EPA/841-B-96-004
September 1997
MONITORING GUIDANCE FOR
DETERMINING THE EFFECTIVENESS
OF NONPOINT SOURCE CONTROLS
FINAL
U.S. Environmental Protection Agency
Nonpoint Source Control Branch
401 M Street, S.W.
Washington, DC 20460
September 1997
-------
-------
FOREWORD
The diffuse nature of nonpoint sources (e.g., agriculture, forestry, urban areas) and the variety of
pollutants generated by them create a challenge for their effective control. Although progress has
been made 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. Monitoring will play an important role in this effort.
This nonpoint source monitoring and evaluation guide is written for use by both those who
monitor and those who evaluate and fund monitoring proposals. For example, the federal, State,
and Tribal agencies that support monitoring activities might use the guide to assess the technical
merits of proposed monitoring and evaluation plans. These same agencies, university personnel,
and others that carry out the monitoring and evaluation might use this guide to formulate their
plans.
This guidance addresses the design of water quality monitoring programs to assess both impacts
from nonpoint source pollution and the effectiveness of control practices and management
measures. There are diverse opinions regarding the most effective way to design a monitoring
program. Since each situation is different and may need a unique monitoring approach, this
guidance presents the theory and information needed to design monitoring programs tailored to
particular situations.
Readers are encouraged to consult, as well, the additional resources listed in this document. For
example, companion documents, Techniques for Tracking, Evaluating, and Reporting the
Implementation of Nonpoint Source Control Measures: I. Agriculture, II. Forestry, and
III. Urban (USEPA, 1997), present guidance on determining the extent of and trends in
implementation of nonpoint source control practices and management measures. 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.
M
Geoffrey H. <£rubbs, Director
Assessment and Watershed Protection Division
-------
-------
ACKNOWLEDGMENTS
Mr. Steven A. Dressing of the Assessment and Watershed Protection Division (AWPD) in
USEPA's Office of Water was the principal author of the 1988 draft document and managed the
development of the final guidance. Contractual support for the final document was provided by
Tetra Tech, Inc. under EPA contract 68-C3-0303. Dr. Jon Harcum wrote much of the text for
Chapters 2 and 4. Drs. Michael Barbour, Mohammed Lahlou, and Sam Stribling and Mr. Sarri
Pett prepared Chapter 3, and Dr. Esther Peters and Mr. Pett prepared Chapter 5. Mr. George
Townsend coordinated Tetra Tech's efforts, and Ms. Marti Martin edited the document.
Special thanks go to the following reviewers for their insights, technical assistance, and critical
review of various drafts of the guidance:
Donald W. Meals of the Vermont Agency of Natural Resources
Dr. R. Peter Richards of Heidelberg College, Ohio
Thomas E. Davenport of USEPA, Region 5
Patricia L. Lietman of US Geological Survey, Harrisburg, Pennsylvania
Gretchen Hayslip of USEPA, Region 10
Dr. Jean D. Spooner of North Carolina State University
Dr. Deanna L. Osmond of North Carolina State University
Dr. Benno P. Warkentin of Oregon State University
Jeanne Goodman of South Dakota Dept. of Environment & Natural Resources
Steven W. Coffey of North Carolina State University
Martin Brossman of USEPA, Headquarters
Donald Martin of USEPA, Region 10
Charles Kanetsky of USEPA, Region 3
Chris Faulkner of USEPA, Headquarters
Keith R. Seiders of Washington Department of Ecology ,
Dr. John C. Clausen of the University of Connecticut
William H. Clark of Idaho Division of Environmental Quality
Dr. Ed Liu of USEPA, Region 9
Dr. Paul D. Robillard of Perm State University
Dr. Richard P. Maas of the University of North Carolina-Asheville
Hoke Howard of USEPA, Region 4
Annie McLeod of South Carolina Dept. of Health and Environmental Control
Ralph Reznick of Michigan Dept. of Environmental Quality
Robert Alpern of New York City Department of Environmental Protection
Dr. James L. Plafkin of USEPA, Headquarters (posthumously)
and many others who reviewed earlier drafts over the past several years. Much appreciation is
due to the many water quality experts whose field and theoretical work has laid the foundation for
this and related monitoring guides.
-------
-------
CONTENTS
PAGE
List of Tables
List of Figures vii
Credits . . . . : ix
Glossary xi
1. Overview of the Nonpoint Source Problem
1.1 Definition of a Nonpoint Source 1-1
1.2 Extent of Nonpoint Source Problems in the United States 1-1
1.3 Effects of Nonpoint Source Pollutants 1-4
1.4 Major Categories of Nonpoint Source Pollution . . . .; 1-6
1.4.1 Agriculture 1-6
1.4.2 Urban Sources 1_7
1.4.3 Removal of Streamside Vegetation 1-7
1.4.4 Hydromodification 1-8
1.4.5 Mining 1_9
1.4.6 Forest Harvesting 1-10
1.4.7 Construction - 1-10
1.4.8 Marinas 1-11
1.5 Water Resource Considerations 1-12
1.5.1 Rivers and Streams 1-12
1.5.2 Lakes, Reservoirs, and Ponds 1-13
1.5.3 Estuaries 1-16
1.5.4 Open Coastal Waters 1-18
1.5.5 Ground Water 1_19
i.6 Climate 1_20
1.7 Soils, Geology, and Topography 1-21
2. Developing a Monitoring Plan
2.1 Introduction 2-1
2.2 Monitoring Objectives 2-4
2.2.1 Monitoring Objective Category: Problem Definition 2-5
2.2.2 Monitoring Objective Category: Model Development 2-6
2.2.3 Monitoring Objective Category: Evaluation 2-6
2.2.4 Monitoring Objective Category: Conduct Research 2-6
2.3 Data Analysis and Presentation Plans 2-7
2.4 .Variable Selection 2-7
2.4.1 Physical and Chemical Water Quality Data 2-10
2.4.2 Biological Data 2-10
2.4.3 Precipitation Data 2-10
-------
Contents
2.4.4 Land Use Data 2-11
2.4.5 Topographic Data 2-12
2.4.6 Soil Characteristic Data 2-12
2.5 Program Design 2-12
2.5.1 Probabilistic Designs 2-14
2.5.2 Targeted Site Location Study Designs 2-21
2.6 Example Program Design 2-25
2.7 Roles and Responsibilities 2-27
2.8 Quality Assurance Project Planning 2-29
2.9 Chemical and Physical Monitoring 2-29
2.10 Recommended References 2-30
3. Biological Monitoring of Aquatic Communities
3.1 Introduction 3-1
3.1.1 Rationale and Strengths of Biological Assessment 3-2
3.1.2 Limitations of Biological Assessment 3-3
3.2 Habitat Assessment 3-4
3.3 Overview of Biological Assessment Approaches 3-6
3.3.1 Screening-Level or Reconnaissance Bioassessment 3-6
3.3.2 Paired-Site Approach 3-7
3.3.3 Composited Reference Site Bioassessment 3-10
3.4 Reference Sites and Conditions 3-11
3.5 Rapid Bioassessment Protocols . . . 3-16
3.6 The Multimetric Approach for Biological Assessment 3-18
3.7 Sampling Considerations 3-20
3.7.1 Benthic Macroinvertebrate Sampling 3-21
3.7.2 Fish Sampling 3-24
3.8 Biomonitoring Program Design 3-26
3.8.1 Process of Randomized Sampling Site Selection 3-29
3.8.2 Targeted Site Selection 3-32
3.8.3 Integrated Network Design 3-32
3.9 Monitoring Trends in Biological Conditions 3-34
3.10 Overview of Some State Programs 3-38
4. Data Analysis
4.1 Introduction 4-1
4.1.1 Estimation and Hypothesis Testing 4-1
4.1.2 Characteristics of Environmental Data 4-4
4.1.3 Recommendations for Selecting Statistical Methods 4-4
4.1.4 Data Stratification 4-8
4.1.5 Recommended Reading List and Available Software 4-9
4.2 Summary (Descriptive) Statistics 4-10
4.2.1 Point Estimation 4-10
4.2.2 Interval Estimation '. 4-18
4.3 Graphical Data Display 4-20
-------
Contents:.'.
4.4 Evaluation of Test Assumptions 4-25
4.4.1 Tests of Normality 4-26
4.4.2 Tests of Equal Variance 4-31
4.4.3 Tests of Randomness 4-33
4.5 Evaluation of One or Two Independent Random Samples 4-33
4.5.1 Tests for One Sample or Paired Data 4-34
4.5.2 Two-sample Tests 4-47
4.5.3 Magnitude of Differences 4-51
4.6 Comparison of More Than Two Independent Random Samples 4-52
4.6.1 One-Factor Comparisons 4-53
4.6.2 Two-Factor Comparisons 4-58
4.6.3 Matched Data 4-61
4.6.4 Multiple Comparisons 4-63
4.7 Regression Techniques 4-64
4.7.1 Overview 4-64
4.7.2 Simple Linear Regression 4-65
4.7.3 Nonlinear Regression and Transformations 4-74
4.7.4 Multiple Regression 4-75
4.7.5 Multivariate Regression 4-78
4.8 Analysis of Covariance 4-79
4.9 Evaluation of Time Series 4-85
4.9.1 Monotonic Trends 4-86
4.9.2 Correlation Coefficients 4-90
4.10 Multivariate Analysis , 4-91
4.10.1 Canonical Correlation 4-92
4.10.2 Cluster Analysis 4-93
4.10.3 Principal Components and Factor Analysis 4-93
4.10.4 Discriminant Analysis 4-94
4.11 Extreme Events 4-94
4.11.1 Rainfall Analyses 4-95
4.11.2 Design Flows 4-101
4.11.3 Frequency of Extreme Events 4-105
5. Quality Assurance and Quality Control
5.1 Introduction 5-1
5.1.1 Definitions of Quality Assurance and Quality Control 5-1
5.1.2 Importance of QA/QC Programs 5-1
5.1.3 EPA Quality Policy 5-2
5.2 Data Quality Objectives 5-3
5.2.1 The Data Quality Objectives Process 5-4
5.2.2 Data Quality Objectives and the QA/QC Program 5-7
5.3 Elements of a Quality Assurance Project Plan 5-8
5.3.1 Group A: Project Management :. 5-8
5.3.2 Group B: Measurements and Acquisition . . . 5-14
5.3.3 Group C: Assessment/Oversight 5-17
-------
Contents
5.3.4 Group D: Data Validation and Usability 5-18
5.4 Field Operations 5_19
5.4.1 Field Design 5_19
5.4.2 Sampling Site Selection 5_2i
5.4.3 Sampling Equipment ' 5_22
5.4.4 Sample Collection 5_22
5.4.5 Sample Handling and Transport 5-23
5.4.6 Safety and Training 5_23
5.5 Laboratory Operations , 5_24
5.5.1 General Laboratory QA and QC 5_24
5.5.2 Instrumentation and Materials for Laboratory Operations 5-24
5.5.3 Analytical Methods 5_26
5.5.4 Method Validation 5_26
5.5.5 Training and Safety ; 5_2g
5.5.6 Procedural Checks and Audits 5_26
5.6 Data and Reports 5_26
5.6.1 Generation of New Data 5.27
5.6.2 Uses of Historical Data 5.27
5.6.3 Documentation and Record Keeping ." 5.23
5.6.4 Report Preparation 5_2g
References R_l
Index ; . . 1-1
Appendices
A. Review of Available Monitoring Guidances A-l
B. Data Sources B_l
C. Example Monitoring Programs C-l
D. Statistical Tables '..'.'.'.'.'.'.'. D-l
-------
TABLE TITLE
LIST OF TABLES
PAGE
1-1 Sources of nonpoint source pollution and their contribution to the impairment of water
quality in the United States 1-5
2-1 General characteristics of monitoring types . . . 2-4
2-2 Applications of six sampling designs to estimate means and totals ,2-21
3-1 General strengths and limitations of biological monitoring and assessment 3-5
3-2 Five tiers of the rapid bioassessment protocols 3-18
3-3 Scoring criteria for the core metrics as determined by the 25th percentile of the metric
values from the Middle Rockies-Central Ecoregion, Wyoming 3-23
3-4 Scoring criteria for the metrics as determined by the 25th percentile of the metric
values for the two aggregated subecoregions for Florida streams 3-24
3-5 Comparison of probabilistic and targeted monitoring designs 3-28
3-6 Waterbody stratification hierarchy 3-30
3-7 Summary of the primary technical issues related to biological monitoring for nonpoint
source evaluations 3-39
3-8 Selected biomonitoring program components, Delaware DNREC 3-40
3-9 , Selected biomonitoring program components, Florida DEP 3-41
3-10 Selected biomonitoring program components, Montana DHES . 3-42
3-11 Selected biomonitoring program components, North Dakota DEH 3-43
3-12 Selected biomonitoring program components, Vermont DEC 3-44
3-13 Fish IBI metrics used in various regions of North America 3-46
3-14 Examples of metric suites used for analysis of macroinvertebrate assemblages 3-49
4-1 Errors in hypothesis testing '; 4-2
4-2 Methods for characterizing data 4-5
4-3 Methods for routine data analysis 4-6
4-4 Total nitrogen runoff concentrations for a single storm event in Florida 4-13
4-5 Total nitrogen runoff concentrations for a single storm event in Florida and example
calculations for the EMC 4-14
4-6 Raw data by time period 4-15
4-7 Loadings rate data 4-15
4-8 Calculation of plotting position for the sulfate data from Station 16 in Figure 4-8 4-27
4-9 Table of skewness test for normality for sample sizes less than 150 4-29
4-10 Selected summary statistics for the sulfate data from Station 16 in Figure 4-8 4-29
4-11 Values of kurtosis test for normality for small sample sizes 4-30
4-12 Example analysis of the Sharpiro-Wilk Wtest using the sulfate data from Station 16
in Figure 4-8 4-32
4-13 Highland Silver Lake TSS data for site 1 4-38
4-14 Evaluation of power using the postimplementation TSS data 4-43
4-15 Nonparametric evaluation of postimplementation data using the Wilcoxon Signed
Ranks test 4-45
-------
List of Tables
4-16 Sign test for comparing paired BOD5 concentrations 4-46
4-17 Summary of parametric tests used to evaluate difference between means 4-49
4-18 Nonparametric evaluation of postimplementation data using the Mann-Whitney test . . . .4-51
4-19 ANOVA notation 4-54
4-20 Common one-way ANOVA output format 4-55
4-21 Trout population from streams in the coastal plain region 4-55
4-22 One-way ANOVA of stream trout data from the coastal plain region using stream
as the treatment 4.56
4-23 Rank of trout population data from streams in the coastal plain region 4-57
4-24 Common two-way ANOVA output format 4-59
4-25 Stream trout population 4-60
4-26 Two-way ANOVA of trout population data using an interaction term 4-60
4-27 Common two-way ANOVA without replication output format 4-62
4-28 Assumptions necessary for the purposes of linear regression 4-66
4-29 Runoff sampler calibration data 4-67
4-30 Regression analysis of runoff sampler calibration data 4-68
4-31 Common ANOVA output format for linear regression 4-72
4-32 ANOVA for regression of treatment watershed runoff on control watershed runoff
during calibration 4-82
4-33 ANOVA for regression of treatment watershed runoff on control watershed runoff
during treatment 4-83
4-34 ANCOVA for comparing regression lines 4-83
4-35 ANCOVA for comparing regression lines from calibration and treatment (hand
calibrations) 4-84
4-36 ANCOVA for comparing regression lines from calibration and treatment (computerized
software) 4.34
4-37 Annual total rainfall for 21 years 4-88
4-38 Analysis of rainfall data using Mann-Kendall T test 4-89
4-39 Analysis of rainfall data using Spearman's rho 4-91
4-40 Theoretical log-probability frequency factors 4-96
4-41 Linearized rainfall frequency variate for equation 4-109 4-96
4-42 Linearized rainfall duration variate for equation 4-109 4-96
5-1 Common QA and QC activities 5-2
5-2 Elements required in an EPA Quality Assurance Project Plan 5-9
5-3 Checklist of items that should be considered hi the field operations section of a QA/QC
program 5_20
5-4 Checklist of items that should be considered in the laboratory operations section of a
QA/QC program . 5.25
-------
FIGURE TITLE
LIST OF FIGURES
PAGE
1-1 Waterbody types affected by nonpoint sources of pollution, by state 1-2
1-2 Leading nonpoint sources of pollution that impair rivers and streams 1-2
1-3 Leading nonpoint sourqes of pollution that impair lakes and reservoirs 1-3
1-4 Leading nonpoint sources of pollution that impair estuaries 1-3
1-5 Leading nonpoint sources of pollution that impair ocean shorelines 1-3
1-6 Leading nonpoint sources of pollution that impair Great Lakes shoreline miles 1-4
1-7 Vertical sediment concentration and flow velocity distribution in a typical stream
cross section 1-13
1-8 Schematic diagram of stream vertical showing relative position of sediment load terms . 1-14
1-9 Important differences between lakes and reservoirs 1-15
1-10 Hydraulic residence time, assuming inflow = outflow 1-15
1-11 A cross-sectional view of a thermally stratified lake in mid-summer 1-16
1-12 Phytoplankton chlorophyll a concentration in Chautaugua Lake's northern basin and
and southern basin, 1977 1-16
1-13 Mixing of salt water and fresh water in an estuary . 1-17
1-14 Chesapeake Bay salinity levels over time and space 1-17
1-15 Estuarine drainage area versus fluvial drainage area 1-18
1-16 Nitrate concentration versus depth below water table 1-19
1-17 Comparison of water movement from irrigation furrows into two different soil types ... 1-21
2-1 Development of a monitoring project 2-2
2-2 Expectations report outline . 2-8
2-3 Simple random sampling for silviculture 2-14
2-4 Stratified random sampling for silviculture 2-16
2-5 Systematic sampling for silviculture 2-18
2-6 Cluster sampling for silviculture 2-19
2-7 Nested paired and paired watershed study designs 2-22
2-8 Map of the Rock Creek Rural Clean Water Program study area 2-26
2-9 St. Albans Bay watershed sampling locations 2-28
3-1 Sample calculations of biological metrics 3-8
3-2 The process for metric selection and validation and development of reference conditions 3-12
3-3 Approach to establishing reference conditions 3-17
3-4 Selection and application of the different tiers of RBP depend on monitoring objectives . 3-19
3-5 Organizational structure of attributes that can serve as metrics 3-21
3-6 Areas in which various fish IBI metrics have been used 3-22
3-7 Some trends that might be observed during the course of a biological monitoring
program 3-35
3-8 Sample power analysis of a bioassessment method 3-36
4-1 Comparison of a and P 4-3
4-2. Precipitation, runoff, total nitrogen, and total phosphorus from a single storm event in
-------
List of Figures
Florida 4-12
4-3 Comparison of several theoretical distributions 4-17
4-4 Dissolved oxygen concentrations from 1980 through 1989 for the Delaware River
at Reedy Island, Delaware, using a time series plot . 4-21
4-5 Dissolved oxygen concentrations from 1980 through 1989 for the Delaware River
at Reedy Island, Delaware, using a histogram 4-22
4-6 Stem and leaf plot of dissolved oxygen concentrations from 1980 through 1989 for the
Delaware River at Reedy Island, Delaware 4-22
4-7 Boxplots of dissolved oxygen concentrations by month from 1980 through 1989 for the
Delaware River at Reedy Island, Delaware 4-23
4-8 Boxplot of sulfate concentrations from 1993 and 1994 for the Rio Grande near
El Paso, Texas 4-24
4-9 Bivariate scatter plot of total suspended solids and flow at 36th Street storm sewer in
Denver, Colorado 4-24
4-10 Time series plot of dissolved orthophosphate from 1989 through 1994 for portions of
the Delaware River 4-25
4-11 Probability plot of sulfate data from Station 16 in Figure 4-8 4-28
4-12 Preimplementation data set 4-36
4-13 Postimplementation data set 4-37
4-14 Log-transformed preimplementation data set 4-39
4-15 Log-transformed postimplementation data set 4-40
4-16 One- and two-sided t test for post-BMP mean TSS concentration . 4-42
4-17 Evaluation of power using the log-transformed postimplementation TSS data 4-43
4-18 Split versus flow rate : . . . 4-67
4-19 Plot of residuals versus predicted values 4-69
4-20 Plot of split residuals 4-70
4-21 Plot of split versus flow rate with confidence limits for mean response and individual
estimates 4-74
4-22 Comparison of regression analyses using raw and log-transformed data 4-76
4-23 Comparison of regression equations for data from two periods 4-80
4-24 Storm runoff calibration and treatment periods in Vermont . 4-81
4-25 One-hour rainfall to be expected at a return period of 2 years 4-97
4-26 24-hour rainfall to be expected at a return period of 2 years 4-98
4-27 One-hour rainfall to be expected at a return period of 100 years . 4-99
4-28 24-hour rainfall to be expected at a return period of 100 years 4-100
5-1 Sample organization chart for a quality assurance project plan . . 5-10
5-2 Sample quality assurance objectives . 5-12
5-3 Sample custody chart 5-15
-------
CREDITS
Figure 2-2,
Tables 4-2, 4-3
Figure 5-3
Table 2-2
Table 4-9
Tables 4-11, D9
Table 4-28
Table 4-40
Tables 4-41,4-42
Tables D4, D5
Table D7
Table D10
Modified and reprinted from R.C. Ward, J.C. Loftis, and G.B. McBride;
Design of water quality monitoring systems; 1990; pages 82-83, page 92, and
page 97, respectively; with permission from Van Nostrand Remhold Company,
New York.
Modified and reprinted with permission from A. Cross-Smiecinski and L.D.
Stetzenback, Quality planning for the life science researcher: Meeting quality
assurance requirements. Copyright CRC Press, Boca Raton, Florida. ®1994.
Modified and reprinted from R.O. Gilbert, Statistical methods for
environmental pollution monitoring, 1987, page 20, with permission from Van
Nostrand Reinhold Company, New York.
Modified from Dr. E.S. Pearson, Tables for Statisticians and Biometricians,
vol. 1, Tables 34B and 34C, with permission from the Biometrika Trustees.
Modified and/or reprinted from Biometrika Tables for Statisticians, 3rd ed.,
vol. I, Table 34, Part A and Table 15, respectively, 1966, with permission
from the Biometrika Trustees.
Modified and reprinted from D.R. Helsel and R.M. Hirsch, Statistical Methods
in Water Resources, 1995, page 225, with kind permission from Elsevier
Science - NL, Sara Burgerhartstraat 25, 1055 KV Amsterdam, The
Netherlands.
Modified and reprinted from V.T. Chow, The log-probability law and its
engineering applications, 1954, Separate No. 536, 80, with permission from
the American Society of Civil Engineering.
Modified and reprinted from Monthly Weather Review, 1962, pp. 87-88, L.L.
Weiss: A general relation between frequency and duration of precipitation,
with permission from the American Meteorological Society.
Reprinted from. Biometrika, 1965, vol. 52, pages 591-611, S.S. Shapiro and
M.B. Wilk, An analysis of variance test for normality (complete samples), with
permission from the Biometrika Trustees.
