reductions due to conversion to prairie (the treatment).
 A negative coefficient on the time variable (-0.119 mg l~1yr~1) indicated a nitrate reduction of 1.2 mg I"1
 over 10 years at this site. It was also found that in the control site, where land was unexpectedly
 converted from grassland to row crops, nitrate concentrations increased during the project period.
If the errors (e.g., residuals) in the statistical models are autocorrelated, a statistical software procedure
should be used that incorporates the autocorrelation structure into the model. For example, PROC
AUTOREG of the SAS software (SAS Institute 2010) is useful with autoregressive autocorrelation
typical of weekly, biweekly, and monthly series. Alternatively, a correction of the standard deviation of
the slope estimate and revised confidence intervals can be used with the correction given in section 7.3.6.

It should be cautioned that changes in pollutant concentrations or loads measured at a downstream station
(either before or after land treatment) versus upstream may be difficult to detect if incoming
concentrations or loads at the upstream station are high and the contribution of the additional area
draining to the downstream station is small. Conversely, if the upstream contribution is very low
compared to that of the treated area, a change or difference due to treatment may be difficult to attribute
to BMPs because of dilution. If the upstream pollutant inputs do not respond similarly to hydraulic
changes (e.g., rainfall), then the design effectively becomes a single watershed design. The Walnut Creek
(IA) NNPSMP project provides an example of the former case where annual mean nitrate concentrations
ranged from 10.0 to 12.7 mg/L at the upstream site and 6.8 to 9.5 mg/L at the site below the treatment
area (Schilling and Spooner 2006). The treatment in this case was conversion of row crops to native
prairie, and the study design (paired watersheds and above/below-before/after) was compromised by the
fact that land conversion began before pre-treatment conditions could be established. See Example 7.8-5
for a discussion of how data from this project were analyzed using multiple linear regression, a technique
typically applied to single watershed trend designs.
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Monitoring and Evaluating Nonpoint Source Watershed Projects                                       Chapter 7


7.8.2.3  Nested Watershed
As described in section 2.4.2.3, it is preferred that the nested subwatershed is used as the control
watershed12 and is located above the remainder of the watershed where treatment occurs (Hewlett and
Pienaar 1973). However, a valid nested design can also entail the treatment watershed in a small
headwater subbasin; the control being the much larger watershed outlet. This design requires calibration
(before) and treatment (after) periods similar to the paired-watershed design.

Analysis of data from a nested watershed design can be done using the same ANCOVA procedure
described in section 7.8.2.1 for the paired-watershed design. In the case of nested watersheds, the paired
data represent observations collected on the same date, time period, or storm at both the nested and main
watershed stations. As noted above, data from the nested watershed should represent the control
watershed, while data from the main watershed outlet represent the treatment watershed.


7.8.2.4  Single Watershed Trend Station
As noted in section 2.4.2.5, monitoring at a single watershed outlet is not a strong design for documenting
the effectiveness of watershed land treatment on water quality. Without the ability to control for the
effects of varying weather and hydrology, it is difficult to attribute any observed changes in water quality
to the land treatment program. However, because the coupling of budget limitations and accountability
requirements often leads to single-station designs, the unfortunate fact that some paired-watershed and
other superior designs fail due to unforeseen circumstances, and the simple reality that some NFS
watershed programs must rely on watershed outlet monitoring conducted by another party (e.g., a state
long-term surveillance program or a USGS network station), it is useful to discuss how best to analyze
data from such stations to assess the  effects of a watershed project. In addition, experience has shown that
projects with failed paired-watershed or above/below-before/after designs may resort to trend analysis as
the best option for analyzing project data (see Example 7.8-6).

Long-term water quality data may show a monotonic trend (a continuous change, consistent in direction,
either increasing or decreasing) or a step trend (an abrupt shift up or down). Trend analysis may be the
best — or perhaps only — approach to documenting response to treatment in situations where water
quality data are collected only at a single watershed outlet station or where land treatment was
widespread, gradual, and inadequately documented. Data from long-term, fixed-station monitoring
programs where gradual responses such as those due to incremental BMP implementation or continual
urbanization are likely to occur are more aptly evaluated with monotonic trend analyses that correlate the
response variable (i.e., pollutant concentration or load) with time  or other independent variables. These
types of analyses are useful in situations where vegetative BMPs like the riparian buffers implemented in
the Stroud Preserve NNPSMP project (Newbold et al.  2008) must mature, resulting in gradual effects
expressed over time. Analysis of step trends, on the other hand, is most appropriate when the change in
response to BMP implementation is rapid and abrupt (e.g., when a municipal stormwater management
regulation is enforced) and the timing of that change is known and well-documented. Biological data can
also be evaluated with either monotonic or step-trend tests. A potential limitation is that most biological
programs will only sample once a year and the time to acquire sufficient samples to  detect a meaningful
trend might be longer than what is practical.
12 A reverse situation, where the downstream subwatershed area is the control is possible in theory, but all effort
would need to be made to ensure that upstream contributions to constituents measured at the downstream control
area are minimized.
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Monitoring and Evaluating Nonpoint Source Watershed Projects                                      Chapter 7
 Example 7.8-6. Single Trend Watershed with Covariates - Sycamore Creek, Ml NNPSMP

This project planned a paired-watershed study with two treatment watersheds (Willow Creek and
Marshall Drain) and one control watershed (Haines Drain), but implementation of no-till and continuous
cover in the control watershed compromised the study (Suppnick 1999). Each watershed was then
analyzed independently, with regression analysis ultimately successful in linking reductions in TSS
(95 percent confidence level) and TP (90 percent confidence level) loads to the percentage of land in no-
till in the Willow Creek watershed (Grabow 1999, Suppnick 1999). Following is a summary of the steps
taken to establish the TSS relationship for Willow Creek (Grabow 1999):
    1.   Regression analysis on sediment yield versus storm discharge and/or peak flow to reduce the
        analysis to water quality change over time independent of hydrologic variability. All variables
        were log-transformed.
    2.   Two methods were then used to answer the question of whether there was a water quality trend
        overtime.
           a.  Regression equation incorporating elapsed time and explanatory variables. This
               addresses the question of whether there has been a change in water quality overtime
               while simultaneously accounting  for hydrologic variability.
           b.  Regression of residuals1 from regression on the water quality variable and explanatory
               variables versus elapsed time. This addresses the question of whether there has been a
               water quality change over time after adjusting for hydrologic variability.
    3.   Correlation of land use change to water quality change via multiple linear regression analysis.
        Terms incorporated in the regression model were percent of land in  no-till, percent of land in
        continuous cover, storm discharge, and peak flow.
Step 1 yielded correlation between TSS load (kg/storm) and both storm discharge (mm) and peak flow
(liters/second). Discharge and peak flow were tested for collinearity which was found to be not an issue
(see Box 7.8-1).
Step 2 analyses indicated statistically significant trends in TSS and TP in Willow Creek watershed.
Method "a" used the following basic equation:

                          log[TSS]  = fa + ^log[Q] + p2log[Qp] + 03t

Where TSS is the TSS storm load (kg), Q is the total storm discharge, QP is the peak stream discharge, t
is elapsed time in days, and the p terms are regression parameter estimates. A significant negative
value for pa indicated a reduction in TSS load overtime. Insertion of average log values of total storm
discharge and peak discharge, and setting the beginning and ending days (1 and 2,629 for tbegin and tend
in this case) would then yield the average change in loadings from the first to last data of data collection.

1 Residuals are the differences between actual and predicted values: Actual-Predicted.
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Monitoring and Evaluating Nonpoint Source Watershed Projects                                      Chapter 7
Example 7.8-6. (continued)

Sycamore Creek, Ml NNPSMP
Method "b" of Step 2 used the following equation:

                                        TSSres = /?„ + ftt

Where TSSres is the residuals (log kg/storm) from the regression in Step 1 and t is again elapsed time.
In this approach, a statistically significant value for pi would indicate a change in the relationship
between TSS and the explanatory variables (total and peak discharge), suggesting an impact due to
land use change. The value pixtend would then estimate the  change in loading (in log units) over the data
collection period. The average  change in loading is determined by then plugging the average values for
log [Q] and log[QP] into the regression equation used in Step 1.

In this case, method "a" indicated a 60 percent reduction in  TSS load, whereas method "b" estimated a
59 percent reduction.

With a statistically significant reduction in TSS load now documented, Step 3 explored the linkage
between that reduction and land use change by adding the  percentage of land in no-till (NoTill) and the
percentage of land in continuous cover (ContCov) as additional terms in the multiple linear regression
used for method "a" in Step 2. Statistically significant regression parameters pa and/or p4 in the following
equation would indicate correlation between log[TSS] and the percentage of land in no-till and/or
continuous cover.

                 log[TSS] = /?„ + ^log[Q] + /32log[Qp] + /33NoTW + /34ContCov + /35t

A statistically significant value of-0.01969 was found for pa,  but p4 was insignificant, suggesting that for
every percent increase in the percentage of land under no-till, the TSS load  (as log kg) would decrease
by 0.01969 log units. Regression estimates based on average storm discharge and peak flow were then
used in conjunction with first-year and last-year values of no-till percentages to estimate a TSS load
reduction of 52 percent, with a  95 percent confidence interval of 18-72 percent. This agreed well with the
estimates of 59 and 60 percent reduction from Step 2.

Combining the results from the above analyses by Grabow  (1999) with additional project information, it
was concluded that it is very likely that streambank stabilization also contributed to the reduction in TSS
observed in Willow Creek  (Suppnick 1999).
                            Box 7.8-1. Collinearity

 What is Collinearity?
 Collinearity in multiple regression analysis occurs when there is a linear relationship
 between two explanatory (x) variables. Although this does not impact the reliability of
 the overall model, it does create great uncertainty regarding the model coefficients.
 There are ways to address Collinearity, including recognizing the ambiguity in the
 interpretation of regression coefficients (USF n.d.) or simply removing one of the
 variables from the regression model (Martz 2013).
 Various statistics programs have tests for Collinearity (or multicollinearity), including
 the Variance Inflation Factor (VIF), Tolerance (1/VIF),  and the Condition Index (SAS
 2016a and 2016c, USF n.d.). Guidelines vary, but VIF values greater than 5 to 10,
 Tolerance values close to 0, and Condition Index values greater than 15 to 30
 indicate problems with Collinearity. See Belsley et al. (1980) for additional details.
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Several statistical trend analysis techniques will be mentioned in this section; the topic of trend analysis is
covered in more detail in Tech Notes 6: Statistical Analysis for Monotonic Trends (Meals et al.
2011). Before proceeding, it is important to recognize some limitations of trend analysis. First, trend
analysis is most effective with long periods of record; general guidelines are >5 years of monthly data for
monotonic trends and >2 years of monthly data before and after a step trend (Hirsch 1988). Short
monitoring periods and small  sample sizes make documentation of trends difficult, and it must be
recognized that - especially over the short term - some increasing or decreasing patterns in water quality
are not trends. A snapshot of water quality data over a few months may suggest a trend, but examination
of a full year may show this "trend" to be part of a regular cycle associated with temperature,
precipitation, or cultural practices. Autocorrelation may also be mistaken for a trend, especially over a
short time period. Changes in  sampling schedules, field methods, or laboratory practices can cause shifts
in data that could be erroneously interpreted as step trends.

Perhaps most importantly, statistical trend analysis can help to identify trends and estimate the rate of
change, but will not provide much insight into attributing a trend to a particular cause (e.g., land
treatment).  Interpreting the cause of a trend requires knowledge of the watershed and a deliberate study
design (see section 7.8.1).

Before proceeding to numerical analysis, it is useful to examine time series plots for visual evidence of a
trend. Visualization of trends in noisy data can be clarified by various data smoothing techniques. Plotting
moving averages or medians,  for example, instead of raw data points, reduces apparent variation and may
reveal general tendencies. Spreadsheets can display a moving-average trend line in time-series
scatterplots with adjustable  averaging periods. A more complex smoothing algorithm, such as LOWESS
(ZOcally Weighted Scatterplot Smoothing), can reveal patterns in very large datasets that would be
difficult to resolve by eye (see Helsel  and Hirsch 2002). Most pollutant concentrations and loads in
surface waters show strong seasonal patterns. Seasonal variations in precipitation and flow are often main
drivers of these patterns, but seasonal  changes in land management and use may also play a role. See
section 4.3  of the 1997 guidance (USEPA 1997b) for additional information on seasonality.

Some techniques to address seasonality beyond controlling for the effects of flow covariates are often
necessary for water quality trend analysis. For example, the relationship between concentration and
discharge may not be consistent over time, perhaps due to seasonal variations in BMP implementation.
The relationship (or slope) can be allowed to change between time periods by the use of interaction terms
between the time periods and  discharge in an analysis of covariance (ANCOVA) statistical model. An
alternative that might develop more traction with experiences is to consider a weighted regressions on
time, discharge  and season (WRTDS) proposed by Hirsch et al. (2010) (see section 7.9.2 for more
information on WRTDS).

When multiple explanatory variables are included in the trend models, it is common that these variables
will be related to each other (collinearity) and/or a few data points may have a lot of 'influence' over the
regression results (Belsley et al. 1980). Regression analysis performed with various software programs
will provide leverage plots as  part of the output to help identify these data features.


7.8.2.4.1  Monotonic Trends

Table 7-8 lists some monotonic trend tests available for different circumstances, including adjustments for
a covariate  and the presence of seasonality. The tests are further divided into parametric, nonparametric,
and mixed types. Regression tests require that the expected value of the dependent variable is a linear
function of each independent variable, the effects of the independent variables are additive, the errors in
                                               7-85
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Monitoring and Evaluating Nonpoint Source Watershed Projects
Chapter 7
the model are independent (e.g., no correlation between consecutive errors in the case of time series data),
and the errors exhibit both normality and constant variance. Nonparametric tests require only constant
variance and independence. Parametric trend tests (see Examples 7.8-7 and 7.8-8) are considered more
powerful and/or sensitive to detect significant trends than are nonparametric tests (see Example 7.8-9),
especially with a small sample number. However, unless the assumptions for parametric statistics are met,
it is generally advisable to use a nonparametric test (Lettenmaier 1976, Hirsch et al. 1991, Thas et al.
1998).

    Table 7-8. Classification of tests for monotonic (nonparametric) or linear (parametric) trend
                              (adapted from Helsel and Hirsch 2002)

No
Seasonality
Seasonality
Other
Explanatory
variables or
covariates
(e.g., stream
discharge)
Type of Test
Parametric
Mixed
Nonparametric
Parametric
Mixed
Nonparametric
Parametric
Mixed
Nonparametric
Not Adjusted for
covariate (X)
Linear regression of Y on t
-
Mann-Kendall
Linear regression of Y on t and
periodic functions or indicator
X's for months
Regression of deseasonalized Y
on t
Seasonal Kendall on Y
Linear regression of Y on t and
covariates (X)
Regression of deseasonalized Y
onX
Seasonal Kendall on Y
Adjusted for covariate (X)
Multiple linear regression of Y on X and t
Mann-Kendall on residuals from regression of Y on X
Mann-Kendall on residuals from LOWESS of Y on X
Multiple linear regression of Y on X, t, and periodic
functions or indicator X's for months
Seasonal Kendall on residuals from regression of Y on X
Seasonal Kendall on residuals from LOWESS of Y on X
Multiple linear regression of Y on t, X covariates
Seasonal Kendall on residuals from regression of Y on X
Seasonal Kendall on residuals from LOWESS of Y on X
Y = dependent variable of interest; X = covariate; t = time

Refer to Tech Notes 6: Statistical Analysis for Monotonic Trends (Meals et al. 2011) for details on
the tests listed in Table 7.8-1.  Chapter 4 (pages 4-86 through 4-89) of the 1997 guidance (USEPA 1997b)
also discusses the computation of Mann-Kendall and Seasonal Kendall statistics.

If the trend model has autocorrelated errors, a statistical model that incorporates the autoregessive errors
should be employed. Alternatively, a correction of the standard error of the slope that is given in section
7.3.6 can be used to calculate the correct confidence interval of the slope on t (time, date) to determine if
it is significantly different from zero (e.g., evidence of a trend over time) in the pollutant concentration or
load.
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Chapter 7
         Example 7.8-7. Simple Linear Regression - Samsonville Brook in Vermont

    •   Eight years of monthly TP concentration data from Samsonville Brook in Vermont
    •   Data satisfy assumptions for regression after log transformation: normal distribution, constant
        variance, independence (low autocorrelation)
< 1.000 q
M
E. ;
«
C 0.100 :
g
5 :
Q.
e 0010 -



/Q-, *CK °S) O O O O
 O ff -Jp 9i_ O (O
Qo0 rt ° QCCT <° % 0%
*-^ '-b O n O p (P

°§> ® o ooo^
0









0 12 24 36 48 60 7? 84 96
Time (months)
                     Q.
                     T5
                         1.000
                         0.100 :
                         0.010
                                    12   24   36   48   60   72    84   96

                                              Time (months)
                  Simple linear regression (using Excel® or any basic statistical package)
                     Log[TP] = -0.8285-0.00414(Time)
                     r2 = 0.18, F = 21.268 P< 0.001

                     Rate of change:
                     Slope of log-transformed date = -0.00414
                     (10-°00414- 1)xiQO = -0.95%/month or about-11%/year

This result suggests that TP concentrations have decreased significantly over the period at a rate of
approximately 11 percent per year.
Note: Data used in this example are taken from the Vermont NNMP project, Lake Champlain Basin agricultural
watersheds section 319 national monitoring program project, 1993 - 2001 (Meals 2001).
                                              7-87
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Monitoring and Evaluating Nonpoint Source Watershed Projects
                                      Chapter 7
 Example 7.8-8. Linear Regression with Monthly Seasons as a Covariate - Corsica River,
                                        MDNNPSMP

A significant trend was detected in a small watershed within the Corsica River Basin, Maryland, using
times series analysis that adjusted for autocorrelation as well as monthly (seasonal) differences for log
transformed, flow-weighted total nitrogen (TN) concentrations. In this example, monthly indicator
variables were used to adjust for seasonality in an ANOVA regression model. See section 7.3.6 for
details on adjustments for autocorrelation and seasonality.
        0.7 •


        0.6-


        0.5-


        0.4 -


        0.3 •
                      01/01/2009


                         Date
0.8-

0.7-

0.6-

0.5-

0.4-

0.3-

0.2-

0.1-

 0-
                                                                        \
                                                                1/2008
                                                                             1/2010
By addressing seasonality in the regression model with monthly indicator variables, most of the
regression degrees of freedom were preserved, a more powerful approach than if each month was
evaluated separately. Each line in the plot on the left represents the trend  line (log transformed, ffflow-
weighted TN concentration) fora single month (i.e., January, February ...  December). The trend slopes
for each month were assumed to be the same,  but the intercept was allowed to vary, enabling the
differences in concentration due to season to be removed from the test for trends and therefore making it
easier to isolate and detect trends due to other factors (e.g., BMPs).

The bottom right graph shows the raw data. The noise due to seasonal differences and other factors
makes it difficult to pick out any trends.  The top right graph shows the predicted value from the seasonal
regression model with the indicator variables. A downward trend is apparent and it is also clear from this
graph that the highest TN concentration is found in February, followed by January, March, May, April,
June, Sept, August, October, November, December, and July (lowest).
                                              7-88
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Monitoring and Evaluating Nonpoint Source Watershed Projects
                                                    Chapter 7
 Example 7.8-9. Mann-Kendall Procedure - Single Trend Watershed - Samsonville Brook
                                            in Vermont.
The data from Samsonville Brook in Vermont:
    •   Eight years of quarterly mean TP concentration data
    •   Data satisfy assumptions for constant variance and independence, but are not normally
        distributed without transformation
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0 12 24 36 48 60 72 84 96
Time (months)
Month
(n=25)
1
5
9
13
17
21
25
29
33
37
41
45
49
53
57
61
65
69
73
77
81
85
89
93
97
[TP]
mg/L
0.180
0.200
0.250
0.068
0.201
0.063
0.099
0.125
0.205
0.078
0.216
0.059
0.098
0.102
0.137
0.037
0.100
0.051
0.180
0.060
0.095
0.021
0.120
0.063
0.035
                        The Mann-Kendall trend test for this example may be evaluated in two ways. First, in a
                        manual calculation, use the formulas below. The value of S (sum  of the signs of
                        differences between all combinations of observations) can be determined either manually
                        or by using a spreadsheet to compare combinations, create dummy variables (-1,0, and
                        +1), and sum for S.

                        Mann-Kendall  S = Ef^1 ZJ=i+isign (y} - yi) = -106
                                i = •
                                   n(n-l)/2
    -106

     300
= -0.353 (decreasing trend)
                        Calculating Zs as (S±1)/as where
                                as = J(-) x (n - 1) x (2n + 5) = 42.817
                                Z =
                                    -105
= -2.454  (USEPA 1997a)
                        This Z statistic is significant at P=0.014, indicating a significant trend, i.e., we are
                        98.6 percent confident there is a decreasing trend in TP. See USEPA (1997a) for the
                        calculation of as when there are ties among the data.

                        To estimate the rate of change, use the Sen slope estimator:

                        ft = median (ZL2l\ 211 individual slopes -000945 to +0.00766
                                     \Xj-xJ
                                Median slope = -0.0011 mg/L/month = -0.013 mg/L/yr

                        This result suggests that TP concentration decreased significantly over the period
                        at a rate of about 0.013 mg/L/yr.
Note: Data used in this example are taken from the Vermont NNMP project, Lake Champlain Basin agricultural
watersheds section 319 national monitoring program project, 1993 -2001 (Meals 2001).
                                                  7-89
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Monitoring and Evaluating Nonpoint Source Watershed Projects                                      Chapter 7


7.8.2.4.2  Step Trends
Monotonic trend analysis may not be appropriate for all situations. Other statistical tests for discrete
changes (step trends) should be applied where a known discrete event (like BMP implementation over a
short period) has occurred. Testing for differences between the "before" and "after" conditions is done
using two-sample procedures such as the Student's t test and ANCOVA (parametric techniques with and
without covariates) and nonparametric alternatives such as the rank-sum test, Mann-Whitney test, and the
Hodges-Lehmann estimator of step trend magnitude (Helsel and Hirsch 2002, Walker 1994). Application
of the Mann-WhitneyAVilcoxon's rank sum test and the Hodges-Lehmann estimator are illustrated in
sections 4.5.2 and 4.5.3, respectively, of the 1997 guidance (USEPA 1997b). A key principle in step trend
analysis is that the hypothesized timing of the step change must be selected in advance (i.e., define the
pre- and post- periods before conducting statistical tests). Knowledge of watershed management activities
and examination of data plots will be helpful in identifying a potential step in time.

For example, the Mann-Whitney test was used to associate changes in P management practices with a
decrease in annual median soluble reactive  P concentration from a 9-ha grassland catchment in Northern
Ireland (Smith et al. 2003). Weekly samples were collected from 1989 through 2000, with the change in P
management instituted in 1998. A comparison of data from 1997 with data from 2000 indicated that the
change from whole-farm to site-specific P management reduced SRP concentrations significantly.

If the trend model has autocorrelated errors, a statistical model that incorporates the autoregessive errors
should be employed. Alternatively, a correction of the standard error of the slope that is given in section
7.3.6 can be used to calculate the correct confidence interval of the step change (difference) between time
periods to determine if it is significantly different from zero (e.g., evidence of a step change)  in the
pollutant concentration or load.


7.8.2.5  Multiple Watersheds
In the simplest case of a multiple watershed design, where monitored watersheds fall into two groups,
treated and untreated, data may be analyzed by Student's t test or the non-parametric Wilcoxon Rank-
Sum test. Such an analysis would test the (null) hypothesis that there was no significant difference in
mean pollutant concentration or load between the treated and untreated watershed groups. Where
monitored watersheds occur in more than two groups (e.g., untreated, treatment A, treatment B, etc.),
significant differences in group means can be evaluated using ANOVA or the Kruskal-Wallis test. For
example, Clausen and Brooks (1983) assessed mining impacts on MN peat lands using a multiple
watershed design. Results - analyzed by ANOVA for normally distributed variables and otherwise by
nonparametric Kruskal-Wallis and Chi-Square tests - documented significant impacts of peat mining on
water quality. Lewis (2006) describes application of fixed-effect and mixed-effect (i.e.,  includes random
effects) regression models to multiple-watershed studies involving logging. A 13-watershed study
involving 3 controls, 5 clear-cuts, and 5 partial cuts was carried out over sixteen years with monitoring  of
storm volumes during four years before cutting, three years of logging, and nine years13 of post-logging.
The best fit was obtained when the proportion harvested, antecedent wetness, regrowth, and spatial
autocorrelation were all incorporated into the model. This study design and analytic approach allows the
prediction of streamflow response to harvesting in other  watersheds considered part of the same
population of watersheds included in the study.
 ! Three years of post-cut monitoring at seven stations and nine years at six stations.


                                               7-90
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Monitoring and Evaluating Nonpoint Source Watershed Projects
                                     Chapter 7
7.8.3 Linking Water Quality Trends to Land Treatment
A central objective of many NFS watershed projects is to determine not only if water quality changes can
be documented but also if water quality changes can be associated with changes in land treatment. Such
documentation is necessary to help build an information base to support continued improvement in
preventing and solving water quality problems. It is also needed in many cases to justify expenditure on
clean-up efforts.

For a range of reasons, including budgets and programmatic constraints, watershed project monitoring
efforts are almost never designed to satisfy the rigorous criteria for establishing true cause and effect
relationships (see Box 7.8-2). Rather, project effectiveness monitoring designs are generally intended to
measure improvements in water quality and, hopefully, relate that improvement to activities undertaken to
influence water quality. A plausible argument that
what was done on the ground improved water
quality is often the best that can be hoped for and
that is usually not a simple task at the watershed
level. The ability to control for factors other than
land treatment (e.g., weather, hydrology, land use
change) is a key ingredient in making such a
plausible argument.

Control refers to eliminating or accounting for all
factors that may affect the response to the treatment
so that the treatment effect can be isolated. In a
laboratory experiment, control is usually obtained
by subjecting the entire system to the same
conditions, varying only the treatment variable and
selecting replicates at random to assure that
unmeasured sources of variability do not affect the
interpretation.  Such an approach is rarely if ever
possible for monitoring projects in watersheds
dominated by nonpoint sources. Instead, we hope to
show an association between change in water
quality and change in land use or management by
selecting a project design that includes monitoring for important explanatory variables (covariates) and
applying appropriate statistical tools to  include and adjust for these covariates in the analysis. By
factoring explanatory variables into trend analyses, we remove some of the noise in the data to uncover
water quality trends that are closer to those that would have been measured had no changes in climatic or
other explanatory variables occurred over time. When performing statistical analyses with both water
quality and land treatment data, it is important to note that it is not necessary to summarize the water
quality data on the same (less frequent) time  scale  as the land treatment data. Rather, land treatment data
can be  incorporated within a trend analysis, for example, as repeating explanatory variables. That is, the
values  of land treatment and land use are treated as X variables in a statistical trend model. Because land
management data are usually taken less frequently than water quality data, the land management
information for a given X variable can be repeated for the time range of water quality samples that is
represented by the land management value.