Reprinted from Journal of the American Statistical Association, 1965, vol. 60,
pp. 320-333, D.B. Owen: The power of Student's t test, with permission from
the American Statistical Association.
Reprinted from Glasser and Winter, 1961, with permission from the Biometrika
Trustees.
-------
Credits
Table Dll
Reprinted by permission of John Wiley & Sons, Inc., New York, from W.J.
Conover, Practical Nonparametric Statistics, Copyright ® (1980 John Wiley &
Sons, Inc.), page 447.
-------
GLOSSARY
Atomic absorption spectrophotometry: A method to determine the elemental composition of a
substance by vaporizing the sample and measuring at specific wavelengths the amount of radiation
absorbed, which is proportional to the concentration of the element in the sample.
Bathymetry: The measurement of depths of water in oceans, seas, and lakes; also, information
derived from such measurements.
Biomagnify: The process by which chemical pollutants become increasingly concentrated in animal
tissue as the pollutants are passed up the food chain.
Biological oxygen demand (BOD): the amount of oxygen required by aerobic organisms to carry out
oxidative metabolism in water containing organic matter, such as sewage.
Chemical oxygen demand (COD): a measure of the quantity of oxidizable components present in
water.
Coliform bacteria: Bacteria present in mammalian feces, used as an indicator of the presence of
human feces, bacteria, viruses, and pathogens in the water column.
Congener: An organism that is a member of the same genus as another animal or plant; a chemical
substance that is related in some way to another.
Coriolis effect: The deflection relative to the earth's surface of an object that is moving on or above
the earth, due to the action of the Coriolis force. An object that is moving horizontally above the
earth's surface in the Northern Hemisphere tends to show a rightward deflection, and one in the
Southern Hemisphere tends to show a leftward deflection.
Dissolved oxygen: The concentration of free molecular oxygen in the water column.
Diurnal: Active primarily during daylight hours.
Estuarine drainage area: The land and water component of a watershed that drains directly into
estuarine waters.
Eutrophic: Rich in nutrients and hence having excessive plant growth, which removes oxygen from
the water. ,
Eutrophication: The process of becoming eutrophic.
Fluvial drainage area: land and freshwater portions of watersheds upstream of estuarine drainage
areas.
-------
Glossary
GIS (geographic information system): A computer system specialized for storage, manipulation, and
presentation of geographical information, such as topography, political subdivisions, geology,
vegetation, flood plains, etc. [Note: Do not confuse GIS with GPS, global positioning system.}
Habitat alteration: Changes hi a habitat that make it less suitable for the organisms inhabiting it,
create conditions favorable to invasion by species not present prior to the changes, or limit its
ecosystem function.
Hydraulic residence time (HRT): amount of time necessary to fill a lake, or average amount of time
water entering a lake stays in the system, calculated as lake volume/flow rate
Hydromodification: Alteration of the hydrologic characteristics of coastal and noncoastal waters,
which in turn could cause degradation of water resources.
Impervious: The characteristic of a surface that prevents or retards the entry of water into or through
it and causes water to run off the surface.
Ion chromatography: A technique for separating components from a mixture using ionic attraction by
placing the mixture hi a mobile phase that is passed over a stationary phase.
Macroinvertebrate: Invertebrate organisms that can be seen with the naked eye.
Macrophyte: Plants visible to the naked eye.
Nonpoint source: Generally, any unconfined and diffuse source of contamination, such as stormwater
or snowmelt runoff, or atmospheric pollution. Legally, a nonpoint source of water pollution is any
source of water pollution that does not meet the legal definition of "point source" in section 502(14) of
the Clean Water Act.
Oligotrophic: Poorly nourished; describes a lake with low plant productivity and high transparency.
Onsite disposal system (OSDS): Sewage disposal system designed to treat waste water at a particular
site. Septic tank systems are common OSDS.
Oxygen-demanding substance: A substance whose decomposition in water uses oxygen.
Pervious: The characteristic of a surface that allows the entry of water into or through it and causes
little water to run off the surface.
Phytoplankter: An organism of phytoplankton—microscopic algae and microbes that float freely in
open water of lakes and oceans.
Point source: Any discernable, confined or discrete conveyance (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.
-------
Pollution (water): The presence of excessive amounts of dredged spoil, solid waste, incinerator
residue, sewage, garbage, sewage sludge, munitions, chemical wastes, biological materials, radioactive
materials, heat, wrecked or discarded equipment, rock, sand, cellar dirt, and industrial, municipal, and
agricultural waste in water.
Riparian areas: Vegetated ecosystems along a waterbody through which energy, materials, and water
pass. Riparian areas characteristically have a high water table and are subject to periodic flooding and
influence from the adjacent waterbody.
SAV (submerged aquatic vegetation): Macrophytes that are rooted and grow beneath the water
surface.
Sluice gate: A gate for regulating or stopping flow in a conduit or passage where surplus water is
carried off
Substrate: The substance, base, or nutrient on which an organism lives and grows, or the surface to
which a fixed organism is attached.
Suspended sediment: The very fine soil particles that remain in suspension in water for a considerable
period of time.
Turbidity: A cloudy condition in water due to suspended silt or organic matter.
Vadose zone: The subsurface zone in soil that contains air or gases generally under atmospheric
pressure, between the land surface and the zone of saturation.
Weir: A device for measuring or regulating the flow of water.
-------
-------
1. OVERVIEW OF THE NONPOINT SOURCE PROBLEM
1.1 DEFINITION OF A NONPOINT SOURCE
Nonpoint sources of water pollution are both diffuse
in nature and difficult to define. Nonpoint source
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 the soil, it picks up and carries away
natural pollutants and pollutants resulting from
human activity, finally depositing them 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 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:
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, such as those
described above, is treated as a point source
discharge and hence is subject to the federal permit
requirements under section 402 of the Clean Water
Act.
1.2 EXTENT OF NONPOINT SOURCE PROBLEMS IN
THE UNITED STATES
Over the last two 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. Although some state,
tribal, and local nonpoint source management
programs have been developed and are being
implemented, pollutant loads from nonpoint sources
present continuing problems for achieving water
quality goals and maintaining designated uses in
many parts of the United States.
Data provided by state water quality officials and
contained in the National Water Quality Inventory
1994 Report to Congress (USEPA, 1995), referred
to as the "1994 305(b) report," indicate that
nonpoint sources negatively affect rivers and
streams in 49 of the 52 states and territories that
reported data; lakes, reservoirs, and ponds in 41 of
the 52 states and territories that reported data;
estuaries and coastal waters in 20 of the 26 coastal
states and territories that reported data; and the
Great Lakes in 4 of the 8 Great Lakes states (Figure
1-1). The categories of nonpoint source pollution
affecting these 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.
-------
Overview of the NFS Problem
Chapter 1
Figure 1-1. Waterbody types affected by nonpoint sources of pollution, by state (USEPA, 1995).
According to the 1994 305(b) report, agriculture is
the primary source of pollution affecting rivers and
streams. Forty-six states and territories list it as a
major source of water quality impairment. Sixty
percent of impaired river and stream miles are
reported to be negatively affected by agricultural
sources of pollution. Other sources that affect
rivers and streams include natural sources1 (reported
as affecting 19% of impaired river miles), municipal
point sources (17%), hydrologic and habitat
modification (17%), urban runoff/storm sewers
(12%), resource extraction (11%), removal of
riparian vegetation (10%), forest harvesting (9%),
industrial point sources (7%), unspecified or other
nonpoint source pollution (7%), stream bank
destabilization (6%), channelization (5%),
petroleum activities (5%), construction (5%), land
disposal (5%), recreational activities (3%), flow
regulation (3%), onsite disposal systems (2%),
highway maintenance and runoff (2%), and land
development (2%) (Figure 1-2).
Agriculture
Hydrological/Habitat modification
Resource Extraction
Removal of Streamslde Veg.
Forestry
60
" 7 Hi Percentage of impaired
miles affected
~" 110
~~I9
I |
I I
25 50 . 75
Natural sources refer to a variety of naturally
occurring water quality problems, including natural deposits
of salts, nutrients, and metals in soils that leach into surface
and ground waters; warm-weather and dry-weather
conditions that raise water temperatures, depress dissolved
oxygen concentrations, and dry up shallow waterbodies; and
low-flow conditions and tannic acids from decaying leaves
that lower pH and dissolved oxygen concentrations in
swamps that drain into streams (USEPA, 1995).
Figure 1-2. Leading sources of nonpoint pollution
that impair rivers and streams (USEPA, 1995).
Agriculture is also considered the most significant
pollution source affecting lakes, reservoirs, and
ponds. Twenty-seven states list agriculture as a
major source of impairment to the water quality of
lakes, reservoirs, and ponds. Fifty percent of
-------
Chapter 1
impaired lake, reservoir, and pond surface acres are
reported to be negatively affected by agriculture,
followed by municipal point sources (which affect
19% of impaired river miles), urban runoff/storm
sewers (18%), unspecified and other nonpoint
source pollution (15%), natural sources (14%),
hydrologic and habitat modification (12%),
industrial point sources (11%), land disposal (11%),
construction (9%), flow regulation (7%), highway
maintenance and runoff (6%), contaminated
sediment (6%), atmospheric deposition (6%), onsite
disposal systems (5%), forest harvesting (5%),
resource extraction (4%), shoreline modification
(3%), land development (3%), recreational activities
(3%), and spills (2%) (Figure 1-3).
Agriculture
Unspecified Nonpoint Source
Hydro/Habitat modification
Land Disposal
Ki- *»:?m%l??Z>--~3~'&gg%xS'-*~:**-r*'-I$i 50
Percentage of impaired
1 2 surface area affected
I i ' '
I I
25 50
Figure 1-3. Leading sources of nonpoint pollution
that impair lakes, reservoirs, and ponds (USEPA,
1995).
The water quality of estuaries is impaired more by
urban runoff/storm sewers than by any other
pollution source. Twelve states list urban
runoff/storm sewers as a major source of
impairment to estuarine water quality. Forty-six
percent of impaired estuarine waters are reported to
be negatively affected by urban runoff/storm
sewers, followed by municipal point sources (39%),
agriculture (34%), natural sources (30%), industrial
point sources (27%), petroleum activities (13%),
construction (13%), land disposal (13%), upstream
sources (11%), unspecified and other nonpoint
source pollution (10%), spills (8%), combined
sewer outfalls (5%), resource extraction (5%),
contaminated sediment (4%), marinas (3%), onsite
disposal systems (3%), wastewater lagoons (3%),
forest harvesting (5%), atmospheric deposition
(2%), and recreational activities (2%) (Figure 1-4).
Agriculture
Construction
Land Disposal of Wastes
34 Percentage of impaired
surface area affected
50
Figure 1-4. Leading sources of nonpoint pollution
that impair estuaries (USEPA, 1995).
Ocean shoreline waters are impaired more by urban
runoff/storm sewers than by other source of
pollution. Two states and Puerto Rico list urban
runoff/storm sewers as a major source of
impairment to ocean shoreline water quality. Forty-
eight percent of impaired ocean shoreline waters are
reported to be negatively affected by urban
runoff/storm sewers, followed by industrial point
sources (34%), natural sources (25%), land disposal
(25%), onsite disposal systems (23%), agriculture
(20%), unspecified and other nonpoint source
pollution (19%), combined sewer outfalls (11%),
recreational activities (11%), municipal point
sources (7%), atmospheric deposition (3%), spills
(3%), ground water loading (3%), and land
development (2%) (Figure 1-5).
Land Disposal of Wastes
Septic Systems
Agriculture
Unspecified Nonpoint Source
Percentage of Impaired
shoreline miles affected
Figure 1-5. Leading sources of nonpoint pollution
that impair ocean shorelines (USEPA, 1995).
Four of the eight Great Lakes states list nonpoint
source pollution as negatively affecting the quality
of their Great Lakes shoreline miles, with
atmospheric deposition as the most damaging
source of pollution to the lakes. Three states list
atmospheric deposition as a source of impairment to
Great Lakes shoreline miles, though none list it as a
major source of impairment. Twenty-one percent of
-------
dverview of the NFS Problem
Chapter 1;
impaired Great Lakes shoreline miles are reported
to be negatively affected by atmospheric deposition,
followed by discontinued discharges (20%),
contaminated sediment (15%), land disposal (9%),
unspecified and other nonpoint source pollution
(6%), agriculture (4%), urban runoff/storm sewers
(4%), industrial point sources (4%), municipal point
sources (4%), combined sewer outfalls (3%), onsite
disposal systems (2%), spills and illegal dumping
(2%), streambank destabilization (1%),
construction (1%), in-place contamination (1%),
contaminated ground water (<1%), highway
maintenance and runoff (<1%), and hydrologic and
habitat modification (<1%) (Figure 1-6).
Air Pollution
ConlimlntM Sidlmtnt
: ' Hs
UfltpidfudNPS
Agriculture
321
Percentage of Impaired
ihorollne miles affected
25
50
Figure 1-6. Leading sources of nonpoint pollution
that impair Great Lakes shoreline miles (USEPA,
1995).
Table 1-1 summarizes the information on the
impairment of the Nation's water quality by
nonpoint sources of pollution.
1.3 EFFECTS OF NONPOINT SOURCE
POLLUTANTS
Nonpoint sources can generate both conventional
pollutants (e.g., bacteria, oxygen-demanding
substances) and toxic pollutants (e.g., pesticides,
petroleum products), just as point sources do. Even
though nonpoint sources can contribute many of the
same kinds of pollutants as point sources, however,
these pollutants are usually generated in different
volumes, combinations, and concentrations.
Pollutants from nonpoint sources are mobilized
primarily during rainstorms or snowmelt.
Consequently, waterborne nonpoint source pollution
is generated irregularly, in contrast to the more
continuous discharges of point sources of pollution.
However, the adverse impacts of NFS pollution
downstream from its source, or on downgradient
waterbodies, can effectively be continuous under
some circumstances. For example, sediment-laden
runoff that is not completely flushed out of a
surface water prior to a subsequent storm can
combine with runoff from that storm 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 NFS
pollution can have a continuous adverse impact
on resident biota. Hence, the noncontinuous
generation of NFS pollution does not necessarily
translate into noncontinuous impacts on receiving
waterbodies.
Sediment, nutrients, pathogens, salts, toxic
substances, petroleum products, and pesticides
are the pollutants contributed to surface and
ground waters by various nonpoint sources.
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.
• Nitrogen and phosphorus are contained in
commercial fertilizers and manure. The
addition of excessive amounts of these nutrients
to marine and freshwater systems, where
nitrogen and phosphorus, respectively, are
generally limiting to plant growth, can lead to
accelerated eutrophication.
• Waste from livestock and pets also contains
bacteria that contaminate swimming, drinking,
and shellfishing waters, as well as oxygen-
demanding substances that deplete dissolved
oxygen levels in aquatic systems. Suspended
sediment generated by construction,
overgrazing, logging, and other activities in
riparian areas, along with that carried in runoff
-------
Chapter 1
Table 1-1. Sources of nonpoint pollution and their contribution to the impairment of water
quality in the United States (USEPA, 1995).
.'','. • , , , , .,.','
Agriculture
Land Disposal
Onsite
Disposal
Systems
Construction0
Recreational
Activities
Atmospheric
Deposition
Hydrologic
and Habitat
Modification
Resource
Extraction
Silviculture
Contaminated
Sediment
Unspecified
and Other
Monpoint
Source
Pollution
Total
lmparedd
Major3
Length/Area
Percent13
Major
Length/Area
Percent
Major
Length/Area
Percent
Major
Length/Area
Percent
Major
Length/Area
Percent
Major
Length/Area
Percent
Major
Length/Area
Percent
Major
Length/Area
Percent
Major
Length/Area
Percent
Major
Length/Area
Percent
Major
Length/Area
Percent
Rivers &
Streams
36
134,557 miles
60%
18
10,360 miles
5%
13
5,428 miles
2%
13
10,365 miles
5%
7
7,796 miles
3%
28
37,080 miles
17%
28
24,059 miles
11%
13
20,31 5 miles
9%
12
16,31 8 miles
7%
224,236 miles
Lakes, Ponds &
Reservoirs
28
3,349,585 acres
50%
10
712,890 acres
11%
6
335,702 acres
5%
13
624,901 acres
9%
4
189,828 acres
3%
5
369,348 acres
6%
13
832,152 acres
12%
14
236,999 acres
4%
9
307,366 acres
5%
8
38 1,1 83 acres
6%
6
988,71 4 acres
15%
6,682,200 acres
Estuaries
4
3,321 mi2
34%
4
1,21 7 mi2
13%
3
271 mi2
3%
2
1,253 mi2
13%
1
180 mi2
2%
0
236 mi2
2%
2
514 mi2
5%
1
235 mi2
2%
1 .
395 mi2
4%
2 '
991 mi2
10%
9,700 mi2
Ocean
Shoreline
1
74 miles
20%
1
92 miles
25%
0
87 miles
23%
0
40 miles
11%
0
12 miles
3%
1
72 miles
19%
374 miles
Great Lakes
Shoreline
1
226 miles
4%
1
458 miles
9%
1
96 miles
2%
1
48 miles
1%
0
1 ,068 miles
21%
0
11 miles
<1%
2
749 miles
15%
2
296 miles
6%
5,077 miles
Source: USEPA, 1995.
bNurriber of states and/or territories in which the source is reported as a major source of water quality impairment.
Length or area of waterbody type impaired by the source, and percent of total impaired length or area of waterbody type (reported at
bottom of column) that is impaired by the particular source,
dDoes not include road construction and maintenance.
Figures in columns might not add to total at bottom if multiple sources impair the same stretch or area of surface water.
-------
Overview of the NFS Problem
Chapter 1
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 concentrate at the
soil surface through evapotranspiration. Salts
used on roads 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, and they can be toxic to plants at high
concentrations.
• 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 that
are toxic to aquatic plants remove a food source
for many aquatic animals, as well as 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 can reduce the
available habitat for aquatic species, increase
erosion, and create flow regimes that are
detrimental to aquatic life.
1.4 MAJOR CATEGORIES OFNONPOINTSOURCE
POLLUTION
1.4.1 Agriculture
Agriculture is the leading source of impairment to
the Nation's rivers, affecting 60 percent of the
impaired river miles in the United States, according
to the 1994 305(b) report (USEPA, 1995).
Agriculture was also reported as a source of
impairment to 50 percent of impaired lake,
reservoir, and pond acres; 34 percent of impaired
estuary square miles; 20 percent of impaired ocean
shoreline miles; and 4 percent of impaired Great
Lakes shoreline miles. Wetland loss and wetland
degradation were attributed to agriculture by 10
states and 8 states, respectively (USEPA, 1995).
The primary agricultural nonpoint source pollutants
are nutrients, sediment, animal wastes, salts, and
agricultural chemicals. Direct impacts on habitats
are also associated with agriculture. Nitrogen and
phosphorus are the two major nutrients from
agricultural land that degrade water quality.
Nutrients 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.
Greatly increased loadings of sediment to runoff
and surface waters can result from land disturbance
and clearing for agricultural operations and from
stream bank erosion due to increased instream
flows. 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; nitrogen, phosphorus, 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
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
-------
Chapter 1
the soil. Salt accumulation leads to soil dispersion,
soil compaction, and possible toxicity to plants and
soil fauna.
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. Normal 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.
Impacts on habitats and adjacent surface waters
result from planting crops too close to surface
waters and from livestock grazing. 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. If
allowed access to streams, cattle can trample
riparian vegetation and disturb stream bank soils,
leading to bank erosion, and can alter riparian
vegetation species composition through selective
grazing. Grazing animals also add fecal
contamination to streams and ponds.
1.4.2 Urban Sources
Urban runoff and pollutants-carried in storm sewers
reportedly impair 12 percent of the Nation's
impaired river miles; 18 percent of impaired lake,
reservoir, and pond acres; 46 percent of impaired
estuary square miles; 48 percent of impaired ocean
shoreline miles, and 4 percent of impaired Great
Lakes shoreline miles (USEPA, 1995). Wetland
degradation is attributed to pollution from urban
runoff/storm sewers by six states (USEPA, 1995).
The major pollutants in runoff from urban areas are
sediment, nutrients, oxygen-demanding substances,
road salts, heavy metals, petroleum hydrocarbons,
pathogenic bacteria, viruses, and toxic chemicals.
These are generated directly from the use of
insecticides, road salts, and fertilizers, and indirectly
from automobile exhaust, oil drippings from trucks
and cars, brake lining wear, and various urban
activities (USEPA, 1977).
During urbanization, pervious, vegetated ground is
converted to impervious, unvegetated land. Land
imperviousness in urban areas—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. Increases
in pollutant loadings generated from human
activities are associated with urbanization, and
imperviousness results in increased stormwater
runoff volumes and altered hydrology in urban
areas. Urban runoff carries these increased ,
pollutant loadings to surface waters, typically
without treatment.
Imperviousness results in large volumes of
stormwater runoff delivered to surface waters much
more quickly than normal, which can result in
scouring of stream banks and streambeds and
increased sediment loadings to surface waters.
Combined with the increased runoff velocities that
occur during spring snowmelts and rain-on-snow
events in urbanized watersheds, floods often occur
more frequently and with greater severity in
urbanized areas (Buttle and Xu, 1988). Major
snowmelt events can produce peak flows with as
much as 20 times the volume of baseflows in urban
areas (Pitt and McLean, 1992). ,
1.4.3 Removal of Streamside Vegetation
Removal of streamside vegetation is reported to be
a leading source of impairment to rivers and streams
and was reported to affect 10 percent of impaired
river and stream miles in the 1994 305(b) report
(USEPA, 1995). Somewhere between 70 and 90
percent of natural riparian ecosystems in the United
-------
Overview of the NFS Problem
Chapter 1
States have been lost to human activities (Windell,
1983, cited in USEPA, 1991).
Losses of riparian vegetation are attributed to
conversion to farmland, drainage for agriculture,
forest harvesting, channelization, damming, creation
of impoundments, irrigation diversions, ground
water pumping, and overgrazing (Brinson et al.,
1981).
The biological communities in streams depend on
inputs of energy from outside sources. The primary
source of energy and nutrients in small, low-order
streams is organic debris (e.g., leaf litter) deposited
from riparian vegetation. When riparian vegetation
is removed, this source of energy and nutrients is
eliminated or reduced. Stretches of streams and
rivers are left with sunlight as the only source of
energy and largely devoid of nutrient inputs. Other
essential inputs to rivers and streams, such as
woody debris—which provides microhabitats for
fish and invertebrates, are also lost when streamside
vegetation is removed (USEPA, 1991).
Riparian habitats, regardless of regional location,
have many characteristics important to surrounding
communities. They have a high rate of energy,
nutrient, and species exchange; they are highly
productive; they provide a unique microclimate with
respect to upslope conditions; they have high edge-
to-area ratios (similar to ecotone areas); and they
support diverse faunal assemblages that are often
unique within the local environment (USEPA,
1991). Loss of riparian vegetation therefore has
negative effects on surrounding biotic communities.
Riparian vegetation also has an enormous capacity
to store water. When it is removed, the natural
hydroperiods of streams and rivers are altered and
the loss of the buffering effects of water released by
riparian vegetation during low flow periods and
water stored by riparian vegetation during periods
of flooding can cause severe stress to aquatic plant
and animal communities. Riparian vegetation
protects stream banks from erosion due to flowing
water, and this protection is also lost when the
vegetation is removed. Increases in erosion,
turbidity, and sedimentation usually result (Brinson
etal.,1981).
Riparian vegetation also removes sediment as water
passes through it, rebuilds floodplains, provides
shelter for aquatic animals and wildlife under
overhanging banks, provides food to aquatic and
terrestrial wildlife, buffers water temperatures, and
improves water quality for downstream users
(USDOI, 1991). Degraded water quality, increased
severity of flooding, loss of wildlife, increased
stream temperatures, and increased expense to
purify water for public uses are therefore some of
the consequences of the removal of riparian
vegetation.
1.4.4 Hydromodification
Hydromodification and habitat alteration are
reported to be a source of impairment to 17 percent
of impaired river and stream miles and 12 percent
of impaired lake, reservoir, and pond acres, and a
source of wetland degradation in five states and
wetland loss in one state (USEPA, 1995).
Hydromodification 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, and reduction of
channel migration potential (Brookes, 1990).
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 are examples of channel
modification. Channel modification typically
results in more uniform channel cross sections,
steeper stream gradients, and reduced average pool
depths.
Flow alteration describes a category of
hydromodification activities that result in either an
increase or a decrease in the usual supply of fresh
-------
Chapter 1
water to a stream, river, 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 both 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). Hydromodification often diminishes the
suitability of instream and riparian habitat for fish
and wildlife through reduced flushing, lowered
dissolved oxygen levels, saltwater intrusion,
interruption of the life cycles of aquatic organisms,
and loss of streamside vegetation.
7.4.5 Mining
Mining, or resource extraction, is reported as a
source of impairment to 11 percent of impaired
river and stream miles; 4 percent of impaired lake,
pond, and reservoir acres; and 5 percent of impaired
estuary square miles. It also accounts for wetland
loss in two states and wetland degradation in one
state (USEPA, 1995).
Numerous pollutants are released from coal and ore
mining. Acid mine drainage from coal mining
contains sulfates, acidity (low pH), heavy metals,
ferric hydroxide or "yellow boy," and silt
(USEPA/USDOI, 1995; Zielinski, n.d.). The heavy
metals released from mining activities include
silver, arsenic, copper, cadmium, mercury, lead,
antimony, and zinc (Horowitz et al., 1993).
Ore mining, both past and present, is a significant
source of mercury contamination (Leigh, 1994).
Mercury was used to separate gold and silver from
ore and is contained in waste piles from the
amalgamation process (Oak Ridge National
Laboratory, 1993). It is estimated that 5.5 x 109 g
of metallic mercury was released into the Carson
River Drainage Basin during processing of the
Comstock Ore at Virginia City, Nevada, in the
1800s. The mill no longer stands, but mercury-
contaminated tailings were left behind to create a
long-term, significant source of mercury
contamination of soil and air (Gustin et al., 1995).
Mercury was also used in eastern mining. Gold
mining in the Georgia piedmont from 1829 to 1940
left mercury-contaminated alluvium. Mercury
concentrations in historical alluvium have been
found to exceed background by as much as two
orders of magnitude near the core of the mining
district (Leigh, 1994).
Public health can be threatened by contaminants
released from ore mining. Exposure pathways for
this contamination include ingestion of fish and
waterfowl, as well as ingestion, inhalation, and
direct contact with contaminated soil, sediment, and
surface water. Potential exposure is also possible
through ingestion of crops irrigated with
contaminated surface water or grown in
contaminated soil (Oak Ridge National Laboratory,
1993).
Coal mining creates significant acidity problems.
The results of a study done to characterize the
causes of acidity in lakes and streams in the United
States show that 26 percent of streams were
acidified primarily by acid mine drainage and 47
percent by atmospheric deposition (Kaufmann et al.,
1992). ;
Three types of mines are created for coal extraction.
Drift or slope mines are driven into valley walls to
expose coal. Shaft mines are driven perpendicular
to the ground. These mines must be pumped
continuously to extract infiltrating water, and when
abandoned they fill with water. Surface mining
extracts coal from the surface after overlying soil
and rock have been removed. Surface mines leach
metals and acids as seeps or springs, and they can
have flows of up to 500 gallons per minute (SCRIP,
-------
Overview of the NFS Problem
Chapter 1
1996). Abandoned, self-draining underground
mines and coal cleaning refuse piles are the worst
potential sources of acid mine drainage.
Contamination from them can continue for 800 to
3,000 years as all of the exposed acidic materials
from the mines slowly leach pollutants to ground
waters (USDOI, n.d.; Zielinski, n.d.). Acid mine
drainage from surface mines is also a problem but is
more controllable (USDOI, n.d.). Of the
acidification caused by acid mine drainage, an
estimated 40 percent is from active surface and
underground mines and 60 percent is from
abandoned mines (Zielinski, n.d.).
The effects of acid mine drainage can be
devastating. In severely affected streams, ferric
hydroxide blankets stream bottoms, smothering
eggs, and covers gills and body surfaces (Zielinski,
n.d.). Once a stream's acid-neutralizing capacity
has been depleted by acidity entering the stream, the
acidity begins to alter the biota. Fish are absent
from streams with a pH less than 4.5, and vascular
plants are lacking in streams with a pH less than 4
(Zielinski, n.d.).
Approximately 11,990 miles of streams are
reported to be degraded by acid mine drainage in
Pennsylvania, West Virginia, Ohio, Kentucky,
Maryland, Indiana, Illinois, Oklahoma, Iowa,
Missouri, Kansas, Tennessee, Virginia, Alabama,
and Georgia (USDOI, n.d.; Zielinski, n.d.). Eighty
percent of these (10,507 miles) are in Pennsylvania,
West Virginia, Ohio, Kentucky, Tennessee,
Maryland, and Alabama (Zielinski, n.d.). The worst
acid mine drainage pollution is in Pennsylvania and
West Virginia and a few areas of southeastern Ohio
(USDOI, n.d.). Pennsylvania alone has 7,800
abandoned or inactive underground mines below the
water table; one billion gallons is the estimated
daily influx of acid mine drainage to surface waters
from these mines (Zielinski, n.d.).
Most water quality problems associated with
mining, however, are considered to be point source
problems and are regulated under state and federal
NPDES permits (USEPA, 1978a).
1.4.6 Forest Harvesting
Forest harvesting is reported as a source of
impairment to 9 percent of impaired river and
stream miles; 5 percent of impaired lake, pond, and
reservoir acres; and 5 percent of impaired estuarine
square miles. Two states attribute wetland
degradation to forest harvesting and three states
attribute wetland loss to forest harvesting (USEPA,
1995). On federal lands, such as national forests,
many water quality problems can be attributed to
the effects of timber harvesting and related
activities (Whitman, 1989).
Forest harvesting operations can degrade water
quality in several ways in waterbodies that receive
drainage from forest lands. Sediment, organic
debris, nutrients, and silvicultural chemicals are
pollutants associated with forest harvesting
operations. Construction of forest roads and
yarding areas, as well as log dragging during
harvesting, can accelerate erosion and sediment
deposition in streams, which fouls instream
habitats. Removal of overstory riparian shade can
increase stream water temperatures; harvesting
operations can leave slash and other organic debris
to accumulate in waterbodies, which can deplete
dissolved oxygen and alter instream habitats.
Fertilizer applications can add excessive nutrients to
aquatic habitats and accelerate eutrophication.
Pesticide applications can increase organic and
inorganic chemical concentrations in waterbodies,
which can lead to adverse wildlife and habitat
impacts (Brown, 1985).
1.4.7 Construction
Construction is reported as a source of impairment
to 5 percent of impaired river and stream miles; 9
percent of impaired lake, pond, and reservoir acres;
13 percent of impaired estuarine square miles; and 1
percent of impaired Great Lakes shoreline miles.
Two states attribute wetland degradation to
construction and four states attribute wetland loss to
construction (USEPA, 1995).
-------
Chapter 1
Many potential pollutants are associated with
construction activities. These include sediment;
pesticides (insecticides, fungicides, herbicides, and
rodenticides); fertilizers used for vegetative
stabilization; petrochemicals (oils, gasoline, and
asphalt degreasers); construction chemicals such as
concrete products, sealers, and paints; paper; wood;
garbage; and sanitary wastes (Washington State
Department of Ecology, 1991). 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 nonpoint
pollutant source.
Runoff from construction sites is by far the largest
source of sediment in urban areas under
development (York County Soil and Water
Conservation District, 1990). Soil erosion accounts
for over 90 percent of sediment losses by tonnage in
urbanizing areas, where most construction activities
occur (Canning, 1988). Uncontrolled construction
site sediment loads have been reported to be on the
order of 35 to 45 tons per acre per year (Novotny
and Chesters, 1989; Yorke and Herb, 1976,1978).
Loadings from undisturbed woodlands are typically
less than 1 ton per year (Leopold, 1968).
Petroleum products used during construction
include fuels and lubricants for vehicles, power
tools, and general equipment maintenance. Asphalt
paving also can be particularly harmful since 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.
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
used on construction sites and carried in runoff.
1.4.8 Marinas
Marinas are reported as a source of impairment to 3
percent of impaired estuarine square miles. Puerto
Rico reports port construction as a source of
wetland degradation, and the construction of
wharves, piers, and bulkheads is reported as a
source of wetland loss by two states (USEPA,
1995).
Marinas are located right at the water's edge, so
there is often no buffering of the release of
pollutants from them to waterways. Consequently,
the concentrations of pollutants in marina waters
and sediment, and in the tissues of organisms living
in or near marinas, can be elevated.
The primary pollutants associated with marinas are
sewage discharged from boats, which contains high
concentrations of fecal coliform bacteria and
organics; metals; and petroleum hydrocarbons.
These pollutants enter the water in marinas through
discharges and spills from boats and docks, and
stormwater runoff from marina uplands. The
concentration of dissolved oxygen in marina basins
can be lowered by inadequate flushing and the
decomposition of organics, such as those in sewage
and fish offal.
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 and jetties are
built near marinas to prevent damage to boats and
shoreline structures, and marinas and ports are areas
of concentration of boat traffic. Both the .
attenuation of waves by in-water structures and the
creation of waves by boat passage affect shoreline
processes, which caii increase turbidity, resuspend
pollutants in sediment, and increase shoreline
erosion (USFWS, 1982).
-------
Overview of the NFS Problem
Chapter .1
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.
Metals and metal-containing compounds are
contained in fuel additives, antifoulant paints,
ballast, and other marina structures. Arsenic is used
in paint pigments, pesticides, and wood
preservatives. Zinc anodes are used to deter
corrosion of metal hulls and engine parts. Copper
and tin are used as biocides in antifoulant paints.
Other metals (iron, 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
(NCDEM, 1991). Elevated levels of copper, zinc,
cadmium, chromium, lead, tin, and PCBs have been
found in oysters, other bivalves, and algae in some
marinas (CARWQCB, 1989; Marcus and Stokes,
1985; McMahon, 1989; NCDEM, 1991; Nixon et
al., 1973; SCDHEC, 1987; Wendt et al., 1990;
Young etal., 1979).
1.5 WATER RESOURCE CONSIDERATIONS
Before a monitoring plan that will provide sufficient
information for meeting monitoring objectives can
be developed, the water resource to be monitored
must be understood. Each type of water
resource—rivers and streams; lakes, reservoirs, and
ponds; estuaries; open coastal waters; and ground
waters—possesses unique hydrologic and biological
features that must be taken into consideration, and a
monitoring program must be structured to either
adapt to those features or avoid them.
All water resource types exhibit both temporal
(long- and short-term) and spatial (small- and large-
scale) variability. Placement of monitoring stations
and timing of sampling are affected by these
variabilities. For instance, suspended sediment
concentrations vary across the width and length of
reservoirs; salinity concentrations in estuaries vary
vertically and temporally as they are affected by
relatively light fresh water flowing over heavier salt
water; and ground water quality varies with soil
type and geozone. The monitoring guidance
provided in this document is appropriate for
temporal variability of minutes to a few years.
1.5.1 Rivers and Streams
Generally, streams are of two types, intermittent
and perennial. Clearly, sampling cannot be done in
intermittent streams when they do not have flow,
and year-to-year variations in precipitation affect
the duration of their flows, their pollutant loads, and
their water quality. Variability in perennial streams
and rivers is also affected by seasonal variations in
precipitation, including snowfall, reservoir
discharge management, and irrigation management.
The highest concentrations of suspended sediment
and nutrients often occur during spring runoff,
winter thaws, or rainstorms.
Other features of streams and rivers that affect
monitoring program design include, but are not
limited to:
• Lateral spatial variability is most important in
streams. Velocity varies vertically and
horizontally in streams and affects pollutant
concentrations at a given location (Figure 1-7)
(USDA-NRCS, 1996).
• Tributary mixing affects lateral variability.
Mixing below tributary junctions might be
incomplete, with tributary flow primarily
following one bank. Meanders produce
increased velocities at the outside bank and
reduced velocities at the inside bank (USDA-
NRCS, 1996).
-------
Chapter 1
Concentration
Velocity
Figure 1-7. Vertical sediment concentration and flow velocity
distribution in a typical stream cross section (Brakensiek et al.,
1979).
The complexity of currents at obstructions
makes them poor monitoring sites (USDA-
NRCS, 1996).
Vertical variability is particularly important
during runoff and in slow-moving streams
because suspended solids, dissolved oxygen,
and algal productivity can vary
substantially with depth (Figure 1-8).
• Toxic contaminants in bed
sediment vary laterally and
vertically.
• Biological communities vary with
type of bed substrate, water
temperature, and amount and type
of aquatic and riparian vegetation.
Also, when designing a stream or river
monitoring program, the effects of
tributary flows must be considered.
Such flows can add pollutant loads,
dilute pollutant loads, and create
lateral gradients. Segmentation of a
stream into fairly homogeneous
segments prior to monitoring might be
necessary or prudent. One to several
monitoring stations might be
necessary in each segment (Coffey et
al., 1993). When dividing a stream
into homogeneous segments, both
land use and drainage area should be
considered, since both affect the
quantity and quality of flows.
1.5.2 Lakes, Reservoirs, and
Ponds
Shape is an important factor that
affects spatial variability in lakes,
reservoirs, and ponds. Lakes and
ponds commonly have simple,
roundish shapes, whereas reservoirs
usually have complex, dendritic
shapes (Figure 1-9). Some lakes and
reservoirs are well mixed and homogeneous, while
others are stratified and heterogeneous.
Stratification in lakes and reservoirs depends on
depth and hydraulic-residence time (HRT) (Figure
1-10). Deeper lakes and reservoirs are more likely
to be strongly stratified, while shallow lakes and
reservoirs are usually more uniform vertically.
-------
Overview of the NFS Problem
Chapterh
BY
PARTICLE
SIZE
Transport
Rates
BY
METHOD OF
TRANPORT
BY
SAMPLING
CAPABILITY
Suspended
Load
Saltation
Load
Measured
Load
Total
Load
Unmeasured
Load
Figure 1-8. Schematic diagram of stream vertical showing relative position of sediment load terms
(Brakensieketal.,1979).
Stratification creates vertical variability in
temperature, dissolved oxygen, and nutrient
concentrations (Figure 1-11). Stratified lakes and
reservoirs can also exhibit little vertical variability
in DO and nutrient concentrations, depending on
their productivity. Deep, eutrophic lakes and
reservoirs exhibit more vertical variability than
deep, oligotrophic lakes and reservoirs (USEPA,
1990a).
The implication of HRT for monitoring and the
water quality effects of pollution control is that
there can be a delay between changes in inflow
water quality and a noticeable effect on lake water
quality. The length of the delay depends on the
HRT of the lake or reservoir and other water quality
factors (e.g., biota, sediment, existing water
chemistry) (USEPA, 1990a).
Features of lakes, reservoirs, and ponds to keep in
mind when designing a monitoring program for
them include, but are not limited to, the following:
• Lakes and reservoirs with HRTs of days or
weeks might respond to seasonal pollutant
loads, whereas lakes and reservoirs with much
longer HRTs might not respond so quickly to
seasonal loads (USEPA, 1990a).
• The distribution and concentrations of water
quality parameters in individual lakes and
reservoirs vary seasonally (Figure 1-11).
-------
Chapter 1
LAKES
RESERVOIRS
• Small watershed area
• Long hydraulic residence time
• Simple shape, shoreline
• Surface outlet
• Large watershed area
• Short hydraulic residence time
• Complex shape, shoreline
• Surface or subsurface outlet
Figure 1 -9. Important differences between lakes and reservoirs.
Inflow =
10 acre-ft/day
Outflow =
10 acre-ft/day
Hydraulic residence time = 500 acre-ft/day +10 acre-ft/day = 50 days
Figure 1-10. Hydraulic residence time, assuming inflow = outflow
(After USEPA, 1990).
Separate lakes and reservoirs in the same
geographic area do not necessarily undergo
seasonal changes at the same time.
Pollutants are generally not
distributed uniformily
throughout lakes and
reservoirs due to inflow points
and circulation'patterns.
Pollutants such as phosphorus
can have residence times in
lakes and reservoirs very
different from the HRTs of the
lakes and reservoirs in which
they are found. Some lakes
and reservoirs do not respond
to reductions in phosphorus
loads because of phosphorus
contained in lake sediment,
which is released when the
phosphorus concentration in
the water column decreases
and sequestered when it
increases.
Short-term variability is an
inherent characteristic of most
still (lentic) waterbodies.
Dissolved oxygen, pH, and
temperature can vary
considerably over the course of
a day.
Small lakes and reservoirs can
respond rapidly to the
addition of runoff, which has
implications if lake water
quality is to be correlated
with land treatment or stream
water quality.
The lateral variability of
chlorophyll a concentrations
can vary based on water depth
and the diurnal migrations of
phytoplankters (Davenport
and Kelly, 1984a).
In summary, some important lake and reservoir
characteristics and processes that must be
-------
Overview of the NFS Problem
Chapter 1
EPWMION OR MIXED LAYER -WARM (LIGHT) WATER y/THERMOCUNE
41 50 59 68
•
DEGREES FARENHEIT
4
•I
DISSOLVED OXYGEN (ms/L)
Figure 1-11. A cross-sectional view of a thermally stratified lake in mid-
summer. The water temperature profile (curved solid line) illustrates how
rapidly the water temperature decreases in the metalimnion compared to
the nearly uniform temperatures in the epilimnion and hypolimnion. Open
circles represent the dissolved oxygen (DO) profile in an unproductive
(oligotrophic) lake: the DO concentration increases slightly in the
hypolimnion because oxygen solubility is greater in colder water. Solid
circles represent the DO profile in a productive (eutrophic) lake in which
the rate of organic matter decomposition is sufficient to deplete the DO
content of the hypolimnion (USEPA, 1990).
considered when designing a
monitoring program include
productivity, depth,
stratification, seasonality, HRT,
and the locations and sources of
inflows. Since sampling
locations must accurately
represent lake or reservoir
conditions, monitoring a round,
simply shaped lake might
require only a single sampling
station, whereas monitoring a
dendritic reservoir might require
numerous stations to reflect its
spatial variability accurately
(USEPA, 1990a).
To simplify both sample
collection and data
interpretation, it can be useful to
monitor within the strata of
stratified lakes and reservoirs,
and to achieve some monitoring
objectives, it could be necessary
to monitor during periods of
peak stratification. Finally,
biological monitoring programs
must be tailored to the diurnal
variations in lakes and
reservoirs.
J'F'M'A'M'J'J'A S'O'N'D'
Figure 1-12. Phytoplanktbn chlorophyll a
concentration in Chautaugua Lake's northern basin
and southern basin, 1977 (Storch, 1986).
1.5.3. Estuaries
The major difference between estuaries and
freshwater bodies is in the mixing of fresh water
with salt water, and the influence of tides on spatial
and temporal variability in estuaries. Incoming
tides affect estuaries by pushing salt
watershoreward while fresh water is entering(Figure
1-13). Fresh water is lighter, so it flows over the
top of salt water, while the force of the tide forces
the salt water shoreward and under the inflowing
fresh water. Outgoing tides pull the entire water
mass oceanward, and the freshwater input fills the
gap left by the receding submerged salt water.
-------
Chapter 1
River
Ocean-
Lighted
Zone
Figure 1-13. Mixing of salt water and fresh water in
an estuary (Chesapeake Bay Program, 1995).
These processes affect daily and seasonal salinity
distributions (Figure 1-14).
Features to consider when designing a monitoring
plan for estuaries include, but are not limited to, the
following:
• The volume of an estuary is an important factor
in determining its ability to dilute pollutants
(NOAA, 1990).
Short-term variability is related to tidal cycles,
which affect the mixing of fresh and salt waters
and the position of the fresh water-salt water •
interface.
The size of the estuarine drainage area (EDA;
Figure 1-15) of an estuary relative to watershed
size determines the overall impact of pollutant
inputs from the EDA on the estuary (NOAA,
1990).
In estuaries with large fluvial drainage areas
(FDA; Figure 1-15), pollutants added from
sources in the EDA might have less overall
impact on estuarine water quality than in
estuaries with small PDAs (NOAA, 1990).
Freshwater inflow is a major determinant of the
physical, chemical, and biological
characteristics of most estuaries. It affects
theconcentration and retention of pollutants, the
SPRING
SALINITY
AUTUMN
SALINITY
in parts per
thousand
Figure 1-14. Chesapeake Bay salinity levels over time and space (Chesapeake Bay Program, 1995).
-------
Overview of the NPS Problem
Chapter 1
Figure 1-15. Estuarine drainage area versus fluvial drainage area (NOAA, 1990).
distribution of salinity, and the stratification of
fresh water and salt water in an estuary (NOAA,
1990).
• Temperature profiles vary seasonally in
estuaries.
• Freshwater input to estuaries varies seasonally,
and spatial variability in estuaries is affected by
the location of freshwater inflows.
• Due to spring runoff, salinity in estuaries is
generally higher in fall and lower in spring
(Chesapeake Bay Program, 1995).
• The earth's rotation (Coriolis effect),
barometric pressure, and bathymetry
(submerged sills and banks, islands) affect
circulation and spatial variability in estuaries.
For instance, Puget Sound contains numerous
islands that affect circulation within it.
Stellwagen Bank, a submerged sand bank located
across the boundary of Massachusetts Bay and the
Atlantic Ocean, has a strong effect on circulation
within the bay and within neighboring Cape Cod
Bay.
In summary, the most important factors that
determine the characteristics of individual estuaries
are the sizes of the EDA and the FDA, water
surface area, water volume, tidal range, salinity
regime, and freshwater inflow. Also, an estuary
might contain subestuaries—portions of a large
estuary having definable subbasin drainage areas
and constituting a significant percentage of either
freshwater inflow or water surface area (NOAA,
1990). For instance, San Francisco Bay has very
separate northern and southern reaches. The
southern reach has a longer HRT, less inflow, and
more sewage input, which give it characteristics
very different from those of the northern reach and
would require a different monitoring program
design.