Although association by itself is not sufficient to infer causal relationships, it can contribute to a plausible
argument that pollution control activities have resulted in environmental improvement. Thus, knowledge
  Box 7.8-2. Cause-effect requirements
       (Mosteller and Tukey 1977).
A cause-effect relationship must satisfy the
following criteria:
  •   Consistency- the direction and degree of
      the relationship between the measured
      variables (such as TP loads and acres
      treated with nutrient management) holds
      in each data set.
  •   Responsiveness - as one variable
      changes in a known manner, the other
      variable changes similarly. For example,
      as the amount of land treatment
      increases, further reduction of pollutant
      delivery to the water resource is
      documented.
  •   Mechanistic - the observed water quality
      change is that which is expected based
      on the known or hypothesized physical
      processes involved in the installed BMPs.
                                               7-91
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Monitoring and Evaluating Nonpoint Source Watershed Projects                                      Chapter 7
of land management and land treatment in the watershed is essential to demonstrate an association
between changes on the land and changes in water quality. For example, section 7.8.2.2 described how
the Sycamore Creek (MI) NNMP project used multiple linear regression to link log[TSS] load to the
percentage of land under no-till cropping (Grabow 1999). Additional explanatory variables included the
logs of total storm discharge and peak stream discharge.

Data on both the temporal progress and spatial extent of land treatment and other watershed land
use/management activities should be used to build an association between land treatment and observed
water quality. For example,  on a temporal scale, land treatment and management data can be analyzed
and linked to water quality in these ways:

Define monitoring periods: Documentation of BMP implementation can be used to define critical
project periods, like pre- and post-treatment periods in before/after and paired-watershed designs or to
establish a hypothesis on the timing of a step trend.

Explain observed water quality:  Knowledge of not only BMP implementation history but also dates of
tillage, manure or agrichemical applications, street sweeping, and other watershed management activities
can be extremely useful in qualitatively explaining observed water quality patterns, especially extreme or
unusual values.

Quantify the level of treatment: Quantitative expressions of land treatment can become the independent
variable in an analysis of correlation between land management and water quality. Analyze land treatment
data collected in the watershed monitoring program to form such variables as:
  «   Number or percent of watershed animal units under animal waste management
  *   Acres or percent of cropland in cover crops
  «   Acres or percent of cropland under conservation tillage
  «   Annual manure or fertilizer application rate and extent
  *   Extent and capacity of storm water infiltration practices

Such variables can be tested for correlation with mean total P concentration, annual suspended sediment
load, or other annual water quality variables.

Document areas receiving  BMPs: Use knowledge of locations of land treatment to:
  «   Select appropriate watersheds for analysis in a multiple watershed design
  «   Confirm conditions in above/below and nested watershed designs
  *   Document the integrity of the control and treatment watersheds in  a paired-watershed design

Relate land treatment to critical  source areas: A comparison of critical pollutant sources to locations
that received treatment can assist in evaluating effectiveness of land treatment efforts and establish
expectations for how much of the NPS problem the land treatment program potentially addresses.

The Walnut Creek (IA) NNPSMP project, for example, monitored stream NOs-N concentrations and
tracked conversion of row crop land to restored prairie vegetation (Schilling and Spooner 2006). By
linking the two monitored variables, the project was able to suggest a clear association between restoring
native prairie  and reducing stream  nitrate levels (see Figure 7-26).
                                               7-92
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Monitoring and Evaluating Nonpoint Source Watershed Projects
Chapter 7



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Change in Row Crop Land Cover
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Relationship between change in percentage of land cover in row crops and

change in stream NO -N concentrations in Walnut Creek, IA
Figure 7-26. Linking stream nitrate concentration to land cover (Schilling and Spooner 2006)
7.9  Load Estimation
Determination of pollutant load is a key objective for many NFS monitoring projects. The mass of
nutrients delivered to a lake or estuary drives the productivity of the waterbody. The annual suspended
sediment load transported by a river is usually a more meaningful indicator of soil loss in the watershed
than is a suspended sediment concentration. The foundation of water resource management embodied in
the TMDL concept lies in assessment of the maximum pollutant load a waterbody can accept before
becoming impaired and in the measurement of changes in pollutant loads in response to implementation
of management measures.

Estimation of pollutant load through monitoring is a complex task that requires accurate measurement of
both pollutant concentration and water flow and careful calculation, often based on a statistical approach.
It is imperative that an NFS monitoring program be designed for good load estimation at the start. This
section addresses important considerations and procedures for developing good pollutant load estimates in
NFS monitoring projects. Much of the material is taken from an extensive monograph written by Dr. R.
Peter Richards, of Heidelberg University, Estimation of Pollutant Loads in Rivers and Streams: A
Guidance Document for NFS Programs. The reader is encouraged to consult that document and its
                                              7-93
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Monitoring and Evaluating Nonpoint Source Watershed Projects
                                       Chapter 7
associated tools for additional information on load estimation. Much of this information is also
summarized in Meals et al. (2013).


7.9.1  General Considerations

7.9.1.1  Definitions
Load may be defined as the mass of a substance that passes a particular point of a river (such as a
monitoring station on a watershed outlet) in a specified amount of time (e.g., daily, annually).
Mathematically, load is essentially the product of water discharge and the concentration of a substance in
the water. Flux is a term that describes the
loading rate, i.e., the instantaneous rate at which
the load passes a point in the river. Water
discharge is defined as the volume of water that
passes a cross-section of a river in a specified
amount of time, while flow refers to the
discharge rate, the instantaneous rate at which
water passes a point. Refer to Meals and
Dressing (2008) for guidance on appropriate
ways to estimate or measure surface water flow
for purposes associated with NFS watershed
projects.

If we could directly and continuously measure the flux of a pollutant, the results might look like the plot
in Figure 7-27. The load transported over the entire period of time in the graph would simply be equal to
the shaded area under the  curve.
               Basic Terms
Flux- instantaneous loading rate (e.g., kg/sec)
Flow rate - instantaneous rate of water passage
(e.g., L/sec)
Discharge  - quantity of water passing a
specified point (e.g., m3)
Load - mass of substance passing a specified
point (e.g.,  metric tons)
                                time-
Figure 7-27. Imaginary plot of pollutant flux over time at a monitoring station (Richards 1998)
                                               7-94
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Monitoring and Evaluating Nonpoint Source Watershed Projects                                      Chapter 7
However, we cannot measure flux directly, so we calculate it as product of instantaneous concentration
and instantaneous flow:
                                    Load = k I  c(f)q(f)dt
where c is concentration and q is flow, both a function of time (t), and k is a unit conversion factor.
Because we must take a series of discrete samples to measure concentration, the load estimate becomes
the sum of a set of n products of concentration (c), flow (q), and the time interval (At) over which the
concentration and flow measurements apply:

                                      Load =
The main monitoring challenge becomes how best to take the discrete samples to give the most accurate
estimate of load. Note that the total load is the load over the timeframe of interest (e.g., one year)
determined by summing a series of unit loads (individual calculations of load as the product of
concentration, flow, and time over smaller, more homogeneous time spans). The central problem is to
obtain good measures of concentration and flow during each time interval; calculation of total load by
summing unit loads is simple arithmetic.


7.9.1.2  Issues of Variability
Both flow and concentration vary  considerably overtime, especially in NFS situations. Accurate load
estimation becomes an exercise in both how many samples to take and when to take them to account for
this variability.

Sampling frequency has a major influence on the accuracy of load estimation, as shown in Figure 7-28.
The top panel shows daily suspended solids load (calculated as the products of daily total suspended
solids (TSS) concentration and mean daily discharge measured at a continuously recording USGS  station)
for the Sandusky River in Ohio. The middle panel represents load calculated using weekly TSS samples
and mean weekly discharge; the lower panel shows load calculated from monthly TSS samples and mean
monthly discharge data. Clearly, very different pictures of suspended solids load emerge from different
sampling frequencies, as decreasing sampling frequencies tend to miss more and more short-term but
important events with high flow or high TSS concentrations.

Because in NFS situations most flux occurs during periods of high discharge (e.g.,  -80 - 90 percent of
annual load may be delivered in -10 - 20 percent of time), choosing when to sample can be as important
as how often to sample. The top panel in Figure 7-29 shows a plot of daily suspended solids load derived
from weekly sampling superimposed on daily flux data; the bottom panel shows daily loads derived from
monthly and quarterly sampling on top of the same daily flux data. Weekly samples give a reasonably
good visual fit over the daily flux pattern. The monthly series gives only a very crude representation of
the daily flux, but it is somewhat better than expected, because it happens to include the peaks of two of
the four major storms for the year. A monthly series based on dates about 10 days later than these would
have included practically no storm observations, and would have seriously underestimated the suspended
solids load. Quarterly samples result in a poor fit on the actual daily flux pattern.
                                              7-95
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Monitoring and Evaluating Nonpoint Source Watershed Projects
                                               Chapter 7
        60DO
        50001

        4000
        3000-

        2000-
        1000
1

c
1 .
laily samples
1 A
                          100
200
300
        6000

        5000
        4000-

        3000-

        2000
        10DO
     Weekly samples
                                         :co
               300
Figure 7-28. Plot of suspended solids loads for the Sandusky River, water year 1985 (Richards
1998). Top, daily TSS samples; Middle, weekly samples; Bottom, monthly samples. Weekly and
monthly sample values were drawn from actual daily sample data series. Flux is on y-axis, time is
on x-axis, and area under curve is load estimate.

The key point here is that many samples are typically needed to accurately and reliably capture the true
load pattern. Quarterly observations are generally inadequate, monthly observations will probably not
yield reliable load estimates, and even weekly observations may not be satisfactory, especially if very
accurate load estimates are required to achieve project objectives.


7.9.1.3  Practical Load Estimation
Ideally, the most accurate approach to estimating pollutant load would be to sample very frequently and
capture all the variability. Flow is relatively straightforward to measure continuously (see Meals and
Dressing 2008), but concentration is expensive to measure and in most cases impossible to measure
continuously. It is therefore critically important to choose a sampling interval that will yield a suitable
characterization of concentration.
                                              7-96
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Monitoring and Evaluating Nonpoint Source Watershed Projects
                                           Chapter 7
   lODOQi
                                            200
                  300
   10000
                          100
200
300
Figure 7-29. Weekly (red line in top panel), monthly (red line), and quarterly (black line in bottom
panel) suspended solids load time series superimposed on a daily load time series (Richards
1998). Log of flux is on y-axis, time is on x-axis, and area under curve is load estimate.

There are three important considerations involved in sampling for good load estimation: sample type,
sampling frequency, and sample distribution in time. Grab samples represent a concentration only at a
single point in time and the selection of grab sampling interval must be made in consideration of the
issues of variability discussed above. Integrated samples (composite samples made up of many individual
grab samples) are frequently used in NFS monitoring. Time-integrated or time-proportional samples are
either taken at a constant rate over the time period or are composed of subsamples taken at a fixed
frequency. Time-integrated samples are poorly suited for load estimation because they are taken without
regard to changes in flow (and concentration) that may occur during the integration period and are usually
biased toward the low flows that occur most often. Flow-proportional samples (where a sample is
collected for every n units of flow that pass the station), on the other hand, are ideally suited for load
estimation, and in principle should provide a precise and accurate load estimate if the entire time interval
is properly sampled. However, collecting flow-proportional samples is technically challenging and may
not be suitable for all purposes. Also, even though a flow-proportional sample over a time span (e.g., a
week) is a good summation of the variability of that week, ability to see what happened within that week
(e.g., a transient spike in concentration) is lost. Flow-proportional sampling  is also not compatible with
some monitoring demands, such as monitoring for ambient concentrations that are highest at low flow or
for documenting exceedance of critical values (e.g., a water quality standard).
                                              7-97
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Monitoring and Evaluating Nonpoint Source Watershed Projects                                      Chapter 7
Sampling frequency determines the number of unit load estimates that can be computed and summed for
an estimate of total load. Using more unit loads increases the probability of capturing variability across
the year and not missing an important event (see Figure 7-29); in general, the accuracy and precision of a
load estimate increases as sampling frequency increases. Over a sufficiently short interval between
samples, a sampling program will probably not miss a sudden peak in flux. If, for example, unit loads are
calculated by multiplying the average concentration for the time unit by the discharge over the same time
unit, the annual load that is the sum of four quarterly unit loads will be considerably less accurate than the
annual load that is the sum of twelve monthly loads. Note that this example does not mean that an annual
load calculated from 12 monthly loads is sufficiently accurate for all purposes.

There is a practical limit to the benefits of increasing sampling frequency, however, due to the fact that
water quality data tend to be autocorrelated (see section 7.3.6). The concentration or flux at a certain point
today is related to the concentration or flux at the same point yesterday and, perhaps to a lesser extent, to
the concentration or flux at that spot last week. Because of this autocorrelation, beyond some point,
increasing sampling frequency will accomplish little in the way of generating new information. This is
usually not a problem for monitoring programs, but can be a concern, however, when electronic sensors
are used to collect data nearly continuously.

Consideration of the basic sampling frequency - n samples per year - does not address the more complex
issue of timing. The choice of when to collect concentration samples is critical. Most NFS water quality
data have a strong seasonal component as well as a strong association with other variable factors such as
precipitation, streamflow, or watershed management activities such as tillage or fertilizer application.
Selecting when to collect samples for concentration determination is essentially equivalent to selecting
when the unit loads that go into an annual load estimate are determined. That choice must consider the
fundamental characteristics of the system being monitored. In northern climates, spring snowmelt is often
the dominant export event of the year; sampling during that period may need to be more intensive than
during midsummer in order to capture the most important peak flows and concentrations. In southern
regions, intensive summer storms often generate the majority of annual pollutant load; intensive summer
monitoring may be required to obtain good load estimates. For many agricultural pesticides, sampling
may need to be focused on the brief period immediately after application when most losses tend to occur.
Issues of random sampling, stratified random sampling, and other sampling regimes should be
considered. Simple random sampling may be inappropriate for accurate load estimation if, as is likely, the
resulting schedule is biased toward low flow conditions. Stratified random sampling - division of the
sampling effort or the sample set into two or more parts which are different from each other but relatively
homogeneous within - could be a better strategy. In cases where there is  a conflict between the number of
observations a program can afford and the number needed to obtain an accurate and reliable load
estimate, it may be possible to use flow as the basis for selecting the interval between concentration
observations. For example, planning to collect samples every x thousand ft3 of discharge would
automatically emphasize high flux conditions while economizing on sampling during baseflow
conditions. Sampling levels following this strategy could be based on an annual average flow, recognizing
that the number of samples per year would vary.
                                               7-98
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Monitoring and Evaluating Nonpoint Source Watershed Projects                                      Chapter 7


7.9.1.4  Planning for Load Estimation
Both discharge and concentration data are needed to calculate pollutant loads, but monitoring programs
designed for load estimation will usually generate more flow than concentration data. This leaves three
basic choices for practical load estimation:
    1.  Find a way to estimate un-measured concentrations to go with the flows observed at times when
       chemical samples were not taken;
    2.  Throw out most of the flow data and calculate the load using the concentration data and just those
       flows observed at the same time the samples were taken; and
    3.  Do something in between - find some way to use the more detailed knowledge of flow to adjust
       the load estimated from matched pairs of concentration and flow.

The second approach is usually unsatisfactory because the frequency of chemical observations is likely to
be inadequate to give a reliable load estimate when simple summation is used. Thus almost all effective
load estimation approaches are variants of approaches 1 or 3.

Unfortunately, the decision to calculate loads is sometimes made after the data are collected, often using
data collected for other purposes. At that point, little can be done to compensate for a data set that
contains too few observations of concentration, discharge, or both, collected using an inappropriate
sampling design. Many programs choose monthly or quarterly sampling with no better rationale than
convenience and tradition. A simulation study for some Great Lakes tributaries revealed that data from a
monthly sampling program, combined with a simple load estimation procedure, gave load estimates
which were biased low by 35 percent or more half of the time (Richards and Holloway 1987).

To avoid such problems, the sampling regime needed for load estimation must be established in the initial
monitoring design, based on quantitative statements of the precision required for the load estimate. The
resources necessary to carry out the sampling program must be known and budgeted for from the beginning.

The following steps are recommended to plan a monitoring  effort for load estimation:
   •  Determine whether the project goals require knowledge of load, or if goals can be met using
     concentration data alone. In many cases, especially when trend detection is the goal, concentration
     data may be easier to work with and be more accurate than crudely estimated load data.
   "  If load estimates are required, determine the accuracy and precision needed based on the uses to
     which they will be put. This is especially critical when the purpose of monitoring is to look for a
     change in load. It is foolish to attempt to document a 25 percent load reduction from a watershed
     program with a monitoring design that gives load estimates +50 percent of the true load (see
     Spooner et al. 201 la).
   "  Decide which approach will be used to calculate the loads based on known or expected attributes of
     the data.
   "  Use the precision goals to calculate the sampling requirements for the monitoring program.
     Sampling requirements include both the total number of samples and, possibly, the distribution of
     the samples with respect to some auxiliary variable such as flow or season.
   "  Calculate the loads based on the samples obtained after the first full year of monitoring, and
     compare the precision estimates (of both flow measurement and the sampling program) with the
     initial goals of the program. Adjust the sampling program if the estimated precision deviates
     substantially from the goals.
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It is possible that funding or other limitations may prevent a monitoring program from collecting the data
required for acceptable load estimation. In such a case, the question must be asked: is a biased, highly
uncertain load estimate preferable to no load estimate at all? Sometimes the correct answer will be no.


7.9.2 Approaches to Load Estimation
Several distinct technical approaches to load estimation are discussed below. The reader is encouraged to
consult Richards (1998) for details and examples of these calculations. Do not estimate annual loads
based on simple multiplication of an annual average concentration and average discharge as load
estimates will be biased low for positively correlated parameters such as suspended sediment and total
phosphorus.


7.9.2.1  Numeric Integration
The simplest approach is numeric integration, where the total load is given by
                                                 n
                                        Load = y  c^ti


where c; is the concentration in the ith sample, qi is the corresponding flow, and t; is the time interval
represented by the ith sample, calculated as:
                                         1
It is not required that t; be the same for each sample.

The question becomes how fine to slice the pie - few slices will miss much variability, many slices will
capture variability but at a higher cost and monitoring effort. Numeric integration is only satisfactory if
the sampling frequency is high - often on the order of 100 samples per year or more, and samples must be
distributed so that all major runoff events are captured. Selection of sampling frequency and distribution
over the year is critical - sampling must focus on times when highest fluxes occur, i.e., periods of high
discharge.

As noted above, flow-proportional sampling is a special case of mechanical rather than mathematical
integration that assumes that one or more samples can be obtained that cover the entire period of interest,
each representing a known discharge and each with a concentration that is in proportion to the load that
passed the sampling point during the sample's accumulation. If this assumption is met, the load for each
sample is easily calculated as the discharge times the concentration, and the total load for the year is
derived by summation. In principle, this is a very efficient and cost-effective method of obtaining a total
load.
7.9.2.2  Regression
When, as is often the case with NPS-dominated systems, a strong relationship exists between flow and
concentration, using regression to estimate load from continuous flow and intermittent concentration data
can be highly effective. In this approach, a regression relationship is developed between concentration
and flow based on the days for which concentration data exist. Usually, these data are based on grab
samples for concentration and mean daily flow for the sampling day (see Example 7.9-1). This
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relationship may involve simple or multiple regression analysis using covariates like precipitation. In
most applications, both concentration and flow are typically log-transformed to create a dataset suited for
regression analysis (see section 7.3.2 and Meals and Dressing 2005) for basic information on data
transformations). The regression relationship may be based entirely on the current year's samples, or it
may be based on samples gathered in previous years, or both. This method requires that there be a strong
linear association between flow and concentration that does not change appreciably over the period of
interest. If BMP implementation is expected to affect the relationship between flow and concentration,
such relationships must be tracked carefully - if BMPs change the relationship, the concentration
estimation procedure must be corrected.

Once the regression relationship is established, the regression equation is used to estimate concentrations
for each day on which a sample was not taken, based on the mean daily flow for the day.  The total load is
calculated as the sum of the daily loads that are obtained by multiplying the measured or estimated daily
concentration by the total daily discharge.

The goal of chemical sampling under this approach is to accurately characterize the relationship between
flow and concentration. The monitoring program should be designed to obtain samples over the entire
range of expected flow rates. If seasonal differences in the flow/concentration relationship are likely, the
entire range of flows should be sampled in each season. In some cases, separate seasonal  flow-
concentration regressions may need to be developed and used to estimate seasonal loads.  Examples of
such flow-concentration regressions are shown in Figure 7-30 and example 7.9-1.

This approach is especially applicable to situations where continuous flow data already exist, e.g., from
an ongoing USGS hydrologic station. Grab samples can be collected as needed and then associated with
the appropriate flow observations. Economy is another significant advantage of this approach. After an
initial intensive sampling period to develop the regression, it may be possible to maintain the regression
model with -20 samples a year for concentration, focusing on high-flow or critical season events.
Software exists to calculate and manage this approach, e.g. Flux32 (Walker 1990, Soballe 2014). Flux32
is an interactive program designed for use in estimating the loadings of nutrients or other water quality
components passing a tributary sampling station over a given period of time. Data requirements include
(a) grab-sample nutrient concentrations, typically measured at a weekly to monthly frequency for a period
of at least 1 year, (b) corresponding flow measurements (instantaneous or daily mean values), and (c) a
complete flow record (mean daily flows) for the period of interest.  Using six calculation techniques,
Flux32 maps the flow/concentration relationship developed from the sample record onto the entire flow
record to calculate total mass discharge and associated error statistics. An option to stratify the data into
groups based upon flow, date, and/or season is also included. The USGS program LOADEST is also
available and is widely used to estimate loads together with an estimate of precision using the regression
approach. LOADEST includes an adjusted maximum likelihood estimation method that can be used for
censored data sets and a least absolute deviation method to use when the regression residuals are not
normally distributed. A web-based version of LOADEST program is available at
https://engineering.purdue.edu/~ldc/LOADEST/. Another USGS load estimation calculation tool -
FLUXMASTER - has been used in the SPARROW (SPAtially Referenced Regressions On Watershed
attributes) watershed modeling technique to compute unbiased detrended estimates of long-term mean
flux, and to provide an estimate of the associated standard error (Schwarz et al. 2006). These models
include seasonal and temporal terms in their formulation that can improve the estimate of load; however,
care is needed to ensure the model form is correct by reviewing the diagnostic plots.
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                                                                           Chapter 7
  GO
7-


6-


5-


4-


3-


2-
       6-
       4-
       2-
                                        10
                         h(flow)
            summer
                                      10
                                            12
                                          12
                         Inflow)

Figure 7-30. Flow-concentration regressions from the Maumee River, Ohio (Richards 1998). Top
panel, regression relationship between log of total suspended solids concentration and log of
flow for the 1991 water year dataset; Bottom panel, plot of same data divided into two groups
based on time of year. Within each season, the regression model is stronger, has lower error, and
provides a more accurate load estimate.
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                     Example 7.9-1. Mill Creek Watershed, PA NNPSMP

  In this project, loads per unit area of nutrients and suspended sediment were estimated by combining
  the non-storm (i.e., low flow) and storm-flow loads (Galeone et al. 2006). Low-flow and storm-flow
  loads were computed using a multiple regression technique that included explanatory variables such
  as discharge, season,  and time to estimate concentrations (and subsequently loads). Regressions
  were developed separately for low-flow and storm-flow periods, and for both low flow and storm flow,
  separate models were generated for the  pre- and post-treatment periods for each site. Models were
  selected on the basis of the highest adjusted R2 and residuals plots to detect trends, and all F-values
  had to exceed the value for the F distribution for the appropriate degrees of freedom and an alpha
  equal to 0.05.

  Continuous discharge  data for the four sites was first separated into low-flow and storm-flow periods
  using site-specific criteria defining a storm event. Sampled storms were reviewed to determine the
  typical rate of stage-height increase that  initiated storm sampling. The recession and subsequent
  completion of storm sampling was also reviewed to determine the typical endpoint of storm sampling
  at each of the four sites. This information was used with 5- or 15-minute stage data to manually
  separate storm-flow discharge data from low-flow data.

  For low-flow periods, a subset of the grab-sample data was used to develop the relation between
  constituent concentrations and explanatory variables. Prior to using the grab-sample data, the
  cumulative frequency distribution for each site was determined using the continuous discharge data
  for the entire period of record. Grab samples collected at flows above the 97th percentile were deleted
  prior to load analysis. With these higher flows deleted, the  relation between constituent concentrations
  and explanatory variables was developed. The low-flow constituent concentrations were estimated  on
  a daily basis using the daily-mean discharge data for low-flow periods.  The estimated concentrations
  were multiplied by the  daily-mean discharge to estimate daily loads.
  Storm-flow loads for nutrients and suspended sediment were estimated by use of the mean discharge
  and mean constituent concentration for sampled storms. The mean discharge-concentration relation
  developed for sampled storms using regression analysis was used to predict the concentrations for
  unsampled storms. The mean discharge was calculated for unsampled storms using the 5- or
  15-minute continuous-stage data for the  sites. This mean discharge was applied to the predicted
  concentration to estimate constituent loads for unsampled  storms. Increases in stage caused by
  snowmelt events were analyzed separately by subsetting the storm events sampled during snowmelts
  and using these regression relations to estimate loads for non-sampled snowmelt events. The
  percentage of the storms sampled at each site was somewhat dependent on the location of the
  surface-water site, ranging from about 50-60 percent at outlet sites and 35-45 percent at upstream
  sites where flashiness  was greater and defined storms more  frequent.
  Constituent loads for each continuous surface-water site were estimated by summing the low-flow and
  storm-flow loads. The annual load data for the constituents were divided by the basin drainage areas
  to determine constituent yields. The percentage of the total yield in storm-flow was determined by
  summing the sampled  and unsampled storm yields and dividing by the total yield. The remaining yield
  was attributed to low-flow periods. Data also were separated  into pre- and post-treatment periods.
There are a few potential disadvantages to this approach. First, as mentioned earlier, potential changes or
trends in the concentration-flow relationship - sometimes a goal of watershed projects - must be tracked.
If the relationship changes a new regression model must be constructed. Second, the monitoring program
must be managed to effectively capture the entire range of flows/conditions that occur; the use of data
from fixed-interval time-based sampling is not appropriate for this purpose because of bias toward low
flow conditions.
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Hirsch et al. (2010) propose a weighted regression on time, discharge, and season (WRTDS) method that
addresses some of these shortcomings. Principally, the WRTDS method relies on the same function
regression structure as LOADEST; however, the fitted coefficients are allowed to vary with time. For
example, the amplitude of the seasonal cycle could be relatively large in some periods of the record and
then dampen to smaller cycles in other portions of the record. This is achieved through using a weighted
regression that "windows in" on a portion of the record in time, flow and season. It is noteworthy that the
researchers recommend that this method is primarily developed for data sets with more than 200 samples
collected over 20 years. Like other flow adjustment tools there is a requirement of flow stationarity, that
is, there isn't a basis for expecting a change in flow over time such as a new reservoir whether that change
is observed over the entire year or just during a portion of the year. Extended dry or wet periods are
simply an expected part of the long term record. WRTDS is generally intended for gradual changes that
might be expected with NFS projects or sites that represent the cumulative effect of multiple point
sources, and less for abrupt changes. WRTDS has been built into Exploration and Graphics for RivEr
Trends (EGRET): An R-package for the analysis of long-term changes in water quality and streamflow.
User guidance is available at https://github.com/USGS-R/EGRET/wiki although more current releases are
available through R (R Core Team 2013). The WRTDS method was applied to eight monitoring sites on
the Mississippi River investigating nitrate (Sprague et al. 2011) and compared to the more traditionally
recommended ESTIMATOR by Moyer et al. (2012) in an evaluation using data from the Chesapeake
Bay.
7.9.2.3  Ratio Estimators
The concept of ratio estimators is a powerful statistical tool for estimating pollutant load from continuous
flow data and intermittent concentration data. Ratio estimators assume that there is a positive linear
relationship between load and flow that passes through the origin. On days when chemistry samples are
taken, the daily load is calculated as the product of grab-sampled concentration and mean daily flow, and
the mean of these loads over the year is also calculated. The mean daily load is then adjusted by
multiplying it by a flow ratio, which is derived by dividing the average flow for the year as a whole by the
average flow for the days on which chemical samples were taken. A bias correction factor is included in
the calculation, to compensate for the effects of correlation between discharge and load. The adjusted
mean daily load is multiplied by 365 to obtain the annual load.