7.5.4 Open Coastal Waters
The major difference between open coastal waters
and estuaries is that open coastal waters are not
directly influenced by freshwater inflows. To ,
design a monitoring program for open coastal
-------
Chapter 1
waters, knowledge of local salinity and circulation
patterns is necessary. This helps to identify
relatively discrete units of coastal water for
monitoring purposes. In open coastal waters, it is
particularly important to identify such discrete
segments or units from which to sample in order to
be able to track conditions over time. An example
of a discrete segment or unit of open coastal water
is a semienclosed embayment.
Features to consider when designing a monitoring
program for open coastal waters include, but are not
limited to, the following:
• Monitoring should focus on units for which
there is reasonable likelihood that changes in
water quality will result from BMP
implementation.
• Consider segment or unit size and circulation
patterns to estimate the likelihood of water
quality improvements from pollution reduction
efforts.
• Open coastal waters exhibit gradients in
salinity, temperature, and water chemistry both
spatially and temporally.
• Surface salinity varies with amount of rainfall
input and evaporation (Tchernia, 1980).
1.5.5 Ground Water
Ground water is monitored less than surface waters
because of the complexity of ground water systems
and the difficulty of obtaining samples. However,
ground water monitoring is important because of
contamination of drinking water supplies and the
NO3-N (mg/l) vs. DEPTH BELOW WATER
2469 Samples From May 1984 to December 1989
20 feet below tha water tahla
< — 5 mg/l nitrate as nitrogen
-50
10
15 20 25 30 35
NO3-N Concentrations (mg/I)
Figure 1-16. Nitrate concentration (mg/L) versus depth below water table (Goodman, 1991).
-------
Overview of the NFS Problem
Chapter 1
interplay between surface water quality and ground
water quality. In 1990, ground water was the
source of drinking water for over half of the
Nation's population and for 95 percent of the
population in rural areas (USEPA, 1995).The
purposes of ground water monitoring include the
following:
• 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 background water quality.
• To identify trends and variations in water
quality.
• To determine the effectiveness of BMPs.
Ground water monitoring often requires a two-stage
approach. The first stage consists of a
hydrogeologic survey to determine ground water
surface elevations and flow directions. This survey
requires numerous sampling locations. The second
stage is an investigation of water quality, with
stations selected based on the results of the first
stage and monitoring objectives (Bishoff et al.,
1995; Goodman et al., 1996; USDA-NRCS, 1996).
More than with surface waters, site-specific
information is absolutely necessary to design a
ground water monitoring program. Water quality in
an aquifer can vary considerably with depth and
location (Figure 1-16).
Features to consider when designing a ground water
monitoring program include, but are not limited to,
the following:
• Local soils and geology.
• The direction of ground water flow.
• The type of ground water system. There are
two general types of aquifers, confined and
unconfined. Unconfined (water table) aquifers
are in direct contact with the atmosphere
through the soil. Confined (artesian) aquifers
are separated from the atmosphere by an
impermeable layer (USDA-NRCS, 1996).
• Selection of well locations depends on the
variability of the aquifer's water quality and is
complicated by the presence of confining beds,
multiple aquifer systems, effects of pollutant
density on pollutant transport, and changes in
permeability.
• Spatial and temporal variabilities in aquifers
cannot be generalized. Some respond to
precipitation quickly, whereas others respond
slowly.
• Sampling depth and depth to aquifer are
important variables to consider in determining
initial sampling frequency.
1.6 CLIMATE
Climate introduces elements of temporal variability
into a monitoring program's design. When
designing a monitoring program, the characteristics
of seasonal variability, measurable as both the
degree to which seasons are distinguishable (e.g.,
the difference between winter and summer in
Alabama versus the difference in Maine), and the
severity of the seasons (e.g., winter in Minnesota
versus winter in South Carolina), must be
considered. These characteristics determine when
and for what amount of time specific variables can
be monitored. For some monitoring objectives,
year-round monitoring might be necessary, and in
cold climates this could call for some means of
heating if automated sample collection is to be used
(USDA-NRCS, 1996).
Climate also affects the quantity, timing, and
intensity of precipitation, the likelihood of
catastrophic events (e.g., hurricanes, floods,
droughts) that could interrupt sampling, seasonal
variations in biological activity, and seasonal
-------
Chapter 1
IRRIGATION FURROW
SANDY LOAM
CLAY LOAM
Figure 1-17. Comparison of water movement from irrigation furrows into two different soil types. The zone
of infiltration represents water movement after 24 hours (after Brady, 1984).
variations in water quality parameters (e.g., addition
of organic matter to streams and rivers from ,
overhanging vegetation). These regional and local
features must be factored into monitoring program
design.
A few examples of climatic factors that affect
monitoring program design follow (MacDonald et
al., 1991):
• Variability in precipitation is inversely
proportional to average annual precipitation.
Drier areas tend to have more year-to-year
variation in precipitation.
• Areas with more rainfall tend to have lower
concentrations of nutrients and other dissolved
ions.
• Climate affects the weathering rate, erosion
rate, and vegetation type and the productivity of
aquatic biota.
1.7 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). The
permeability, depth, and porosity of soil and
bedrock affect background levels of nutrients and
dissolved ions in ground water and surface waters.
The texture, depth, and permeability of soils also
influence the quantities of fertilizers, pesticides, and
herbicides that are leached into surface waters and
the amount of leaching that occurs immediately
after application and during runoff events (Figure
1-17). Slope is an important factor that must be
considered when designing a monitoring program.
Slope affects 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. DEVELOPING A MONITORING PLAN
2.1 INTRODUCTION
Since the relationship between public health and
water quality began to influence legislation in the
early 1900s, water quality management and its
related information needs have evolved
considerably. Today, the Intergovernmental Task
Force on Monitoring Water Quality (ITFM,
1995b) defines water quality monitoring as an
integrated activity for evaluating the physical,
chemical, and biological character of water in
relation to human health, ecological conditions,
and designated water uses. Water quality
monitoring for nonpoint sources of pollution
includes the important element of relating the
physical, chemical, and biological characteristics
of receiving waters to land use characteristics.
Without current information, water quality and
the effects of land-based activities on water
quality cannot be assessed, effective management
and remediation programs cannot be
implemented, and program success cannot be
evaluated.
The most fundamental step in the development of
a monitoring plan is to define the goals and
objectives, or purpose, of the monitoring
program. In the past, numerous monitoring
programs did not document this aspect of the
design process and the resulting data collection
efforts led to little useful information for decision
making (GAO, 1986; MacDonald et al., 1991;
National Research Council, 1986; Ward et al.,
1990). As a result, the identification of
monitoring goals is the first component of the
design framework outlined by the ITFM (1995b).
In general, monitoring goals are broad statements
such as "to measure improvements in Elephant
Butte Reservoir" or "to verify nutrient load
reductions into the Chesapeake Bay." Designing
a monitoring plan also includes selecting sampling
variables, a sampling strategy, station locations,
data analysis techniques, the length of the
monitoring program, and the overall level of
effort to be invested. Figure 2-1 presents one
approach for developing a monitoring plan.
Monitoring programs can be grouped according
to the following general purposes or expectations
(ITFM, 1995b; MacDonald et al., 1991):
• Describing status and trends
• Describing and ranking existing and emerging
problems
• Designing management and regulatory
. programs
• Evaluating program effectiveness
• Responding to emergencies
• Describing the implementation of best
management practices
• Validating a proposed water quality model
• Performing research
The remainder of the design framework outlined
by the ITFM (1995b) includes coordination and
collaboration, design, implementation,
interpretation, evaluation of the monitoring
program, and communication. Numerous
guidance documents have been developed, or are
in development, to assist resource managers in
developing and implementing monitoring
programs that address all aspects of the ITFM's
design framework. Appendix A presents a
review of more than 40 monitoring guidances for
both point and nonpoint source pollution. These
guidances discuss virtually every aspect of
nonpoint source pollution monitoring, including
monitoring program design and objectives,
sample types and sampling methods, chemical and
physical water quality variables, biological
monitoring, data analysis and management, and
quality assurance and quality control.
Once the monitoring goals have been established,
existing data and constraints should be
considered. A thorough review of literature
pertaining to water quality studies previously
conducted in the geographic region of interest
-------
Developing a Monitoring Plan
Chapter 2
QA Planning
QA Implementation
&
QA Assessment
Define personnel and budgetary constraints
Define monitoring parameters,
sampling frequency, sampling location
and analytic procedures
Evaluate hypothetical
or, if available, real data
Will the data meet the
proposed monitoring objectives?
Yes
No
Is the proposed monitoring
program compatible with
available resources?
Yes
No
Initiate monitoring activities on a pilot basis
Analyze and evaluate data
1
r
Does the pilot project meet
the monitoring objectives?
Yes
No
Continue monitoring and data analysis
Reports and recommendations
Revise the
objectives
or the
monitoring
procedures
Revise
monitoring
plan as
needed
Figure 2-1. Development of a monitoring project (after MacDonald et ai., 1991).
-------
Chapter^
should be completed before starting a new study.
The review should help determine whether
existing data provide sufficient information to
address the monitoring goals and what data gaps
exist.
Identification of project constraints should address
financial, staffing, and temporal elements. Clear
and detailed information should be, obtained on
the time frame within which management
decisions need to be made, the amounts and types
of data that must be collected, the level of effort
required to collect the necessary data, and the
equipment and personnel needed to conduct the
monitoring. From this information it can be
determined whether available personnel and
budget are sufficient to implement or expand the
monitoring program.
As with monitoring program design, the level of
monitoring that will be conducted is largely
determined when goals and objectives are set for
a monitoring program, although there is some
flexibility for achieving most monitoring
objectives. Table 2-1 provides a summary of
general characteristics of various types of
monitoring.,
The overall scale of a monitoring program has
two components—a temporal scale and a
geographic scale. The temporal scale is the
amount of time required to accomplish the
program objectives. It can vary from an
afternoon to many years. The geographic scale
can also vary from quite small, such as plots
along a single stream reach, to very large, such as
an entire river basin. The temporal and
geographic scales, like a program's design and
monitoring level, are primarily determined by the
program's objectives. Hence, unspecific or.
unclear monitoring objectives present a barrier to
selecting the appropriate temporal and geographic
scales.
If the main objective is to determine the current
biological condition of a stream, sampling at a
few stations in a stream reach over 1 or 2 days
might suffice. Similarly, if the monitoring
objective is to determine the presence or absence
of a nonpoint source impact, a synoptic survey
might be conducted in a few select locations. If
the objective is to determine the effectiveness of a
nutrient management program for reducing
nutrient inputs to a downstream lake, however,
monitoring a subwatershed for 5 years or longer
might be necessary. If the objective is to
calibrate or verify a model, more intensive
sampling might be necessary.
Depending on the objectives of the monitoring
program, it might be necessary to monitor only. :
the waterbody with the water quality problem or-,,
it might be necessary to include areas that have-,
contributed to the problem in the past, areas
containing suspected sources of the problem, or a
combination of these areas. A monitoring
.program conducted on a watershed scale must
include a decision about a watershed's size. The
effective size of a watershed is influenced by
drainage patterns, stream order, stream
permanence, climate, number of landowners in
the area, homogeneity of land uses, watershed
geology, and geomorphology. Each factor is
important because each has an influence on
stream characteristics, although no direct
relationship exists.
There is no formula for determining appropriate
geographic and temporal scales for any particular
monitoring program. Rather, once the objectives
of the monitoring program have been determined,
a combined analysis of them and any background
information on the water quality problem being
addressed should make it clear what overall
monitoring scale is necessary to reach the
objectives.
Other factors that should be considered to
determine appropriate temporal and geographic
-------
Developing a Monitoring Plan
Chapter 2
Table 2-1. General characteristics of monitoring types .
Type of
Monitoring
Trend
Baseline
Implementation
Effectiveness
Project
Validation
Compliance
Number and Type
of Water Quality
Parameters
Usually water
column
Variable
None
Near activity
Variable
Few
Few
Frequency of
Measurements
Low
Low
Variable
Medium to high
Medium to high
High
Variable
Duration of
Monitoring
Long
Short to
medium
Duration of
project
Usually short to
medium
Greater than
project duration
Usually medium
to long
Dependent on
project
Intensity of
Data Analysis
Low to
moderate
Low to
moderate
Low
Medium
Medium
High
Moderate to
high
Source: MacDonatd et al., 1991.
scales include the type of water resource being
monitored and the complexity of the nonpoint
source problem. Some of the constraints
mentioned earlier, such as the availability of
resources (staff and money) and the time frame
within which managers require monitoring
information, will also contribute to determination
of the scales of the monitoring program.
2.2 MONITORING OBJECTIVES
Identifying and concisely stating the monitoring
objectives are critical steps in the development of
a monitoring program. Unlike monitoring goals,
monitoring objectives are more specific
statements that can be used to complete the
monitoring design process including scale,
variable selection, methods, and sample size
(Plafkin etal., 1989; USDA-NRCS, 1996 ).
Monitoring program objectives must be detailed
enough to allow the designer to define precisely
what data will be gathered and how the resulting
information will be used. Vague or inaccurate
statements of objectives lead to program designs
that provide too little or too much data, thereby
failing to meet management needs or costing too
much.
Monitoring programs can be implemented for one
or many reasons. The more common types of
monitoring program objectives are summarized
below. The emphasis of this guidance is on
-------
Chapter 2,
evaluation monitoring, but information contained
herein might also be used to address other types
of monitoring. The reader is cautioned that even
though two different monitoring programs might
share some objective listed below, their designs
can be radically different.
2.2.1 Monitoring Objective Category:
Problem Definition
(1) Determine whether an impairment
exists
Meeting this objective involves an investigation of
key parameters to determine the general condition
of a habitat or water quality. Measurements of
.individual pollutants in waterbodies are often
'taken to determine whether violations of water
quality standards are occurring. Biological
monitoring is also useful when evaluating whether
designated uses are supported. Monitoring
associated with this type of objective might reveal
'that a suspected problem is more complicated or
.serious than originally thought and that more
intensive monitoring studies will be necessary.
(2) Determine the extent of the impairment
Even if a problem is known to exist, the
geographic and temporal extent of the problem
might not be known. Does the problem affect a
stream reach, or does the problem extend to the
downstream lake? Some pollution sources are
emitted only during certain parts of the year or in
association with certain events, such as storms, or
might be a problem only during a particular time
of the year, such as fish spawning season.
Determining the geographic and temporal aspects
of a pollution problem will help focus
management on BMP systems that will have the
most benefit.
(3) Determine the causes and sources of
impairment
Monitoring might be required to determine the
cause of an environmental problem, such as
degraded fish habitat or an algal bloom.
Determining the pollution's source is often more
difficult than determining its presence because
there are often many potential sources whose
influences overlap. When conducting monitoring
for this purpose, it is important to monitor the
appropriate water quality characteristics and
account for climatic factors to establish a cause-
and-effect relationship, even though it might be
difficult to prove.
Point and nonpoint sources often affect the same'
waterbody, and monitoring might also be required
to determine the contribution and relative
importance of each to water quality impairment.
It might also be necessary to determine which
areas are the most critical in causing waterbody
impairment. For instance, a high erosion rate on
land far from a receiving waterbody might have a
lower pollution-causing potential than an area
with a lower erosion rate near to a receiving
waterbody. Factors such as the timing of
pollutant contributions relative to the hydrologic
cycle of the waterbody and the ecology of the
biological communities must be factored into the
analysis. In addition, the distance of pollutant
sources from receiving waters, the fate and
transport of pollutants from different sources, the
magnitude of pollutant contributions from each
source, and the distance to the impaired resource
of concern (as distinguished from distance to a
point of entry into a receiving waterbody, which
might be some distance from the actual
impairment) should be considered. This type of
information can often be used in developing load
allocations for nonpoint pollution sources and
wasteload allocations for point sources, although
extensive monitoring might be required.
-------
Peveloping a Monitoring Plan
Chapter 2
2.2.2 Monitoring Objective Category:
Model Development
(1) Calibrate models
Model calibration is the first stage of testing a
model and tuning it to a set of field data. Field
data are necessary to guide die modeler in
choosing the empirical coefficients in a model
before the model can be used to predict the effect
of management techniques or activities.
(2) Validate models
Model validation involves the testing of a model
using a second set of field data. In most cases,
the second set of field data should represent an
independent data set that extends the range of
conditions for which the model is valid. If an
independent data set is not available, a set of
randomly selected data should be used for
validation. Once a model has been validated, it
can be used to assist managers with management
decisions within the range of the calibration and
validation data sets.
2.2.3 Monitoring Objective Category:
Evaluation (emphasis of this
guidance)
(1) Measure the effectiveness of best
management practice (BMP) systems
Individual BMPs or groups of BMPs are
monitored to determine the extent of pollution
control. Monitoring for individual BMPs can •
typically be conducted at a plot or field scale,
whereas monitoring for BMP systems is usually
conducted on a watershed scale because the
combined effect of a few or several BMPs is
being investigated. Studies of some individual
practices can be conducted in a relatively short
time (less than 5 years), while others might take
longer. Evaluation of BMP systems is typically
conducted over a long term (more than 5 years)
because BMP implementation can take years to
affect water quality. This type of monitoring is
difficult due to the presence of pollutant reserves
in soil and sediments, the effect of many land uses
within a study area, the variety of approaches that
landowners use to implement similar systems of
BMPs, and the need to track land management as
well as water quality and climatic variables.
(2) Analyze trends
The objective here is to answer the question, "Is
water quality changing over time?" Baseline
monitoring is part of trend analysis because
establishing a baseline is essential to analyzing
trends. However, baseline monitoring is
generally thought of as determining a condition
prior to pollutant entry or prior to a change in
waterbody condition, whether beneficial or
detrimental. Controlling for influencing factors
such as climate is necessary if baseline monitoring
is to be used as a reference point for trend
analysis and management decisions. The ability
to relate water quality changes to changes in land
management depends on the quality and quantity
of data collected on land management practices.
2.2.4 Monitoring Objective Category:
Conduct Research
Research monitoring is done to address specific
research questions. Research monitoring is
usually conducted on a plot scale, is well
controlled, and is limited to a very specific
question. Monitoring and data analysis
techniques for research and for other types of
monitoring are often very similar, and the
difference between them is often one of objective
rather than approach. A critical examination of
articles about relevant and well-conducted
research projects in which monitoring is a key
element can provide excellent guidance for the
design of a monitoring program.
-------
phaptQif!?
2.3 DATA ANALYSIS AND PRESENTATION PLANS
Ward et al. (1990) point out that one of the most
important and difficult tasks is to identify what
information is to be produced by the monitoring
effort. It is particularly critical to ensure that
policy makers and other stakeholders know the
type of information that a monitoring program
can produce and that realistic monitoring program
expectations are developed. Ward et al. (1990)
identify key steps to ensure that realistic
expectations are placed on the monitoring
program and the associated data analysis:
• Perform a thorough review of the legal basis
for the management effort and define the
resulting "implications" for monitoring.
« Review the administrative structure and
procedures developed from the law in order
to define the information expectations of the
management staff.
• Review the ability of the monitoring
program to supply information.
• Formulate an information expectations
report for the monitoring system.
• , Present the information expectations report
to all users of the information.
« Develop consensus as to an agreeable
formulation of information expectations and
related monitoring system design criteria.
This process is typically performed as an iterative
process that involves the technical staff and the
decision makers who developed the monitoring
objectives. To develop an information
expectations report, the data analyst might need to
have formal meetings, develop questionnaires,
and conduct interviews to learn what the
managers need. In some cases this iterative
process might require modifying or redesigning
the monitoring program. The data analyst should
remember that complete consensus might not be
possible.
When developing an information expectations
report, the presentation of results should be
selected depending on the audience reviewing the
information and the objectives of the monitoring
program. How quickly must information be
presented to information users? To what kind of
information and how much information do the
decision makers respond favorably? At a
minimum, the data analyst should prepare
example report formats to be approved by the
decision makers, keeping in mind that "a picture
is worth a thousand words." In all cases, the goal
should be to present clear and accurate
information that is not subject to misinterpre-
tation. Ward et al. (1990) present an example
outline (Figure 2-2) of what might be considered
in an expectations report. (The data analyst
should modify this outline to suit individual
needs.)
2.4 VARIABLE SELECTION
In these days of increasing monitoring and
evaluation needs and relatively small monitoring
and evaluation budgets, it is extremely important
for program managers to design efficient
monitoring and evaluation programs. The
variables selected for a monitoring program
should be tied directly to the monitoring
objectives. It is often the case that some variables
in addition to those of prime interest are
monitored because they are relatively cheap to
monitor and might provide some useful
information for purposes not yet outlined. This is
generally reasonable, but the technical staff
should (1) anticipate these undefined purposes so
that the extra variables are monitored in a manner
that yields useful information (e.g., support
statistical analyses) and (2) make sure the extra
cost associated with monitoring additional
variables does not preclude necessary expansions
-------
Developing a Monitoring Plan
Chapter 2
Expectations Report Outline
Evolution of Water Quality Management Program
- Geographical/Hydrological Setting
- Water Quality Problems
- Water Quality Laws
- Management Program Structure
- Management Procedures
Information "Expected" by Management Program
- Implications of the Law Establishing the Program
Legal Goals
- Management Powers and Functions
- Monitoring Requirements Directly Stated
Information Needs of Management Operations
- Water Quality Criteria
- Water Quality Standards
- Permits
- Compliance
Enforcement
- Construction Loans
Planning
- Water Quality Assessment
Ability of Monitoring Systems to Produce Water Quality Information
- Narrative Information
- Numerical Information-Data
- Graphical Information
- Statistical Information
- Average Conditions
- Changing Conditions
- Extreme Conditions
- Water Quality Indices
Suggested Information Expectations for Monitoring System
- Management Information Goa!(s)
Definition of Water Quality
- Monitoring System Goal(s)
- Information Product of Monitoring System
- Narrative
- Graphical
- Statistical
Resulting Monitoring Network Design Criteria
- Variable Selection
- Site Selection
- Sampling Frequency Determination
Figure 2-2. Expectations report outline (Ward et al., 1990).
-------
Chapter?
or extensions of the monitoring and evaluation
program for the variables of prime interest.
In many instances the water quality problem will
directly indicate what variables should be
monitored. For example, a dissolved oxygen
problem would strongly suggest monitoring of
dissolved oxygen. (Typically, biochemical
oxygen demand, sediment oxygen demand,
temperature, and nutrients would be monitored as
well.) Or, if the goal is to assess the impact of
nonpoint source controls in terms of standards
violations, then the variables selected should be
those required for the analysis of standards
violations.
In some cases, it might be more beneficial to use
surrogate measures instead of the variables
mentioned in the monitoring goals and objectives.
In these cases, objectives for the surrogates that
are consistent with the overall monitoring and
evaluation goals should be established. The key
to using surrogate measures is to be certain that a
reliable relationship exists between the true
measure and the surrogate measure. For
example, if the objective is to monitor the
condition of salmon spawning areas, surrogate
measures are necessary because the condition of
salmon spawning areas is a composite of many
factors. Good surrogate variables would be
stream bank undercut, embeddedness, and
vegetative overhang (Platts et al., 1983). The
corresponding surrogate goals could be to reduce
cobble embeddedness and to increase vegetative
overhang to appropriate levels for salmon
spawning. The monitoring goals would then be to
document changes in cobble embeddedness and
vegetative overhang.
Poor surrogate selection results when a known
relationship between the monitoring goals and
objectives and the chosen surrogate measures
does not exist. For example, a poor surrogate for
estimators of sediment delivery to water resources
is the unqualified use of erosion rates. Without
the existence of a known relationship between
these two measures (i.e., sediment delivery ratio),
the surrogate will produce misleading results.
Variable selection should also reflect the nonpoint
source data analysis and presentation plan. For
example, if the plan involves data normalization
or grouping prior to data analysis, the variable list
should include those variables used to normalize
and/or group the data. Some analyses might
require discrete observations, whereas others
might use continuous data. All monitoring sites
should be characterized sufficiently for
meaningful data interpretation, including
georeferencing. For surface water sites the
relevant information may include waterbody
name, river reach number and milepoint,
location, prevailing winds, shading, bottom
sediment, elevation, slope, stream width and
depth, drainage area, upstream land use, lake
depth, and more. In the case of ground water
monitoring, this information includes the aquifer
tapped by a well, the depth of the well, the type
of well construction, and the well elevation
(USGS, 1977). Water level measurements should
be included in all ground water studies.
Since there are numerous variables to choose
from but monitoring budgets are limited, some
method to prioritize variable selection is often
necessary. When available, existing data should
be used to guide variable selection. Further
discussion on variable selection, prioritization,
and optimization are provided by USDA-NRCS
(1996), MacDonald et al. (1991), and Sherwani
and Moreau (1975). In some cases, optimal
variable selection is not possible, perhaps due to
lack of local data. In such cases, the researcher
might need to rely on professional judgment and
the review of monitoring programs of similar
nature and scope.
Some data requirements for nonpoint source
monitoring and evaluation efforts can be met
using nationally available data sources. Appendix
-------
Developing a Monitoring Plan
Chapter 2
B describes some of these data sources and
includes information for those interested in
accessing the data. Other data sources are
available to nonpoint source professionals as well,
and state, regional, or local sources of data in
particular should be investigated. State
agriculture, forestry, and other environmental
agencies; counties; municipalities; and state and
local health departments are likely sources of
water quality, health-related, and land use data
and information. Regional planning
commissions, local universities, and
environmental consultants might also be able to
provide data. The sources summarized in
Appendix B focus on the major data sources made
available to EPA or known to reviewers of this
document. The remainder of this section
summarizes key data that would normally be
considered in a nonpoint source monitoring
program.
2.4.1 Physical and Chemical Water
Quality Data
Physical and chemical water quality data are
essential to almost all nonpoint source monitoring
and evaluation efforts, due to the relationships
between flow and pollutant characteristics. For
example, it might be necessary to establish
watershed water budgets so that the location and
magnitude of nonpoint sources or background
sources can be determined. In other cases, the
extent of the floodplain might prove critical to
assessments of BMP control needs. Important
physical and chemical water quality variables to
monitor include flow (streams), temperature,
transparency, suspended sediment, sedimentation
rate, dissolved oxygen, pH, conductivity,
alkalinity/acid neutralizing capacity (lakes), and
nutrients. Other factors, such as cobble
embeddedness, woody debris, and salinity, might
be important depending on type of water body
and monitoring goals.
2.4.2 Biological Data
Biological data can be very useful for evaluating
water resource impairment due to nonpoint source
impacts because aquatic organisms integrate the
exposure to various nonpoint sources over time.
Measures of biological communities integrate the
effects of different pollutant stressors—excess
nutrients, toxic chemicals, increased temperature,
excessive sediment loading, and others—and thus
provide an overall measure of the aggregate
impact of the stressors. Monitoring changes in
aquatic communities over time can serve as a
measure of improvement due to BMPs. The
biological survey approach used depends on
waterbody type, i.e., stream, river, lake, wetland,
or estuary. Important biological parameters to
monitor include bacteria, algal biomass,
macrophyte biomass and location,
macroinvertebrates, and fish populations.
2.4.3 Precipitation Data
Precipitation data, including total rainfall, rainfall
intensity, storm interval, and storm duration, have
proven to be key to successful interpretation of
nonpoint source data in the Nationwide Urban
Runoff Program (NURP), Model Implementation
Program (MIP), and Rural Clean Water Program
(RCWP) studies. By combining precipitation data
with pollutant loading evaluations, it has been
found that a few storms can account for a large
proportion of the total annual pollutant load.
Johengen and Beeton (1992) found that, in the
Saline Valley RCWP, a few storms accounted for
more than 50 percent of the annual loading.
Interestingly, they found that initial estimates of
suspended solids and phosphorus loadings were
only 20 and 50 percent of loadings estimated by
adjusting for daily precipitation. The project-
mandated weekly sampling had missed the
loading spikes that lasted for only a few days.
Research has shown that average annual soil loss
can be .estimated using only a few site-specific
-------
Chapter 2
factors, among which is a rainfall-runoff erosivity
factor (R). The other factors used to estimate soil
loss are soil erodibility, topography, and land use
and management. The Universal Soil Loss
Equation (USLE) has been revised and is now
known as the Revised USLE (RUSLE), based on
research by Renard et al. (1991) and Wischmeier
and Smith (1978). The rainfall-runoff erosivity
factor found in the RUSLE is also used in several
nonpoint source models, including the
Agricultural Nonpoint Source Pollution Model
(AGNPS) (Young et al., 1985). The Water
Erosion Prediction Project (WEPP) Hillslope
Profile version erosion model is a "new
generation" soil erosion model that can be run
both as a continuous simulation model and on a
single-storm basis. The model requires a large
number of data on management practices, which
might be difficult to obtain (Singh and Fiorentino,
1996). A procedure derived from the NURP
program uses storm frequency and other factors
to determine recurrence intervals for instream
pollutant concentrations resulting from urban
nonpoint source pollution (USEPA, 1984b).
2.4.4 Land Use Data
Landuse data include information on treatments
applied to land, current and historical use of the
land, spatial and temporal information on land use
activities, and changes in land use made before
and during a project. Data on these elements are
important for evaluating correlations between land
surface activities and water quality. Establishing
a correlation between a change hi water quality
and a change in land treatment must be based on
both the detection of a water quality trend and
detailed information on changes in land use or
management, and it requires rigorous statistical
analysis (Goodman, 1991; Meals, 1991, 1992).
Land treatment can be linked to water quality
impacts at the field, subwatershed, watershed, or
project level. In general, the larger the drainage
area, the harder it is to associate land treatment
and water quality. Subwatershed monitoring is
the most effective means for demonstrating water
quality improvements from a system of BMPs
because at this scale the confounding effects of
external factors, other polllutant sources, and
other BMPs or BMP systems are minimized
(Coffeyetal., 1993).'
Two key points must be considered in nonpoint
source monitoring with respect to linking water
quality and land treatment. First, weather and
season are important confounding influences on
nonpoint source activities because they strongly
influence the types of land-based activities that
can occur, and hence the timing and quantity of
runoff from treated lands and the consequential
water quality effects. Second, spatial variation
must be considered. The location of land
treatments relative to surface waters is likely to
vary from year to year, and this adds variation to
the effect of land treatment on water quality
(Meals, 1991).
Correlations between water quality and land
treatment can be made much more easily if land
use and land treatment monitoring are considered
as part of monitoring design in a project's
preliminary stages. It is also very important to
control for the effects of hydrologic variation.
Paired regression is an effective method to control
for background variability and is recommended
(Meals, 1991, 1992).
Geographic information systems (GIS) are
effective management tools for land use data
(Meals, 1991). They allow for tracking and
manipulating spatial land use data and remarkably
improve the ease of visual inspection and
comprehension of the data. Data for GIS are
available from a variety of sources, including
state agencies, GIS user groups, GIS vendors,
universities, consultants, conferences, and
numerous publications dedicated to GIS topics
(Griffin, 1995).
-------
Developing a Monitoring Plan
Chapter 2
2.4.5 Topographic Data
Topographic data are also required for many
nonpoint source monitoring and evaluation
efforts, particularly when soil erosion, water
runoff, and sedimentation are estimated with
models. For example, the USLE includes both
slope length and slope steepness factors
(Wischmeier and Smith, 1978). AGNPS input
includes a slope shape factor, field slope length,
channel slope, and channel side slope (Young et
al., 1985).
2.4.6 Soil Characteristics Data
Other data such as soil chemistry and soil physical
characteristics might be required for some
monitoring and evaluation efforts. Recent
approaches to assessing the potential for ground
water contamination from nonpoint sources have
emphasized the need for data such as hydrologic
soil group, soil organic carbon content, depth to
water, net recharge, aquifer media, and vadose
zone characteristics (Aller et al., 1985; Dean et
al., 1984).
2.5 PROGRAM DESIGN
Numerous program designs can be used to
evaluate the monitoring objectives identified
earlier in this chapter. To select the program
design, the researcher should develop clear,
quantitative monitoring objectives; understand the
watershed or waterbody to be monitored; and
know something about the locations of and
pollutant transport from point and nonpoint
sources. In developing the information
expectations report described earlier in this
chapter, the technical staff will typically decide
whether parameter estimation or hypothesis
testing is the primary evaluation tool. This choice
has an impact on the program design. As an
example, balanced designs (e.g., two sets of data
with the same number of observations in each set)
are generally more desirable for hypothesis
testing, whereas parameter estimation might
require unbalanced sample allocations to account
for spatial and temporal variabilities (Gaugush,
1986). Hypothesis testing is likely to be used in a
program evaluation (e.g., water quality before
and after pollution controls are implemented),
whereas parameter estimation can be applied in
assessments when determining pollutant loads
from various sources. Hypothesis testing will
typically require more intensive databases than
those needed for objectives that entail general
water quality assessments. As a result, the
sampling methodologies required to meet different
objectives for the same waterbody may differ
considerably. \
Most monitoring programs are based on either a
probabilistic or a targeted design, or some
combination of the two. Probabilistic designs
include random selection of station locations
and/or sampling events to provide an unbiased
assessment of the waterbody. In targeted designs,
monitoring sites are selected based on known
existing problems or knowledge of upcoming
events in the watershed such as installation of a
BMP. The most common types of targeted
designs employed for the evaluation of nonpoint
source pollution sources and BMP systems
include monitoring single watersheds, nested
watersheds (e.g., above-and-below
implementation), two watersheds, paired
watersheds, multiple watersheds, and trend
stations. Statistical procedures to analyze the data
from these study designs are presented in Chapter
4.
Simply identifying the site location and sampling
frequency is not sufficient to describe the where
and when of sampling programs. Additional
considerations include the depth of sampling, the
origins of the aliquot(s) taken in each sample
bottle, the time frame over which measurements
are made, and others. For example, if a stream is
well mixed, a single grab sample from the center
of the stream might be sufficient, whereas it
-------
Chapter 2
Example Objective: Determine the annual
loading of phosphorus from a watershed with
no point sources.
Sampling Methodology: Assuming no
snowmelt inputs and that the majority of
phosphorus is delivered under high-flow
conditions, the investigator should perform
flow-proportional sampling during events.
This, of course, assumes that a stage-
discharge relationship has been establishedt
Vertical and horizontal concentration and flow
profiles should be assessed to determine the
need for transect and/or depth-integrated
sampling*
might be more appropriate to take an integrated
sample from a wider stream. In deeper estuaries,
it is a common practice to collect samples near
the top and bottom of the waterbody as well as
just above and just below the pycnocline.
Frequency of sampling should be based on several
factors (Sherwani and Moreau, 1975):
Response time of the system
Expected variability of the parameter
Half-life and response time of constituents
Seasonal fluctuation and random effects
Representativeness under different
conditions of flow
Short-term pollution events
Variability and types of the inputs
Magnitude of response
Examples of sample type classifications include
instantaneous and continuous; discrete and
composite; surface, soil profile, and bottom; time-
integrated, depth-integrated, and flow-integrated;
and biological, physical, and chemical. Several
existing guidance manuals (Brakensiek et al.,
1979; Koterba et al., 1995; Lapham et al., 1995;
Platts et al., 1983; Scalf et al., 1981; Shelley,
1979; Shelton, 1994; Shelton and Capel, 1994;
USDA-NRCS, 1996; USEPA, 1978b, 1981,
1987a; USGS, 1977) and other reference
materials (Wetzel and Likens, 1979) describe
these various sample types and the equipment
used to collect them.
Selecting an appropriate sampling design for
nonpoint source monitoring and evaluation efforts
can be a complicated and frustrating experience
for the program manager. In addition to
balancing multiple (and sometimes competing)
objectives, program managers must contend with
large variabilities in measured parameters. These
variabilities are caused by several factors,
including distance to the pollutant source;
nonuniform distribution of the pollutant due to
physical, biological, or chemical influences;
buildup or degradation over time; temporal and
spatial variation in background levels; diversity in
the biological community; and other
nonuniformities such as those in topology,
climatic conditions, and waterbody geometry.
These factors, in turn, make collecting accurate
and unbiased environmental samples more
difficult. Biased samples are those which result in
consistently higher or lower values than what
exists in the waterbody. For example, suspended
solids samples taken only during base flow
conditions will most likely result in low estimates
of annual solids loadings. Accuracy is a measure
of how close the sample value is to the true
population value. It is necessary to design
sampling efforts that meet accuracy requirements
while not placing unreasonable burdens on
personnel or budgets. Data that are biased or do
not meet the project's accuracy requirements are
of little use to program managers. An exception
might be volunteer data, which often do not meet
accuracy requirements but are highly useful in
gaining public support for projects.
Other types of sampling uncertainty include
random sampling errors and gross errors.
Random sampling errors arise from the variability
of population units (Gilbert, 1987) and explain
why the sample means from two surveys are
-------
Developing a Monitoring Plan
iChapter 2
never equal. Gross mistakes can occur at any
point in the process beginning with sample
collection and ending with the reporting of study
results. Adherence to accepted sampling and
laboratory protocols combined with thorough
quality control and data screening procedures and
experience, dedication, and care will minimize
the chances for gross errors.
2.5.1 Probabilistic Designs
In a probabilistic sampling program, the entity
about which inferences are made (e.g.,
watershed) is die population or target population
and consists of population units. The sample
population is the set of population units that are
directly available for measurement. As an
example, in a watershed impacted by nonpoint
sources, the target population could be defined as
storm-event dissolved phosphorus concentrations
at the inlets to all impoundments, and phosphorus
concentrations in 1-liter grab samples could be
population units. Note that both spatial and
temporal limits of the water quality variable
should be established in defining the target
population (Gaugush, 1986). This focuses the
sampling program better, in this case eliminating
the need to monitor at upstream and in-lake sites,
and during baseflow conditions. As a further
refinement, the technical staff may define the
population units as the dissolved phosphorus
concentrations in half-hour composite samples
taken during all storms. By sampling and
statistically evaluating selected population units,
inferences can be made about the entire
waterbody.
Simple random sampling
In simple random sampling, .each unit of the
target population has an equal chance of being
selected (Figure 2-3). This type of sampling is
appropriate when there are no major trends,
cycles, or patterns in the target population
(Gilbert, 1987). Random sampling can be applied
in a variety of ways, including site selection along
the length of a river or areally throughout a lake.
Samples may also be taken at a single station
using random time intervals. The number of
random samples required to achieve a desired
margin of error when estimating the mean is
(Gilbert, 1987)
n =
1 + (t , s/d):
^ I-a/2,n~l . '
IN
(2-1)
where
n = number of samples,
t = Student's rvalue,
s = sample standard deviation,
Figure 2-3. Simple random sampling for silviculture.
Dots represent harvest sites. All harvest sites of interest
are represented on the map, and the sites to be sampled
(open dots—O) were selected randomly from all harvest
sites on the map. The shaded lines on the map could
represent county, watershed, hydrologic, or some other
boundary, but they are ignored for the purposes of
simple random sampling.
-------
Chapter 2
d = absolute margin of error,
N = number of population units, and
a = confidence interval.
If N is large, the above equation can be simplified
to
where the standard deviation now refers to the
sample standard deviation yields
n =
(2-2)
Since the Student's t value is a function of n, both
of the above equations are applied iteratively. If
the population standard deviation is known, rather
than estimated, Equation 2-2 can be further
simplified to
(2-3)
where Z is the standard normal deviate and a is
the population standard deviation. In most cases,
N is large enough to apply Equation 2-2 or 2-3.
Values of Z and t can be found in Appendix D.
Suppose, for example, that the monitoring
objective is to estimate the mean dissolved
orthophosphate concentration (mg/L as P) during
August in a waterbody segment such that there is
a 95 percent chance that the mean concentration is
within ±0.025 mg/L of the estimated mean.
Assuming a population standard deviation of 0.05
mg/L, the number of samples can be estimated
using Equation 2-3 as
0.025
= 15.4 ~ 16 samples
In most cases the standard deviation is not known
and Equation 2-2 would be applied. Intuitively,
more samples are required due to the uncertainty
associated with the standard deviation. To apply,
Equation 2-2, it is reasonable to initially assume
that n is equal to some value greater than 16, say
18, which will correspond to a t statistic of 2.110.
Substituting the above values into Equation 2-2
2.110X
0.05
0.025
= 17.8 -18 samples
Since the computed 18 samples correspond to the
initial assumption, no iterations are necessary. In
practice, this type of analysis would be performed
for several variables and a judgment between
sampling size, allowable error, and cost would be
made.
Applying any of these equations is difficult when
no historical data set exists to quantify the
standard deviation. To estimate the population
standard deviation, Cochran (1977) recommends
four sources:
• Existing information on the same population
or a similar population.
• Informed judgment, or an educated guess.
• A two-step sample. Use the first-step
sampling results to estimate the needed
factors, for best design, of the second step.
Use data from both steps to estimate the
final precision of the characteristic(s)
sampled.
• A "pilot study" on a "convenient" or
"meaningful" subsample. Use the results to
estimate the needed factors. Here the results
of the pilot study generally cannot be used in
the calculation of the final precision because
the pilot sample often is not representative
of the entire population to be sampled.
Gilbert (1987) and Cochran (1977) address
additional aspects of simple random sampling.
Included in these texts are estimation of the mean
and total for sampling with and without
-------
Developing a Monitoring Plan
Chapter 2
Example Objective: Determine the monthly
mean total suspended solids concentration
(to within ± 15 mg/L at the 95 percent
confidence level) fora tributary from an
agricultural watershed.
Sampling Methodology; Since the
concentration may vary with stream depth,
Width, and flow, the investigator should select
a site that is well mixed so that a single grab
sample can be taken. If a well-mixed site
cannot be found, an integrated sample would
be required. Samples would be collected
during high and low flow conditions to obtain
a representative mean. Random or stratified
random samples would then be collected as
grab or composite samples depending on the
averaging time selected.
replacement, equations for determining the
number of samples required for both independent
and correlated data, and the impact of measure-
ment errors. In most cases, environmental
sampling is done without replacement (e.g.,
aliquots of stream water are not placed back into
the stream), Nis relatively large, the samples are
assumed to be independent, and measurement
error is ignored, thus making many of these
specialized cases less critical. However, the
reader should be aware that these issues might
become paramount depending on the monitoring
objectives and sampling design.
Stratified Random Sampling
In stratified random sampling, the target
population is divided into groups called strata for
the purpose of obtaining a better estimate of the
mean or total for the entire population (Figure
2-4). Simple random sampling is then used
within each stratum. Stratification involves the
use of categorical variables (e.g., season, flow
condition) to group observations into more units
that reduce the variability of observations within
each unit. As an example, stratified random
sampling can be used to evaluate chemical
concentrations in waterbodies when evaluating
nonpoint source loadings. One approach would
be to stratify stream flow into base and various
storm flow periods to account for the energy
relationship between precipitation and pollutant
generation. Random sampling would then be
performed in each stratum.
Cochran (1977) found that stratified random
sampling provides a better estimate of the mean
for a population with a linear trend, followed in
Figure 2-4. Stratified random sampling for silviculture.
Letters represent harvest sites, subdivided by type of
ownership (P = private nonindustrial, I = industrial, F =
federal, S = state). All harvest sites of interest are
represented on the map. From all of the sites in one
ownership category, sites were randomly selected for
sampling (highlighted sites). The process was repeated
for each ownership category. The shaded lines on the
map could represent county, soil type, or some other
boundary, and could have been used as a means for
separating the harvest sites into categories for the
sampling process.
-------
Chapter 2
order by systematic sampling (discussed later) and
simple random sampling. He also states that
stratification normally results in a smaller
variance for the estimated mean or total than is
given by a comparable simple random sample.
In a stratified random sampling program when N,
the number of population units, is large, the
optimum number of samples can be estimated
with (Cochran, 1977)
n = Z
(2-4)
*=1
where
n = number of samples across all strata,
Z = standard normal variate,
L = number of strata,
Wh = stratum weight,
sh = sample standard deviation for stratum h,
d = absolute margin of error for weighted
mean, and
a = confidence interval.
The stratum weight, Wh, is the relative size of
each stratum. Once the total number of samples
is determined, the samples may be allocated to
each stratum by (Gilbert, 1987)
(2-5)
h=l
Alternatively, the samples may be proportionally
allocated, with each stratum given a percentage of
the total samples in accordance with the stratum
size. The above equation allocates more samples
to a stratum that is larger or has a higher
variability. Cochran (1977) provides an approach
for optimizing the sampling when the sampling
cost per population unit, ch, is different among the
strata:
(2-6)
h=l
In general, a larger number of samples would be
taken in a stratum that is more variable, larger, or
less costly to sample than other strata.
The mean for stratum h, x^, is the simple mean of
all samples within the stratum. The weighted
mean, ~xst, is given by
"»**
(2-7)
Systematic Sampling
Systematic sampling is used extensively in water
quality monitoring programs, usually because it is
relatively easy to do from a management
perspective. In systematic sampling the first
sample is taken from a random starting point (or
at a random starting time) and each subsequent
sample is taken at a set distance (or time interval)
from the first sample (Figure 2-5). For example,.
if budgetary constraints limit the number of
samples to 10 and the objective is to characterize
a 10-mile river using systematic sampling, the
first observation would be taken randomly in the
first river mile. Subsequent samples would be
taken at 1-mile increments up the river. In
comparison, a stratified random sampling
approach would divide the river into 10 1-mile
segments (strata) and one random sample would
be taken in each segment.
Gilbert (1987) recommends systematic sampling
when estimating long-term trends, defining
-------
Developing a Monitoring Plan
A
O
O
Chapter 2
Figure 2-5. Systematic sampling for silviculture. Dots (•
and O) represent harvest sites of interest. A single point
on the map (a) and one of the harvest sites were
randomly selected. A line was stretched outward from the
point to (and beyond) the selected harvest site. The line
was then rotated about the map and every fifth dot that it
touched was selected for sampling (open dots—O). The
direction of rotation was determined prior to selection of
the point of the line's origin and the beginning harvest
site. The shaded lines on the map could represent county
boundaries, soil type, watershed, or some other
boundary, but were not used for the sampling process.
seasonal or other cycles, or forecasting pollution
concentrations. In general, systematic sampling is
superior to stratified random sampling with one or
two samples per stratum for estimating the mean
(Cochran, 1977). Gilbert (1987) reports that
systematic sampling is equivalent to simple
random sampling in estimating the mean if the
target population has no trends, strata, or
correlations among the population units.