When used in a stratified mode (e.g., for distinct seasons), the same process is applied within each
stratum, and the stratum load is calculated by multiplying the mean daily load for the stratum by the
number of days in the stratum. The stratum loads are then summed to obtain the total annual load. The
Beale Ratio Estimator is one technique, with an example provided by Richards (1998). Several formulas
are available to calculate the number of samples (random or within  strata) required to obtain a load
estimate of acceptable accuracy based on known variance of the system. Stratification may improve the
precision and accuracy of the load estimate by allocating more of the sampling effort to the aspects which
are of greatest interest or which are most difficult to characterize because of great variability such as high
flow seasons.


7.9.2.4 Comparison  of Load Estimation Approaches
Although strongly driven by available resources, the monitoring program design (that should have
included consideration of load estimation issues from the beginning), and the natural system itself, the
choice  of load estimation approach can make an enormous difference in the resulting load estimate.
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In an analysis of total suspended solids data from the Maumee River in water year 1991, Richards (1998)
demonstrated that different methods of load estimation applied to different datasets can result in
substantially different estimates of pollutant load. Richards (1998) found that loads were often
underestimated with the Beale Ratio Estimator and regression techniques, attributing this finding to
missed high flow/TSS events and/or the estimation methods being biased toward low flow conditions.
Notably, the Beale Ratio Estimator gave a load estimate closer to the true load (estimated through
numeric integration) than did the regression method. For the full daily dataset, the single flow-
concentration regression over the entire year appeared to seriously underestimate suspended solids load;
while separating the data into summer and winter seasons improved the fit and the accuracy of the load
estimate. In a summary of findings, Harmel et al. (2006) reported that the USGS regression method could
result in annual constituent loads to within 10 percent of true loads in larger watersheds but no less than
30 percent for smaller watersheds.

Harmel and King (2005) and Harmel et al. (2006) concluded that flow-proportional, composite sampling
was the most effective method to obtain high quality data for estimating loads from small agricultural
watersheds. They concluded that composite sampling extended the sampler capacity with little effect on
error, noting that intensive sampling strategies could achieve errors less than 10 percent. In their study,
smaller sampling intervals should be used for constituents such as sediment which varies more during the
course of a rainfall event in  comparison to other constituents which vary less during a rainfall event.

Dolan et al. (1981) evaluated total phosphorus loadings to Lake Michigan from Grand River in 1976-77'.
They found that the Beale ratio estimator performed better than regression  or other simplified
calculations. Quilbe et al. (2006) evaluated a 1989-1995 nutrient and sediment data set from the
Beaurivage River (Quebec,  Canada). They chose to estimate loadings with a Beale ratio estimator because
they found  that the correlation between flow and various water quality parameters was too weak to
develop regression equations while noting that regression techniques would have been preferred if good
correlations were found. Marsh and Waters (2009) also found few cases with strong correlations in their
evaluation of 31 storm events in Queensland. They concluded that there was no clear best technique, but
noted that the ratio methods were more robust and regression techniques worked well when there was a
"tight" correlation. Using hourly model output, Zamyadi et al. (2007) found that the Beale ratio did not
perform well in comparison to averaging and interpolation procedures.

Taking the  above literature into account, this guidance recommends that numeric integration be used
when the full time series of water quality and flow data are available as in the case of flow-proportional
composited samples. Regression approaches are appropriate for incomplete water quality records if good
correlations between water quality and flow exist, with the Beale ratio recommended otherwise. It is
important to take into account stratification by flow regime, season,  and other covariates for both
regression and the Beale ratio.


7.9.3 Load Duration Curves
A particularly useful diagnostic tool for load estimation data is the load duration curve. Simply stated, a
duration curve is a graph representing the percentage of time during which the value of a given parameter
(e.g., flow,  concentration, or load) is equaled or exceeded. A load duration curve is therefore a cumulative
frequency plot of mean daily flows, concentrations, or daily loads over a period of record, with values
plotted from their highest value to lowest without regard to chronological order. For each flow,
concentration, or load value, the curve displays the corresponding percent of time (0 to 100) that the value
was met or exceeded over the specified time  - the flow, concentration, or load duration interval.
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Extremely high values are rarely exceeded and have low duration interval values; very low values are
often exceeded and have high duration interval values.

The process of using load duration curves generally begins with the development of a flow duration
curve, using existing historical flow data (e.g., from a USGS gage), typically using mean daily discharge
values. A basic flow duration curve runs from high to low along the x-axis, as illustrated in Figure 7-31.
The x-axis represents the duration or percent of time, as in a cumulative frequency distribution. The y-
axis represents the flow value (e.g., ftVsec (cfs)) associated with that percent of time. Figure 7-31
illustrates that the highest observed flow for the period of record was about 5,400 cfs, while the lowest
flow was 6 cfs. The  median flow - the flow exceeded 50 percent of the time - was about 200 cfs.

In the next step, a load duration curve is created from the flow duration curve by multiplying each of the
flow values by the applicable numeric water quality target (usually a water quality criterion) and a unit
conversion factor, then plotting the results as for the flow duration curve. The x-axis remains as the flow
duration interval, and the y-axis depicts the load rather than the flow. This curve represents the allowable
load (e.g., the TMDL) at each flow condition  over the full range of observed flow. An example is shown
in Figure 7-32 for the same site as shown in the flow duration curve, using a target of 0.05 mg/L total P.
Then, observed P load observations associated with the flow intervals are plotted along the same axes.
Points located above the curve represent times when the  actual loading is exceeding the target load, while
those plotting below the curve represent times when the actual loading is less than the target load.

A key feature of load duration curve analysis  is that the pattern of loads - and impairment - can be easily
visualized over the full range of flow conditions. Because flow variations usually correspond to seasonal
patterns, this feature can address the requirement that TMDLs account for seasonal variations. The pattern
of observed loads  exceeding target loads can be examined to see if impairments occur only at high flows,
only during low flows, or across the entire range of flow conditions. A common way to look at a load
duration curve is by dividing it into zones representing, for example: high flows (0-10 percent flow
duration interval), moist conditions (10-40 percent), mid-range flows (40-60 percent), dry conditions
(60-90 percent), and low flows (90-100 percent). Data may also be grouped by season (e.g., spring runoff
versus summer base flow). Sometimes, analysis of the load duration curve can provide insight on the
source of pollutant loads.  Measured loads that plot above the curve during flow duration intervals above
80 percent (low flow conditions), for example, may suggest point sources that discharge continuously
during dry weather.  Conversely, measured loads that plot above the curve during flow duration intervals
of about 10 to 70 percent tend to reflect wet weather contributions by NPS such as erosion, washoff, and
streambank erosion. Figure 7-32 illustrates that allowable total P loads in the Sevier River were exceeded
during all flow intervals, and that P concentrations were independent of flow.

It should be noted that an individual load duration curve  applies only to the point in the stream where the
data were collected.  A load duration curve developed at a watershed outlet station (e.g., for a TMDL)
applies only to loads observed at that point. If significant pollution sources exist upstream, a single load
duration analysis at the watershed outlet can underestimate the extent of impairment in upstream
segments. For this reason, it is usually wise to develop multiple load duration curves throughout the
watershed to address the spatial distribution of impairments. Such an exercise can also be useful in
targeting land treatment to critical watershed source areas.
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     Chapter 7
      10,000
       1,000
   I
           0%     10%     20%     30%    40%    50%     60%     70%    80%    90%     100%

                                           Flow Duration Interval

Figure 7-31. Flow duration curve for the Sevier River nearGunnison, UT, covering the period
January 1977 through September 2002
                          —Allowable TP Load at USGS Gage 10217000 (kg/day)
                           a Observed TP Load at Station 494247 (kg/day)
    10,000
     1,000
  I
  5
  CL
      100-
10%
20%     30%     40%     50%     60%    70%    80%

    Observed Flow Duration interval at USGS Gage 10217000
90%
                                                                                       100%
Figure 7-32. Load duration curve for the Sevier River near Gunnison, UT, January 1977 through
September 2002. Blue line represents allowable total P load calculated as the product of each
observed flow duration interval and the target total P concentration of 0.05 mg/L. Yellow points
represent observed total P loads at the same flow duration intervals.
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For more detailed discussion of load duration curves, particularly their application to the TMDL process,
refer to:
  •  USEPA. 2007. An Approach for Using Load Duration Curves in the Development of TMDLs

7.9.4 Assessing Load Reductions
The same statistical tools recommended for flow and concentration data in section 7.8.2 and elsewhere in
this chapter can be used to analyze program effectiveness with regard to load reductions. For example,
loads might be estimated on a weekly basis using numeric integration and flow-proportional, composite
sample data. Under a paired-watershed approach, the weekly-paired loads would be grouped as pre- and
post-treatment and analyzed using ANCOVA.

For comparisons of annual loadings, the analyst will have limited data to perform analyses (i.e., one
annual loading value per site-year) and will be generally limited to reporting simple change in loading and
drawing anecdotal comparisons to the control watershed. Normalizing the loadings based on watershed
size, annual rainfall, and other covariates might prove helpful.

Depending on the watershed and the types of installed BMPs, it is also appropriate to compare storm
loadings from individual storms before and after BMP implementation in a single watershed. The particular
challenge here is to control for other covariates and select/analyze storms of a certain size (e.g., rainfall
between 2.5-5.0 cm) and occurring at key times during the year (e.g., within 6 weeks of spring planting).
This type of analysis might also be limited to drawing simple comparisons due to sample size.


7.10 Statistical Software
Modern computers and software packages make it simple to perform the statistical analyses described in
this chapter. Most standard spreadsheet programs include basic statistical functions and graphing
capabilities, but more sophisticated and powerful statistical  software packages might be needed for
advanced analyses such as ANCOVA or cluster analysis. An extensive list and comparison of statistical
software packages is available at Wikipedia. Practical Statistics, a web site maintained by Dennis Helsel,
provides a more environmental-centric review of low-cost software tools. Table 7-9 lists some examples
and websites to visit for more information about the many statistical packages available.

                  Table 7-9. Sampling of available statistics software packages
Package Name
Analyse-lt (add in for MS Excel)
DataDesk
JMP
Mathematica
MATLAB
MINITAB
R
SAS/Stat, SAS/lnsight
SPSS
SYSTAT
WINKS
Web Site URL
http://www.analyse-it.com
http://www.datadesk.com
http://www.imp.com/en qb/software.html
http://www.wolfram.com/mathematica/
http://www.mathworks.com/products/matlab/
https://www.minitab.com/en-us/
https://www.r-proiect.orq/
http://www.sas.com/technoloqies/analvtics/statistics/index.html
http://www.spss.com/spss/
http://www.SYstat.com/products/Svstat/
http://www.texasoft.com/
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7.11 References
Belsley, D.A., E. Kuh, and R.E. Welsch. 1980. Regression Diagnostics: Identifying Influential Data and
       Sources ofCollinearity. John Wiley and Sons, New York.

Bernstein, B.B. and J. Zalinski.  1983. An optimum sampling design and power tests for environmental
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Bishop, P.L., W.D. Hively, J.R. Stedinger, M.R. Rafferty, J.L. Lojpersberger, and J.A. Bloomfield. 2005.
       Multivariate analysis of paired watershed data to evaluate agricultural best management practice
       effects on stream water phosphorus. Journal of Environmental Quality 34:1087-1101.

Box, G.E.P. and D.R. Cox. 1964. An analysis of transformations - series B (methodological). Journal of
       the Royal Statistical Society 26(2):211-252.

Box, G.E.P. and G.M. Jenkins. 1976. Time Series Analysis: Forecasting And Control. Revised Edition.
       Holden-Day, Oakland, CA.

Carpenter, S.R., T.M. Frost,  D. Heisey, and T.K. Kratz. 1989. Randomized intervention analysis and the
       interpretation of whole-ecosystem experiments. Ecology 70(4): 1142-1152.

Chambers, J.M., W.S. Cleveland, B. Kleiner, P.A. Tukey. 1983. Graphical Methods for Data Analysis.
       Duxbury Press, Boston.

Clausen, J.C. 2007. Jordan Cove Watershed Project Final Report. University of Connecticut, College of
       Agriculture and Natural Resources, Department of Natural Resources Management and
       Engineering. Accessed January 8, 2016.
       http://iordancove.uconn.edu/jordan cove/publications/final report.pdf

Clausen, J.C. and K.N. Brooks.  1983. Quality of runoff from Minnesota peatlands: II. a method for
       assessing mining impacts. Water Resources Bulletin 19(5):769-772.

Clausen, J.C. and J. Spooner. 1993. Paired Watershed Study Design. 841-F-93-009. Prepared for S.
       Dressing, U.S. Environmental Protection Agency, Office of Water, Washington, DC. Accessed
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Cleveland, W.S.  1993. Visualizing Data. AT&T Bell Laboratories/Hobart Press, Murray Hill, NJ/Summit,
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Clifford, R., Jr., J.W. Wilkinson, and N.L. Clesceri. 1986. Statistical Assessment of a Limnological Data
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       Canada Centre for Inland Waters, October 7-10, 1985, Volume 27 of Developments in Water
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Dolan, D.M., A.K. Yui and R.D. Geist.  1981. Evaluation of river load estimation methods for total
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Dolan, D.M. and K.P. McGunagle. 2005. Lake Erie total phosphorus loading analysis and update: 1996-
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       http://www.cee.mtu.edu/~nurban/classes/ce5508/2007/Readings/dolan05.pdf
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Drake, D. 1999. Multivariate Analysis of Fish and Environmental Factors in the Grande Ronde Basin of
       Northeastern Oregon. Oregon Department of Environmental Quality, Biomonitoring Section,
       Laboratory Division, Portland. Accessed March 24, 2016.
       http://www.deq.state.or.us/lab/techrpts/docs/Bio012.pdf.

Elliott, A.C. 2012. Descriptive Statistics Using Microsoft Excel. Texa Soft Mission Technologies, Cedar
       Hill, TX. Accessed March 24, 2016.
       httD://www.stattutorials.com/EXCEL/EXCEL-DESCRIPTIVE-STATISTICS.html.

Erickson, A.J., P.T. Weiss, J.S. Gulliver, R.M. Hozalski. 2010a. Analysis of Individual Storm Events. In
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Monitoring and Evaluating Nonpoint Source Watershed Projects                                    Chapter 8
8  Quality Assurance and  Quality Control
    By S.A. Lanberg, J.G. O'Donnell, and J.B. Harcum


8.1   Introduction
Quality assurance and quality control (QA/QC) are commonly thought of as procedures used in the
laboratory to ensure that all analytical measurements made are accurate. Yet QA/QC extends beyond the
laboratory and includes a wide range of issues that nonpoint source (NFS) managers consider when
addressing the challenges of developing a monitoring program (see chapters 2 and 3). When considered
independently from monitoring program design, QA/QC may seem burdensome. Yet the purpose of
QA/QC is the same as a well-intentioned NFS manager, which is to ensure that the monitoring data
generated are complete, accurate, and suitable for the intended purpose.  By integrating certain QA/QC
aspects with monitoring program design, NFS managers can reduce repetition and ultimately reduce total
costs by developing a more efficient monitoring design.

The remainder of this section defines QA/QC, discusses their value in NFS monitoring programs, and
explains EPA's policy on these topics. Section 8.2 provides an overview of the Data Quality Objectives
(DQO) process. EPA recommends that organizations use the DQO process to systematically plan their
monitoring programs. Typically, written QA/QC documentation takes the form of a quality assurance
project plan (QAPP). As discussed in section 8.3, a QAPP details the technical activities and  QA/QC
procedures that should be implemented to ensure the data meet the specified standards.

The QAPP should identify who will be involved in the project and their responsibilities; the nature of the
study or monitoring program; the questions to be addressed or decisions to be made based on the data
collected; where, how, and when samples will be taken and analyzed; the requirements for data quality;
the specific  activities and procedures to be performed to obtain the requisite level of quality (including
QC checks and oversight);  how the data will be managed,  analyzed, and checked to ensure that they meet
the project goals; and how the data will be reported. The QAPP should be implemented and maintained
throughout a project.

Sections 8.4, 8.5, and 8.6 provide more specific information for preparing QAPPs with respect to field
operations, laboratory operations, and data and reporting requirements, respectively. Although there are
many commonalities, QAPP development to support modeling and secondary data usage is beyond the
scope of this chapter. The reader is referred to CREM (2009) and USEPA (2002b) for guidance on the
development and application of environmental models and related QAPPs. EPA also provides guidance
about the  evaluation of existing (secondary) data quality (USEPA 2012) and information needed to
develop QAPPs for secondary data projects (USEPA 2008a).
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8.1.1  Definitions of Quality Assurance and Quality Control


8.1.1.1  Quality assurance:
An integrated system of management activities involving planning, implementation, documentation,
assessment, reporting, and quality improvement to ensure that a process, item, or service is of the type
and quality needed and expected by the client (USEPA 200Ic).


8.1.1.2  Quality control:
The overall system of technical activities that measures the attributes and performance of a process, item,
or service against defined standards to verify that they meet the stated requirements established by the
customer; operational techniques and activities that are used to fulfill requirements for quality (USEPA
2001c).

In a laboratory setting, QC procedures include the regular inspection of equipment to ensure it is
operating properly and the collection and analysis of blank, duplicate, and spiked samples and standard
reference materials to ensure the accuracy and precision of analyses. QA activities are more managerial in
nature and include assignment of roles and responsibilities to project staff, staff training, development of
data quality objectives, data validation, and laboratory audits. Table 8-1 lists some common activities that
fall under the heading of QA/QC. Such procedures and activities are planned and executed by diverse
organizations through carefully designed quality management programs that reflect the importance of the
work and the degree of confidence needed in the quality of the results.


                                Table 8-1. Common QA/QC activities
 QA Activities
       Organization of the project into component parts
       Assignment of roles and responsibilities to project staff
       Determine the number of QC samples and sampling sites needed to obtain data of a required confidence level
       Tracking of sample custody from field collection through final analysis
       Development and use of data quality objectives to guide data collection efforts
       Auditing of field and laboratory operations
       Maintenance of accurate and complete records of all project activities
       Training of personnel to ensure consistency of sample collection techniques and equipment use
 QC Activities
       Collection of duplicate samples for analysis
       Analysis of blank, duplicate, and spike samples
       Regular inspection and calibration of analytical equipment
       Regular inspection of reagents and water for contamination
       Regular inspection of refrigerators, ovens, etc. for proper operation
       Regular evaluation of data against QC objectives
Adapted from Drouse et al., 1986, and Erickson et al., 1991.
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8.1.2 Importance of QA/QC Programs
While it is desirable to stay below 10 percent, development and implementation of a QA/QC program can
require up to 10 to 20 percent of project resources (Cross-Smiecinski and Stetzenback 1994). This cost,
however, can be recaptured in lower overall costs of a well-planned and executed project. Likely
problems are anticipated and accounted for before they arise, eliminating the need to resample, reanalyze
data, or revisit portions of the project to determine where an error was introduced. A QAPP can serve as a
foundation for documenting standard operating procedures for all project activities, ensuring that project
tasks are conducted consistently by all personnel and can support training for new personnel as the project
moves forward. During a project, QA/QC information can provide essential feedback to ongoing project
management. Most importantly, a QA/QC program helps ensure that project data are of known accuracy
and precision, that errors  are minimized, and that all critical project activities are conducted consistently.
As long as the QA/QC procedures are followed, the data and information collected by the project will be
adequate to support technical conclusions and choices from among alternative courses of action. These
conclusions and actions will be defensible based on quality of the data and information collected. In short,
QA/QC procedures and activities are cost-effective measures used to determine how to allocate project
energies and resources toward improving the quality of research and the usefulness of project results
(Erickson et al, 1991).


8.1.3 EPA  Quality Policy
EPA has established a QA/QC program to ensure that data used in research and monitoring projects are of
known and documented quality to satisfy project objectives. The use of different methods, lack of data
comparability, unknown data quality,  and poor coordination of sampling and analysis efforts can delay
the progress of a project or render the  data and information collected from it  unsuited for decision
making. QA/QC practices should be integral parts of the development, design, and implementation of an
NPS monitoring project to minimize or eliminate these problems (Erikson et al. 1991; Pritt and Raese
1992; USEPA 2001b).

EPA Order CIO 2105.0 (formerly EPA Order 5360.1 A2), EPA's Policy and Program Requirements for
the Mandatory Agency-wide Quality System (USEPA 2000b), provides requirements for the conduct of
quality management practices, including QA/QC activities, for all environmental data collection and
environmental technology programs performed by or for EPA. The EPA Quality Manual for
Environmental Programs (USEPA 2000a) provides program requirements for implementing EPA's
mandatory quality system. In accordance with EPA Order CIO 2105.0, EPA  requires that environmental
programs be supported by a quality system that complies with the quality system  standard developed by
the American National Standard ANSI/ASQC E4-1994, Specifications and Guidelines for Quality
Systems for Environmental Data Collection and Environmental Technology Programs (ANSI/ASQC
1994). The ANSI/ASQC E4-1994 quality system standard was later updated  as ANSI/ASQ E4-2004,
Quality Systems for Environmental Data and Technology Programs - Requirements with Guidance for
Use (ANSI/ASQ 2004).

EPA's mandatory agency-wide Quality System Policy (EPA Policy CIO 2106.0)  requires each office or
laboratory generating data to implement minimum procedures to ensure that  precision, accuracy,
completeness, comparability, and representativeness of data are known and documented (Erickson et al.
1991; USEPA 2008b). This policy is now based on the quality system standard developed by the
American National Standards Institute and the American Society of Quality Control (ANSI/ASQ 2004).
Each office or laboratory is required to specify the quality levels that data must meet to be acceptable and
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satisfy project objectives. This requirement applies to all environmental monitoring and measurement
efforts mandated or supported by EPA through regulations, grants, contracts, or other formal agreements.
To ensure that this responsibility is met uniformly across EPA, each organization performing work for
EPA must document in a Quality Management Plan (QMP) that is approved by its senior management
how it will plan, implement, and assess the effectiveness of QA/QC operations applied to environmental
programs (USEPA 200Ib). In addition, each non-EPA organization must have an approved QAPP that
covers each monitoring or measurement activity associated with a project (Erickson et  al. 1991, USEPA
1983, 2008b). Additional implementation guidance is provided in EPA Quality Manual for
Environmental Programs (USEPA 2000a).

The purpose of writing a QAPP prior to undertaking an NPS monitoring project is to establish clear
objectives for the program, including the types of data needed and the quality of the data generated
(accuracy, precision, completeness, representativeness, and comparability) in order to meet the project's
water quality and land treatment objectives.  See  section 2.1 for a discussion of appropriate objectives for
NPS monitoring projects.

The QAPP should specify the policies, organization, objectives, functional activities, QA procedures, and
QC activities designed to achieve the data quality goals of the project. It should be distributed to all
project personnel, and they should be familiar with the policies and objectives outlined in the QAPP to
ensure proper interaction of the sampling  and laboratory operations and data management. Although a
QA/QC officer oversees major aspects of QAPP implementation, all persons involved in an NPS
monitoring project who either perform or supervise the work done under the project are responsible for
ensuring that the QA/QC procedures and activities established in the QAPP are adhered to.

The QMP and each QAPP must be submitted for review to the EPA organization responsible for the work
to be performed, and they must be approved by EPA or its  designee (e.g., federal or state  agency) as part
of the contracting or assistance agreement process before data collection can begin. In addition, it is
important to note that the QMP and QAPP are "live" documents and programs in the sense that once they
have been developed they cannot be placed on a shelf for the remainder of the project. All QA/QC
procedures should be  evaluated and plans updated as often as necessary during the course of a project to
ensure that they are in accordance with the present project direction and efforts (Knapton and Nimick
1991, USEPA 2001c).


8.2  Data Quality Objectives
When monitoring data are being used to assess water quality and the effects of land-based activities on
water quality or the effectiveness of best management practices, EPA recommends that states, tribes, and
non-governmental organizations (NGOs)  consider using the systematic planning tool called the Data
Quality Objectives (DQO) Process. The DQO process should be part of project planning and development
of a proposed monitoring strategy.

The DQO process is used to establish performance or acceptance  criteria that serve as the basis for
designing a plan for collecting data of sufficient  quality and quantity to support the objectives of a study.
The DQO process consists of seven iterative steps (USEPA 2006):
    1)  State the problem: define the problem that necessitates the study; identify the planning team,
        examine budget, schedule.
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    2)  Identify the goal of the monitoring program: state how monitoring data will be used in
       meeting objectives and solving the problem, identify study questions, define alternative
       outcomes.
    3)  Identify information inputs: identify data and information needed to answer questions.
    4)  Define the boundaries of the study: specify the target population and characteristics of interest,
       define spatial and temporal limits, scale of inference.
    5)  Develop the analytic approach: define the parameters of interest, specify the type of inference,
       and develop the logic for drawing conclusions from findings.
    6)  Specify performance or acceptance criteria: develop performance criteria for new data being
       collected or acceptance criteria for existing data being considered for use.
    7)  Develop the plan for obtaining data: select the resource-effective monitoring plan that meets
       the performance criteria.

Several iterations of the process might be required to  specify the DQOs for a monitoring program.
Because DQOs are continually reviewed during data collection activities, any needed corrective action
can be  planned and executed to minimize problems before they become significant. General guidance and
examples of planning for monitoring programs are also provided in related guidance (USEPA 2003a).


8.2.1  The Data Quality Objectives Process
The DQO process takes into consideration the factors that will depend on the data (most importantly, the
decision(s) to be made) or that will influence the type and amount of data to be collected (e.g., the
problem being addressed, existing information, information needed before a decision can be made, and
available resources). From these factors the qualitative and quantitative data needs are determined. The
purpose of the DQO process is to improve the effectiveness, efficiency, and defensibility of decisions
made based on the data collected, and to do so in a resource-effective manner (USEPA 2006).

DQOs  are qualitative and quantitative statements that clarify the study objective, define the most
appropriate type of data to collect, and determine the most appropriate conditions under which to collect
them. DQOs also specify the minimum quantity and quality of data needed by a decision maker to make
any decisions that will be based on the results of the project. By using the DQO process, investigators can
ensure  that the type, quantity, and quality of data collected and used in decision making will be
appropriate for the intended use. Similarly, efforts will not be expended to collect information that does
not support defensible decisions. The products of the  DQO process are criteria for data quality and a data
collection design that ensures that data will meet the criteria.

A brief description of each step of the DQO process and a list of activities that are part of each step
follow. For a detailed discussion of the DQO development process, refer to EPA's Guidance on
Systematic Planning Using the Data Quality Objectives Process (USEPA 2006). This reference contains a
case study example of the DQO process. A computer program, Data Quality Objectives Decision Error
Feasibility  Trials (USEPA 200la), is also available to help the planning process by generating cost
information about several simple sampling designs based on the DQO constraints before the sampling and
analysis design team begins developing  a final sampling design in the last step of the  DQO process.
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8.2.1.1  (1) State the problem
In this first step, concisely describe the problem to be studied . A review of prior studies and existing
information is important during this step to gain a sufficient understanding of the problem in order to
define it. The specific activities to be completed during this step (outputs) are:
   •  Identify members of the planning team.
   •  Identify the primary decision maker of the planning team and define each member's role and
      responsibilities during the DQO process.
   •  Develop a concise description of the problem.
   •  Specify the available resources and relevant deadlines for the study.