Estimates of variance from systematic samples
may differ from those determined from random
samples. Cochran (1977) notes that "on the
average the two variances are equal." However,
Cochran also states that for any single population
for which the number of sampling units is small,
the variance from systematic sampling is erratic
and may be smaller or larger than the variance
from simple random sampling.
Gilbert (1987) cautions that any periodic variation
in the target population should be known before
establishing a systematic sampling program.
Sampling intervals equal to or multiples of the
target population's cycle of variation may result
in biased estimates of the population mean.
Systematic sampling can be designed to capitalize
on a periodic structure if that structure can be
characterized sufficiently (Cochran, 1977). A
simple or stratified random sample is
recommended, however, in cases where the
periodic structure is not well known or where the
randomly selected starting point is likely to have
an impact on the results (Cochran, 1977).
Quantitative procedures for estimating the
population mean and variance from systematic
sampling data are presented by Gilbert (1987).
Gilbert (1987) notes that assumptions about the
population are required in estimating population
variance from a single systematic sample of a
given size. However, there are systematic
sampling approaches that do support unbiased
estimation of population variance, including
multiple systematic sampling, systematic stratified
sampling, and two-stage sampling (Gilbert, 1987).
In multiple systematic sampling more than one
systematic sample is taken from the target
population. Systematic stratified sampling
involves the collection of two or more systematic
samples within each stratum.
Cluster Sampling
Cluster sampling is applied in cases where it is
more practical to measure randomly selected
groups of individual units than to measure
randomly selected individual units (Gilbert,
1987). In cluster sampling, the total population is
divided into a number of relatively small
-------
Chapter 2
subdivisions, or clusters, and then some of these
subdivisions are randomly selected for sampling
(Figure 2-6). For one-stage cluster sampling, the
selected clusters are sampled totally. In two-stage
cluster sampling, random sampling is then
performed within each cluster (Gaugush, 1986).
An example of one-stage cluster sampling is the
collection of all macroinvertebrates on randomly
selected rocks within a specified sampling area.
The stream bottom might contain hundreds of
rocks with thousands of organisms attached to
them, thus making it difficult to sample the
organisms as individual units. However, it is
often possible to randomly select rocks and then
inspect every organism on each selected rock.
Gaugush (1986) states that the "analysis of cluster
samples requires the estimation of variance at two
levels, the between-cluster variability and the
within-cluster variability. The total variability is
a recombination of these two levels." Freund
(1973) notes that estimates based on cluster
sampling are generally not as good as those based
on simple random samples, but they are more
cost-effective. As a result, Gaugush believes that
the difficulty associated with analyzing cluster
samples is compensated for by the reduced
sampling requirements and cost. Cochran (1977)
discusses one-stage cluster sampling for clusters
of either equal or unequal sizes and provides
equations for determining the optimal population
unit size using the relative sizes of possible
population units, the variance among the
population unit totals, and the relative cost of
measuring one population unit. He notes that
many factors come into play when determining
optimal population size, including cost versus unit
size.
Two-stage Sampling
Two-stage sampling involves dividing the target
population into primary units, randomly selecting
a subset of these primary units, and then taking
random samples (second-stage units) within each
of the selected primary units. This is a common
practice when a large sample is taken and then a
smaller aliquot is actually measured from the
original sample. The process of subsampling
introduces additional uncertainty and becomes
significant if the pollutant is in particulate form
and very small subsamples are used (Gilbert,
1987).
Two-stage sampling might also include systematic
sampling within a randomly selected subset of the
population primary units. For example, if the
target population is the average annual pollutant
concentration in a stream, the primary units could
be daily average concentrations (n = 365). A
Figure 2-6. Cluster sampling for silviculture. All harvest
sites in the area of interest are represented on the map
(closed {•} and open {O} dots). The shaded lines on the
map represent county boundaries. Some of the counties
were randomly selected, and all harvest sites within
those counties (open dots - O) were selected for
sampling. Some other type of boundary, such as soil
type or watershed, could have been used to separate the
harvest sites for the sampling process.
-------
Developing a Monitoring Plan
Chapter 2
subset of these daily concentrations (e.g., n = 24)
could be selected at random for further systematic
sampling of hourly concentrations. For example
if four systematic, hourly samples could be taken
on each of 24 different days, with the hour for the
first sample determined randomly, followed by
three more hourly samples taken every sixth
hour, 96 hourly composite samples would be
available for the calculation of the population
mean and variance.
Cochran (1977) describes two-stage sampling in
great detail and presents methods for determining
the mean and variance in two-stage sampling with
units of equal size. In Cochran1 s discussion, he
notes that if all population units are sampled, the
formula for estimating the variance is the same as
that used to estimate the variance for proportional
stratified random sampling. This means that two-
stage sampling is a type of incomplete
stratification, with the primary units treated as
strata.
For further information regarding two-stage (and
three-stage) sampling, the reader is referred to
Gilbert (1987) and Cochran (1977). The authors
provide equations for estimating the number of
samples (primary units) and subsamples for two
conditions: (1) primary units of equal size and (2)
primary units of unequal size. Equations for
estimating the mean and total values in composite
samples of equal- and unequal-sized units are also
provided. The authors also provide equations for
calculating the number of composites and
composite subsamples needed.
Double Sampling
Double sampling is often used when two
techniques exist for measuring a pollutant.
Initially, both methods are used. Then, after a
correlation has been established, only the cheaper
or simpler technique is used. Gilbert (1987)
provides an approach for calculating the sample
size when the cost and variability associated with
both methods has been determined during the
initial sampling. This same procedure can also be
used when it is less expensive to measure a
surrogate variable (Gilbert, 1987). This
technique can be used for stratification, ratio
estimates, and regression estimates (Cochran,
1977).
Regression analyses are used to predict values for
one variable (i.e., the dependent variable) using
one or more independent variables based on a
mathematical relationship. As an example, total
suspended solids concentration is typically a
covariate of total phosphorus concentration in
watersheds impacted by agricultural runoff.
Measurement of total suspended solids may help
increase the precision of total phosphorus
estimates. Gaugush (1986) discusses sampling to
support regression analyses using spatial or
temporal gradients as the independent variable,
the latter being for trends over time. Some key
points in his discussion related to using a spatial
independent variable are as follows:
• Whenever the type of relationship (e.g.,
linear, log-linear) is known, relatively few
sampling points are needed along the
gradient. More samples may then be used
as replicates.
• Whenever the relationship is not known,
more sampling points are needed along the
gradient. More replicates are also needed to
test the proposed model.
• It is usually acceptable to place sampling
points equal distances from each other along
the gradient as long as the sampling does not
fall in step with some natural phenomenon
that would bias the data collected.
Some key points in the discussion regarding time
sampling are as follows:
-------
Chapter 2
• Time can be used either as a covariate or as
a grouping variable. Grouping by time
might be desirable when changes in the
variable of interest either are small over
time or occur only during short periods with
long periods of little or no change.
• Considerations in using time as a covariate
are similar to those for spatial gradients, but
(1) time is usually only a surrogate for other
variables that truly affect the variable of
interest, and (2) the relationship with time is
likely to be complex.
• If time is to be used as a covariate, relatively
frequent sampling will be needed, with some
replication within sampling periods.
Random sampling within the periods is also
recommended.
The sampling designs most common to
environmental monitoring are summarized in
Table 2-2.
2.5.2 Targeted Site Location Study
Designs
Paired and nested paired watershed approaches
are the two most appropriate approaches when
trying to evaluate the impact or benefit of a BMP
or system of BMPs at the watershed scale
(Spooneretal., 1985). A nested paired
watershed design (Figure 2-7A) is sometimes
referred to as an "above-and-below" design
where one monitoring station is located above the
treatment area and one station is located below
the treatment area. The paired watershed design
(Figure 2-7B) is based on identifying two
watersheds where one watershed is the control
and the second is the treatment. In both study
designs, data are collected before treatment
(calibration) and after treatment is implemented so
that differences between watersheds (or nested
watersheds) can be evaluated. The key advantage
of these two approaches is that the variation due
to year-to-year climatic differences and
differences between watersheds are statistically
Table 2-2. Applications of six sampling designs to estimate means and totals
Sampling Design
Simple Random
Sampling
Stratified Random
Sampling
Two-stage Sampling
Cluster Sampling
Systematic Sampling
Double Sampling
Conditions for Application
Population does not contain major trends, cycles, or patterns of
contamination.
Useful when a heterogeneous population can be broken down into parts
that are internally homogeneous.
Needed when measurements are made on subsamples or aliquots of the
field sample.
Useful when population units cluster together and every unit in each
randomly selected cluster can be measured.
Usually the method of choice when estimating trends or patterns of
contamination over space. Also useful for estimating the mean when
trends and patterns in concentrations are not present.
Useful when there is a strong linear relationship between the variable of
interest and a less expensive or more easily measured variable.
-------
Developing a Monitoring Plan
Chapter 2
Calibration
A) Nested Paired Watershed Study Design
Control Watershed
Treatment Watershed
Calibration
Treatment
B) Paired Watershed Study Design
Figure 2-7. Nested paired and paired watershed study designs.
-------
Chapter 2
controlled, provided that a sufficient calibration
period has been used. Clausen (1991) states that
the cost of conducting a paired watershed
experiment in Vermont ranged from $30,000 to
$50,000 per year for 3 or 4 years. This cost
included continuous discharge and water
sampling, as well as the analysis of approximately
six water quality characteristics.
In St. Albans Bay, Vermont, in another RCWP,
two small watersheds received proper manure
management during a 2-year calibration period,
followed by a period in which one watershed
. received winter-spread manure (Clausen, 1985).
This is an interesting example of the paired
watershed approach since BMPs were removed
from, instead of applied to, a watershed after the
calibration period. Data from this type of nested
paired or paired watershed design can be
evaluated by an analysis of covariance as
described by USEPA (1993c). Unfortunately,
both study designs are limited because the
experiment is not repeated to account for spatial
variability, and transferability of BMP
effectiveness to other regional watersheds is not
appropriate (MacDonald et al., 1991).
Nested watershed designs can also be used to
document the severity of a nonpoint source
pollution problem. In an example from the Rock
Creek, Idaho, RCWP, paired data were collected
using an upstream-downstream approach. These
data were used in regressions of water quality
against time.
The downstream concentrations (below the
nonpoint pollution source) were adjusted for
upstream concentrations (above the nonpoint
pollution source), transformed, and then
regressed against time as a continuous variable
(Spooner et al., 1986). Results of this approach
indicated that decreasing pollutant concentrations
from nonpoint pollution sources were due to
implementation of BMPs.
Single-watershed designs, which collect data
before and after BMP implementation, and two-
watershed designs, which collect data after BMP
implementation in one watershed, should
generally be avoided for evaluating BMP
effectiveness. The single-watershed design does
not account for year-to-year climatic variability.
The two-watershed design does not account for
differences between watersheds since no
calibration data are collected.
An alternative approach, when collecting data
during a calibration period is not viable, is to use
a multiple-watershed design, in which numerous
watersheds are monitored. In this design,
multiple watersheds in a region are selected,
including some that have a particular BMP
implemented and others that do not have the BMP
implemented. Alternatively, numerous paired
upstream and downstream stations (i.e., nested
watersheds) are selected. In the case of paired
upstream and downstream stations, the
designation of controls or treatments is not
random, and it is necessary to add additional
station pairs where no treatment or BMP is
implemented (MacDonald et al., 1991). By
monitoring numerous watersheds, the true
variability between watersheds is considered and
the results from this study design can be
transferred to other watersheds in the region.
Fifteen paired stations were established in the
Snohomish River basin (Washington State) to
determine the effect of commercial agriculture on
water quality along with other objectives over a 3-
year period (Luchetti et al., 1987). The pairs
varied considerably in terms of stream size and
agricultural activity. Combining the monitoring
data with land use and BMP implementation data,
the project documented the impact of commercial
agriculture on water quality.
Use of trend stations, or long-term ambient
monitoring, is based on establishing monitoring
stations that are routinely monitored. This type of
study design is generally most appropriate for
-------
Developing a Monitoring Plan
Chapter 2
watersheds where a variety of BMPs are being
implemented over a period of time or gradual
water quality changes are expected. The
difficulty in using trend stations is developing a
causal link between water quality and the various
land use activities. To use trend stations,
variables associated with land treatment,
hydrology, and meteorology should be accounted
for to increase the likelihood of successful
documentation of water quality-BMP
relationships. The long-term commitment
required from management to monitor these
stations is one of the key disadvantages of this
approach. The U.S. Geological Survey has
systematically sampled the national stream quality
accounting network (NASQAN) once a month for
more than 20 years to monitor the water quantity
and quality (Smith et al., 1987).
One key to establishing the study design, which is
often overlooked, is site selection. Site location
and establishment are discussed in several existing
monitoring guides and texts (Brakensiek et al.,
1979; Ponce, 1980a; USEPA, 1978b, 1981;
USGS, 1977; Wetzel and Likens, 1979). Few
differences exist between nonpoint source site
location strategies and the approaches discussed in
these documents. Within any given budget, site
location is a function of water resource type,
monitoring objectives, and data analysis plans.
When evaluating the effectiveness of nonpoint
source control measures, it might be necessary to
locate monitoring sites above known point sources
to remove them as confounding influences hi the
study. Additional considerations in site selection
are site accessibility and landowner cooperation in
data collection efforts (e.g., farm management
records). It is strongly recommended that
nonpoint source monitoring stations be located
near or at USGS gaging stations, when possible,
due to the extreme importance of obtaining
accurate flow records for estimating pollutant
loads. In the absence of a USGS gaging station,
monitoring stations should be located at sites that
offer adequate flow monitoring capabilities.
Some station requirements may be such that, with
careful station siting, one particular station can
meet multiple monitoring objectives. Caution
should be exercised, however, to avoid
compromising the worth of a station for the sake
of false economy.
For evaluating the overall background or
performing a problem assessment, a panel of
federal and state monitoring professionals
(USEPA, 1975) determining several points for
establishing site locations for physical and
chemical water column sampling, which should
be considered as appropriate. The process of site
selection for biological monitoring is described in
Chapters.
• Sites should be located at representative sites
in mainstem rivers, estuaries, coastal areas,
lakes, and impoundments. These sites can
be used to characterize the overall quality of
the area's surface waters and will provide
water quality baselines against which
progress can be measured.
• Sites should be located in water quality-
limited and major water use areas. Sites in
water quality-limited areas can be used to
evaluate the overall pollution control
strategy ,and BMP system effectiveness.
Sites in major water use areas, such as
public water supply intakes, commercial
fishing areas, and recreational areas, serve a
dual purpose—public health protection and
overall water quality characterization.
• Sites should be located upstream and
downstream from representative land use
areas (e.g., mining, silviculture) and
morphologic zones. These sites can be. used
to compare the relative effects of pollution
sources and morphologic zones on water
quality and to document baseline water
quality.
-------
Chapter 2
• Sites should be located at the mouths of
major or significant tributaries to mainstem
streams, lakes, impoundments, estuaries, or
coastal areas. Data from these sites, when
taken in concert with permit monitoring data
and intensive survey data, can be used to
determine the major sources of pollutants to
the area's major waterbodies. By
comparison with other tributary data, the
relative magnitude of the pollution sources
can be evaluated and problem areas can be
identified.
• Sites should be located to measure the input
and output of nutrients and other pertinent
substances into and from waterbodies (i.e.,
lakes, impoundments, estuaries, or coastal
areas) that exhibit eutrophic characteristics,
as well as at critical locations within the
waterbody. The information from these
stations, when taken in combination with the
pollution source data, can be used to
establish cause-and-effect relationships,
identify problem areas, and indicate
appropriate corrective measures.
Sediment sampling sites should be located in sink
areas as determined by intensive surveys,
reconnaissance surveys, and historical data. A
major concern of sediment monitoring is' to assess
the accumulation of toxic substances and
sediment-bound nutrients. The location for a
sediment sampling site should be chosen by
considering the sediment mechanics and the
hydrological characteristics of the waterbody
(USEPA, 1975).
2.6 EXAMPLE PROGRAM DESIGN
The RCWP includes several examples of nonpoint
source monitoring and evaluation strategies. Two
project strategies are described here. Several
additional examples are provided in Appendix C.
The Idaho RCWP's major focus was to control
sediment from irrigation return flows. Using a
targeted study design, seven ambient monitoring
stations (Figure 2-8) were used (Clark, 1986):
S-l: Near mouth - integrated all pollution sources
flowing into Rock Creek and measured the
pollutant load that going into the Snake
River (river mile (RM) 0.75). Water
quality, benthic macroinvertebrates, and
fisheries data were collected.
S-2: At Poleline Road - a benthic invertebrate
and fisheries monitoring site as well as water
quality (RM 3.75).
S-3: Above Highway 93 - below the confluence
of the high-priority agricultural drains and
city of Twin Falls urban runoff (RM 7.3).
Water quality, benthic macroinvertebrates,
and fisheries data were collected.
S-4: At Twelvemile - above the influence of
Twin Falls urban area and the high-priority
drains (RM 13.5). Water quality, benthic
macroinvertebrates, and fisheries data were
collected.
S-5: At 3500 East Road - a benthic invertebrate
and fisheries monitoring site only (RM
21.1).
S-6: Near Rock Creek townsite - measured the
quality of the natural surface water above
the irrigation tract (RM 30.3). Water
quality, benthic macroinvertebrates, and
fisheries data were collected.
C-l: Twin Falls Main Canal - source of water for
the irrigation tract. Only water quality data
were collected.
Intensive monitoring stations were placed on
irrigation drains to track changes in sediment load
and associated pollutants close to their source and
-------
Developing a Monitoring Plan
Chapter 2
3 miles
LEGEND
-A. monitoring stations
• subbasin monitoring stations
JU1I1 streambank erosion study sites
1H town
project boundary
NOTE: Diversions from the High and
Low Line canals are controlled.
High and Low Line canals bypass
Rock Creek.
s-6
Figure 2-8. Map of the Rock Creek Rural Clean Water Program study area, Twin Falls County, Idaho.
(Source: Clark, 1986)
-------
Chapter, 2
associated BMPs. In this way, changes in water
quality due to the RCWP could be detected.
Nineteen stations were located in six subbasins
(Figure 2-8). Stations measured the source of
water to the subbasins (7-1, 5-1, 4-1, 4-3, 2-1,
and 1-1), the input of the subbasins to Rock
Creek (7-7, 7-4, 5-2, 4-2, 4-3, 2-2, and 1-2), and
key intermediate sites (7-2, 7-3, and 7-6).
Additional stations were added in other subbasins
as they were needed (2-3, 2-4, and 10-1).
The St. Albans Bay, Vermont, RCWP project
used a four-level monitoring and evaluation
program to meet three objectives (Vermont
RCWP Coordinating Committee, 1986):
• Document changes in the water quality of
specific tributaries within the watershed
resulting from implementation of manure
management practices.
• Measure changes in suspended sediment and
nutrients entering St. Albans Bay resulting
from implementation of water quality
management programs within the watershed.
• Evaluate trends in the water quality of St.
Albans Bay and the surface waters within
the St. Albans Bay watershed during the
period of the St. Albans Bay RCWP
Watershed Project. Monitoring sites for all
four levels of monitoring and evaluation are
shown in Figure 2-9. The Level 1 bay
sampling was designed to determine long-
term water quality trends in St. Albans Bay
over the life of the project (Vermont RCWP
Coordinating Committee, 1984). The Level
2 tributary sampling was designed to
determine the long-term water quality trends
for the major tributaries including the Bay
and the St. Albans City wastewater
treatment plant (Vermont RCWP
Coordinating Committee, 1984). The Level
3 monitoring was directed toward evaluating
the effect of best manure management
practices on the quality of surface runoff
from individual fields; Level 4 was designed
to supplement the Level 2 monitoring by
sampling additional tributaries to St. Albans
Bay and to isolate subunits within the Level
2 subwatersheds (Vermont RCWP Coordin-
ating Committee, 1984).
2.7 ROLES AND RESPONSIBILITIES
Designing and implementing a monitoring
program is an interdisciplinary and interagency
activity. In many cases, technical staff will need
to integrate "new" monitoring with what is
already being done in order to demonstrate to
program managers that duplicate work is not
proposed. The most effective way to achieve this
goal is to bring all the involved agencies and
other stakeholders in the monitoring effort
together. One or a few agencies acting as project
coordinator(s) should seek to obtain an agreement
from each involved party with respect to their
role(s) and responsibilities in the performance of
the project. These agreements can be formalized
as commitments and specified in the quality
assurance project plan, which is discussed at
greater length in Chapter 5.
Such coordinated cooperation permits each
involved party to offer the results of its ongoing
activities to the monitoring effort and lessens the
burden on the proposed budget. For example, the
U.S. Geological Survey might already have a
gaging station in place and the Natural Resources
Conservation Service might already have a
tracking system for BMPs in place. Other
agencies, including the U.S. Fish and Wildlife
Service and EPA, might have other ongoing
monitoring programs. When multiple agencies
are involved in the monitoring program, each can
benefit from the efforts of the others.
-------
Developing a Monitoring Plan
Chapter 2
LAKE
CHAMPLAIN
LEGEND
• level 1
A level 2
t\ levels
A level 4
® precipitation
project boundary
2 miles
Figure 2-9. St. Albans Bay watershed, Franklin County, Vermont, sampling locations. (Source: Vermont
RCWP Coordinating Committee, 1986)
-------
Chapter 2
2.8 QUALITY ASSURANCE PROJECT PLANNING
An integral part of the design phase of any
nonpoint source pollution monitoring project is
the development of a quality assurance project
plan (QAPP). The QAPP is a critical document
for the data collection effort inasmuch as it
integrates the technical and quality aspects of the
planning, implementation, and assessment phases
of the project. The QAPP documents how quality
assurance (QA) and quality control (QC) elements
will be implemented throughout the life of a
project. It contains statements about the
expectations and requirements of those for whom
the data are being collected (i.e., the decision
makers) and provides details on project-specific
data collection and data management procedures
that are designed to ensure that these
requirements are met. Development and
implementation of a QA/QC program, including
preparation of a QAPP, can require up to 10 to
20 percent of project resources (Cross-Smiecinski
and Stetzenback, 1994), but this cost is recaptured
in lower overall costs due to the project's being
well planned and executed. A thorough
discussion of QA/QC is provided in Chapter 5.
2.9. CHEMICAL AND PHYSICAL MONITORING
Chemical and physical monitoring and the
mechanics of sampling are important topics and
need to be considered as carefully as other
monitoring topics discussed in this guide, such as
data analysis and biological monitoring.
However, these aspects of monitoring are covered
in detail in other documents (e.g., USDA-NRCS,
1996; USGS, 1977) and it would be redundant to
duplicate the information here. Therefore, these
types of monitoring are only briefly mentioned
here.
Important topics related to chemical and physical
monitoring and sampling procedures that
managers of nonpoint source pollution monitoring
programs should consider include the following:
Type of sample. Water quality varies
temporally and spatially, and samples must
be taken that will accurately reflect overall
water quality and overall water quality
impacts of nonpoint source pollutants.