8.2.1.2  (2) Identify the goal of the monitoring program
Identify what questions the study will attempt to resolve and what actions might be taken based on the
study. This information is used to prepare a "decision statement" or an objective that will link the
principal study question to one or more possible actions that should solve the problem. Example NFS
monitoring program objectives might be to "determine the sources of bacteria causing the water quality
standard violation in Duck Creek" or "determine the effects of land treatment program xyz on phosphorus
loads to Lake Eutrophy." Results from the monitoring program would then support management
decisions to take action, modify an  action, or take no action.

The specific activities to be completed during this step are:
   •  Identify the principal study question.
   •  Define the alternative actions that could result from resolution of the principal study question.
   •  Combine the principal study question and the alternative actions into a decision statement.
   •  If applicable,  organize multiple decisions to be made by priority.


8.2.1.3  (3) Identify information inputs
Identify the information that needs to be obtained and the measurements that need to be taken to resolve
the decision statement. The specific activities to be completed during this step are:
   •  Identify the information that will be required to resolve the decision statement.
   •  Determine the sources for each item of information identified above.
   •  Identify the information that is needed to establish the threshold value that will be the basis of
      choosing among alternative actions.
   •  Confirm that appropriate measurement methods exist to provide the necessary data.


8.2.1.4  (4) Define the boundaries of the study
Specify the time periods and spatial area to which decisions will apply and determine when and where
data should be collected. This information is used to define the population(s) of interest. The term
population refers to the total collection or universe of objects from which samples will be drawn. The
population could be the concentration of a pollutant in sediment, a water quality variable, algae in the
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river, or bass in the lake. It is important to define the study boundaries to ensure that data collected are
representative of the population being studied (because every member of a population cannot be sampled)
and will be collected during the time period and from the place that will be targeted in the decision to be
made. The specific activities to be completed during this step are:
  •   Specify the characteristics that define the population of interest.
  •   Identify the geographic area to which the decision statement applies (such as a county) and any
      strata within that area that have homogeneous characteristics (e.g., recreational waters, dairy farms).
  •   Define the time frame to which the decision applies.
  •   Determine when to collect data.
  «   Define the scale of decision making, or the actual areas that will be affected by the decision
      (e.g., first-order streams, dairy farms with streams running through them, a county).
  •   Identify any practical constraints on data collection.


8.2.1.5  (5) Develop the analytic approach
Define the statistical parameter of interest, specify the threshold at which action will be taken, and
integrate the previous DQO outputs into a single statement that describes the logical basis for choosing
among alternative actions. This statement is known as a decision rule. It is often phrased as an
"If...then..." statement. For example, "If septic systems are contributing to water quality standard
violations, then failing  septic systems will be remediated; otherwise, no action will be taken." The
specific activities to be completed during this step are:
  •   Specify the statistical parameter that characterizes the population (the parameter of interest), such
      as the mean, median, or percentile.
  •   Specify the numerical value of the parameter of interest that would cause a decision maker to take
      action, i.e., the threshold value.
  •   Develop a decision rule in the form of an "if...then..." statement that incorporates the parameter of
      interest, the scale of decision making, the threshold level, and the actions that would be taken.


8.2.1.6  (6) Specify  performance or acceptance criteria
Define the decision maker's tolerable limits of making an incorrect decision (or decision error) due to
incorrect information (i.e., measurement and sampling error) introduced during the study.  These limits are
used to establish performance goals for the data collection design. Base the limits on a consideration of
the consequences of making an incorrect decision. The decision maker cannot know the true value of a
population parameter because the population of interest almost always varies over time and space and it is
usually impractical or impossible to measure every point (sampling design error). In addition, analytical
methods and instruments are never absolutely perfect (measurement error). Thus, although it is
impossible to eliminate these two errors, the combined total study error can be controlled to reduce the
probability of making a decision error. The specific activities to be completed during this step are:
  •   Determine the possible range (likely upper and lower bounds) of the parameter of interest.
  •   Identify the decision errors and choose the null hypothesis. Decision errors for NFS pollution
      problems might take the  general form of deciding there is an impact when there is none [a false
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      positive, or type I error] or deciding there is no impact when there is [a false negative, or type II
      error].
  •  Specify the likely consequences of each decision error. Evaluate their potential severity in terms of
      ecological effects, human health, economic and social costs, political and legal ramifications, and
      other factors.
  •  Specify a range of possible parameter values where the consequences of decision errors are
      relatively minor (gray region). The boundaries of the gray region are the threshold level and the
      value of the parameter of interest where the consequences of making a false negative decision begin
      to be significant.
  •  Assign probability limits to points above and below the gray region that reflect the tolerable
      probability for the occurrence of decision errors.


8.2.1.7  (7) Develop the plan for obtaining data
Evaluate information from the previous steps and generate alternative data collection designs. Some
aspects of this may be considered informally during the project planning process, and less attention can be
given to some alternatives. The designs should specify in detail the monitoring that is required to meet the
DQOs, including the types and quantity of samples to be collected; where, when, and under what
conditions they should be collected; what variables will be measured; and the QA/QC procedures that will
ensure that the DQOs are met. The QA/QC procedures are fully developed when the QAPP is written
(see below). Choose the most resource-effective design that meets all of the DQOs. As resources dictate,
it may be necessary to reduce or restate the DQOs. The specific activities to be completed during this step
are:
  •  Review the DQO outputs and existing environmental data.
  •  Develop general data collection design alternatives.
  •  Formulate the mathematical expressions needed to solve the design problem for each data
      collection design alternative. This  involves selecting a statistical test method (e.g., Student's ^test),
      developing a statistical model that relates the measured value to the "true" value,  and developing a
      cost function that relates the number of samples to the total cost of sampling and analysis.
  •  Select the optimal sample size that satisfies the DQOs for each data collection design alternative.
  •  Select the most resource-effective  data collection design that satisfies all of the DQOs.
  •  Document the selected design's key features and the statistical assumptions of the selected design. It
      is particularly important that the statistical assumptions be documented to ensure that, if any
      changes in analytical methods or sampling procedures are introduced during the project, these
      assumptions are not violated.

The DQO process should be used during the planning stage of any study that requires data collection, and
before the data are collected. EPA's policy is to use the DQO process to plan all data collection efforts
that will require or result in a substantial commitment of resources. The DQO process is applicable to all
studies, regardless  of size; however, the depth and detail of the DQO development effort depends on the
complexity of the study. In general, more complex studies benefit more from more detailed DQO
development.
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8.2.2 Data Quality Objectives and the QA/QC Program
The DQOs and the quality objectives for measurement data that will be specified in the QAPP are
interdependent. The DQOs identify project objectives; evaluate the underlying hypotheses, experiments,
and tests to be performed; and then establish guidelines for the data collection effort needed to obtain data
of the quality necessary to achieve these objectives (Erickson et al. 1991, USEPA 2006). The QAPP
presents the policies, organization, and objectives of the data collection effort and explains how particular
QA/QC activities will be implemented to achieve the DQOs of the project, as well as to determine what
future research directions might be taken (Erickson et al, 1991, USEPA 2006). At the completion of data
collection and analysis, the data are validated according to the provisions of the QAPP and a Data Quality
Assessment (DQA), using  statistical tools, is conducted to determine:
  •  Whether the data meet the assumptions under which the DQOs and the data collection design were
     developed.
  •  Whether the total error in the data is small enough to allow the decision maker to use the data to
     support the decision within the tolerable decision error rates expressed  by the decision maker
     (USEPA 2006).

Thus, the entire process is designed to assist the decision maker by planning and obtaining environmental
data of sufficient quantity and quality to satisfy the project objectives and allow decisions to be made
(USEPA 200 Ic, 2006). The DQO process is the part of the quality system that provides the basis for
linking the intended use of the data to the QA/QC requirements for data collection and analysis (USEPA
2006).


8.3   Elements of A Quality Assurance Project Plan
QAPPs must be prepared in accordance with EPA Requirements for Quality Assurance Project Plans
(USEPA, 2001b) and Guidance for Quality Assurance Project Plans (USEPA 2002a).  EPA requires that
four types of elements be discussed in a Quality Assurance Project Plan (QAPP): Project Management,
Measurement and Acquisition, Assessment and Oversight, and Data Validation and Usability. These
elements are listed in Table 8-2. For complete descriptions and requirements, see USEPA (200Ib).
Additional information on the contents of a QAPP is contained in Drouse et al. (1986), Erickson et al.
(1991), and Cross-Smiecinski and Stetzenback (1994). Drouse et al. (1986) and Erickson et al. (1991) are
examples of EPA QAPPs prepared under previous guidance.

The elements in Table 8-2  should always be addressed in the QAPP, unless otherwise directed by the
overseeing or sponsoring EPA organization(s). Both laboratory and field operations should be included.
The types, quantity, and quality of environmental data collected for each project could be quite different.
The level of detail in each QAPP will vary according to the nature of the work being performed and the
intended use of the data (USEPA 200 Ib). If an element is not applicable or required, then this should be
stated in the QAPP. For some complex projects, it might be necessary to add special requirements to the
QAPP. Again, the QAPP must be approved by the sponsoring EPA organization before data collection
can begin.
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                                                                Chapter 8
      Table 8-2. Elements required in an EPA Quality Assurance Project Plan. (USEPA, 2001 b)
                                            QAPP Element
                   A1
                   A2
                   A3
                   A4
                   A5
                   A6
                   A7
                   A8
                   A9
                    B1
                    B2
                    B3
                    B4
                    B5
                    B6
                    B7
                    B8
                    B9
                   B10
                    C1
                    C2
                    D1
                    D2
                    D3
Title and Approval Sheet
Table of Contents
Distribution List
Project/Task Organization
Problem Definition/Background
Project/Task Description
Quality Objectives and Criteria
Special  Training/Certification
Documents and Records
Sampling Process Design (Experimental Design)
Sampling Methods
Sampling Handling and Custody
Analytical Methods
Quality Control
Instrument/Equipment Testing, Inspection, Maintenance
Instrument/Equipment Calibration and Frequency
Inspection/Acceptance of Supplies and Consumables
Non-direct Measurements
Data Management


Assessments and Response Actions
Reports to Management


Data Review, Verification, and Validation
Verification and Validation Methods
Reconciliation and User Requirements
Standard Operating Procedures (SOPs) must be provided or referenced in the QAPP such that they are
available to all participants. An SOP typically presents in detail the method for a given technical
operation, analysis, or action in sequential steps and it includes specific facilities, equipment, materials
and methods, QA/ QC procedures, and other factors necessary to perform the operation, analysis, or
action for the particular project. By following the SOP, the operation should be performed the same way
every time. Activities typically include field sampling, laboratory analysis, software development, and
database management. EPA presents examples of the format and content of SOPs (USEPA, 2007). The
format and content requirements for an SOP are flexible because the content and level of detail in SOPs
vary according to the nature of the procedure. SOPs should be revised when new equipment is used, when
comments by personnel indicate that the directions are not clear, or when a problem occurs. Organizations
should ensure that current SOPs are used. SOPs are critical in the training of new personnel during the
conduct of a long-term project.
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                             Definitions of selected data quality terms

  Precision (reproducibility) is an expression of mutual agreement of multiple measurements of the same property
  (e.g., duplicate field samples or duplicate lab samples) conducted under similar conditions. It is evaluated by
  recording and comparing multiple measurements of the same parameter on the same exact sample under the
  same conditions. Relative percent difference (RPD) is a measure of precision and is calculated with the following
  formula (Cross- Smiecinski and Stetzenback, 1994):
     where
        x-i =  analyte concentration of first duplicate and
        X2 =  analyte concentration of second duplicate.

  Accuracy (bias) is the degree of agreement of a measurement (or an average of measurements), X, with an
  accepted reference or true value, T. Accuracy is expressed as the percent difference from the true value {1 00 [(X-
  T)/T]} unless spiking materials are used and percent recovery is calculated (Erickson et al., 1991). Accuracy can
  be determined by analyzing a sample and its corresponding matrix spike. Accuracy can be expressed as percent
  recovery and calculated using the following formula (Air National Guard, 1993):
     where
        A =   spiked sample result;
        B=   sample result; and
        C=   spike added.

  Comparability is defined as the confidence with which one data set can be compared to another (Erickson et al.,
  1991). Consistent sampling methodology, handling, and analyses are necessary to ensure comparability. Also,
  assurance that equipment has been calibrated properly and analytical solutions prepared identically is necessary
  to attain data comparability (Air National Guard,  1993).
  Representativeness is a measure of how representative the data obtained for each parameter are compared
  with the values of the same parameter within the population being measured. Because the total population cannot
  be measured, sampling must be designed to ensure that the samples are representative of the population being
  sampled (Air National Guard, 1 993). A relevant sampling design issue, for example, is to determine how a sample
  will be collected to ensure it is representative of the desired characteristic (Erickson et al., 1991).
  Completeness is defined as the  amount of valid data obtained from a measurement system compared to the
  amount that was expected to be obtained under anticipated sampling/analytical conditions (Erickson et al., 1991).
  An assessment of the completeness of data is performed at the end  of each sampling event, and if any omissions
  are apparent, an attempt is made to resample the parameter in question, if feasible. Data completeness should
  also be assessed prior to the preparation of data reports that check the correctness of all data. An example of a
  formula used for this purpose is

                                                        \V
                                              %C = 100
-}
.n\
     where
        %C  =  percent complete;
        V    =  number of measurements judged valid; and
        n    =  total number of measurements necessary to achieve a specified level of confidence in decision
                making (Cross-Smiecinski and Stetzenback, 1994).
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8.4  Field  Operations
Field operations are an important activity in an NFS monitoring program. Field operations involve the
organization and design of the field operation, selection of sampling sites, selection of sampling
equipment, sample collection, sample handling and transport, and safety and training issues. For the
purposes of QA/QC, the process of conducting field operations should be broken down into as many
separate steps as are necessary to ensure complete consideration of all of the elements  and processes that
are a part of field activities. Field operations described in this section have been broken down into the
phases mentioned above, but individual monitoring programs might require the use of more or fewer
phases. For example, if the sample collection phase is very complex or if it is anticipated that sample
collection will often be done under inclement weather conditions when field personnel might experience
discomfort and feel rushed, it is advisable to break sample collection into separate preparation, sampling,
and termination phases and discuss QA/QC for each of the phases separately. This will ensure that no
details are omitted.


8.4.1 Field Design
Adherence to the procedures specified in the QAPP for field operations and documentation of their use
for all aspects of field operations are extremely important if the data obtained from the project are to be
useful for decision making, supportable if questioned, and comparable for use by future researchers
(Knapton and Nimick 1991). Data sheets, for recording site visit information and field data, should be
prepared beforehand. Where applicable data sheets should include data quality reminders to help ensure
that all data are collected and QA/QC procedures are followed during all field activities.

General information that should be included in the documentation of the design for field operations
includes the scale of the operations (laboratory, plot, hillslope, watershed); size of plots/data collection
sites; designation of control sites; basin characteristics; soil and vegetation types; maps with the location
of plots/data collection sites within the basin/catchment;  weather conditions under which sampling is
conducted; equipment and methods used; problems that might be encountered during sampling; dates of
commencement and suspension of data collection; temporal gaps in data collection; frequency of data
collection; intensity of data collection; and  sources of any outside information (e.g., soil types, vegetation
identifications) (Erickson et al., 1991). Some of these aspects are discussed in greater detail in the
following sections.


8.4.2 Sampling Site Selection
The  selection of sampling sites is important to the validity of the results. Sites must be selected to provide
data to meet the goals/objectives of the project. The QAPP should provide detailed information on
sampling site locations (e.g., latitude and longitude); characteristics that might be important to data
interpretation (e.g., percent riparian cover, stream order); and the rationale for selecting the sites used
(Knapton and Nimick, 1991). Sites from other studies can be convenient to use due to their familiarity
and the availability of historical data, but such sites should be scrutinized carefully to be certain that data
obtained from them will serve the objectives of the project. If during the course of the project it is found
that one or more sampling sites are not providing quality data, alternative sites might be selected and the
project schedule adjusted accordingly. The  adequacy of the  sampling locations and the sampling program
should be reviewed periodically by project  managers, as  determined by data  needs (Knapton and Nimick,
1991).
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Sampling sites should be visited before sampling begins. It is important to verify that the sites are
accessible and are suitable for collection of the data needed. Consideration should be given to
accessibility in wet or inclement weather if samples will be taken during such conditions. The sites should
be visited, if possible, in the type(s) of weather during which sampling will occur.

Plastic-laminated pictures of each sampling site with an arrow pointing to each monitoring location can
assist field personnel in finding the sites during inclement weather when the sites might appear different.

If permission to access a site is needed (for instance, if one or more sites are on or require passage
through private property), such permission must be obtained before sampling begins. The person(s)
granting the permission should be fully informed about the number of persons  who will be visiting during
each sampling event, frequency of sampling, equipment that will have to be transported to the sampling
site(s), any hazardous or dangerous materials that will be used during sampling, and any other details that
might affect the decision of the person(s) to grant access permission. A lack of full disclosure of
information to gain access permission creates a risk of the  permission's being revoked at some point
during the project. A copy of the site entry permission letter or document should be taken to the site at the
time of field visit.


8.4.3  Sampling Equipment
Equipment for field operations includes field-resident equipment such as automatic samplers and stage-
level recorders and nonresident sampling equipment such as flow, pH, and conductivity meters;
equipment needed to gain access to sampling sites such as boats; and equipment for field personnel health
and safety,  such as waders, gloves, and life vests. The condition and manner of use of the field equipment
determines the reliability of the collected data and the success of each sampling event. Therefore,
operation and maintenance of the equipment are important elements of field QA/QC. All measurement
equipment must be routinely checked and calibrated to verify that it is operating properly and generating
reliable results, and all access and health and safety equipment should be routinely checked to be certain
that it will function properly under all expected field conditions.

A manual with complete descriptions of all field equipment to be used should be available to all  field
personnel. The manual should include such information as model numbers for all measurement
equipment, operating instructions, routine repair and adjustment instructions, decontamination techniques,
sampling preparation instructions (e.g., washing with deionized water), and use limitations (e.g.,
operating temperature range). If any samples are to be analyzed in the field, the techniques to be used
should be thoroughly described in the manual.


8.4.4  Sample Collection
A Sampling Plan should be developed and approved prior to sampling. The process of sample collection
should be described with the same amount of detail as the equipment descriptions. A thorough description
of the sample collection process includes when the sampling is to be done (e.g., time of day, month, or
year; before and/or after storms); the frequency with which each type of sample will be collected; the
location at which samples are to be taken (i.e., depth, distance from shore, etc.); the time between samples
(if sampling is done repetitively during a single sampling site visit); and how samples are to be labeled.
Each field person must be thoroughly familiar with the sampling techniques (and equipment) prior to the
first sampling event. Holding practice sampling events prior to the commencement of actual sampling is
an excellent way to prepare all field personnel and will help to identify potential problems with the
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sampling sites (access, difficulty under different weather conditions), sampling equipment, and sampling
techniques.

Quality control activities for field operations must ensure that all field operations are conducted so that
sampling is done in a consistent manner and that all generated information is traceable and of known and
comparable quality. Each field activity should be standardized. Standard operating procedures (SOPs) for
field sampling have been developed and might be required depending on the agency for which the
sampling is being conducted. Elements of the field operations section of a QAPP should include clear
statements of the regulatory requirements applicable to the project. Any SOPs that are part of regulatory
requirements should be followed precisely. The pictures taken of each sampling  site to aid in locating the
sampling sites also help ensure consistency of field monitoring across time and personnel by ensuring that
the same spot is used at each sampling event.

Depending on the DQOs and data requirements of the program (type of data and frequency of collection),
additional quality control  samples might be needed to monitor the performance of various field (as well as
laboratory) operations including sampling, sample handling, transportation,  and storage.

As the samples are collected, they must be labeled and packaged for transport to a laboratory for analysis
(or other facility for nonchemical analyses). Computer-generated sample bottle labels prepared before the
sampling event and securely attached to each bottle help minimize mistakes. Sampling location and
preservation, filtration,  and laboratory procedures to be used for each sample should be recorded on each
label. Be sure these labels are printed with waterproof ink on waterproof paper, and use aNo. 2 pencil or
waterproof/solvent-resistant marker to record information.


8.4.5 Sample Handling and Transport
Once samples have been collected, they must be analyzed, usually in a laboratory. Handling and transport
of sampling containers and custody of sample suites is also a part of field operations. Sample transport,
handling, and preservation must be performed according  to well-defined procedures. The various persons
involved in sample handling and transport should follow  SOPs for this phase of the project. This will help
ensure that samples are handled properly, comply with holding time and preservation requirements, and
are not subject to potential spoilage, cross-contamination, or misidentification.

The chain of custody and  communication between the field operations and other units such as the
analytical laboratory also need to be established so that the status of the samples is always known and can
be checked by project personnel at any time. The chain of custody states who the person(s) responsible
for the samples are at all times. It is important that chain of custody be established and adhered to so that
if any problem with the samples occurs, such as loss, the  occurrence can be traced and possibly rectified,
or it can be determined  how serious the problem is and what corrective action needs to be taken. Field
data custody sheets are  essential for this effort (Cross-Smiecinski and Stetzenback, 1994). Chain-of-
custody seals must be applied to sample containers and shipping  containers.


8.4.6 Safety and Training
When dealing with NPS monitoring, sampling activities often occur during difficult weather and field
conditions. It is necessary to assess these difficulties and  establish a program to ensure the safety of the
sampling personnel. The following types of safety issues, at a minimum, should be considered and
included in training and preparation activities for sampling: exposure, flood waters, debris in rivers and
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streams, nighttime collecting, criminal activity, and first aid for minor injuries. The trade-off between the
need for data quality and the safety of personnel is a factor that project staff should consider collectively.

Finally, the QAPP for the field operations should include provisions for dealing with any foreseeable
problems such as droughts, floods, frozen water, missing samples, replacement personnel during sickness
or vacation, lost samples, broken sample containers, need for equipment spare parts, and other concerns.


8.5  Laboratory Operations
Laboratory operations should be conducted with great care and attention to detail. Often, an independent
laboratory conducts sample analyses, so QA/QC for the laboratory are not under the direct control of
project personnel. However, it is important that project personnel are certain that the laboratory chosen to
do analyses follows acceptable QA/QC procedures so that the data produced meet the DQOs established
for the project. Laboratories should be selected based on quality assurance criteria established early in the
project. The  Quality Assurance  Officer for the project should be certain that these criteria are used for
selecting a laboratory to perform any necessary analyses for the project and that any laboratories selected
meet all criteria. Laboratories can be evaluated through the following measures (Air National Guard,
1993):
  •   Performing proficiency testing through analysis of samples similar to those which will be collected
      during the project.
  «   Performing inspections and audits.
  •   Reviewing laboratory QA/QC plans.
  •   One or more  of these measures should be used by the project manager, and the laboratories should
      be visited before entering into a contract for sample analyses.


8.5.1 General Laboratory  QA/QC
EPA recommends using an accredited laboratory with an established QA/QC policy to ensure that results
will be defensible. The National Environmental Laboratory Accreditation Conference (NELAC) Institute
provides accreditation of environmental testing laboratories. Numerous references are available on
laboratory QA/QC procedures, and one or more should be consulted to gain an understanding of
laboratory QA/QC requirements if project personnel are  not familiar with them already. The details of a
laboratory's QA/QC procedures must be included in the QAPP for the NPS monitoring project. Some
elements to look for in a laboratory QA/QC plan include (Cross-Smiecinski and Stetzenback, 1994):
  •   How samples are received
  •   Proper documentation of their receipt
  •   Sample handling
  •   Sample analysis
  •   QC requirements (procedures and frequencies of QC checks, criteria for reference materials, types
      of QC samples  analyzed and frequencies)
  •   Waste disposal
  •   Cleanliness and contamination
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  •   Staff training and safety
  •   Data entry and reporting
  •   Confidentiality

This section provides some information on laboratory QA/QC procedures to which managers of
monitoring programs should pay particular attention when deciding to use a particular laboratory for
sample analysis.


8.5.2 Instrumentation and Materials for Laboratory Operations
The laboratory chosen to do chemical analyses should have all equipment necessary to perform the
analyses required, including organic analysis, inorganic analysis, and assessments of precision and
accuracy. If any specialized analyses are required (e.g., microbiology, histopathology, toxicology), be
certain that the laboratory has the appropriate equipment and that laboratory staff are adequately trained
to perform the desired analyses. As noted in the elements of the  QAPP, periodic calibration checks that
are conducted to ensure that measurement systems (instruments, devices, techniques) are operating
properly should be described in the QAPP, including procedures and frequency (Cross-Smiecinski and
Stetzenback, 1994).


8.5.3 Analytical Methods
The laboratory chosen for sample analysis should use analytical methods approved by the agency for
which the sampling is being conducted or by project personnel,  as appropriate. Standard methods include
those published by the U.S. Geological Survey (USGS), the USEPA, and the American Society for
Testing and Materials (ASTM), or those published in Standard Methods for Examination of Water and
Wastewater (Rice et al., 2012). A compendium of methods for environmental analysis is maintained by
the National Environmental Methods Index (NEMI), supported by both USGS and USEPA. If any
methods to be used are not published, they should first be validated and verified as acceptable for the
project. Each approved and published method should be accompanied by an SOP that is followed
rigorously by the laboratory (Pritt and Raese 1992).


8.5.4 Method Validation
The laboratory chosen for sample analysis should have well-developed procedures for method validation.
Method validation should account for and  document the following (at a minimum): Known and possible
interferences; method precision; method accuracy, bias, and recovery; method detection level;  and
method comparability to superseded methods, if applicable (Pritt and Raese 1992).


8.5.5 Training and Safety
An analytical laboratory should be able to  ensure its customers that its personnel are adequately trained to
perform the necessary analyses. Individual laboratory staff should be independently certified for each of
the analyses they will be allowed to perform in the laboratory. Selection of a laboratory for sample
analysis should be based on queries about how often training is conducted,  whether employees are limited
to using equipment for which they have been adequately trained, whether the training program is
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independently certified, who conducts the training, how the staffs competence with individual
instruments is measured, and other factors (Pritt and Raese 1992).

Safety for staff is an important consideration when choosing a laboratory because, aside from the paramount
concern for human well-being, accidents can seriously delay sample analyses or create a need for
resampling. Prospective laboratories should be inspected for their attention to safety procedures, including
the availability of safety equipment such as fire extinguishers, safety showers and eyewashes, fume
hoods, and ventilation systems; use and disposal practices for hazardous materials; and compliance with
environmental regulations. Safety equipment should be tested on a regular basis (Pritt and Raese 1992).

Additionally, laboratory safety includes procedures for ensuring that the laboratory is accessible only to
authorized personnel to ensure confidentiality of the data. The laboratory should have a system for
accounting for and limiting (or denying) laboratory access to all visitors, including persons affiliated with
projects for which the laboratory is analyzing samples (Pritt and Raese 1992).


8.5.6  Procedural Checks and Audits
A laboratory should have established procedures (SOPs) for conducting internal checks on its analyses
and taking corrective action when necessary. If more than one laboratory is used for sample analyses, it
will be important to know that the data obtained from the two are of the same quality and consistency. A
protocol for conducting interlaboratory comparisons should also be an element of a laboratory's QA/QC
plan. For many projects occasional samples are analyzed by a second laboratory to determine whether
there is any bias in the data associated with the primary laboratory's  analyses.

Laboratory audits by independent auditors are normally conducted on a prescribed basis to ensure that
laboratory operations are conducted according to accepted and acceptable procedures (Cross- Smiecinski
and Stetzenback, 1994). Determination that a laboratory undergoes such audits and reviews audit results
might be sufficient to determine that a laboratory will be adequate for conducting analyses of samples
generated by the NPS monitoring project.


8.6  Data and Reports
It is essential  during the conduct of an NPS monitoring project to document all data collected and used, to
document all  methods and procedures followed, and to produce clear, concise, and readable reports that
will provide decision makers with the information they need to choose among alternative actions, as
described in the DQOs. See sections 3.9 and 3.10 for additional details on data management, reporting,
and presentation.


8.6.1  Generation of New Data
All data generated during the project, whether in the field, laboratory, or some other facility,  should be
recorded. Include with the data any reference materials or citations to materials used for data analyses.
These include computer programs, and all computer programs used for data reduction should be validated
prior to use and verified on a regular basis. Calculations should be detailed enough to allow for their
reconstruction at a later date if they need to be verified (Cross-Smiecinski and Stetzenback 1994). Data
generated by a laboratory should be accompanied by pertinent information about the laboratory, such as
its name, address, and phone number, and names of the staff who worked directly with the project
samples.
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8.6.2  Use of Historical Data
Historical data are data collected for previous projects that concerned the same resource in the same area
as the project to be implemented. Historical data sometimes contain valuable information, and their use
can save time and effort in the implementation and/or data analysis phases of a new project. Before new
data are collected, all historical data available should be obtained and their validity and usability should
be assessed. Data validity implies that individual data points are considered accurate and precise because
the field and laboratory methods used to generate the data points are known. Data usability implies that a
database demonstrates an overall temporal or spatial pattern, though no judgment of the accuracy or
precision of any individual data point is made (Spreizer et al., 1992). The validity of historical data can be
difficult to ascertain, but data usability  can be assessed through a combination of graphical and statistical
techniques (Spreizer etal. 1992).

Specifically, historical data that can be  shown to be either valid or usable can be applied to a new project
in the following ways (Coffey 1993, Spreizer et al.  1992, USEPA 200Ic):
  "   If the quality (i.e., accuracy and precision) of historical data is sufficiently documented, the data can
      be used alone or in combination with new data. The quality of historical data should be evaluated
      relative to the project requirements.
  "   Characteristics derived from the historical data, such as the variability or mean of data, can be used
      in the development or selection of a data collection design. Knowledge of expected variability
      assists in determining the number of samples needed to attain a desired confidence level, the length
      of monitoring program necessary to obtain the necessary data, and the required sampling frequency
      (see section 3.4.2).
  "   Spatial analysis of historical data can indicate which sampling locations are most likely to provide
      the desired data.
  "   Historical data can provide insights about past impacts and water quality that can be useful in
      defining an NFS pollution problem.
  "   Past trends can be ascertained, and the present tendency of water quality characteristics (degrading,
      stable, or improving) can be established for trend analysis (see section 7.8.2.4).


8.6.3  Documentation, Record Keeping, and Data Management
  "   All information and records related to the NFS monitoring project should be kept on file and kept
      current. This documentation should include:
  "   A record of decisions made regarding the monitoring project design
  "   Records of all personnel, with their qualifications, who participated in the project
  •   Intended and actual implementation schedules, and explanations for any differences
  "   A description of all sampling sites
  •   Field records of all sampling events, including any sampling problems and corrective actions taken
  "   Copies of all field and laboratory SOPs
  "   Equipment manuals and maintenance schedules (intended and actual,  with explanations for any
      discrepancies)
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  •   Printouts from any equipment
  •   Sample management and custody records
  •   Laboratory procedures
  •   Copy of the laboratory QA/QC plan
  «   Personnel training sessions and procedures, including any training manuals or other materials
  •   All data generated during the project in hard copy and electronic forms
  •   All correspondence related to the project
  •   Project interim and final reports

One aspect that merits further discussion is documentation and management of data, from the collection
process through the data analysis. Data management activities include documenting the nature of the data
and subsequent analyses so that the data from different sites are comparable. Data management also
includes handling and storing both hard copies  and electronic files containing field and laboratory data. A
data management system that addresses project needs should be selected at the beginning of the
monitoring program (see section 3.9). It is also important to understand and comply with applicable state
agency and/or grant policies and standards regarding data collection and generation.

Some grants might require local NPS and water resources managers to add their data to EPA's storage
and retrieval (STORET) database (https://www.epa.gov/waterdata/storage-and-retrieval-and-water-
quality-exchange). STORET contains raw biological,  chemical, and physical data on surface water and
ground water collected by federal, state, and local agencies; tribes; volunteer groups; academics; and
others. Each sampling result in STORET is accompanied by information on where  the sample was taken
(latitude, longitude, state, county, hydrologic unit code, and brief site identification), when the sample
was gathered, the medium sampled (e.g., water, sediment, fish tissue), and the name of the organization
that sponsored the monitoring. Staff working with the database should have expertise and training in the
software and in the procedures for data transport, file transfer, and system maintenance.

The operation of the data management system should include QA oversight and QC procedures. If
changes in hardware or software become necessary during the course of the project, the data manager
should obtain the most appropriate equipment and test it to verify that the  equipment can perform the
necessary jobs. Appropriate user instructions and system documentation should be  available to all staff
using the database system. Developing spreadsheet, database, and other software applications involves
performing QC reviews of input data to ensure  the validity of computed data.


8.6.4 Report Preparation
The original project description should include a schedule and format for required reports, including the
final report. Adherence to this schedule is important to provide information and documentation of project
progress, problems encountered, and corrective actions taken. Reports are also valuable for supporting
continuation of a project if at any point during the project its continuation is scrutinized or if additional
funding must be secured to ensure its completion. Reports can also become the primary sources of
historical information on projects if there  are changes in project personnel during the project. Project
managers should decide on the necessary  content and format of all reports prior to commencement of the
project, and these  will differ depending on funding and intended audience.
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8.7   Geospatial Data
Projects should incorporate procedures for documenting geospatial data appropriately. Geospatial
Information System (GIS) data can vary from relatively simple site locations to complex with many
overlapping contextual boundaries. For example, the development of a watershed implementation plan
may involve analyzing water samples from industrial dischargers, developing water quality models,
creation of new geospatial data, or even updating existing geospatial data. Use of geospatial data from
external sources may require the development of a secondary data QAPP. QAPPs also apply to geospatial
data (USEPA 200 Ib), but should vary with the complexity of the project (see Table 8-3). The project
planning phase should determine the scope and complexity of the project that will inform the complexity
of the QAPP (USEPA, 2003b).

            Table 8-3. Continuum of Geospatial Projects with Differing Intended Uses
Purpose of Project
Regulatory compliance
Litigation
Congressional testimony

Regulatory development
Spatial data development
(Agency infrastructure development)

Trends monitoring
(non-regulatory)
Reporting guidelines
(e.g., Clean Water Act)
"Proof of principle"

Screening analyses
Hypothesis testing
Data display
Typical Quality Assurance Issues
Legal defensibility of data sources
Compliance with laws and regulatory mandates
applicable to data gathering
Legal defensibility of methodology

Compliance with regulatory guidelines
Existing data obtained under suitable QA program
Audits and data reviews

Use of accepted data-gathering methods
Use of accepted models/analysis techniques
Use of standardized geospatial data models
Compliance with reporting guidelines

QA planning and documentation as appropriate
Use of accepted data sources
Peer review of products
Level of QA
>
^
Source: USEPA, 2003b.
8.7.1 Performance Criteria fora Geospatial Data Project
Projects with geospatial components will likely follow the same DQO process described in section 8.2 of
this chapter. In decision-making programs taking the form of the DQO process, data quality to achieve a
desired level of confidence in the decision takes a number of typical forms as listed below (USEPA
2003b):
  •   A description of the resolution and accuracy needed in input data sources
  •   Statements regarding the speed of applications programs written to perform data processing
      (e.g., sampling at least "n" points in "m" minutes)
  •   Criteria for choosing among several existing data sources for a particular geospatial theme
      (e.g., land use); geospatial data needs are often expressed in terms of using the "best available"
      data, but different criteria—such as scale, content, time period represented, quality, and format—
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      may need to be assessed to decide which are the "best available" (when more than one is available)
      to use on the project
  •   Specifications regarding the accuracy needs of coordinates collected from GPS receivers
  •   Criteria for aerial photography or satellite imagery geo-referencing quality, such as specifications as
      to how closely these data sources need to match spatially with ground-based reference points or
      coordinates
  •   Criteria for minimum overall match rate, tolerances including whether or not spatial offsets are to
      be supplied in the resulting coordinates procedures, and if so, the offset factor in address matching
  •   Topology, label errors, attribute accuracy, overlaps and gaps, and other processing quality
      indicators for map digitizing
  •   Criteria to be met in ground-truthing classified satellite imagery


8.7.2 Spatial Data Quality Indicators for Geospatial Data
The most comprehensive way to track the quality and applicability of a geospatial data set is through the
use of metadata. EPA requires that appropriate metadata accompany every data set, in accordance with
Federal Geographic Data Committee standards (FGDC 1998). There are five components applicable to
the Federal Geographic Data Committee metadata requirements (FGDC 1998, USEPA 2003b):
  «   Accuracy - positional: The closeness of the locations of the geospatial features to their true
      position.
  •   Accuracy - attribute: The closeness of attribute values (characteristics at the location) to their true
      values.
  •   Completeness: The degree to which the entity objects and their attributes in a data set represent all
      entity instances of the abstract universe (defined by what is specified by the project's data use in
      systematic planning). It is in the metadata where the user may define the abstract universe with
      criteria for selecting features to include in the data set. The information is relevant to any user who
      wishes to independently replicate geospatial procedures. Missing, or incomplete data can affect
      logical consistency needed for correct processing of data  by software.
  •   Logical consistency: The data in any spatial data set is logically consistent when it complies with
      the structural characteristics of the data model and is compatible with attribute constraints defined
      for the system.
  •   Lineage: The description of the origin and processing history of a data set.
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8.8  References
Air National Guard. 1993. Draft Final Remedial Investigation/Feasibility Study Work Plan. Prepared for
       Wyoming Air National Guard 153rd Tactical Airlift Group, Cheyenne Municipal Airport,
       Cheyenne, Wyoming, by Aepco, Inc. and Tetra Tech, Inc.

ANSI/ASQ (American National Standards Institute/American Society for Quality). 1994. Specifications
       and Guidelines for Quality Systems for Environmental Data Collection and Environmental
       Technology Programs. American Society for Quality Control (ASQC). ASQC Quality Press,
       Milwaukee, Wisconsin.

ANSI/ASQ (American National Standards Institute/American Society for Quality). 2004. American
       National Standard, Quality systems for environmental data and technology programs -
       Requirements with guidance for use. American Society for Quality (ASQ). ASQ Quality Press.
       Milwaukee, Wisconsin.

Coffey, S.W., J. Spooner, and M.D. Smolen. 1993. The Nonpoint Source Manager's Guide to Water
       Quality and Land Treatment Monitoring. North Carolina State University, Department of
       Biological and Agricultural Engineering, NCSU Water Quality Group, Raleigh, NC.

CREM (Council for Regulatory Environmental Modeling). 2009. Guidance on the Development,
       Evaluation, and Application of Environmental Models. EPA/100/K-09/003. U.S. Environmental
       Protection Agency, Council for Regulatory Environmental Modeling, Washington, DC.

Cross-Smiecinski, A., and L.D. Stetzenback. 1994. Quality Planning for the Life Science Researcher:
       Meeting Quality Assurance Requirements. CRC Press, Boca Raton, Florida.

Drouse, S.K.,  D.C. Hillman, J.L. Engles, L.W. Creelman and S.J. Simon.  1986. National Surface Water
       Survey. National Stream Survey (Phase 1 - Pilot, Mid-Atlantic Phase 1 Southeast Screening, and
       Episodes Pilot) Quality Assurance Plan. EPA/600/4-86/044. NTIS No. PB87-145819. Prepared
       for U.S. Environmental Protection Agency, Office of Research and Development, Environmental
       Monitoring Systems Laboratory, Las Vegas, Nevada, by Lockheed Engineering and Management
       Services Co., Inc., Las Vegas.

Erickson, H.E., M. Morrison, J. Kern, L. Hughes, J. Malcolm and K. Thornton. 1991. Watershed
       Manipulation Project: Quality Assurance Implementation Plan for 1986-1989. EPA/600/3-
       91/008. NTIS No. PB91-148395. Prepared for Corvallis Environmental Research Laboratory,
       Oregon, by NSI Technology Services Corporation, Corvallis, OR.

FGDC (Federal Geographic Data Committee). 1998. Content Standard for Digital Geospatial Metadata.
       FGDC-STD-001-1998. Federal Geographic Data Committee, Washington, DC.

Knapton, J.R., and D.A. Nimick.  1991. Quality Assurance for Water-Quality Activities of the
       U.S. Geological Survey inMontana. Open File Report 91-216. U.S. Geological Survey, Helena,
       Montana.

Pritt, J.W., and J.W. Raese, ed. 1992.  Quality Assurance/Quality Control Manual. Open File Report
       92-495. U.S. Geological Survey, National Water Quality Laboratory, Reston, Virginia.

Rice, E.W., R.B. Baird, A.D. Eaton, and L.S. Clesceri, ed. 2012. Standard Methods for the Examination
       of Water and Wastewater. 22nd ed. American Public Health Association, American Waterworks
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       Association, Water Environment Federation. American Public Health Association Publications,
       Washington, DC.

Spreizer, G.M., T.J. Calabrese and R.S. Weidner. 1992. Assessing the Usability of Historical Water
       Quality Data for Current and Future Applications. In Current Practices in Ground Water and
       Vadose Zone Investigations, ASTM STP 1118. Ed. D.M. Nielsen and M.N. Sara, pp. 377-390.
       American Society for Testing and Materials, Philadelphia, Pennsylvania.

USEPA (U.S. Environmental Protection Agency). 1983. Interim Guidelines and Specifications for
       Preparing Quality Assurance Project Plans. EPA-600/4-83-004. QAMS-005/80. U.S.
       Environmental Protection Agency, Office of Monitoring Systems and Quality Assurance, Office
       of Research and Development, Washington, DC.

USEPA (U.S. Environmental Protection Agency). 2000a. EPA Quality Manual for Environmental
       Programs. CIO 2105-P-01-0. U.S. Environmental Protection Agency, Office of Environmental
       Information. Washington, DC.

USEPA (U.S. Environmental Protection Agency). 2000b. Policy and Program Requirements for the
       Mandatory Agency-wide Quality System. CIO  2105.0. U.S. Environmental Protection Agency,
       Office of Environmental Information, Washington, DC.

USEPA (U.S. Environmental Protection Agency). 2001a. EPA Data Quality Objectives Decision Error
       Feasibility Trials Software (DEFT) - USER'S GUIDE, EPA QA/G-4D. EPA/240/B-01/007. U.S.
       Environmental Protection Agency, Office of Environmental Information. Washington, DC.

USEPA (U.S. Environmental Protection Agency). 2001b. EPA Requirements for Quality Assurance
       Project Plans, EPA QA/R-5. EPA 240/B-01/003. U.S. Environmental Protection Agency, Office
       of Environmental Information. Washington, DC.

USEPA (U.S. Environmental Protection Agency). 2001c. EPA Requirements for Quality Management
       Plans, EPA QA/R-2. EPA/240/B-01/002. U.S.  Environmental Protection Agency, Office of
       Environmental Information. Washington, DC.

USEPA (U.S. Environmental Protection Agency). 2002a. Guidance for Quality Assurance Project Plans,
       EPA QA/G-5. EPA 240/R-02/009. U.S. Environmental Protection Agency, Office of
       Environmental Information, Washington, DC.

USEPA (U.S. Environmental Protection Agency). 2002b. Guidance for Quality Assurance Project Plans
       for Modeling, EPA QA/G-5M. EPA/240/R-02/007. U.S. Environmental Protection Agency,
       Office of Environmental Information, Washington, DC.

USEPA (U.S. Environmental Protection Agency). 2003a Elements of a State Water Monitoring and
       Assessment Program. EPA 841-B-03-003. U.S. Environmental Protection Agency, Office of
       Water, Office of Wetlands, Oceans and Watersheds, Assessment and Watershed Protection
       Division, Washington, DC. Accessed January  28, 2016.

USEPA (U.S. Environmental Protection Agency). 2003b. Guidance for Geospatial Data Quality
       Assurance Project Plans, EPA QA/G-5G. EPA/240/R-03/003.  U.S. Environmental Protection
       Agency, Office of Environmental Information, Washington, DC.
                                             8-23
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Monitoring and Evaluating Nonpoint Source Watershed Projects                                     Chapter 8
USEPA (U.S. Environmental Protection Agency). 2006. EPA Guidance on Systematic Planning Using
       the Data Quality Objectives Process, EPA QA/G-4. EPA/240/B-06/001. U.S. Environmental
       Protection Agency, Office of Environmental Information. Washington, DC.

USEPA (U.S. Environmental Protection Agency). 2007. Guidance for Preparing Standard Operating
       Procedures (SOPs), EPA QA/G-6. EPA 600/B-07/001. U.S. Environmental Protection Agency,
       Office of Environmental Information, Washington, DC.

USEPA (U.S. Environmental Protection Agency). 2008a. NRMRL QAPP Requirements for Secondary
       Data Projects. U.S. Environmental Protection Agency, National Risk Management Research
       Laboratory, Cincinnati, OH.

USEPA (U.S. Environmental Protection Agency). 2008b.  U.S. Environmental Protection Agency Quality
       Policy. CIO 2106.0. U.S. Environmental Protection Agency, Office of Environmental
       Information. Washington, DC.

USEPA (U.S. Environmental Protection Agency). 2012. Guidance for Evaluating and Documenting the
       Quality of Existing Scientific and Technical Information Addendum to: A Summary of General
       Assessment Factors for Evaluating the  Quality of Scientific and Technical Information. U.S.
       Environmental Protection Agency, Science and Technology Policy Council, Washington, DC.
                                             8-24
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Monitoring and Evaluating Nonpoint Source Watershed Projects                                     Chapter 9
9   Monitoring Costs

     By S.A. Dressing and D.W. Meals


9.1   Introduction
Monitoring plans must be designed to help achieve watershed project or program goals. This could be a
relatively simple task with an unlimited budget, but perhaps the most frequently cited problems for those
who design and carry out water quality monitoring programs are the limitations and unpredictability of
funding. Although cost should not be the defining factor in the design of monitoring plans, it must be
considered from the start. Both "cheap" monitoring programs that are inadequate to achieve project
objectives or great monitoring programs that are discontinued because funding disappears are worse than
no monitoring at all because much or all the money spent is essentially wasted.

While funding can almost never be guaranteed over the course of a multi-year monitoring effort, careful
cost analysis at the beginning can help design a monitoring plan that will meet objectives and fit within a
cost range that can be sustained until the project ends. In some cases, project budgets might be
insufficient to carry out meaningful monitoring; in such cases, monitoring should not be done. In all other
cases, project staff must seek a balance that provides the ability to achieve monitoring objectives that are
supportive of project or program goals at an affordable cost.

Although an exact monitoring budget will be highly specific to the setting of a particular project,
monitoring costs can be estimated reasonably well as part of project planning. Even a very good cost
estimate, however, will miss the mark on category specific costs. For example, sampling trips may take
more or less time than anticipated, equipment costs can change drastically if equipment is washed away
or needed equipment suddenly becomes available from a discontinued monitoring effort, or data analysis
and reporting requirements change under new management or because of unexpected findings or
additional requests for information. While the total budget allotted to a monitoring project may not
change, projects should maintain flexibility to shift resources within a budget to ensure that project
objectives are met with maximum cost efficiency.

In this chapter, potential monitoring costs for the types of monitoring described in this guidance
document are illustrated using a spreadsheet tool that has been developed to estimate monitoring costs for
nonpoint source watershed projects (Dressing 2012, 2014). Two user-editable versions of the spreadsheet
can be downloaded at this site: (https://www.epa.gov/polluted-runoff-nonpoint-source-
pollution/monitoring-and-evaluating-nonpoint-source-watershed). The master spreadsheet allows users to
determine every detail  in their cost estimation, whereas the simplified spreadsheet includes default
assumptions for monitoring designs, sampling types, and parameters, as well as basic algorithms to  allow
users to generate cost estimates with as little input as possible. See Appendix 9-1 for additional details on
the cost estimation spreadsheets.


9.2  Monitoring Cost Items and  Categories
A complete accounting of monitoring costs begins with watershed characterization and development of a
QAPP (see chapter 9) and ends with data analysis (see chapter 8) and reporting. Costs incurred by
                                               9-1
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Monitoring and Evaluating Nonpoint Source Watershed Projects
Chapter 9
monitoring efforts can include monitoring site selection, construction of monitoring stations, installation
and setup of monitoring equipment, sample collection, laboratory analysis, and the ultimate removal of
monitoring sheds at the conclusion of the project (see chapters 2 and 3 for details on monitoring designs).
Some monitoring efforts include the cost of contracts or grants for monitoring support.

Specific cost items can be grouped and summarized in many (and often overlapping) ways, including the
categories shown in Table 9-1. These categories are basically people and things, whereas the categories
shown in Table 9-2 are organized by project phases and key project elements. Some costs are incurred
once during a project (e.g., site establishment) while others are recurring (e.g., sampling site visits), so
annual costs often vary, particularly for the first and last years of a project.
                       Table 9-1. Costs grouped by type of item or activity
Cost Category
Labor
Installed Structures
Other Site Establishment Costs
Purchased Equipment
Rental Equipment
Monitoring Supplies
Office Equipment
Office Supplies
Travel/Vehicles
Laboratory Analysis
Data Purchases
Printing/Media
Electricity/Fuel
Site Service and Repair
Annual Site Fees
Contracts
Grants
Items Included In Category
All labor costs (inclusion of fringe benefits optional).
Materials and labor costs.
One-time fee, electricity connection, setup, etc.
All purchased monitoring equipment.
All rented monitoring equipment.
All startup and annual monitoring supplies.
All purchased office equipment.
All startup and annual office supplies.
All use of vehicles for travel, construction, sample pickup, etc.
Annual laboratory analysis.
Maps, data, satellite & aerial photography.
Printing and other report output media (e.g., CD, web).
All fuel and power costs for operating sites.
All service, repair, and replacements of sites and equipment.
All annual fees for site access. Does not include initial fee.
All non-itemized contracts costs.
All non-itemized grants costs.
                      Table 9-2. Costs grouped by project phase or element
Cost Category
Items Included in Category
One-Time Costs
Proposal and QAPP
Watershed Characterization
Site Establishment
Portable Sampling Equipment and
Startup Supplies Costs
Cost for development of proposal and QAPP or equivalent document (added to Year
1 cost).
Cost for characterization of watershed to aid monitoring design (added to Year 1
cost). Includes windshield surveys and analysis of existing data and maps.
Includes one-time costs for setting up station, including purchase of equipment that
remains at site. Site selection, preparation, and excavation costs are all included.
Includes one-time costs for all portable sampling equipment or instruments that are
taken to the site for use and then taken away for use at another site or time.
Equipment includes such items as kick nets, pH meters, etc. Also includes one-time
cost for initial purchase of supplies such as pipettes, vials, and bottles.
                                               9-2
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Monitoring and Evaluating Nonpoint Source Watershed Projects
Chapter 9
Cost Category
One-Time Office Equipment and
Startup Supplies Costs
Station Demolition and Site
Restoration
First-Year Report
Final Report
Items Included in Category
Includes computer hardware and software and related items.
Includes all costs associated with tearing down the station and restoring the site at
the end of the project.
This is the cost for data analysis and writing, printing, and distribution of the first-year
report. Data analysis and reporting can be combined or kept separate.
This is the cost for data analysis and writing, printing, and distribution of the final
report. Data analysis and reporting can be combined or kept separate.
Annual Costs
Access Fees
Sampling Trips to Sites
Volunteer Training
Sample Analysis
Annual Data Analysis and Reports
Site Operation and Maintenance
Supplies and Rental Equipment
Land Use Tracking
Total Cost of Monitoring
Any fees paid to landowners for allowing access to the site.
Includes labor, vehicle use, and other equipment (e.g., boat) costs for site visits.
Annual cost to train volunteers or others collecting data for the project.
Cost for laboratory analysis of samples. Includes travel to and from laboratory if done
in addition to sampling trip travel. Can include costs for shipping samples to
laboratories as "Other" cost.
This is the cost for annual analysis of project data and annual or more frequent
reporting in years other than the first and last year. Includes labor and materials. Data
analysis and reporting can be combined or kept separate.
Includes service/repair/replacement of equipment and structures, electric and fuel
bills (e.g., for heating), and annual cost to establish and update stage/discharge
relationship.
This cost is primarily for consumable supplies (e.g., sample preservative), but can
include sample bottles and other items. Also includes rental equipment and office
supplies.
Labor, travel, and services (e.g., aerial photography or data purchase) needed to
track land use and land treatment.
Total cost of monitoring for the entire project period.
9.3  Cost Estimation Examples
The cost spreadsheets have been used to estimate costs for a wide variety of monitoring designs and
applications. The cost estimates highlighted here were developed for three different purposes. First, the
master spreadsheet was used to provide a range of estimates for a diverse set of monitoring options, with
estimated costs generated for eight different monitoring scenarios covering a wide range of timeframes
(see section 9.3.1). The ten cost estimates summarized in section 9.3.2 cover various monitoring
approaches relevant to assessing the watershed-scale water quality impacts of programs such as USDA's
National Water Quality Initiative (NWQI). Finally, the simplified spreadsheet was used to estimate costs
for 60 basic, 5-year monitoring scenarios that are summarized in section 9.3.3.  It is important to note that
assumptions regarding the need and cost for labor, equipment, monitoring parameters, sampling
frequency, and sampling duration are all important determinants of the final cost estimates, so costs are
presented in this section more for a comparative analysis than as accurate estimates for any specific
monitoring type or effort. The examples are particularly useful to contemplate trade-offs among cost
categories and to evaluate where cost-effectiveness can be improved, e.g., offsetting high labor costs with
the purchase of automated equipment.
                                               9-3
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Monitoring and Evaluating Nonpoint Source Watershed Projects                                      Chapter 9


9.3.1  Cost Estimates for a Diverse Range of Monitoring Options
Cost estimates for the following eight monitoring scenarios are presented in this section.
    1.   Synoptic Survey
    2.   TMDL - Water Quality Standards
    3   TMDL - Loads
    4.   Paired-Watershed - Loads
    5.   Long-term Single Station - Biomonitoring
    6.   Above/Below BMP Effectiveness - Biomonitoring
    7.   Input/Output Urban Low Impact Development (LID) Effectiveness
    8.   Photo-Point Monitoring

These eight scenarios were chosen to represent a wide range of monitoring approaches, addressing both
problem assessment and project evaluation, using chemical, physical, and biological (Barbour et al. 1999)
monitoring methods. With the exception of 1-year synoptic surveys (Scenario 1), costs are estimated for
1, 2, 5, and 8 years. A more detailed comparison of Scenarios 2-8 is based on five-year cost estimates. See
Appendix 9-2 for additional details on these eight scenarios.