There are four basic types of samples to
consider—grab, composite, integrated, and
continuous (USDA-NRCS, 1996):
Typically, a grab sample is a sample taken at
one place a single time. Care should be taken
to make sure that a grab sample is represen-
tative. If there is spatial variability (e.g.,
across a stream, at different depths in a lake)
or temporal variability (e.g., during a storm
event) it might be more appropriate to take a
composite or time-integrated sample rather
than a grab sample.
Composite samples consist of a series of grab
samples, usually collected in the same
location but at different times with the results
averaged. Composite samples are usually
either time-weighted or flow-weighted.
Time-weighting means that a fixed volume is
collected at a predetermined time interval.
Flow-weighting means that a sample is taken
after a specified quantity of water has passed
the monitoring station. Both types of
composite sampling are amenable to
automatic sampling equipment. Composite
samples are appropriate for most monitoring
objectives.
Integrated samples account for variations in
water quality with depth or distance from a
stream bank at a monitoring station.
Subsamples are taken at various depths or
distances from the stream bank, and
integrated into a single sample.
Continuous sampling requires electronic
measuring devices and is therefore limited to
variables that are amenable to this type of
sampling, such as dissolved oxygen,
-------
Developing a Monitoring Plan
Chapter 2
conductivity, pH, and salinity. It is generally
not suitable for measurements of metals,
organics, or pesticides. Continuous sampling
is typically used for research and fate and
transport studies.
Some factors that influence the type of sample
to collect include the objectives of the study,
waterbody type, and variables to be sampled.
Type of sample collection. Samples can be
collected manually or with automated
equipment. Sampling location, sample site
accessibility, and staffing are factors to
consider when determining which approach to
use.
Type of sample collection equipment.
Sampling equipment can be either
mechanically operated or powered, and the
use of one or the other approach again
depends on project-specific considerations and
constraints. Commonly used sampling
equipment includes flow recorders, staff
gauges, and precipitation gauges.
Station type. Various monitoring stations
might be necessary to measure the variables
of interest. Discharge stations might be
installed to measure runoff from a sampling
plot in a field or at the edge of a field, or to
measure stream discharge. Other monitoring
stations might be necessary to collect water
samples, record precipitation, analyze soil
water, assess biological factors, or monitor
sediment.
Sampling equipment operation and
maintenance. It is important to ensure that
all sampling equipment is in good operational
condition prior to sampling and during
sampling to ensure that reliable data are being
collected. The use of automated sampling
equipment does not mean that project staff are
relieved of the responsibility to regularly
check equipment operation. Staff should be
, thoroughly trained to use and maintain
sampling equipment properly.
• Record keeping. Proper record keeping is
important to make the process of data analysis
less burdensome and to aid in tracking any
anomalies in data to possible influences, such
as equipment malfunctions or variations in
sample collection timing. Detailed records
are also valuable when writing reports and
preparing presentations.
2.10 RECOMMENDED REFERENCES
Important monitoring references that should be
consulted include the following:
American Public Health Administration. 1995.
Standard methods for the examination of water
and wastewater. 19th ed. American Public
Health Association, Washington, DC.
Discussion of how to collect samples and the
required volume of sample material for numerous
water quality parameters.
Bauer, S.B., and T.A. Burton. 1993.
Monitoring protocols to evaluate water quality
effects of grazing management of western
rangeland streams. EPA 910/R-93-017.
Submitted to U.S. Environmental Protection
Agency, Region 10, Water Division, Surface
Water Branch, by Idaho Water Resources
Research Institute, University of Idaho, Moscow,
ID. October.
Temperature, nutrients, bacteria, stream channel
morphology, stream bank stability, sediment,
streamside vegetation. For each, parameters to
measure, sample collection procedures, sample
analysis.
Clark, W.H. 1990. Coordinated nonpoint source
water quality monitoring program for Idaho.
-------
Chapter 2
Idaho Department of Health and Welfare,
Division of Environmental Quality, Boise, ID.
January.
Appendix with suggested monitoring parameters
and protocols, including suggested protocols for
various types of BMP implementation and
pollutant sources and transport mechanisms.
MacDonald, L.H., A.W. Smart, and R.C.
Wissmar. 1991. Monitoring guidelines to
evaluate effects of forestry activities on streams in
the Pacific Northwest and Alaska. EPA/910/9-
91-001. U.S. Environmental Protection Agency,
Region 10, Seattle, WA.
Forestry focus; parameter selection; discussion of
many parameters, including a definition, relation
to designated uses, how the parameter responds to
management activities, parameter-specific
measurement notes, applicable standards, present
uses of the parameter, and parameter assessment.
Parameter recommendations for various land
treatments.
USDA. 1979. Field manual for research in
agricultural hydrology. Agricultural Handbook
224. U.S. Department of Agriculture,
Washington, DC.
USDA-NRCS. 1996. Water quality monitoring.
U.S. Department of Agriculture, Natural
Resources Conservation Service, Washington,
DC.
Numerous recommendations for good references
on.a variety of sampling topics. Also includes
tables with recommendations of variables to
measure based on the above considerations.
Topics covered include variable selection, sample
types (grab, composite, integrated, continuous),
station type (discharge, concentration,
precipitation, soil water, biotic, sediment), sample
collection (volume), sample preservation.
USGS. 1977. National handbook of
recommended methods for water-data acquisition.
U.S. Geological Service, Office of Water Data
Coordination, Reston, Virginia.
-------
-------
3 BIOLOGICAL MONITORING OF AQUATIC COMMUNITIES
3.1 INTRODUCTION
Biological monitoring, as described here, consists
of assessing the condition of the physical habitat
and specific biological assemblages—typically
benthic macroinvertebrates and fish— that inhabit
the aquatic environment. In the true definition of
the term, biological monitoring includes toxicity
testing, fish tissue analyses, and single population
surveys conducted over time. However, for
purposes of this document, the definition of
biological monitoring is limited to the concept of
community-level assessments.
Community: Any group of organisms
of different specfes that co-occur in
the same habitat or area.
This chapter discusses the rationale behind using
biological monitoring as part of a nonpoint source
(NPS) monitoring program, gives basic guidance
on conducting biological assessments, provides
biological monitoring program design
considerations, and discusses ways in which
biological assessment data can be used to detect
trends in NPS impacts. Numerous texts and
papers have been written on biological monitoring
methods (see references), but methods and means
of interpreting the results from them are still under
development for many habitats and regions of the
country. The U.S. Environmental Protection
Agency (EPA) has produced two guidance
documents that are the foundation for the majority
of state biological monitoring programs:
• Biological criteria: Technical guidance for
streams and small rivers. EPA 822-B-96-001.
(Gibson et al., 1996)
• • Rapid bioassessment protocols for use in
streams andwadable rivers. EPA/444/4-89-
001. (Plafkin et al., 1989)
Many state agencies, such as the Ohio
Environmental Protection Agency (OEPA),
Illinois Environmental Protection Agency (IEPA),
Delaware Department of Natural Resource and
Environmental Control (DNREC), Florida
Department of Environmental Protection (DEP),
Connecticut DEP, and New York Department of
Environmental Conservation (DEC), are
incorporating biological monitoring into ongoing
and new monitoring programs. In addition,
numerous ongoing biological monitoring programs
have been implemented by federal, local, and
tribal entities, including the U.S. Geological
Survey (USGS), National Water Quality
Assessment Program (NAWQA); U.S. Department
of the Interior, Bureau of Land Management; U.S.
Department of Agriculture (USDA), Forest
Service; King County, Washington; Prince
George's County, Maryland; and the Yakama
Tribe. The methods for biological monitoring are
thereby being improved, and this in turn is making
biological assessment a more widely accepted and
applicable tool for monitoring programs.
The common use of EPA methods for sampling
and analysis is documented in a recent work that
reviews state programs (Summary of state
biological assessment programs for streams and
wadable rivers, Davis et al., 1996; EPA230-R-96-
007). It shows that 45 states are using rapid
bioassessment protocol (RBP)-type sampling and
analysis methods or are in the process of
establishing such programs.
Biological survey approaches differ depending on
the waterbody, i.e., stream, river, lake, estuary, or
-------
Biological Monitoring
Chapter 3
wetland. EPA has developed and or is currently
developing bioassessment survey methods
appropriate for use in these different waterbody
types.
3.1.1 Rationale and Strengths of
Biological Assessment
The central purpose of assessing biological
condition is to determine how well a waterbody
supports aquatic life and what kind of aquatic life
it supports. Biological communities reflect the
cumulative effects of different pollutant
stressors—excess nutrients, toxic chemicals,
increased temperature, excessive sediment
loading, and others—and thus provide an overall
measure of the aggregate impact of the stressors.
Although biological communities respond to
changes in water quality more slowly than water
quality actually changes, they respond to stresses
of various degrees over time. Because of this,
monitoring changes in biological communities can
be particularly useful for determining the impacts
of infrequent or low-level stresses, such as highly
variable NFS pollutant inputs, which are not
always detected with episodic water chemistry
measurements. Improvements in waterbody
condition after the implementation of best
management practices (BMPs) can sometimes be
difficult to detect, and biological assessment can
be useful for measuring such improvements.
Several biosurvey techniques that can be used for
detecting aquatic life impairments and assessing
their relative severity are discussed in this chapter.
A small number of factors are often key in
determining the structure of a community and its
response to stress; e.g., the type of substrate or the
riparian vegetation that provides organic material
to the stream, regulates temperature, and provides
bank stability (National Research Council, 1986).
Landscape features such as soil type, vegetation,
surrounding land use, and climate also have a
well-documented influence on water chemistry
and hydrologic characteristics. Finally, water
quality, as influenced by landscape features and
anthropogenic sources of pollutants, has a direct
effect on aquatic biological communities.
The quality of the physical habitat is an important
factor in determining the structure of benthic
macroinvertebrate, fish, and periphyton
assemblages. The physical features of a habitat
include substrate type, amount of debris in the
waterbody, amount of sunlight entering the
habitat, water flow regime (in streams and rivers),
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 them
are often taxonomically and biologically distinct
(Hawkins et al., 1993). Habitat quality is assessed
during biological assessment and is a measure of
the extent to which the habitat provides a suitable
environment for healthy biological communities.
Biological assessment: An evaluation
of the biological condition of a waterbody
using biological surveys and other dir&ot
measurements of biota in surface
waters.
Biological monitoring: Multiple, routine
biological assessments over time using
consistent sampling and analysis
methods for the detection of changes in
biological condition.
Natural biological communities are usually
diverse, comprising species at various trophic
levels (e.g., primary producers, secondary
producers, carnivores) and levels of sensitivity to
environmental changes. Adverse impacts from
NFS pollution or other stressors such as habitat
-------
Chapters
alteration can reduce the number of species in a
community, change the relative abundances of
species within a community, or alter the trophic
structure of a community. Biological surveys of
select species or types of organisms that are
particularly sensitive to stressors, such as fish,
periphyton, or benthic macroinvertebrates, take
advantage of this sensitivity as a means to evaluate
the collective influence of the stressors on the
biota (Cummins, 1994).
Some NFS pollutant inputs such as wet-weather
runoff of chemical contaminants or sediment are
highly variable in time, and biological monitoring
can be a useful approach for monitoring this type
of NFS impact. Biological monitoring can be used
to assess the overall impact of multiple stressors,
although it might not provide information about
the relative magnitude of each stressor.
Knowledge of the natural physical habitat and
biological communities in an area is important for
interpreting biological assessment data. Biological
and habitat data collected from numerous sites that
are 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. In
areas where natural conditions do not exist (due to
past disturbances), historical data or the best
professional judgment of knowledgeable experts
can sometimes be used to select reference sites and
define the reference condition. 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). Paired watersheds or upstream-
downstream approaches are examples. A
knowledge of the natural condition is still valuable
for accurate data interpretation when control sites
are used (Cowie et al., 1991).
3.1.2 Limitations of Biological
Assessment
Although biological assessment is useful for
detecting impairments to aquatic life and assessing
the severity of the impairments, it is not
necessarily a measure of specific stressors. Thus,
it usually does not provide information about the
cause of impairment, i.e., specific pollutants or
their sources. Certain biological indicators do
provide information about the types of stress
affecting a biological community. However, if
stress to a stream community is chemical,
chemical monitoring in addition to biological
monitoring is required to determine the actual
pollutants responsible for biological or water
quality impairments and their sources. Chemical
monitoring and toxicity tests are also necessary to
design appropriate pollution control programs.
Biological integrity: "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" (Karr and Dudley, 1981).
-------
Biological Monitoring
Chapter 3
A detailed biological monitoring program requires
the development of the analysis protocols for
biological assessments (including reference
conditions and metrics, which are discussed
later). Establishing the analysis approach can ,
require a considerable level of effort and ,
therefore can be somewhat expensive. Also,
detailed biological assessments generally require
precise training and broad experience with
taxonomic identification of the samples.
Experience in the region where sampling is to
occur is helpful, but not required. The biological
assessment methods and the means of interpreting
the results from assessments need to be tailored to
many habitats and regions of the country.
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 waterbody and a
detectable biological response. Consequently,
biological monitoring is not 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
communities following habitat restoration or
pollution problem abatement. The extent of this
lag time is difficult, if not impossible, to predict.
Other factors also determine the rate at which a
biological community 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). In extreme cases, a
biological community might not recover following
pollution abatement or habitat restoration. Both
the possibility of the failure of a biological
community to recover 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 3-1
summarizes the strengths and limitations of the
biomonitoring approach.
3.2 HABITAT ASSESSMENT
Habitat assessment is an important component of
biological assessment and monitoring, both in
describing the biological potential of a system and
in addressing the Clean Water Act emphasis on
"physical integrity." As mentioned in the
introduction, the quality of the physical habitat is
important in determining the structure of benthic
macroinvertebrate and fish communities. Habitat
quality refers to the extent to which habitat
structure provides a suitable environment for
healthy biological communities to exist. Habitat
quality encompasses the three factors, habitat
structure, flow regime, and energy source.
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 velocity and
volume of water moving through a stream.
Energy enters streams as the input of nutrients in
runoff or ground water, as debris (e.g., leaves)
falling into streams, or from photosynthesis by
aquatic plants and algae.
These three factors—habitat structure, flow
regime, and energy source—are interrelated and
make stream environments naturally
heterogeneous. Habitat structural features that
-------
Chapter 3
Table 3-1. General strengths and limitations of biological monitoring and assessment approaches.
Strengths
Limitations
Properly developed methods, metrics, and
reference conditions provide a tool that
provides a means to assess the ecological
condition of a waterbody
Simpler bioassessments can be relatively
inexpensive and easily performed with minimal
training
Bioassessment indicates the cumulative
impacts of multiple stressors on biological
communities, not only water quality
Biological assessment data can be interpreted
based on regional reference conditions where
reference sites for the immediate area being
monitored are not available
Bioassessments involving 2 or more organism
groups at different trophic levels provide a
reasonable assessment of ecosystem health
Biological community condition reflects both
short- and long-term effects
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 higher 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
The optimal season for biological sampling
season varies regionally, and sampling during
multiple seasons may be required in some
areas
Biological assessment does not always
distinguish between the effects of different
stressors in a system impacted by more than
one stressor
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 protection to aquatic
organisms. Curvature (sinuosity) in a stream
affects currents and thereby deposition of
sediment on the inner and outer banks. Rocks
and boulders create turbulence, which affects
dissolved oxygen levels; deep, wide portions are
areas of lowered velocity where material can
settle out of the water and increased
decomposition occurs.
Microhabitat; In streams, any small-
scale, physical feature contributing to
the texture of the habitat such as the
type and structure of substrate
particles; submerged, emergent, or
floating aquatic vegetation; algal
growths; snags and woody debris; or
leaf litter.
-------
Biological Monitoring
Chapter 3
The aspects of habitat structure mentioned above
(channel morphology, floodplain size and shape,
etc.) should be inspected during habitat
assessment. The aspects are separated into
primary, secondary, and tertiary groupings
corresponding to their influence on small-,
medium-, and large-scale aquatic habitat features
(Plafkin et al., 1989). The status or condition of
each aspect of habitat is characterized as falling
somewhere on 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 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; Ohio EPA, 1987; Plafkin et al., 1989;
Plans etal., 1983).
The relationship between habitat quality and
biological condition is generally one of three
types (Barbour and Stribling, 1991):
• The biological community varies directly with
habitat quality—water quality is not the
principal factor affecting the biota.
• The biological community is degraded
relative to the potential of its actual
habitat—water quality degradation is
implicated as a cause of the biotic condition.
• The biological community is elevated above
what actual habitat conditions should
support—organic enrichment in the water or
alteration of energy source is suspected as a
cause.
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.
3.3 OVERVIEW OF BIOLOGICAL ASSESSMENT
APPROACHES
3.3.1 Screening-Level or Reconnaissance
Bioassessment
The simplest bioassessment approach that can be
used to obtain useful information about the status
of an aquatic community and the condition of a
site is a screening-level, or reconnaissance,
bioassessment (Plafkin et al., 1989; USEPA,
1994a). This type of survey can be done
inexpensively and with few resources. If
conducted by a trained and experienced biologist
with a knowledge of aquatic ecology, taxonomy,
and field sampling techniques, the results of
screening-level bioassessment will have the
greatest validity. This bioassessment method is
most often conducted using benthic
macroinvertebrates and is described in detail in
Plafkin et al. (1989) and by the U.S.
Environmental Protection Agency (USEPA,
1994a).
The first element of the screening-level approach,
as in many biological assessment approaches, is
an assessment of physical habitat. The instream
habitat should be inspected for the amount of
embeddedness, type of bottom substrate, depth,
flow velocity, presence of scoured areas or areas
of sediment deposition, relative abundance of
different habitat types (pools, riffles, runs),
-------
Chapter 3
presence of woody debris, and aquatic vegetation.
When conducting the assessment in a stream,
record whether the stream channel has been
altered. If the assessment is in a lake, reservoir,
or pond, determine whether artificial bottoms or
shorelines (beach sand, cement) have been
installed. The riparian habitat must also be
inspected for the amount of riparian cover,
evidence of bank erosion, areas where livestock
enter to water, and proximity of altered land uses
(e.g., residential, agricultural, silvicultural, or
urban). Determine the width of any natural
vegetation buffer areas. The surrounding land
use should be noted as a percentage of each type
(e.g., 40 percent agriculture, 40 percent wooded,
20 percent residential).
The biological sampling portion of the streamside
bioassessment is relatively simple. No laboratory
work is involved, and it can be conducted by a
person with a basic knowledge of aquatic biology.
Macroinvertebrates should be collected from
different instream habitats, with data from each
habitat kept separate. Calculations of relative
abundance and number of orders/families
represented are then made. Calculations of basic
community structure can also be made if
specimen identifications are sufficiently detailed
to allow determination of the functional feeding
group the organisms occupy. Sample calculations
of relative abundance and community structure
are presented in Figure 3-1. Different functional
feeding groups dominate in different habitats
(filterers and scrapers dominate in riffle/run
habitats whereas shredders dominate areas with
large amounts of woody debris), so these
calculations require that distinct habitats be
sampled. Samples of invertebrates from woody
debris of all types should be taken, including
sticks, twigs, leaves, needles, and so forth.
Freshly fallen debris will generally support a less
representative macrobenthos than debris that is at
least 50 percent decomposed.
A reference collection of biological organisms,
usually available at a museum or university,
should be used to positively identify any
specimens whose identification is in doubt. A
reference collection is a collection of preserved
specimens of organisms from an area that is the
same as or similar to that where monitoring is
done.
Reference collection: A biological
collection of positively identified
specimens with one or more
individuals representing each taxon
likely to be sampled in the study
area.
Judgment of biological condition is made using
the presence or absence of indicator taxa, the
dominance of nuisance or sensitive taxa in the
sampled habitats, or the evenness of taxonomic .
distribution and comparison with what is expected
at unimpaired locations. A trained biologist will
be able to determine whether the biota at a site
are moderately or severely impaired using this
approach, but subsequent sampling is often
necessary to confirm any findings. The most
useful application of this approach is for problem
identification or screening and for setting
pollution abatement priorities. Florida DEP has
developed a biological screening tool, the
BioRecon, that is used for this purpose in its
nonpoint source pollution control program.
3.3.2 Paired-Site Approach
The paired-site approach for biological
monitoring involves the use of control and
treatment sites for the detection of changes in
biological condition. This approach is useful for
the detection of changes due to changes in water
quality, habitat quality, or land use features. A
-------
Biological Monitoring
Chapter 3
Example of transforming benthic macroinvertebrate data
into biological metrics
The data presented here are from a single sampling event at one site in New England.
They are a 200-organism subsample of organisms collected with the 20-jab method
(USEPA, 1997) for low-gradient streams. The 11 metrics calculated from the data (note
that 7 metrics fall under the category "Percent of Total Sample") are described below.
Taxa Richness - the number of distinct taxa in the sample.
HUsenhoff Biotic Index - measures the abundance of tolerant and intolerant individuals in
a sample by the following formula: HBI = £Xjtj / n, where x, is the number of individuals in
the ith species, tt is the tolerance value of the ith species, and n is the total number of
species in the sample.
EPT Index - the number of taxa in the insect groups Ephemeroptera (mayflies), Plecoptera
(stoneflies), and Trichoptera (caddisflies).
Percent Dominance - the number of individuals in the numerically most dominant taxon as
a proportion of the total sample [(Number individuals in dominant taxon / Total individuals in
sample) x 100].
Percent of Total Sample - (1) Pisidium, (2) Simuliidae (= Prosimulium + Simulium),
(3) Isopoda (= Caecidotea), (4) Diptera, (5) EPT (= mayflies, stoneflies, caddisflies),
(6) filters FFG, and (7) collectors FFG.
Figure 3-1. Sample calculations of biological metrics.
key element of the approach, as the name implies,
is the simultaneous monitoring of sites that are not
affected by the changes for which the monitoring
is being conducted (control sites) and separate
sites that are affected by a "treatment" (treatment
sites), which might be BMP implementation or
another form of NFS pollution control. To
provide reliable and valuable data, the control and
treatment sites must be as similar as possible.
"Similarity" in this context means that the
biological populations to be monitored at both the
control and treatment sites must respond similarly
to changes in environmental parameters (Richards
and Minshall, 1992; Skalski and McKenzie,
1982). Paired sites can be similar watersheds
within a region or separate sites within a
watershed that are located upstream and
downstream from a nonpoint source of pollution.