9.3.1.1  Discussion
Table 9-3  summarizes the total costs for each scenario for 1, 2, 5, and 8 years. Cost totals are taken from
the base scenarios in which all equipment is purchased and all monitoring is  stand-alone; that is, there are
no cost savings assumed for monitoring activities that may be combined with other activities (e.g.,
another monitoring effort in the same area) to save on travel or labor. It should be no surprise that
biological (Scenarios 5 and 6) and photo-point (Scenario 8) monitoring are the least expensive monitoring
approaches in this analysis. Sampling frequency (2x/year) for biological and photo-point monitoring is far
less than is assumed for water quality monitoring and load estimation, and laboratory and equipment costs
are generally lower as well.

While total cost provides the best measure for comparing the costs for alternative monitoring designs, the
breakout of costs by category gives a better picture of where cost savings can be found within each
monitoring design. For example, labor accounted for the greatest share of total costs in all five-year
scenarios, ranging from 68 percent for Scenario 7 (urban LID) to 90 percent  for quantitative  photo-point
monitoring (Figure 9-1). Labor accounted for only 45 percent of the total cost for the 1-year  synoptic
survey.

Equipment costs ranged from 2 percent for Scenario 2 (TMDL water quality standards) to 12 percent of
total 5-year costs for qualitative photo-point monitoring. About 45 percent of the 1-year budget  for
synoptic surveys was devoted to equipment. Laboratory analysis costs accounted for 16 percent of total
5-year costs for Scenario 7 (urban LID), 9 percent of the 1-year cost for a synoptic survey, and 5 percent
of the 5-year cost for Scenario 2 (TMDL water quality standards), but were responsible for less  than
1 percent of costs for all other scenarios.

Vehicle (mileage) costs ranged from 1 percent for Scenario 1 and quantitative photo-point monitoring to
10 percent of total 5-year costs for Scenario 2. Both Scenario 3 and Scenario 7 had  5-year budgets in
which vehicle costs accounted for 9 percent of the total cost.
                                               9-4
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Monitoring and Evaluating Nonpoint Source Watershed Projects
                                                            Chapter 9
It is important to ensure that whatever monitoring approach is used will provide the type, quality, and
quantity of information necessary to meet project monitoring objectives. Despite its lower cost, photo-
point monitoring will usually not be appropriate as a stand-alone monitoring approach for tracking
progress in achieving a TMDL. Likewise, biological monitoring cannot be used to estimate pollutant
loads. On the other hand, weekly grab sampling for water chemistry may be wasteful if monitoring is
intended to track attainment of aquatic life support, and photo-point monitoring could be appropriate for a
trash TMDL such as that established for the Anacostia River (MDOE and DDOE 2010).
          Table 9-3. Summary of scenario costs for diverse range of monitoring options
Scenario
1 . Synoptic Survey
2. TMDL WQS
3. TMDL Loads
4. Paired-Watershed Load
5. Long-Term Biological
6. Above/Below BMP Effectiveness - Biological
7. Input/Output Urban LID Effectiveness
8. Photo-Point Monitoring - Qualitative Analysis
8. Photo-Point Monitoring - Quantitative Analysis
Total Cost ($1,000)
1Year
30
47
62
93
16
17
68
8
25
2 Years
n/a
90
107
158
26
28
115
11
39
5 Years
n/a
215
238
348
53
58
252
19
75
8 Years
n/a
339
368
537
80
88
388
26
111
       Above/Below Biological
                                   LID
           Paired-Load
         ©
            Synoptic
Photo Qualitative
  TMDL-Load
                    Long-Term Biological
Photo Quantitative
                       TMDL-WQS
                                           Category
                                        H Labor
                                        I  I Equipment
                                        I  I Laboratory Analysis
                                        D Vehicles
                                        fj Monitoring Structures
                                        Q Supplies
Figure 9-1. Breakout of costs for diverse range of monitoring options
                                                 9-5
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Monitoring and Evaluating Nonpoint Source Watershed Projects                                     Chapter 9


9.3.2 Cost Estimates for Watershed-Scale Evaluation of Agricultural BMP
       Implementation
This cost analysis was performed to explore different options for planning and assessing the water quality
impacts of watershed-scale implementation of agricultural BMPs. The setting assumed for the cost
scenarios is a 12-digit HUC watershed covering 10,117 ha (25,000 ac), primarily in agricultural use.
Monitoring is performed in perennial streams with the exception of the paired-watershed scenario that
assumes intermittent flow.

Cost estimates were generated for a total of 84 scenarios, including synoptic surveys, compliance
monitoring, soil testing, multiple-watershed monitoring, and paired-watershed, trend, and above/below
monitoring. Cost estimates were developed for three different driving distances to the watershed to
illustrate how that factor influences costs, particularly the labor share of total costs. Three timeframes
were considered (three, five, and seven years) for all but synoptic surveys which were assumed to be
completed within one year.

For simplicity, all labor was  assumed to be performed by contractors, but this may not be affordable in
many situations. Pay rates assumed (including fringe and overhead) and basic job functions are
summarized in Table 9-4. Rates for government or university employees and volunteers would clearly
differ, and contractor rates would vary depending on location.

Additional assumptions about number  of sampling sites, monitoring frequency, monitoring variables, and
various other aspects of the monitoring designs are documented in Appendix 9-3.

            Table 9-4. Labor costs assumed for watershed-scale evaluation scenarios
Pay Level
4
3
2
1
Rate ($/hr)1
130
80
56
34
Job Functions
Monitoring design, statistical analysis, oversight, etc.
Lead field person for monitoring, data collection, bulk of writing
Field technician, lab tech, etc.
Secretarial and support staff
Includes fringe and overhead.


9.3.2.1   Discussion
Results for 5-year monitoring efforts are summarized in Figure 9-2. Not shown in this figure are 1-year
synoptic surveys which had the lowest cost, ranging from $12,000 to $18,000 depending on distance
traveled to the watershed. The low cost of synoptic surveys compared to the cost of other scenarios
indicates that they can be a very good investment for generating additional information to support final
decisions on both the land treatment plan and long-term monitoring design.

Compliance monitoring is also relatively inexpensive as defined in these scenarios, ranging from $21,000
to $55,000 for 5-year efforts depending on distance traveled. The cost for a soil testing program ranges
from $32,000 to $50,000 for five years with a far smaller influence of distance traveled on total cost
compared to compliance monitoring. This is because soil testing requires a large  amount of time
collecting samples at the site, whereas sampling for compliance monitoring is relatively quick once the
site is reached.
                                              9-6
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Monitoring and Evaluating Nonpoint Source Watershed Projects
Chapter 9
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Figure 9-2. Cost estimates for watershed-scale assessment of agricultural BMP projects

Trend monitoring costs can range from $68,000 to $275,000 for a 5-year effort with grab sampling to a
range of $172,000 to $630,000 for a 5-year effort with automated sampling and pollutant load estimation.
Although not shown in Figure 9-3, this analysis presents an interesting choice between a 7-year grab
sampling effort ($92,000-$382,000) and a 3-year load estimation effort ($112,000-$391,000) fortrend
analysis. This cost information coupled with an MDC analysis (see section 9.4) could lead to cost-
effective solutions to monitoring needs.

The cost of above/below monitoring ranges from $152,000 to $553,000 for a 5-year grab sampling effort
to $268,000 to $799,000 for a 5-year load estimation effort. Costs for above/below monitoring designs are
roughly twice the cost of the parallel trend monitoring designs for grab sampling, but can be much less
than double the cost for load estimation. For example, comparing 5-year costs for above/below with trend
concentration monitoring shows that the "near" cost for above/below ($329,000) is about twice the "near"
cost for the trend design  ($159,000). However, the 5-year cost for above/below load monitoring
($466,000) is far less than double the cost for trend load monitoring ($371,000). The different patterns are
largely explained by the  costs for site establishment and automated sampling equipment for load
estimation.

Paired-watershed monitoring (loads) are found to be similar to above/below monitoring in this analysis.
Costs ranged from $176,000 to  $455,000 for a 5-year effort on an intermittent stream to $294,000 to
$824,000 for 5 years on a perennial stream. The major difference between paired-watershed and
above/below monitoring costs is the travel between watersheds and larger area involved in land
use/treatment tracking for paired-watershed monitoring.
                                               9-7
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Monitoring and Evaluating Nonpoint Source Watershed Projects
Chapter 9
The cost of monitoring 20 subwatersheds in a multiple-watershed design is estimated to range from
$125,000 to $185,000 for a 5-year effort. Grab sampling is assumed for multiple-watershed monitoring
scenarios in this analysis.

For scenarios assuming 5 years of monitoring and the "near" distance (monitoring team 241 km from
watershed), labor consumes 72 to 86 percent of total cost estimates. The proportion of total costs devoted
to labor often changes with project duration, however, as illustrated in Figure 9-3. In this comparison, the
labor share of cost decreases with increasing monitoring duration for soil testing (assuming 20 sites), but
increases for a paired study measuring loads on a perennial stream. The different trends result primarily
from differences in first-year costs. The paired design assumes significant labor and equipment (-equal)
costs for site establishment and purchased equipment, while the soil testing design assumes substantial
labor cost to select sites via desktop analysis. It should be noted that for both scenarios total labor costs
increase overtime, whereas equipment, site selection, and site establishment costs are incurred in the first
year only.
90-
80-
70-
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Figure 9-3. Comparison of labor cost category percentage overtime
9.3.3 Cost Estimates for Five-Year Trend and Above/Below Monitoring
Cost estimates were generated for 160 scenarios that address two different designs (trend and
above/below); four different monitoring variable sets (nutrient and sediment grab samples - [NSC],
nutrient and sediment loads - [NSL], biological/habitat with kick net - [BioK], and sondes for nutrients
and turbidity - [SNT]); four watershed sizes (202, 2023,  10117, and 20234 ha)1; and five different
 500; 5,000; 25,000; and 50,000 acres
                                              9-8
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Monitoring and Evaluating Nonpoint Source Watershed Projects                                      Chapter 9


distances to the watershed (0, 40, 80, 121, and 161 km)2. All scenarios assume 5 years of monitoring,
while it was assumed that sampling frequency was 2 and 26 times per year for biological and all other
monitoring, respectively. In addition to sample collection and analysis, total monitoring costs also include
watershed characterization,  site establishment, land use/treatment tracking, data analysis, and reporting.
This cost analysis was designed to test application of the simplified spreadsheet to the designs most
commonly used by NFS watershed projects. See Appendix 9-4 for additional details.

Additional scenarios using all combinations of the following conditions were run to illustrate how
assumptions on salary and equipment affect total cost estimates:
  *   Labor cost of $0 and salary adjustment of factors of 0.5, 0.7, and 1 (baseline).
  •   Purchase of all equipment (baseline) and equipment cost of $0.

These scenarios were run for a 2,023-ha (5,000  ac) watershed where the monitoring team was 80 km
(50 mi) from the watershed, parameters that best represent the median total costs  for each design and
variables set.


9.3.3.1  Discussion
Figure 9-4 summarizes the results from this analysis. The box plots on the top show clearly that load
estimation (NSL) is the most expensive approach when compared to concentration monitoring with grab
samples (NSC) and the use of sondes for nutrients and turbidity (SNT). Biological monitoring (BioK) is
the cheapest option overall,  but sampling is only done twice per year versus the assumed 26 times per
year for the other three options. Above/below monitoring is more expensive than trend monitoring for all
variable sets because there are twice as many stations.  The cost, however, is less than double because of
efficiencies in labor, travel,  analysis and other cost categories. It should be noted  that paired designs
would have costs similar to those for the above/below design.

When costs are reduced to cost per sampling trip to each monitoring site (bottom  of Figure 9-4),
biological monitoring is by far the most expensive approach of the scenarios considered. This is due
primarily to the fact that only 2 samples are collected each year versus 26 samples per year for the other
scenarios. Load monitoring  is more expensive than both concentration and sonde monitoring. This figure
also points out the cost efficiency of above/below versus trend monitoring when using a biological
approach; the extra site is relatively inexpensive. Readers should keep in mind that, as described above,
total costs include more than just sample collection and analysis.

In all cases examined here, labor accounted for the largest share of costs, ranging from 63% to 84 percent
of total cost (66 percent to 85 percent if labor for analysis of biological samples is included). Competitive
contractor rates were assumed for labor, but the importance of labor costs can vary greatly because
monitoring efforts may use far less expensive staff (e.g., volunteers) or assume that labor is not an
additional cost because in-house staff are used.

Labor generally accounted for a larger share of total costs for scenarios that required less equipment,
ranging from 63 percent to 74 percent for biological (74-85 percent including  analysis of biological
samples) and 74 percent to 84 percent for nutrient/sediment concentration monitoring. A slightly lesser
share of total cost was devoted to labor in cases where sondes were assumed (66-82 percent) or loads
were estimated with continuous flow measurement and automatic sampling (67-81 percent). Despite the
2 0, 25, 50, 75, and 100 miles


                                                9-9
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Monitoring and Evaluating Nonpoint Source Watershed Projects
                                                                                          Chapter 9
greater importance of labor in costs for biological monitoring, Figure 9-5 illustrates that the dollar amount
is still far less than for other monitoring options, whether labor for analysis of biological samples is
included (BioK-A) or not (BioK).
   o
   8
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   3
   i_
   in
       $350

       $300

       $250

       $200

       $150

       $100

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Variable Set
     Design
              T
1
                     BioK   NSC   NSL    SNT
                           Above/Below
                               BioK   NSC    NSL    SNT
                                         Trend
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     $7,000-

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Variable Set
     Design
1
                     BioK   NSC   NSL    SNT
                           Above/Below
                               BioK   NSC    NSL    SNT
                                         Trend
BioK=Biological monitoring with kick net; NSC=Nutrient and sediment concentration; NSL=Nutrient and sediment load:
SNT=sondes for nutrients and turbidity
Figure 9-4. Box plots summarizing cost estimates for five-year monitoring efforts
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                                        5-Year Labor Costs
   M
   _§
       $250,000-
       $200,000-
       $150,000-
       $100,000-
        $50,000-
             $0^
                     BioK
BioK-A
    NSC
Variable Set
NSL
SNT
          BioK=biological monitoring w/out analysis labor; BioK-A=BioK plus sample analysis labor cost
          NSC=nutrient & sediment cone, NSL=nutrient & sediment load, SNT=sondesfor nutrients & turbidity
Figure 9-5. Box plots summarizing five-year labor costs

Equipment and supplies accounted for 6-27 percent of total costs for BioK, NSL, and SNT, but only a
maximum of 2 percent for NSC. The large difference in importance of this cost category for biological
monitoring versus grab sampling for nutrients and sediments (NSC) hinges largely on the vast differences
in sampling frequencies (2x/yr vs. 26x/yr) and thus labor costs. The difference between NSC and NSL
and SNT is due to the far larger reliance on purchased equipment for monitoring with sondes and
measurement of loads. Sample analysis generally accounted for 2-25 percent of total costs for all
scenarios.

Vehicle costs were typically well under 10% of total  costs for these scenarios, and per diem costs were
zero except in cases where watersheds were very large (10,117 or 20,234 ha) and monitoring teams were
remote (121  or 61 km from the watershed). Overnight stays were associated with watershed
characterization and land use/treatment tracking, not  water quality monitoring. Each cost scenario
assumes that the watershed will be characterized in the first year of monitoring, and that land
use/treatment will be tracked twice per year every year.

Assumptions regarding salary and equipment costs have a substantial impact on total cost estimates as
illustrated in Table 9-5. If pay rates are reduced to 70 percent of the default values, the total cost is
reduced by 23-25 percent for all 64 scenarios3 included in this analysis versus the baseline scenario of full
pay rates (see Table 9-5) and purchase of all equipment. A reduction to 50 percent of default pay rates
reduces the total cost by 38-42 percent. If labor costs are zeroed out, total  costs are reduced by
68-84 percent. If pay rates are maintained at the default values and all equipment is assumed to be in hand
with no purchases required, costs are reduced by 1-20 percent versus the baseline scenario. If equipment
! Two designs, 4 variable sets, 4 salary levels, 2 equipment cost levels (2x4x4x2=64).
                                               9-11
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purchases are assumed to not be needed and labor costs are reduced to 70 percent of the default values,
total costs are reduced by 25-43 percent. If neither labor nor equipment costs are included, total cost is
reduced by 81-98 percent of baseline cost, clearly illustrating the importance of assumptions on pay rates
and equipment needs when estimating total monitoring program costs.

             Table 9-5. Cost reductions due to lowering of labor and equipment costs
Salary Assumption
Full Cost
Reduced to 70%
Reduced to 50%
No cost for Labor
Full Cost
Reduced to 70%
Reduced to 50%
No cost for Labor
Equipment and Supplies
Purchase All
Purchase All
Purchase All
Purchase All
Zero cost
Zero cost
Zero cost
Zero cost
Cost Reduction vs. Base Scenario1
Range
O2
23-25
38-42
68-84
1-20
25-43
41-59
81-98
Median
O2
24
39
77
12
35
51
88
1Base scenario assumes full contractor salary levels and purchase of all equipment and supplies. All scenarios assume 5-yr monitoring in a
2,023-ha watershed 80 km from monitoring team.
2Base scenario of full pay rates (Table 9-4) and purchase of all equipment.
9.3.4  Major Conclusions from Cost Estimation Scenarios
The cost estimates provided in this section are intended to illustrate the importance of estimating the costs
for all elements of monitoring for both the short- and long-term as part of establishing an effective and
sustainable monitoring program that will meet watershed project monitoring objectives. Those who use
either spreadsheet will find that they can tailor assumptions and add localized cost information to improve
their estimation capabilities. With increasing experience, including making adjustments based on
comparison of estimated versus actual costs, users should be able to improve the accuracy of their cost
estimates overtime. In all cases, but especially where budgets for monitoring are limited, accurate cost
estimation is essential to assessing the potential for conducting a monitoring effort that will satisfy project
objectives.  Anything short of that is likely to be a waste  of resources.

Because labor is such an important cost factor for all monitoring designs considered here, it provides the
greatest opportunity for cost savings. These  savings can  be generated a number of ways, including:
  •  Using volunteers whenever possible. (Training costs may be incurred, however, and practical and
      legal limitations apply.)
  •  Using in-house labor. (This is not free and may involve diversion of labor from other projects or
      programs.)
  •  Negotiating contracts to ensure greater use of lower cost staff wherever appropriate.
  •  Using labor sources based within or near the watershed. (This will also reduce vehicle and lodging
      costs, but may limit options.)
  •  Piggybacking sampling trips with other duties to maximize benefits of travel time.
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Monitoring and Evaluating Nonpoint Source Watershed Projects                                      Chapter 9
  •   Strategic use of in-house, volunteer, and contractor/grant labor to more efficiently match functions
      with capabilities and needs.
  •   Substituting higher initial cost equipment for some labor in long-term projects (e.g.,
      telecommunications/data logging to reduce data collection trips).

In many cases the addition of non-instrumented monitoring sites to a watershed project can be relatively
inexpensive because of the labor already invested in getting to the watershed for sampling events, as well
as the labor needed to characterize the watershed, track land use and land treatment, analyze data, and
develop reports. The incremental cost of adding monitoring stations should always be assessed in light of
how they could contribute to achieving project monitoring objectives. For example, paired-watershed and
above/below monitoring designs are inherently more powerful than single-station trend designs for
evaluating the effectiveness of BMP implementation on a localized or watershed scale. The incremental
cost of sampling two  stations instead of one may support a stronger monitoring design that could yield
results in a shorter time period, perhaps reducing overall costs in the end. In addition, findings may be
more conclusive and the  risk of failure reduced.

Equipment is never cheap, but the relatively low cost for equipment in most cost estimates developed here
suggests that it may be cost-effective to use sophisticated equipment and instruments if they can offset
higher personnel costs. Conversely, substituting labor for equipment (e.g., sending staff out to collect
frequent observations vs. using a data logger) is not likely to be cost-effective. Finally, it is very important
that equipment is maintained and operated in accordance with manufacturer recommendations to both
obtain good data and  to ensure that equipment is operable over its expected lifespan.

While this chapter did not focus on how total cost is affected by the selection of monitoring variables, it is
clear that analysis of constituents such as pesticides and metals, as well as advanced methods such as
microbial  source tracking will cost more than in situ measurement of temperature or laboratory analysis
of basic variables such as suspended sediment. Planners can use the spreadsheets to assess tradeoffs
between adding more or  different variables versus increasing  sampling frequency or duration, or adding
monitoring sites. Careful consideration of these and other design options should lead to better decisions
regarding  the makeup of a monitoring plan while both achieving monitoring objectives and staying within
the budget.


9.4  Using Minimum Detectable Change to Guide Monitoring
      Decisions
As noted earlier, cost should not be the defining factor in the design of monitoring programs. Program
designers  must seek a balance that provides the ability to achieve monitoring objectives that are
supportive of watershed  project goals at an affordable cost. Monitoring design, for example, should be
guided by the results  of MDC analysis (see section 3.4.2) whenever possible. To illustrate this approach,
cost estimates were developed for options considered in Example 1 (A  linear trend with autocorrelation
and covariates or explanatory variables;  Y values log-transformed) of a technical note on MDC (Spooner
et al. 2011). In the first scenario, weekly samples are collected for five years, resulting in an MDC of
15 percent, or an average of 3 percent change per year. By extending the monitoring period to 10 years,
the MDC  is increased to  20 percent, but with a lower average change of 2 percent per year required.
Assuming that total P is the monitoring parameter of interest ($20 per sample analysis) the total cost
(including a QAPP, reports, travel, etc.) for five years is estimated at $190,000, with 83 percent devoted
to labor. A 10-year effort would cost $377,000. So, an additional $187,000 is needed to reduce the
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Monitoring and Evaluating Nonpoint Source Watershed Projects
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average annual change needed from 3 percent to 2 percent. This type of analysis would provide project
managers with the cost information needed to determine whether they would prefer to enhance
implementation of BMPs to achieve a faster rate of change or commit to a longer monitoring period to
measure a slower rate of change.

The cost-benefit of adding explanatory variables can also be assessed through a combination of MDC
analysis and cost estimation. For example, Spooner et al. (1987) demonstrated that adding salinity as a
covariate in the  Tillamook Bay, Oregon watershed study decreases the MDC for fecal coliform (yearly
geometric concentration means) over an 11-year period of time (20 samples/yr; 14 sites) from 42 percent
to 36 percent. For this same study, the MDC for fecal coliform decreases from 55 percent to 42 percent
when doubling sampling frequency from 10 to 20 times per year over an 11-year study.

To estimate costs for the Tillamook Bay scenarios, it is assumed that there are 14 monitoring sites and
fecal coliform is measured from one grab sample per site ($20/sample). Salinity is measured using a
hand-held meter ($765). Sample size is increased by 10 percent for QA/QC. Sampling trips are assumed
to involve 2 people for 8 hours each, including a 322-km (200 mi) round-trip to cover all 14 sites. The
cost for a QAPP is assumed to be $1,400 and data  analysis and reporting costs are $2,268 for the first and
last years and $622 for the other nine years. The costs for watershed characterization, site establishment,
and land use/treatment tracking are assumed to be  zero.

These scenarios are summarized in Table 9-6. Adding salinity to the base scenario increases the  11-year
cost by only $800 ($75/year) while improving the  MDC by 8 percent from 55 percent (5 percent per year)
to 47 percent (4.3% per year). Increasing sampling frequency nearly doubles the total 11-year cost while
improving the MDC by 13 percent, from 55 percent to 42 percent (3.8 percent per year). Adding salinity
measurement to the increased sampling frequency  adds just $800 to the total 11-year cost, but reduces the
overall MDC by an additional 6 percent to 36 percent (3.3 percent per year). Clearly, with or without an
increase in sampling frequency, the additional $800 cost for salinity, while almost negligible, buys
substantial additional sensitivity to detect a change in fecal coliform counts.

Table 9-6. Illustration of costs and MDC in response to changes in sampling program in Tillamook
                                Bay, Oregon (Spooner et al. 1987)
Scenario
Base
Add salinity
Double frequency
Double frequency, add salinity
Sampling Program
10x/yr, FC
10x/yr, FC, salinity
20x/yr, FC
20x/yr, FC, salinity
Cost
(1 1 years)
$182,600
$183,400
$347,400
$348,200
Cost Change1
-
$800
$164,800
$165,600
MDC
55%
47%
42%
36%
MDC Change1
-
8%
13%
19%
1Change versus Base scenario.
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Monitoring and Evaluating Nonpoint Source Watershed Projects                                      Chapter 9


9.5   References
Barbour, M.T., J. Gerritsen, B.D. Snyder, and J.B. Stribling. 1999. Rapid Bioassessment Protocols for
       Streams and Wadeable Rivers: Periphyton, Benthic Macroinvertebrates and Fish. 2nd ed.
       EPA/841-D-99-002. U.S. Environmental Protection Agency, Office of Water, Washington, DC.
       Accessed February 9, 2016. http://water.epa.gov/scitech/monitoring/rsl/bioassessment/index.cfm.

Dressing, S.A. 2012. Monitoring Cost Estimation Spreadsheet - Master Version. TetraTech, Inc.,
       Fairfax, VA. Accessed April 29, 2016. https://www.epa.gov/polluted-runoff-nonpoint-source-
       pollution/monitoring-and-evaluating-nonpoint-source-watershed.

Dressing, S.A. 2014. Monitoring Cost Estimation Spreadsheet- Simplified Version. Tetra Tech, Inc.,
       Fairfax, VA. Accessed April 29, 2016. https://www.epa.gov/polluted-runoff-nonpoint-source-
       pollution/monitoring-and-evaluating-nonpoint-source-watershed.

MDOE (Maryland Department of the Environment) and DDOE (District of Columbia Department of the
       Environment). 2010. Total Maximum Daily Loads of Trash for the Anacostia River Watershed,
       Montgomery and Prince George's Counties, Maryland and the District of Columbia-Final.
       Maryland Department of the Environment and District Department of the Environment-Natural
       Resources Administration. Accessed April 6, 2016.

NEMI (National Environmental Methods Index). 2006. National Environmental Methods Index. National
       Water Quality Monitoring Council. Accessed February 5, 2016. www.nemi.gov.

Spooner, J., R.P. Maas, M.D. Smolen, and C.A. Jamieson. 1987. Increasing the Sensitivity of Nonpoint
       Source Control Monitoring Programs. In Symposium On Monitoring, Modeling, and Mediating
       Water Quality, American Water Resources Association, Bethesda, Maryland, pp. 243-257.

Spooner, J., S.A. Dressing, and D.W. Meals. 2011. Minimum detectable change  analysis. Tech Notes #7.
       Prepared for U.S. Environmental Protection Agency, by Tetra Tech, Inc., Fairfax, VA. Accessed
       March 24, 2016. https://www.epa.gov/polluted-runoff-nonpoint-source-pollution/nonpoint-
       source-monitoring-technical-notes.
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Monitoring and Evaluating Nonpoint Source Watershed Projects                                    Chapter 9
Appendix 9-1. Overview of Cost Estimation
Spreadsheets
Both the master and simplified spreadsheets support cost estimation covering all items shown in Table
9A1-1 and Table 9A1-2. Various options exist for users to change spreadsheet costs (e.g., for labor or
laboratory analysis) based on local information or experience, as well as assumptions regarding labor,
equipment, and other requirements for monitoring designs of interest to the user. The master spreadsheet
provides total flexibility in changing cost assumptions, whereas the simplified spreadsheet is designed to
provide a set of default assumptions that facilitates development of cost estimates with minimal data
entry. The master spreadsheet supports costing of virtually any monitoring design, while the simplified
spreadsheet supports cost estimation for only above/below, paired, and trend monitoring designs.
Data entry requirements for the simplified worksheet are:
  •  Beginning year for monitoring (for inflation estimates).
  •  Monitoring design (above/below, paired,  or trend - results in 1 or 2 sites).
  •  Watershed size and size of second watershed for paired design.
  •  Distance monitoring team is from watershed.
  •  Extra distance to drop  samples off at laboratory.
  •  Average speed limit for drive to watershed.
  •  Average speed limit within watershed.
  •  Mileage rate paid for vehicles.
  •  Per diem rate (food and non-lodging expenses).
  •  Lodging rate (including taxes).
  •  Type of sampling (biological/habitat, grab, sondes, loads).
  •  Variable set (2 or 3 options per sampling type).
  •  Sampling frequency (same at each site).
  •  Duration of monitoring effort.
The simplified spreadsheet provides the sample type and variable set options shown in Table 9A1-1
(Note: codes are used in Appendix 9.4). Variable sets for these options are shown in Table 9A1-2 through
Table 9A1-5. The number of units needed is calculated for each cost item based on the number of
monitoring sites, sampling frequency, and monitoring plan duration. Note that inclusion of specific
vendor products does not indicate EPA endorsement.
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           Table 9A1-1. Sample type and variable set options for simplified spreadsheet
Sample Type
Variable Set
Options
Grab
Variables
Nutrients, Sediment
Bacteria, Nutrients,
Sediment
Metals, Sediment
Code
NSC
BNSC
MSC
Load
Variables
Nutrients, Sediment
Bacteria, Nutrients,
Sediment
Metals, Sediment
Code
NSL
BNSL
MSL
Biological/Habitat
Variables
Biological Monitoring
with Kick Net
Biological Monitoring
with D-Frame Dip Net

Code
BioK
BioD

Sondes
Variables
Nutrients, Turbidity
Nutrients, Turbidity,
Metals

Code
SNT
SNTM

                            Table 9A1-2. Grab sampling variable sets
Variable Set
Nutrients and
Sediment (NSC)
Bacteria,
Nutrients, and
Sediment (BNSC)
Metals (Total and
Dissolved) and
Sediment (MSC)
Cost Items
Equipment and Supplies
Style A Staff Gage (13.5 ft), T-style post, and post
driver
Rain Gage (plastic)
Cooler (54-quart) and ice for cooler
Bottles-1000 ml wide mouth (HOPE, Box of 24)
Sulfuric Acid (1 ON) Liter
Same as above



Above items, minus sulfuric acid and plus the
items below:
Geopump Series 1 Peristaltic Pump AC/DC
Silicone Tubing, Size 24, 25'L (for use with
peristaltic pumps)
12V Battery and Charger (for peristaltic pumps)
Solinst Model 860 Disposable Filters
(0.45 |jm) 1 filter
1:1 Nitric acid 500ml
Laboratory Analysis
Total N using EPA Method 351. 4
Total P using EPA Method 365.4
Suspended Sediment Concentration (USGS Method)


Total N using EPA Method 351.4
Total P using EPA Method 365.4
Suspended Sediment Concentration (USGS Method)
E. coli and total coliform via Micrology Labs Coliscan
Easygel
Suspended Sediment Concentration (USGS Method)
Hardness EPA Method 130.2 - Titrimetry using EDTA
Metals Scan (5 metals) using EPA Method 200.7
($12/metal)



As shown below, the simplified spreadsheet allows users to apply labor adjustment factors (0 to 1.5 times
default assumptions) to better simulate local labor costs. Inflation can also be factored into cost estimates.
The base year assumed for inflation is 2012 because most costs in the spreadsheet are from that year.
Users can also change default assumptions in the simplified spreadsheet to tailor them to local costs, but
this requires a level of effort that mimics what is required for the master spreadsheet.
Salary Adjustment Factor:
Inflation Rate (vs. 2012)
1
0.0

%
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Monitoring and Evaluating Nonpoint Source Watershed Projects
Chapter 9
The simplified spreadsheet generates simple pie charts to show costs by category (see Figure 9A1-1).
Total cost is also broken down as in Table 9A1-6. Total costs are given with and without inflation
estimates. Annual costs are also generated by the simplified spreadsheet as shown in Table 9A1-7. The
effect of inflation is illustrated by the change in costs between the years 2017 through 2021 which would
all be the same without inflation.
                           Table 9A1-3. Load monitoring variable sets
Variable Set
Nutrients and
Sediment (NSL)
Bacteria,
Nutrients, and
Sediment
(BNSL)
Metals (Total
and Dissolved)
and Sediment
(MSL)
Cost Items
Equipment and Supplies
USGS portable steel gage house (2'x3'x5' tall),
connection to power grid, and surge protector
Style A Staff Gage (13.5 ft), T-style post, and post
driver
Isco Model 6712FR Fiberglass Refrigerated Sampler,
2-bottle kit (7.5-liter polyethylene), 2 extra 7.5-liter
polyethylene bottles for each site, intake line with
strainer, battery-backed power pack, and Flowlink
Software
Isco 730 Bubbler Flow Module
Isco 581 RTD (rapid transfer device) for field retrieval of
Model 6712FR data
Pygmy-type Current Meter w/ AquaCount data logger
Tipping Bucket Rain Gauge
HOBO Event Rainfall Logger (for tipping bucket rain
gauge) and Boxcar Software
Cooler (54-quart) and ice for cooler
Sulfuric Acid (1 ON) Liter
Same as above



Above items, minus sulfuric acid and plus the items
below:
Bottles-1000 ml wide mouth (HOPE, Box of 24)
Geopump Series 1 Peristaltic Pump AC/DC
Silicone Tubing, Size 24, 25'L (for use with peristaltic
pumps)
12V Battery and Charger (for peristaltic pumps)
Solinst Model 860 Disposable Filters ( 0.45 pm) 1 filter
1:1 Nitric acid 500ml
Laboratory Analysis
Total N using EPA Method 351. 4
Total P using EPA Method 365.4
Suspended Sediment Concentration (USGS
Method)







Total N using EPA Method 351.4
Total P using EPA Method 365.4
Suspended Sediment Concentration (USGS
Method)
E. coli and total coliform via Micrology Labs
Coliscan Easygel ($18.50 for 10 tests)
Suspended Sediment Concentration (USGS
Method)
Hardness EPA Method 130.2 - Titrimetry using
EDTA
Metals Scan (5 metals) using EPA Method 200.7
($12/metal)




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Chapter 9
                         Table 9A1-4. Biological monitoring variable sets
Variable Set
Kick Net Option (BioK)
D-Frame Dip Net Option
(BioD)
Cost Items
Style A Staff Gage (13.5 ft), T-style post, and post driver
YSI 556 D.O., pH, conductivity, temperature meter with pH kit
pH buffer, conductivity, and ORP calibration solutions for YSI 556
Hach Model 21000 Portable Turbidimeter with USB+Power Module for 21000 (for data transfer
to PC) and Gelex Secondary Standards Kit
Silicone oil and portable turbidimeter sample cells for Hach Turbidimeter
Pentax Option W30 waterproof digital camera
Garmin eTrex 30 GPS
Current meter outfit (Pygmy-type). Meter, headphones, and rod.
Bottom kick net (500 urn mesh)
Forceps (straight fine point)
Sieve bucket
First aid kit, 1 19-piece, economy
STEARNS neoprene chest waders and fluorescent orange PVC gloves
Bottles-1000 ml wide mouth (HOPE, Box of 24)
Low plastic specimen jars and black molded caps
Ice (cooler full)
95% Ethanol (3.8 L)
Above items, minus bottom kick net and plus item below
D-Frame dip net (500 urn mesh)
                          Table 9A1-5. Sondes monitoring variable sets
Variable Set
Nutrients and
Turbidity Set (SNT)
Cost Items
Equipment and Supplies
Style A Staff Gage (13.5 ft), T-style post, and post driver
Rain Gage (plastic)
Hydrolab DataSonde 5 - DS5 w/ built-in data logger, temperature
sensor, and connecting cable (takes 10 sensors, measures up to
15 parameters simultaneously)
pH, polarographic DO, temperature (comes with unit), nitrate,
self-cleaning turbidity, ammonia, chlorophyll a, and conductivity
sensors for DS5
5-meter communication cable and battery pack for DS5
Bottles-1000 ml wide mouth (HOPE, Box of 24)
Cooler (54-quart) and ice for cooler
1:1 Nitric acid 500ml
Sulfuric Acid (1 ON) Liter

Laboratory Analysis
Total P using EPA Method 365.4








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Monitoring and Evaluating Nonpoint Source Watershed Projects
Chapter 9
Variable Set
Nutrients,
Turbidity, and
Metals (Total and
Dissolved) Set
(SNTM)
Cost Items
Equipment and Supplies
Above items plus the items below:
Geopump Series 1 Peristaltic Pump AC/DC
Silicone Tubing , Size 24, 25'L (for use with peristaltic pumps)
12V Battery and Charger (for peristaltic pumps)
Solinst Model 860 Disposable Filters ( 0.45 pm) 1 filter

Laboratory Analysis
Total P using EPA Method 365.4
Hardness EPA Method 130.2 -
Titrimetry using EDTA
Metals Scan (5 metals) using EPA
Method 200.7 ($12/metal)


             11%
                                           I Total Labor Cost
                                           iTotal Equipment and
                                            Supplies Cost

                                            Total Lab Chemical
                                            Analysis Cost

                                           I Total Vehicle Cost
                                            Total Per Diem Cost
Figure 9A1-1. Pie chart from simplified spreadsheet
                     Table 9A1-6. Tabular output from simplified spreadsheet
Cost Category
Labor
Equipment and Supplies
Sampling Analysis
Vehicles
Per Diem
TOTAL COST
Average Annual Cost
Total Cost with Inflation
Average Annual Cost with Inflation
Total Cost
$205,167
$2,158
$53,654
$22,921
$0
$283,900
$40,557
$325,887
$46,555
% of Total
72
1
19
8
0
100



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Monitoring and Evaluating Nonpoint Source Watershed Projects
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                     Table 9A1-7. Annual costs from simplified spreadsheet
Inflation Rate: 2%
Begin: 2016
End: 2022
Year
2016
2017
2018
2019
2020
2021
2022

Inflation Factor
Applied
1.08
1.10
1.13
1.15
1.17
1.20
1.22
TOTAL
Annual Cost
without Inflation
$47,349
$39,279
$39,279
$39,279
$39,279
$39,279
$40,157
$283,900
Annual Inflation-
Adjusted Cost
$51,253
$43,367
$44,234
$45,119
$46,021
$46,942
$48,951
$325,887
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Monitoring and Evaluating Nonpoint Source Watershed Projects                                   Chapter 9
Appendix 9-2.  Cost Estimates for a Diverse  Range

of Monitoring  Options

As described in Appendix 9-1, a large number of assumptions must be made to estimate costs for various
monitoring scenarios. Thus, while these cost estimates are intended to be informative, they are more or
less relevant to any particular monitoring effort based on how well the assumptions match the realities of
that specific situation. Cost estimates given here are more likely to be high than low because it is always
assumed that contractors perform the monitoring (i.e., no use of in-house labor that was hired to do
monitoring) and all monitoring equipment must either be leased or purchased.


Cost Scenarios and Assumptions
Cost estimates for the following eight monitoring scenarios are presented in this section.
    1.  Synoptic Survey
    2.  TMDL - Water Quality Standards
    3.  TMDL-Loads
    4.  Paired-Watershed - Loads
    5.  Long-term Single Station - Biomonitoring
    6.  Above/Below BMP Effectiveness - Biomonitoring
    7.  Input/Output Urban LID Effectiveness
    8.  Photo-Point Monitoring

These eight scenarios address both problem  assessment and project evaluation, using chemical, physical,
and biological (Barbour et al. 1999) monitoring methods. Five-year total costs are used for comparing
Scenarios 2-8, but costs are also provided for 1, 2, and 8 years. The synoptic survey is considered a one-
year effort.


The Watershed
The setting assumed for the cost scenarios is a 3,035 ha (7,500 ac) watershed, primarily in agricultural use
with some urban influence. Monitoring is performed in perennial streams.

For the synoptic survey (Scenario 1) it is assumed that the nature and extent of water quality problems in
the watershed are totally unknown. Thus, water chemistry sampling includes a wide range of variables.
For Scenarios 2-7, the problems are assumed to be associated with sediment, nutrients, aquatic life use
support, and cadmium toxicity. Stream channel restoration is the focus of Scenario 8.
                                          9-22
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Monitoring and Evaluating Nonpoint Source Watershed Projects                                      Chapter 9


Labor Costs
All monitoring is assumed to be performed by contractors; different pay rates would apply to government
and university employees, and volunteers would work for free. Pay rates assumed (including fringe and
overhead) and basic job functions are summarized in Table 9A2-1.

                        Table 9A2-1. Labor costs assumed for scenarios
Pay Level
4
3
2
1
Rate ($/hr)1
130
80
56
34
Job Functions
Monitoring design, statistical analysis, oversight, etc.
Lead field person for monitoring, data collection, bulk of writing
Field technician, lab tech, etc.
Secretarial and support staff
includes fringe and overhead.
Other Cost Assumptions
Monitoring proposals are assumed to be QAPPs (Quality Assurance Project Plans) prepared in 16 hours
by a team that includes an expert and support staff at a cost of $ 1,400 for each scenario.

Transportation costs (vehicle and labor) include driving to and from the watershed, driving to monitoring
sites within the watershed, and delivering samples to a laboratory for analysis. It is assumed that the
watershed is 160 km (100 mi) from the base of those performing the monitoring. The sample analysis
laboratory is assumed to be "on the way," so no additional mileage is added for delivering samples to the
laboratory.

Watershed characterization (windshield survey) costs are included only in Scenario 1.  Monitoring site
selection and establishment (as needed) costs are included in all scenarios. While it is a very important
part of most NPS monitoring designs and is addressed by the spreadsheet, costs for meteorological
monitoring were not included in these scenarios.

Analytical methods for water quality variables were obtained from various sources such as NEMI
(http://www.nemi.gov/). Constraints associated with these methods (e.g., cooling samples to 4°C for
suspended sediment, and pre-acidification for hardness) are reflected in the cost estimates through, for
example, the purchase of refrigerated samplers or the use of both pre-acidified and non-acidified sample
containers.

For safety reasons, all sampling is assumed to be performed by teams of at least two people. In some
cases, one or two additional people are added for a limited number of sampling trips. Larger teams are
assumed necessary for QA/QC checks, stage-discharge calibration during a regularly scheduled sampling
event, and scenarios where both intensive water chemistry and biological monitoring are performed. In all
cases where continuous flow is measured, additional labor is assumed for stage-discharge calibration.
                                              9-23
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Monitoring and Evaluating Nonpoint Source Watershed Projects                                     Chapter 9


Scenario Description and Results

Scenario 1: Synoptic Survey
Under this scenario, a windshield survey is performed to characterize the watershed and select monitoring
sites. It is assumed that the survey covers 512 km (320 mi) within an 8-hour day. Water quality
monitoring at six sites (1.6 km [1 mi] apart from each other) is performed on two separate sampling dates
to cover both high-flow and low-flow conditions. Each sampling run is assumed to require 400 km
(250 mi) and a 12-hour day (1 hour per site, plus driving time) for a team of three in a single vehicle.

Equipment and sampling assumptions for this scenario are:
  "   Equipment: sample bottles and jars, water quality sonde with 6 sensors (D.O., pH, temperature,
      conductivity, turbidity, and chlorophyll a), pygmy-type meter with data logger, kick net
  "   Sampling for all 6 sites: B.O.D., hardness, SSC, TP, TKN, NO2+NO3 -N, E. coli and total
      coliforms, biological monitoring, flow
  "   Sampling for 3 sites (targeted locations to keep costs down): grab sample for pesticides scan and
      metals scan (5 metals)

As shown in Table 9A2-2, the total cost for this one-year effort is estimated at $30,000. Equipment and
labor each account for 45% of the total cost. Assuming that the contractor already has the basic
monitoring equipment, however, the one-year total cost is reduced to j ust over $17,000.


Scenario 2: TMDL - Water Quality Standards
Scenario 2 envisions a TMDL under which water quality monitoring is performed at a single site to both
track dissolved cadmium concentration (weekly grab samples) and assess aquatic life use support through
biological monitoring.

Equipment and sampling assumptions for this scenario are:
  "   Equipment: sample bottles, multi-probe water quality meter for in situ D.O., pH, conductivity, and
      temperature measurements, kick net
  "   Sampling: cadmium and hardness, biological monitoring (2x/yr)

As shown in Table 9A2-2, the total cost for five years is about $214,900. Costs for one year, 2 years, and
8 years are estimated at $47,100, $90,300, and $339,400, respectively. Nearly 83% of the total cost is
associated with sampling trips, with another 7% for analysis of samples for cadmium and hardness. Labor
accounts for 81% of the total budget, and equipment  account for only 2% of the total 5-year budget.


Scenario 3: TMDL - Pollutant Load
Under this scenario, weekly flow-weighted composite samples are taken for suspended sediment load
estimation at a single site. Continuous discharge is measured with a bubbler water level sensor and a
pygmy-type current meter is used for calibration.
                                             9-24
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Monitoring and Evaluating Nonpoint Source Watershed Projects                                      Chapter 9


Equipment and sampling assumptions for this scenario are:
  •   Equipment: sample shed, refrigerated automatic sampler (with bubble flow module, battery backup,
      2-bottle kit), data transfer device and software, surge protector, pygmy-type current meter and data
      logger
       OO
  «   Sampling: discharge and suspended sediment concentration

As shown in Table 9A2-2, the total five-year cost for this scenario is estimated at $237,500. Total costs
for 1, 2, and 8 years are $61,800, $106,500, and $368,400, respectively. Sampling trips and labor account
for 87% and 84% of the total cost, respectively.


Scenario 4:  Paired-Watershed Loads
This scenario is in many ways a doubling of Scenario 3, but shared equipment (e.g., pygmy-type current
meter) is not duplicated and incremental costs for analyzing and reporting on data from the second
monitoring station are assumed to be  half the cost for the first monitoring station. The watersheds are
assumed to be  12.8 km (8 mi) apart. Weekly flow-weighted composite  samples are taken for suspended
sediment load estimation at each site  using an automatic sampler. Continuous discharge is measured with
a bubbler water level  sensor and a pygmy-type current meter is used for calibration. Unlike for Scenario
3, tracking of land use and land treatment is included in the analysis, with the cost essentially twice that
for Scenario 5.

Equipment and sampling assumptions for this scenario are:
  •   Equipment: 2 sample sheds, 2 refrigerated automatic samplers (with bubble flow module, battery
      backup, 2-bottle kit), data transfer device and software, 2 surge protectors, pygmy-type current
      meter and data logger
  «   Sampling: discharge and suspended sediment concentration

As shown in Table 9A2-2, this is the  most expensive scenario considered here with a total five-year cost
estimated at $347,800. Total costs for 1, 2, and 8 years are $93,400, $158,100, and $537,400,
respectively. Sampling trips account for about three-quarters of the total cost. Site establishment cost is
significant under this  scenario, accounting for nearly 7% of the total cost, while sample analysis
represents about 2% of the total cost.  Labor is the largest cost category at 84% of the total cost.


Scenario 5:  Long-Term Trend  Monitoring-Biological
This scenario assumes long-term biological monitoring (2x/yr) at a single site. Stage is measured as a
covariate, but discharge is not estimated. Land use and BMP implementation are tracked via two whole-
watershed surveys per year.

Equipment and sampling assumptions for this scenario are:
  «   Equipment: multi-probe water quality meter for in situ D.O., pH, conductivity, and temperature
      measurements,  staff gage, kick net, sample bags
  «   Sampling: biological monitoring (2x/yr)

The total cost for five years is estimated at $52,800, while the total costs for 1, 2, and 8 years are
estimated at $16,100, $25,800, and $79,800, respectively. As shown in Table  9A2-2, land use tracking
                                              9-25
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Monitoring and Evaluating Nonpoint Source Watershed Projects                                     Chapter 9
accounts for about 28% of the total five-year cost, while annual sampling trips consume 26% of the five-
year budget. An additional 20% is used for data analysis and reporting. The largest cost category is labor
at 85% of the total cost.


Scenario 6: Above/Below BMP Effectiveness Monitoring-Biological
This scenario assumes long-term biological monitoring (2x/yr) at two monitoring sites in an above/below
design to evaluate individual BMP effectiveness. Stage at the time of sampling is measured as a covariate,
but discharge is not estimated. Land use and BMP implementation are tracked via two partial-watershed
surveys per year.

Equipment and sampling assumptions for this scenario are:
  •  Equipment: multi-probe water quality meter for in situ D.O., pH, conductivity, and temperature
      measurements, 2 staff gages, kick net, sample bags
  •  Sampling: biological monitoring (2x/yr)

As shown in Table 9A2-2, the five-year total cost is estimated at $58,000. One-year, two-year, and eight-
year total costs are estimated at $17,200, $28,000, and $88,000, respectively. The total cost for this
scenario nearly matches that for Scenario 5. Despite having two sites instead of one, annual sampling trips
for  Scenario 6 ($15,720) cost only slightly more than for Scenario 5 ($13,860). The time spent tracking
land use/land treatment is substantially greater for Scenario 5 because the entire watershed is tracked
versus only a portion of the watershed under the Scenario 6 above/below study. This difference explains
the  greater amount and percentage of the Scenario 5 budget devoted to land use tracking ($14,640, 28%)
versus that for  Scenario 6 ($11,800, 20%). Labor accounts for 85% of the five-year budget.


Scenario 7: Input/Output Urban LID Effectiveness
The analysis of inflow-outflow monitoring  of urban LID practices assumes two monitoring stations, one
storm event sampled per week at each station, discharge measurement, and analysis of both suspended
sediment and five metals.

Equipment and sampling assumptions for this scenario are:
  •  Equipment: 2 small sample sheds, 2 refrigerated automatic samplers (with 2-bottle kit), data
      transfer device and software, 2 submersible pressure transducers with data logger, 2 V-notch weir
      boxes, 2  surge protectors
  •  Sampling: discharge, suspended sediment concentration, metals scan (5 metals)

As shown in Table 9A2-2, the five-year total cost for this scenario is estimated at $251,400, while
estimated total  costs for 1, 2, and 8 years are $68,000, $114,900, and $387,800. Costs for monitoring site
establishment and equipment contribute to the high first-year cost of this study design. After five and
eight years, however, the average annual costs drop to about $50,300 and $48,500, respectively. Annual
sampling trips account for nearly 71% of the total five-year budget, while annual sample analysis
accounts for 16%, and equipment and site establishment combine for just over 8%. Labor is the largest
cost category at 68% of the total five-year budget.
                                              9-26
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Monitoring and Evaluating Nonpoint Source Watershed Projects                                       Chapter 9


Scenario 8: Photo-Point Monitoring
This scenario assumes repeat photography of a riparian zone restoration project using two photo points
(see chapter 5). Each photo point has a single camera point. Cost estimates were developed for both
qualitative and quantitative approaches, with digital image analysis assumed for the quantitative
approach.

Equipment assumptions for qualitative photo-point monitoring are:
  •  2 meter boards, digital camera with tripod, GPS unit, field computer, compass, level, sledge
      hammer, measuring tape, rebar, shovel, whiteboard, metric staff gage

As shown in Table 9A2-2, the five-year cost for qualitative photo-point monitoring is estimated to be
about $ 18,600, with 81% of the cost devoted to labor. If it is assumed that the contractor already has the
major equipment, the total cost for five years is reduced to about $16,300. Total costs for 1, 2, and 8 years
are  estimated at $8,100, $11,100, and $26,000, respectively.  Annual sampling trips account for about 48%
of the total five-year budget, while site establishment, portable sampling equipment, and startup supplies
consume a combined 22% of the budget. Labor is the largest cost category at 81% of the total.

When considering photo-point as an add-on monitoring activity (e.g., the same individuals who perform
biological monitoring or collect water chemistry samples also take the photos), the five-year cost is
reduced to $8,500 due primarily to savings in labor and vehicle costs. Coupled with the assumption  that
the  contractor already has the major equipment the 5-year cost drops to about $6,200.

Quantitative photo-point analysis requires  image processing  software, and labor requirements for data
analysis are increased substantially. Because quantitative photo-point analysis has not been used to any
measurable extent in watershed projects, the cost estimates provided here are highly uncertain. The total
cost for five years is estimated at $74,900 with 90% of the cost for labor. Assuming the contractor has all
major equipment and software, the 5-year cost  is reduced to  about $68,700. If quantitative photo-point
monitoring is added to a water chemistry or biological monitoring program, the cost is estimated at just
over $53,000 for five years. Coupled with the assumption that the contractor already has the major
equipment and software the 5-year cost drops to $46,800.
                                               9-27
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Monitoring and Evaluating Nonpoint Source Watershed Projects
Chapter 9
                        Table 9A2-2. Total costs for eight diverse scenarios
Cost Phase or Element
Proposal and QAPP
Watershed
Characterization
Site Establishment
Portable Sampling
Equipment and Startup
Supplies Costs
One-Time Office
Equipment and Startup
Supplies Costs
Station Demolition and
Site Restoration
First- Year Report
Final Report
Annual Access Fees
Annual Sampling Trips to
Sites
Annual Volunteer
Training
Annual Sample Analysis
Annual Data Analysis
Annual Reports
Annual Site Operation
and Maintenance
Annual Supplies and
Rental Equipment
Annual Land Use
Tracking
TOTAL
Scenario
1
1Year
$1,400
$1,858
$0
$13,332
$0
$0
$3,610
$0
$0
$6,008
$0
$3,774
$0
$0
$0
$59
$0
$30,040
2
5 Years
$1,400
$0
$0
$4,210
$0
$0
$1,952
$3,608
$0
$177,660
$0
$15,880
$2,268
$3,588
$0
$4,313
$0
$214,879
3
5 Years
$1,400
$0
$11,409
$4,803
$0
$808
$1,692
$2,976
$0
$205,660
$0
$2,860
$2,268
$3,588
$0
$0
$0
$237,464
4
5 Years
$1,400
$0
$22,829
$4,803
$0
$1,616
$2,448
$4,422
$0
$266,539
$0
$5,720
$3,402
$5,316
$0
$0
$29,280
$347,775
5
5 Years
$1,400
$0
$2,110
$3,595
$0
$0
$1,952
$2,656
$0
$13,860
$0
$4,960
$2,268
$3,588
$0
$1,795
$14,640
$52,824
6
5 Years
$1,400
$0
$2,234
$3,595
$0
$0
$1,952
$2,656
$0
$15,720
$0
$9,920
$2,268
$3,588
$0
$2,855
$11,800
$57,988
7
5 Years
$1,400
$0
$17,598
$3,056
$0
$136
$2,250
$3,336
$0
$177,840
$0
$40,040
$2,412
$3,288
$0
$0
$0
$251,356
8
Qualita-
tive
5 Years
$1,400
$0
$1,860
$2,260
$0
$114
$720
$1,224
$0
$8,820
$0
$0
$342
$1,818
$0
$0
$0
$18,558
8
Quantita-
tive
5 Years
$2,440
$0
$1,860
$6,241
$0
$114
$11,676
$10,980
$0
$13,960
$0
$0
$17,280
$10,368
$0
$0
$0
$74,919
                                                9-28
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Monitoring and Evaluating Nonpoint Source Watershed Projects                                  Chapter 9
Appendix 9-3. Cost Estimates for Watershed-Scale

Evaluation of Agricultural BMP Implementation

As described in Appendix 9-1, a large number of assumptions must be made to estimate costs for various
monitoring scenarios. Thus, while these cost estimates are intended to be informative, they are more or
less relevant to any particular monitoring effort based on how well the assumptions match the realities of
that specific situation. Cost estimates given here are more likely to be high than low because it is always
assumed that contractors perform the monitoring (i.e., no use of in-house labor that was hired to do
monitoring) and all monitoring equipment must either be leased or purchased.


Cost Scenarios
Cost estimates for the following seven monitoring scenarios are described in this section. Results of the
cost analysis are summarized in Figure 9-2. One year is assumed for the synoptic survey, and costs for
other scenarios are estimated for 3, 5, and 7 years.
    1.  Preliminary Synoptic Survey
   2.  Compliance Monitoring
   3.  Above/Below Monitoring (sub-scenarios for concentration and load: 3C, 3L)
   4.  Multiple-Watershed Monitoring
   5.  Trend Monitoring (sub-scenarios for concentration and load: 5C, 5L)
   6.  Paired-Watershed Monitoring (sub-scenarios for perennial and intermittent flows: 6P, 61)
   7.  Soil Testing


The Watershed

The setting assumed for these cost scenarios is a 12-digit HUC watershed covering 10,117 ha (25,000 ac),
primarily in agricultural use. Monitoring is performed in perennial streams with the exception of Scenario
61 which assumes intermittent flow. Scenario 1 assumes that the nature and extent of water quality
problems in the watershed are totally unknown, so a wider range of monitoring variables is included. For
Scenarios 2-7, the problems are assumed to be associated with nutrients from agricultural sources.


Labor Costs

Labor cost assumptions are the same as described in Appendix 9-2 (Table 9A2-1).


Driving Distances and Sampling Times
Transportation costs include driving to and from the watershed, driving to monitoring sites within the
watershed, and delivering samples to a laboratory for analysis. To bracket a wide range of possibilities for
transportation costs and sampling times, three one-way distances and associated drive times are assumed:
                                         9-29
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Monitoring and Evaluating Nonpoint Source Watershed Projects                                      Chapter 9
  *   "In" - Monitoring staff are within the watershed: distance and travel time are zero.
  •   "Near" - Monitoring staff are based 240 km (150 mi) from the watershed, with a one-way drive
      time of 2 hour and 45 minutes.
  •   "Far" - Monitoring staff are based 480 km (300 mi) from the watershed, with a one-way drive time
      of 5.5 hours.

Drive distances and times for sampling runs within (i.e., in addition to travel distance and times to the
watershed) the watershed are assumed to be:
  *   Zero miles and time for trend monitoring (1 station)
  *   25 km (16 mi) and 0.5 hours R/T for compliance and above/below monitoring (2 stations)
  *   48 km (30 mi) and 0.75 hours R/T for paired-watershed monitoring (2 stations, 1 in nearby
      watershed 24 km [15 mi] away)
  «   96 km (60 mi) and 2.5 hour R/T for multiple-watershed study (20 sub-watershed stations all within
      same watershed)
  *   80 km (50 mi) and 2 hours R/T for a soil testing study (20 fields within the same watershed)

For all scenarios in which driving to the watershed is required, it is assumed that collected samples are
dropped off at the laboratory in transit with no additional driving mileage. For scenarios in which the
contractor is based in the watershed, 80 km (50 mi) is added for delivery of the samples to the nearest
laboratory, except for Scenario 7 for which soil samples are assumed mailed to the laboratory.

It is assumed that contractors within the watershed will not incur lodging fees, while lodging is
(generally) assumed for others when work days exceed 12 hours. Efforts were made to combine activities
(e.g., site establishment and discharge observation) to reduce the need for overnight stays.

For safety reasons, all  sampling is assumed to be performed by teams of at least two people. Two-person
teams are assumed for grab sampling and 3 people are assumed necessary for runs including discharge
observations. Periodic trips for QA/QC (e.g., 4 times per year for weekly sampling) by a QA/QC expert
are also included.

The time required for grab sampling is assumed to be 0.5 hours per site, whereas sampling at sites with
automatic sampling and discharge measurements is assumed to require 1.5 hours per site. Scenario 7
incorporates an assumption that 45 minutes is required to collect a composite soil  sample for each 4-ha
(10-acre) field that is monitored.

The cost of establishing a stage-discharge relationship is included for Scenarios 3L, 5L, 6P, and 61.  It is
assumed that all monitoring is performed on wadeable streams, so time assumed for a discharge
observation is set at 1.5 hours. Requirements for discharge observations on larger streams would be more
expensive. Costs assume eight discharge observations per year, with 6 of these as  separate trips and 2 as
additional time during normal sampling runs. The driving distances and hours assumed necessary for
discharge observations made within each study area as separate trips are summarized in Table 9A3-1.
                                               9-30
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Monitoring and Evaluating Nonpoint Source Watershed Projects
Chapter 9
   Table 9A3-1. Driving and labor assumptions for discharge observations as stand-alone trips
Scenario
3L. Above/Below
5L. Trend (Load only)
6P. Paired
61. Paired
Discharge Observation
Number of
Stations
2
1
2
2
Total Drive Distance Within
Watershed1
25 km (16 mi)
0 km (0 mi)
48 km (30 mi)
48 km (30 mi)
Total Hours Within
Watershed1
3.50
1.50
3.75
3.75
1Does not include driving distance and time to arrive at watershed.

Table 9A3-2 summarizes assumptions regarding driving distances and time spent within (and between for
paired-watershed design) each watershed for sampling runs. This does not include round-trip (R/T) travel
to or from the watershed, nor does it include add-ons such as discharge observations.
                 Table 9A3-2. Sampling distances and times within watersheds
Scenario
1 . Synoptic
2. Compliance
3C. Above/Below (Cone.)
3L. Above/Below (Load)
4. Multiple
5C. Trend (Cone.)
5L. Trend (Load)
6P. Paired (Perennial)
61. Paired (Intermittent)
7. Soil Test
No. of
Sites
8
2
2
2
20
1
1
2
2
20
Travel Within
Watershed
km
32
25
25
25
96
0
0
0
0
80
Hours
0.75
0.5
0.5
0.5
2.5
0
0
0
0
2
Travel Between
Watersheds
km
0
0
0
0
0
0
0
48
48
0
Hours
0
0
0
0
0
0
0
1
1
0
Time at
Each Site
Hours
0.5
0.5
0.5
1.5
0.5
0.5
1.5
1.5
1.5
0.75
Total PER Site
km
4
12.5
12.5
12.5
4.8
0
0
24
24
4
Hours
0.6
0.75
0.75
1.75
0.625
0.5
1.5
2
2
0.85
Quality Assurance Project Plans (QAPPs)

Monitoring proposals are assumed to be QAPPs prepared in 16 hours by a team that includes an expert
and support staff at a cost of $1,400 for each scenario.


Watershed characterization

Watershed characterization costs apply only to Scenario 1, including a windshield survey (240 km,
8 hours) and a review of available data and maps. For all other scenarios it is assumed that the watershed
has been suitably characterized for development of the monitoring program.
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Monitoring and Evaluating Nonpoint Source Watershed Projects
Chapter 9
9.5.1.1  Site selection and establishment
For Scenarios 1 and 2 (synoptic and compliance) site selection is assumed to be a desktop exercise,
requiring two staff for four hours each. It is assumed that site selection for Scenario 7 involves more time
because information must be gathered to find 20 fields via a random selection process. Two staff for
20 hours each is assumed for this effort, with any additional labor provided by cooperators within the
watershed. For Scenarios 3-6 it is assumed that three staff each devote 2 hours of paper investigation to
each monitoring site prior to traveling to the watersheds for field investigation.

Field costs for site selection include travel  (to and within the watersheds)  and labor. Costs assumed for
field work under Scenarios 3-6 are summarized in Table 9A3-3. It is assumed that an additional person is
needed for site selection that involves installation of a sampling shed and  for Scenario 4 because
20 subwatersheds must be selected.

Monitoring site establishment (as needed) costs are included in Scenarios 3 through 6, with greater cost
for sites with continuous discharge measurement and automated samplers. Major materials and equipment
assumed for stations at which continuous flow is measured are summarized in Table 9A3-4. A tipping
rain gauge, data logger, and software  are purchased for Scenarios 3L, 5L, 6P, and 61. Plastic rain gauges
are purchased for Scenario 3C and 5C, while available local precipitation  records are used for all other
scenarios.

                         Table 9A3-3. Field work costs for site selection
Scenario [# stations]
3C. Above/Below (cone.) [2]
3L. Above/Below (load) [2]
4. Multiple Watershed [20]
5C. Trend (cone.) [1]
5L Trend (load) [1]
6P. Paired (perennial) [2]
61. Paired (intermittent) [2]
1 -Way Distance
from Base
km/vehicle
0
240
480
0
240
480
0
241
483
0
240
480
0
240
480
0
240
480
0
240
480
Travel and Site Investigation and
Selection
km/vehicle
50
530
1,010
50
530
1,010
322
804
1,287
10
490
970
10
490
970
40
520
1,000
40
520
1,000
Hours/person
5
10.5
16
5
10.5
16
48
53.5
59
2.5
8
13.5
2.5
8
13.5
5
10.5
16
5
10.5
16
Number of Staff/
Number of
Vehicles
2/1
2/1
2/1
3/1
3/1
3/1
3/1
3/1
3/1
2/1
2/1
2/1
3/1
3/1
3/1
3/1
3/1
3/1
3/1
3/1
3/1
Number of
Overnight
Stays1
0
0
1
0
0
1
3
4
4
0
0
1
0
0
1
0
0
1
0
0
1
1 Except where the contractor is based within the watershed, overnight lodging was assumed as needed to keep the length of work days
reasonable (generally 12 hours or less).
                                               9-32
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Monitoring and Evaluating Nonpoint Source Watershed Projects
Chapter 9
 Table 9A3-4. Major equipment and materials costs for stations measuring continuous discharge
Cost Item
Build sampling shed (labor and materials)
Connection to power grid
Staff gage, post, and post driver
Automatic sampler with bubble flow module,
battery backup, 2-bottle kit, data transfer device, and software
Pygmy-type current meter w/data logger
Unit Cost
$2,000
$800
$154
$10,530
$2,015
Six hours is added per station (18 person-hours) in cases where a monitoring shed is installed for
automatic sampling equipment. Table 9A3-5 summarizes travel and labor assumptions for site
establishment field work for Scenarios 3 and 6.


           Table 9A3-5. Site establishment costs for sites designed for load estimation
Scenario
[# stations]
3L. Above /
Below [2]
5L Trend [1]
6P. Paired [2]
61. Paired [2]
2-Way Travel to Site1
km/
Vehicle
0
480
960
0
480
960
48
528
1,008
48
528
1,008
Hours /
Person
0
5.5
11
0
5.5
11
1
6.5
12
1
6.5
12
Shed
Construction
and Setup
Hours / Person
12
12
12
6
6
6
12
12
12
12
12
12
# Staff /#
Vehicles
3/1
3/1
3/1
3/1
3/1
3/1
3/1
3/1
3/1
3/1
3/1
3/1
Total Without
Discharge
Observation
Hours / Person
12
17.5
23
6
11.5
17
13
18.5
24
13
18.5
24
Hours Added
for Discharge
Observation2
Hours / Person
0
3
3
0
1.5
1.5
0
3
3
0
3
3
Total
Hours /
Person
12
20.5
26
6
13
18.5
13
21.5
27
13
21.5
27
#
Nights
0
1
2
0
0
1
0
1
2
0
1
2
1 Paired watersheds are assumed to be 24 km apart. Above/below sites are assumed to be less than 1 km apart.
2Hours were added to perform a discharge observation at each site where long-distance travel was involved and pollutant load estimation is
planned.
Site Demolition and Restoration

Site demolition and restoration is only required for sites with sampling sheds. It is assumed that 3 people
are needed for this activity, each working 3 hours at each monitoring station. Assumptions are
summarized in Table 9A3-6.
                                               9-33
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Monitoring and Evaluating Nonpoint Source Watershed Projects
Chapter 9
                       Table 9A3-6. Site demolition and restoration costs
Scenario [# stations]
3L. Above / Below [2]
5L Trend [1]
6P. Paired [2]
61. Paired [2]
2-Way Travel to Site1
km /Vehicle
0
480
960
0
480
960
48
528
1,008
48
528
1,008
Hours /
Person
0
5.5
11
0
5.5
11
1
6
11.5
1
6
11.5
Site Demolition
and Restoration
Hours / Person
6
6
6
3
3
3
6
6
6
6
6
6
# Staff /#
Vehicles
3/1
3/1
3/1
3/1
3/1
3/1
3/1
3/1
3/1
3/1
3/1
3/1
Total
Hours / Person
6
11.5
17
3
8.5
14
7
12
17.5
7
12
17.5
# Nights
0
0
1
0
0
1
0
0
1
0
0
1
1 Paired watersheds are assumed to be 24 km apart. Above/below sites are assumed to be less than 1 km apart.
Sample Analysis
Analytical methods for water quality variables included in the spreadsheet were obtained from various
sources such as NEMI (2006). Constraints associated with these methods (e.g., cooling samples to 4°C for
suspended sediment, and pre-acidification for hardness) are reflected in the cost estimates through, for
example, the purchase of refrigerated samplers and sample preservatives.

Sample analysis for total P assumes EPA Method 365.4 (NEMI 2006) at a cost of $21 per sample. Soil
samples under Scenario 7 are analyzed for soil P (Mehlich 3), textural class, and organic matter, at a total
cost of $26 per sample. Soil samples are assumed to be sent by ground shipment to the laboratory.

The number of samples analyzed is increased by 10% for QA/QC.


Land Use/Treatment Tracking
Tracking of BMP implementation is assumed to occur twice per year under Scenarios 3,5, and 6. The
baseline assumption for tracking effort within a 12-digit HUC is 240 km (150 mi) driving and 8 hours
R/T each time, with variations across scenarios due to differing monitoring scales and specifics. Travel
distances and times to the watershed are added as appropriate.

For Scenario 4 it is assumed that a cooperator (e.g., NRCS) provides the data for the 20 subwatersheds on
an annual basis; additional observations can be made during the 30-minute visits for grab sampling in
each of the 405-ha (1,000-acre)  subwatersheds. Under Scenario 7, annual data on organic and inorganic
nutrient application rates and crop yields  per field are assumed to be provided by a cooperator. The
resulting assumptions are summarized in  Table 9A3-7.
                                              9-34
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Monitoring and Evaluating Nonpoint Source Watershed Projects
Chapter 9
           Table 9A3-7. Driving and labor assumptions for land use/treatment tracking
Scenario
1 . Synoptic
2. Compliance
3. Above/Below
4. Multiple
5. Trend
6. Paired
7. Soil Test
Land Use/Treatment Tracking
Drive Distance
Within
Watershed1
n/a
n/a
128 km (80 mi)
n/a
240 km (150 mi)
290 km (180 mi)
n/a
Hours
Including
Drive Time
n/a
n/a
6
n/a
8
9
n/a
Frequency
(#/Yr)
n/a
n/a
2
n/a
2
2
n/a
Comments
Not done.
Not done.
Only part of watershed is tracked.
Not done. Data provided by a cooperating agency.
Baseline assumption.
Tracking intensity varies by source and location.
Not done. Data provided by a cooperating agency.
1Does not include driving distance and time to arrive at watershed.
Supplies
Cost estimates include the purchase of ice for each sampling event and annual purchases of 1-liter HDPE
bottles and sample preservative.


Data Analysis and Reports
Data analysis and reporting costs are set higher for the first and last years compared to the "middle" years.
For example, involvement of higher paid staff is greater in the first and final years because of the
challenges faced in developing data management and analysis procedures and rules. It is assumed that
lower level staff can play a greater role in the middle years with oversight from senior staff.

The cost for  analysis and reporting is greater for projects estimating pollutant loads versus those simply
collecting concentration data. Synoptic surveys (Scenario 1) and compliance (Scenario 2) monitoring
efforts are assumed to require less time than other scenarios because of greater simplicity.  Data analysis
and reporting for multiple-watershed studies (Scenario 4) is assumed to be the most time consuming
despite less frequent sampling than found in Scenarios 3 and 6 because information is obtained from
20 subwatersheds. More hours are assumed for data analysis than for reporting in all cases for Scenario 7
because reports are assumed to be short and more straight-forward. Table 9A3-8 summarizes assumptions
for data analysis and reporting.
                 Table 9A3-8. Labor assumptions for data analysis and reporting
Scenario
1 . Synoptic
2. Compliance
3C. Above/Below
3L. Above/Below
4. Multiple
First- Year Report
Data
Analysis
(Hours)
12
10
15
20
36
Report
Preparation
(Hours)
18
12
22
28
34
Middle-Year Reports
Data
Analysis
(Hours)
n/a
7
12
14
26
Report
Preparation
(Hours)
n/a
8
12
14
24
Final-Year Report
Data
Analysis
(Hours)
n/a
10
17
22
38
Report
Preparation
(Hours)
n/a
12
22
26
32
                                              9-35
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Monitoring and Evaluating Nonpoint Source Watershed Projects
Chapter 9
Scenario
5C. Trend
5L. Trend
6P. Paired
61. Paired
7. Soil Test
First- Year Report
Data
Analysis
(Hours)
14
16
20
20
16
Report
Preparation
(Hours)
18
26
28
28
12
Middle-Year Reports
Data
Analysis
(Hours)
11
13
14
14
13
Report
Preparation
(Hours)
9
12
14
14
10
Final-Year Report
Data
Analysis
(Hours)
19
20
22
22
20
Report
Preparation
(Hours)
18
23
26
26
11
Scenario Summaries

Scenario 1: Preliminary Synoptic Survey
Under this scenario, grab sampling is performed at 8 sites on two trips (low and high flow conditions). A
team of 3 people conducts a windshield survey to characterize the watershed, but subsequent land
use/land treatment tracking is not performed. Meteorological and flow data are assumed to be obtained as
part of the desktop analysis of the watershed.

Equipment and sampling assumptions for this scenario are:
  •  Equipment: sample bottles and cooler
  •  Sampling: TP, SSC, B.O.D., E. coll, total coliform, discharge, and suspended sediment
     concentration


Scenario 2: Compliance Monitoring
Under this scenario, grab sampling (4x/yr) is performed at 2 sites. Land use/land treatment tracking is not
performed.

Equipment and sampling assumptions for this scenario are:
  •  Equipment: sample bottles and cooler
  •  Sampling: TP


Scenario 3: Above/Below Monitoring
This scenario has two options. Land use/land treatment tracking (e.g., type and number of practices, acres
treated) is performed 2x/yr for both options via windshield survey and collection of data from cooperators
(e.g., USDA, Soil and Water Conservation District); emphasis is placed on the area between the above
and below stations.
                                             9-36
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Monitoring and Evaluating Nonpoint Source Watershed Projects                                      Chapter 9

3C. Concentration Option: Weekly grab samples are collected at 2 sites.
Equipment and sampling assumptions for this scenario are:
  •  Equipment: sample bottles and cooler, 2 plastic rain gages, 2 staff gages
  •  Sampling: TP, stage

3L. Load Option: Weekly flow-proportional composite samples are collected at 2 sites.
Equipment and sampling assumptions for this scenario are:
  •  Equipment: sample bottles and cooler, 2 sample sheds, 2 tipping bucket rain gages and data logger,
     2 staff gages, 2 refrigerated automatic samplers (with bubble flow module, battery backup, 2-bottle
     kit), data transfer device and software, 2 surge protectors, pygmy-type current meter and data
     logger
  •  Sampling: TP, continuous flow

Scenario 4:  Multiple-Watershed Monitoring
Under this scenario there are 10 small watersheds each (n=20) with/without BMPs. Water quality
sampling occurs six times per year. It is assumed that land use/land treatment tracking is performed 2x/yr
by a cooperator, with additional observations made during water quality sampling runs.
Equipment and sampling assumptions for this scenario are:
  •  Equipment: sample bottles and cooler, 20 staff gages
  •  Sampling: TP, stage

Scenario 5:  Trend Monitoring
This scenario  has one monitoring site and two options. Land use/land treatment tracking is performed
2x/yr for both options via windshield survey and collection of data from cooperators (e.g., USDA, Soil
and Water Conservation District). Data are collected on the nature, extent, and timing of BMP
implementation - as  well as operation and maintenance after implementation.

5C. Concentration Option: Twice-monthly grab samples.
Equipment and sampling assumptions for this scenario are:
  •  Equipment: sample bottles and cooler, plastic rain gage, staff gage
  •  Sampling: TP, stage, precipitation

5L. Load Option: Weekly flow-proportional composite samples.
Equipment and sampling assumptions for this scenario are:
  •  Equipment: sample bottles and cooler, sample shed, tipping bucket rain gage and data logger, staff
     gage, refrigerated automatic sampler (with bubble flow module, battery backup, 2-bottle kit), data
     transfer device and software, surge protector, pygmy-type current meter and data logger
  •  Sampling: TP, continuous flow, precipitation
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Monitoring and Evaluating Nonpoint Source Watershed Projects                                      Chapter 9


Scenario 6: Paired-Watershed Monitoring
This scenario has two monitoring sites (treated and untreated) and two options that address load
estimation for a perennial and intermittent stream setting. For continuously flowing streams, a single
weekly composite sample is collected for analysis. For intermittent streams, a flow-proportional
composite sample is collected during each of 20 runoff events each year. Land use/land treatment tracking
is performed 2x/yr in both watersheds for both options via windshield survey and collection of data from
cooperators (e.g., USDA, Soil and Water Conservation District).

61. Intermittent Stream Option: Twenty runoff events sampled per year at each site.
Equipment and sampling assumptions for this scenario are:
  •   Equipment:  sample bottles and cooler, 2 sample sheds, 2 tipping bucket rain gages and data logger,
      2 staff gages, 2 refrigerated automatic samplers  (with bubble flow module, battery backup, 2-bottle
      kit), data transfer device and software, 2 surge protectors, pygmy-type current meter and data
      logger
       OO
  "   Sampling: TP, continuous flow, precipitation

6P. Perennial Stream Option: Weekly flow-proportional composite samples at each site.
Equipment and sampling assumptions for this scenario are:
  •   Equipment:  sample bottles and cooler, 2 sample sheds, 2 tipping bucket rain gages and data logger,
      2 staff gages, 2 refrigerated automatic samplers  (with bubble flow module, battery backup, 2-bottle
      kit), data transfer device and software, 2 surge protectors, pygmy-type current meter and data
      logger
  •   Sampling: TP, continuous flow, precipitation


Scenario 7: Soil Testing
This scenario involves random selection of 20 agricultural fields for annual soil sampling. Ten fields are
beginning to adopt nutrient management, and the other ten  are conventionally managed. Local
precipitation records are used in lieu of on-site collection of precipitation data. Annual data on nutrient
application and crop yields are provided by a cooperator (e.g., the landowner, USDA).

Equipment and sampling assumptions for this scenario are:
  •   Equipment:  2 soil probes, 2 buckets, and a supply of bags and ties for soil samples
  "   Sampling: soil P, textural class (covariate), and organic matter (covariate)
                                              9-38
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Monitoring and Evaluating Nonpoint Source Watershed Projects
Chapter 9
Appendix 9-4. Cost  Estimates for Five-Year Trend

and Above/Below  Monitoring

As described in Appendix 9-1, a large number of assumptions must be made to estimate costs for various
monitoring scenarios. Thus, while these cost estimates are intended to be informative, they are more or
less relevant to any particular monitoring effort based on how well the assumptions match the realities of
that specific situation. Cost estimates given here are more likely to be high than low because it is always
assumed that contractors perform the monitoring (i.e., no use of in-house labor that was hired to do
monitoring) and all monitoring equipment must either be leased or purchased.

The basic scenarios (n=160) assumed for this analysis are summarized in Table 9A4-1. The trend design
assumes one monitoring site and the above/below design assumes two monitoring sites. All monitoring is
assumed to continue for five years. Tracking of land use and land treatment is assumed to occur twice per
year, with costs identical for all scenarios. The five-year costs for this tracking range from about $100 to
$16,500 for all scenarios. Costs vary considerably based on the size of and distance to the watershed.

                Table 9A4-1. Factors used in creating cost estimation scenarios
Scenario
Biological
Nutrient and Sediment
Concentration
Nutrient and Sediment
Load
Sondes for Nutrients
and Turbidity
Monitoring
Variables Set
(and source)
BioK(Table9A1-4)
NSC (Table 9A1 -2)
NSL(Table9A1-3)
SNT(Table9A1-5)
Sampling
Frequency
(times/year)
2
26
Monitoring
Designs
Trend and
Above/Below
Watershed
Sizes (ha)
202
2,023
10,117
20,234
Distances to
Watershed1 (km)
0
40
80
121
161
Distance sampling team must travel to reach the watershed or nearest watershed being monitored.

Labor costs for these estimates use the same rates shown in Table 9A2-1. All scenarios include a mix of
fixed labor assumptions (e.g., QAPP development cost is $1,400 for all4 scenarios) and variable labor
assumptions that are based on the monitoring design and watershed size. For example, watershed
characterization costs vary depending on design and watershed size as illustrated in Table 9A4-2. A
simple algorithm in the simplified spreadsheet estimates travel distances and drive times based on
watershed size, affecting both labor and vehicle costs for watershed characterization.
1 "All" scenarios refers to the base scenarios for which pay rates are those found in Table 9A2-1.
                                           9-39
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Monitoring and Evaluating Nonpoint Source Watershed Projects
Chapter 9
     Table 9A4-2. Watershed characterization costs as function of design and watershed size
Design
Trend
Above/Below
Watershed Size (ha)
202
$1,516
$1,888
2,023
$1,780
$2,152
10,177
$2,952
$3,324
20,234
$4,790
$5,162
Distance to watershed is assumed = 80km.

Labor and vehicle requirements for sampling vary depending upon design, watershed size, and
monitoring variables set. The variability of labor costs for data analysis and report development is
illustrated in Table 9A4-3. These costs reflect the assumption that biological data require more time for
analysis (at species level) than chemical/physical data collected using the other variable sets. Estimation
and analysis of pollutant loads, likewise, is assumed to be more time-consuming than for either sonde or
concentration data. Spreadsheet users, of course, can change these assumptions.

                 Table 9A4-3. Variability of costs for data analysis and reporting
Design
Trend
Above/Below
Variable Set
BioK
NSC
NSL
SNT
BioK
NSC
NSL
SNT
Samples/Year
2
26
26
26
2
26
26
26
5-Year Labor Cost for Data Analysis and
Reporting
$15,889
$10,051
$16,271
$11,899
$27,068
$16,177
$27,047
$19,873
Assumes 2,023-ha watershed and 50 mile distance.

QA/QC is addressed in a number of ways. For sample analysis, sample size is increased by 10% to
account for replicates. In addition, a QA/QC officer is assumed to join the sampling team once per year,
and stage-discharge relationships are checked 8 times per year.

The results of running 160 scenarios for these above/below and trend monitoring designs are discussed in
section 9.3.3 and summarized in Figure 9-4. Paired designs would have costs similar to those for the
above/below design.

Additional cost estimates were run using a salary adjustment factor to see how this would affect total
costs. Salaries were adjusted across the board by reducing them to 70%, 50%, and 0% of those in Table
9A2-1. Similarly, a rough assessment of the effects of equipment costs on total costs was performed by
estimating costs where all or no equipment was purchased. These two equipment scenarios were also
combined with the  four salary options (0%, 50%, 70%, and 100% of the rates in Table 9A2-1) to explore
the impacts of both adjustments on total costs. The results of these analyses are presented in section 9.3.3
and summarized in Table 9A3-3.
                                               9-40
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