Habitat assessment is as important in the paired-
site approach as it is in the other biological
assessment approaches. Because of the influence
of surrounding landscape features on aquatic
biota, control and treatment sites that are
influenced by the same habitat features should be
chosen. This implies that the hydrologic
characteristics (flow, waterbody type, channel
width, etc.) of the waterbodies in which the
-------
Chapter 3
Example of transforming benthic macroinvertebrate data
into biological metrics
RAW DATA
Taxa
Oligochaeta
Valvata
Fossaria
Gyraulus pan/us
Pisidium
Hydracarina
Caecidotea
Hyallela azteca
Arthroplea
Ameletus
Amphinemura
Anabolia
Neophylax
Anomalagrion/lschnura
Hygrotus
Hydrobius
Sialis
Nymphuliella
Diptera
Culicoides
Simulium
Prosimulium
Chrysogaster
Molophilus
Pseudolimnophila
Bittacomorpha
Ptychoptera
Orthocladiinae
Tanypodinae
Chironominae
Number
2
4
3
15
32
1
6
35
2
34
1
4
2
1
1
1
3
1
1
1
40
4
1
1
1
1
2
20
15
2
TOTAL NUMBER 237
FFG
col
scr
col
scr
fil
pre
col
col
fil
col
shr
shr
scr
pre
pre
pre
pre
shr
fil
pre
fil
fil
col
shr
pre
col
col
col
pre
col
TV
8
6
6
8
8
6
6
8
3
0
,3
5
3
9
5
5
4
5
8
2
6
2
10
4
2
8
8
5
7
6
CALCULATED METRICS
Metrics
Taxa Richness
Hilsenhoff Biotic Index
EPT Index
Percent Dominance
Percent Pisidium
Percent Simuliidae
Percent Isopoda
Percent Diptera
Percent EPT
Percent Filterers (fil)
Percent Collectors (col)
Values
30
5.7
5
17
14
19
3
38
18
33
45
LEGEND
FFG functional feeding group
TV tolerance value
scr scrapers
pre predators
shr shredders
fil shredders
col collectors
Figure 3-1. (continued)
-------
Biological Monitoring
Chapter 3
control and treatment sites are located,
surrounding land use, slope and soil type, and
riparian vegetation should be as nearly identical
as possible. For the same reasons, similar
habitats should be sampled at the control and
treatment sites. Types of substrates, locations in
the streams (e.g., center or edge of stream), and
nature of surrounding aquatic vegetation and
debris should be nearly identical. All habitat
features should be thoroughly investigated before
the final selection of the control and treatment
sites to be monitored to ensure their similarity.
Determination that the biota at control and
treatment sites respond similarly to environmental
factors is extremely important and usually
requires separate sampling before any treatment is
introduced at the treatment sites. It is important
that the biota at control and treatment sites vary
similarly both spatially and temporally, and a
critical assumption of the paired-site approach is
that the control and treatment populations do
respond similarly to environmental parameters
(Skalski and McKenzie, 1982). It is this
similarity of response that enables one to detect
changes due to the treatment. If control and
treatment populations that respond differently to
environmental factors are chosen, then the effect
of the treatment cannot be determined.
Identification of specimens to the genus or species
level should be sufficient to determine significant
changes in biological communities at pairs of
sites. Some laboratory work may be necessary or
desirable to be certain that accurate identifications
have been made.
Pretreatment sampling establishes the pattern of
changes at the control and treatment sites.
Skalski and McKenzie (1982) recommend that the
proportional abundance of populations of
macroinvertebrates at control and treatment sites
be the parameter used to determine any change
attributable to the treatment. Further discussion
of monitoring program design and data analysis
for the paired-site approach can be found in
Skalski and McKenzie (1982) and Richards and
Minshall (1992).
3.3.3 Composited Reference Site
Bioassessment
Composited reference site bioassessment is an
approach wherein biological communities at
monitored sites are compared to "reference"
biological communities, or reference conditions,
which represent biological communities in
unimpaired or minimally impaired waterbodies in
the region of interest. Reference conditions are
discussed in greater detail below. The approach
is useful for ranking sites according to the degree
by which they differ from the reference condition,
which is equated with the degree of impairment at
the monitored site. The composited reference site
approach integrates characteristics among broad
geographical areas and watersheds and thus is a
more comprehensive assessment and monitoring
approach than the paired-site approach. Regional
reference conditions are useful for providing
ecological realism in impairment criteria because
they incorporate the geographic distritution, or
biogeography, of organisms. The composited
reference site approach also requires the greatest
Biogeography: The geographic
distribution of plants and animals that
results from a combination of their
evolutionary history, mobility, and ability
to adapt to changing conditions.
amount of time, specialized expertise, and field
and laboratory effort to perform, largely because
to conduct a composited reference site biological
assessment and monitoring program, it is
-------
Chapter 3
necessary to initially establish a reference database
for the region in which monitoring will be
conducted. However, it is recognized as being the
most accurate approach.
Biological sampling for the composited reference
site approach is conducted at each of the reference
sites on a periodic basis, which can vary from
region to region. Once a composite of reference
sites has been established, monitoring can be
conducted on a randomly-selected subset of the
reference sites, thereby reducing the intensity of
monitoring. All monitoring of reference sites and
assessment sites (unknown condition) is done
within a specific index period, which reduces
temporal (seasonal) variation. This approach can
include more than one index period (as in the
Florida nonpoint source program), but it is usually
based on a single index period established to
optimize the evaluation of biological communities
(as is done by Ohio EPA and Delaware DNREC).
The habitat assessment phase of composited
reference site bioassessment is not different from
that of the screening-level bioassessment
approach. Refer to section 3.3.1 for a description
of what is involved.
The biota collection phase for composited
reference site bioassessment is similar to that of a
screening-level bioassessment, but involves the
collection of additional samples to detect subtle
differences in NFS pollution impacts. Specimen
identification is generally done in the laboratory to
the genus or species level. This level of detail
allows for a more accurate analysis of community
structure and biological condition. Data analysis
using genus- and species-level identifications can
provide information on the generic cause of
impairment (nutrient enrichment, toxic pollutants,
or habitat degradation). To gain this level of
insight, however, it is necessary to be able to
distinguish the effects of NFS pollution
impairment from natural variability of the
populations being sampled. Reference conditions
must be established for this purpose.
Additionally, area- or region-specific metrics must
be established before the composited reference site
bioassessment approach can be used effectively.
During the process of establishing reference
conditions for an area, metrics specific to the area
are selected and calibrated. Figure 3-2 describes
the development of metrics and associated
reference conditions in a step-by-step manner.
Metric: An enumerated or calculated
term that represents some aspect of
biological assemblage structure,
function, or other measurabte feature
and that changes in a predictable way
in response to environmental
(including human) fnftuences.
3.4 REFERENCE SITES AND CONDITIONS
In biological assessment, macroinvertebrates and
fish are commonly used as indicators of the
condition of biological communities.
Comparisons are made between
macroinvertebrates and fish found at undisturbed
sites and those found at monitored sites to
determine how closely they resemble one another.
The undisturbed, or reference sites, are aquatic
habitats that are assumed to fully support natural
biological communities. The greater the
difference is between reference and monitored
sites, the more disturbed the monitored sites are
considered to be. The disturbance responsible for
the difference might be a habitat change,
pollution, or some other stress.
A reference condition is a composite
characterization of the natural biological condition
in an ecologically homogeneous region created
-------
Biological Monitoring
Chapter 3
The Process for Metric Selection, Validation, and Development
of Reference Conditions
Step 1. Reference Site Selection. Select candidate reference sites from maps and other available
information and confirm through reconnaissance. Sites are confirmed through the existence of non-
degraded physical habitat and the absence of known contaminant sources.
Assuming that reference sites are available (see explanation in Section 3.4) if reference sites are
not available), candidate sites are selected to represent the "natural" condition within a region or
area. These sites should be representative of:
Extensive natural riparian vegetation
Natural channel structures typical of region
Natural hydrograph (typical flow patterns and discharges)
• Absence (or minimal presence) of sources of perturbation
These sites can be identified from existing CIS or land use maps, historical data, or local "expert"
knowledge, and confirmed through site reconnaissance.
Step 2. Site Classification. Determine site classes based on mapped information or regional
water quality characteristics such as, e.g., ecoregion or subecoregion, gradient, alkalinity, and
hardness.
The purpose of site classification is to partition the variability within each biological metric to
enhance the ability to discriminate impairment from nonimpairment, or to improve the interpretation
of change in monitoring. Physicochemical aspects, e.g., ecoregions, alkalinity, pH, elevation,
drainage area, etc., are analyzed to derive site classes. Then, the biological metrics are used to
confirm site classes and to partition variability.
For example, the number of taxa in a set of reference sites might range between 10 and 40 species.
However, in ecoregion A, the number of taxa is between 10 and 25 to represent a natural
community. In ecoregion B, the number of species ranges between 20 and 40. The classification
of sites by ecoregion, in this case, allows for a better understanding of natural variability than a
universal compositing of all reference sites.
Step 3. Candidate Metric Selection. List ail metrics that are relevant to the biological communities
being used for assessment of a site or waterbody.
Metrics allow the investigator to use meaningful indicator attributes in assessing the status of
communities in response to perturbation, or to monitor trends in the health of the communities. All
metrics that have relevancy to the assemblage under study and will respond to the targeted
stressors are potential metrics for consideration. For example, the number of taxa as a measure
of diversity can be identified for various groups of organisms that are relatively sensitive to
environmental change (i.e., mayflies, darters, diatoms, etc.); the relative dominance of a single taxon
is informative of a pollutant situation; an imbalance in trophic structure is suggestive of an adverse
effect on food source.
Figure 3-2. The process for metric selection and validation and development of reference conditions.
-------
Chapter 3
Step 4. Determination of Core Metrics. Calculate metrics based on biosurvey data (see Figure
3-1). Compare value range of each candidate metric from reference sites to those from impaired
sites. Metrics become part of the core analysis if the data show them able to discriminate between
reference and impaired sites.
Core metrics are those remaining following initial candidate metric screening that will discriminate
between good and poor quality ecological conditions, or will provide a basis for monitoring changes over
time. Metrics that use the relative sensitivity of the monitored assemblage to specific pollutants or
stressors, where these relationships are well characterized, can be useful as a diagnostic tool. The
discriminatory ability of metrics can be evaluated by comparing the distribution of each metric at a set
of assumed reference sites with the distribution of the same metric values from a set of known impaired
sites within each site class. This is done to calibrate the metrics. If there is minimal or no overlap
between the percentile distributions, the metric can be considered to be a strong discriminator between
reference and impaired conditions. The following two figures graphically demonstrate the difference
between strong and weak metrics:
Middle Rockies - Central Ecoregion, Wyoming
Benthic Metrics
8 32
REF IMP
TYPE
(a) Strong metric: Percent Total Sample as Stoneflies
Middle Rockies - Central Ecoregion, Wyoming
-• Benthic Metrics
« 96
I M
I 88
•o
o 84
o 80
•&
M 76
2 72
H
£
REF IMP
TYPE
Min - Max
25% - 75%
Median value
(b) Weak metric: Percent Contribution of 10 Dominant Taxa
Figure 3-2. (continued)
-------
Biological Monitoring
Chapter 3
ecoregions in the conterminous United States.
The size of each ecoregion is a function of its
within-region homogeneity relative to between-
region variation. The ecoregion concept is useful
for water quality management because
waterbodies within ecoregions are relatively
homogeneous and can therefore be managed
similarly.
Omernik (1987, 1995) found that hydrologic units
such as river basins cannot be used to accurately
delineate ecoregions, but that within an ecoregion
there might be separate watersheds or
subwatersheds. Therefore, surveys and
monitoring conducted in several watersheds are
strengthened by the ecoregion framework.
Characteristics other than ecoregion are also
helpful in classifying sites. For example, the
Wyoming Department of Environmental Quality
(DEQ) found that elevation distinguishes stream
classes within the Middle Rockies Ecoregion.
EPA's Biological Criteria: Technical Guidance
for Streams and Small Rivers (Gibson et al.,
1996) describes the process for classifying sites
and selecting reference sites.
In a landscape that is heavily altered by
agricultural activity, silviculture, industrial-
commercial development, or urbanization,
undisturbed streams or reaches might not exist
and reference conditions might need to be
determined based on best professional judgment
of that which is likely attainable, historical
records, or another means of estimation. The
most appropriate approach to establishing
reference conditions is to conduct a preliminary
resource assessment to determine the feasibility of
using reference sites (Figure 3-3). If acceptable,
minimally impaired reference sites cannot be
found for a region, some form of simulation
modeling might be the best alternative.
Biological attributes can be modeled from
neighboring regional site classes, expert
consensus, and/or a composite of "best"
ecological information. Such models might be the
only viable means of examining significantly
altered systems. The expectations derived from
these models should be regarded as hypothetical
until more reliable information is obtained.
3.5 RAPID BIOASSESSMENT PROTOCOLS
EPA has recommended a set of rapid .
bioassessment protocols, or RBPs, that use
benthic macroinvertebrate and fish communities
to assess biological condition in streams and
wadable rivers. The five protocols differ in the
level of effort, taxonomic level, and expertise
required to perform them, and in the applicability
of the data obtained (Table 3-2). More intensive
bioassessments (RBPs III and V) give the most
useful information for trend analysis and
establishment of a baseline for problem diagnosis.
RBPs I and II are less intensive bioassessment
approaches and are useful for setting priorities for
more intensive study. RBP IV, not described
here, is a screening technique used to survey
persons knowledgeable about the fish in an area.
For further information about RBP IV, consult
Plafkin et al. (1989).
Selection of appropriate organisms and protocols
for biological assessment depends on the
objectives of the monitoring study (Figure 3-4).
RBP benthic protocols have been applied in
freshwater streams and wadable rivers, and their
applicability is presently limited to these
waterbodies. Fish RBP protocols have been used
in freshwater streams and larger rivers and are
applicable to both. RBP-type methods for fish
and invertebrates have been adapted for use by
many states and federal agencies and are in use
across the country (Southerland and Stribling,
1995).
.
-------
Chapter 3
PRELIMINARY RESOURCE ASSESSMENT
Reference Sites
Reference
Sites
Acceptable?
No Reference Sites
Ecological
Modeling
Where no
"natural" sites
exist, select
best available
(may require
sampling all
sites).
Use (1) neighboring site
classes, (2) expert
consensus, or (3) composite
of "best" ecological
Figure 3-3. Approach to establishing reference conditions (Gibson et al., 1994).
-------
Biological Monitoring
Chapters
Table 3-2. Five tiers of the rapid bioassessment protocols (Plafkin et al., 1989).
Level
or Tier
I
II
111
IV
V
Organism
Group
benthic
invertebrates
benthic
invertebrates
benthic
invertebrates
fish
fish
Relative Level
of Effort
low; 1-2 hr per site (no
standardized sampling)
intermediate; 1 .5-2.5 hr
per site (all taxonomy
performed in field)
most rigorous; 3-5 hr per
site (2-3 hr of total are for
lab taxonomy)
low; 1-3 hr per site (no
fieldwork involved)
most rigorous; 2-7 hr per
site (1-2 hr per site are
for data analysis)
Level of
Taxonomy/Where
Performed
order, family/field
family/field
genus or species/
laboratory
not applicable
species/ field
Level of Expertise
Required
one highly trained
biologist
one highly trained
biologist and one
technician
one highly trained
biologist and one
technician
one highly trained
biologist
one highly trained
biologist and 1-2
technicians
3.6 THE MULTIMETRIC APPROACH FOR
BIOLOGICAL ASSESSMENT
Accurate assessment of biological condition
requires a method that integrates biotic responses
through an examination of patterns and processes
from the organism to ecosystem level (Karr et al.,
1986). The rapid bioassessment protocols
(Plafkin et al., 1989) discussed above make use of
an array of measures that individually provide
information on diverse biological attributes and,
when integrated, provide an overall indication of
biological condition.
The raw biological data collected during a survey
consist entirely of taxonomic identifications and
numbers of individuals within each taxon. The
level of identification—whether to family, genus,
or species—depends on the method being used.
For instance, RBP II involves identification to the
family level, whereas RBP III involves
identification to the lowest practical level,
generally genus or species. These data are used
to calculate or enumerate a variety of values, or
metrics. Each reflects a different characteristic of
community structure and has a different range of
sensitivity to pollution stress (Plafkin et al.,
1989). Appropriately developed metrics can be
used to draw conclusions about different aspects
of the biological condition at a site, and
measurements of multiple metrics in a biological
assessment will yield a more accurate
representation of the overall biological condition
at a site. Gray (1989) stated that the three best-
documented biological responses to environmental
stressors are a reduction in species richness, a
change in species composition to dominance by
opportunistic species, and a reduction in the mean
-------
Chapter 3
APPROACH
Decide on Monitoring
Objectives
Determine
whether an
impairment
exists
SCREENING
Determine If
further
investigation is
necessary
I
MORE RIGOROUS APPROACHES
x' Determine the
f relative impact NFS
\. pollution
I
Limited effort
impairment noted
Focus on communities.
3 levels of impairment
detected
RBP!
RBPIV
V S
Focus on communities
and populations. 4
levels of impairment
detected
RBPV
Figure 3-4. Selection and application of the different tiers of RBP depend on monitoring objectives.
body size of organisms. Though the last type of
biological response (change in mean body size)
may be well documented, it is rarely used in the
more common bioassessment protocols because the
level of effort for an accurate interpretation can be
prohibitive.
Figure 3-5 illustrates a conceptual structure for the
attributes calculated or measured for a biological
assemblage during a biological assessment.
Generally, the biological assemblage at a site can
be characterized by metrics organized into four
classes—community structure, taxonomic
composition, individual condition, and biological
processes. These are described below.
Community structure is characterized by
measurements of the variety of taxa and the
distribution of individuals among taxa. Taxa
richness is the number of distinct taxa in a sample
and reflects the diversity of the sample. The
relative abundance of each taxon is a comparison
of the number of individuals in one taxon to the
total number of individuals in the sample.
Dominance is calculated as the percent
composition of the dominant taxon within the total
sample. It indicates balance within the
community.
Taxonomic composition refers to the types of
taxa in the sample. Sensitivity to pollution and
other environmental disturbance is the number of
-------
Biological Monitoring
Chapters
pollution-tolerant and intolerant species in the
sample. The presence of exotic and nuisance
species is also noted because they can play
important ecological roles and indicate stressed
conditions.
Individual condition is more easily measured in
fish than in benthos and periphyton; it refers to
the presence or absence and frequency of diseases
and anomalies. Contaminant levels in the tissues
of individuals can also be measured. The
frequency of head capsule deformities in midges
(Chironomidae) has been used by some
researchers.
Biological processes occurring at the sample site
are indicated by measurements of species that
perform specific functions within the community.
For instance, the functional feeding groups (e.g.,
detritivores, filter feeders) indicate the primary
source of energy for the biological system.
Numerous biological metrics have been tested in
various regions of the country (Figure 3-6),
primarily for fish and benthos. Summaries of
those used have recently been presented in tabular
form (Gibson et al., 1996; Barbour et al., 1995)
and are reproduced in Tables 3-13 and 3-14 at the
end of this chapter. Examples of metrics that
have been tested and have had scoring criteria
established are those for the montane region of
Wyoming (Table 3-3) and the plains streams of
Florida (Table 3-4). Figure 3-1 explains five
common metrics and presents sample data and
calculated values for each of the metrics. Readers
should calculate the metrics themselves to be
certain that use of the data for metric calculation
is understood.
3.7 SAMPLING CONSIDERATIONS
The large influence that small environmental
factors, such as amount of sunlight or presence of
woody debris in a waterbody, can have on aquatic
communities means that even though there might
not be easily distinguished boundaries between
habitats, such as those between riffles and pools,
the biota inhabiting them are often taxonomically
and biologically distinct (Hawkins et al., 1993).
The distribution of benthic fauna in lakes and
streams is also heterogeneous because of variable
requirements among species for feeding, growth,
and reproduction, which are satisfied for different
species by different substrata, water chemistry,
and inputs of woody debris (Wetzel, 1983). This
leads to a patchy, nonrandom distribution of
animals.
Because of the influence that habitat has on
biological communities, sampling similar habitats
at all sampling stations is important for data
comparability and for data interpretation (Plafkin
et al., 1989). Collection of habitat quality data
each time biological data are collected helps to
establish the correlation between the two for a
particular ecoregion. Obviously, the more
correlative data that are collected, the more useful
they will be in interpreting sampling data, that is, "
in separating water quality and habitat quality
effects as they relate to biological condition. The
ability to separate the two influences is important
for determining the expected or potential
improvement in biological condition from water
quality improvement programs such as point or
nonpoint source pollution control (Barbour and
Stribling, 1991).
When sampling multiple habitats, it is important
to establish consistency in sampling procedures.
Sampling protocols should be standardized, and
the same level of effort should be applied at each
sampling station. Because differences in gear
efficiency and techniques may affect results,
standardized sampling is needed if direct compari-
sons are to be made between stations or between
data from a single station at different times.
-------
Chapter 3
iBiofoaical Monitorini
COMMUNITY
STRUCTURE
TAXONOMIC
COMPOSITION
INDIVIDUAL
CONDITION
T T j
TAXA
RICHNESS
RELATIVE
ABUNDANCE
DOMINANCE
IDENTITY
SENSITIVITY
(intolerance)
RARE OR
ENDANGERED
TAXA
DISEASE
ANOMALIES
BIOLOGICAL
PROCESSES
1
CONTAMINANT
LEVELS
METABOLIC
RATE
t i 1
t
BIOLOGICAL ASSESSMENT
TROPHIC
DYNAMICS
PRODUCTIVITY
PREDATION
RATE
RECRUITMENT
RATE
1
Figure 3-5. Organizational structure of attributes that can serve as metrics.
3.7.1 Benthic Macroin vertebrate
Sampling
Stream environments contain a variety of macro-
and microhabitat types including pools, riffles,
and runs of various substrate types; snags; and
macrophyte beds. Relatively distinct assemblages
of benthic macroinvertebrates inhabit various
habitats (Hawkins et al., 1993), 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, high-
gradient mountain streams are best sampled from
the cobble substrate of riffles for macroinver-
tebrates, whereas low-gradient coastal streams
lack riffles and are appropriately sampled from
snags and shorezone vegetation. These two
different stream types might be sampled with
different methods, during different times of the
year, or with different biological index periods.
The seasonal variability of the biota and stream
environment are key factors that determine the
proper index period. Established sampling
protocols that are part of existing monitoring
programs should be considered for NPS
bioassessment. However, current and/or
historical sampling approaches should be
evaluated to determine whether they will provide
the required data to address the program
objectives.
-------
Biological Monitoring
Chapter 3
Ontario
Western
Oregon
Sacramento
San Joaquin
New England
Central Appalachia
Midwest
Colorado Front
Range
Figure 3-6. Areas in which various fish IBI metrics (see Table 3-4) have been used.
Sampling a single habitat, such as riffles, limits
the variability inherent in sampling natural
habitats. This can produce a more repeatable
characterization of the biological condition of a
stream because sampling bias is reduced, whereas
sampling of multiple habitats must be carefully
standardized to reduce investigator bias and to
control for sampling efficiency problems.
If the biological assessment strategy is to sample a
single habitat, the most representative stable
habitat conducive to macroinvertebrate
colonization should be chosen. The most suitable
habitat choice will vary regionally. The key is to
select one habitat that supports a similar
assemblage for benthos within a range of stream
sizes, is the most representative of the stream type
or class under investigation, and is likely to
reflect anthropogenic disturbances within the
watershed. Suitable habitat alternatives to riffles
for sampling benthos include snags, downed
trees, submerged aquatic vegetation beds,
emergent shoreline vegetation, and the most
prevalent substrate.
The RBPs recommended by EPA specify that a
subsample of 100 organisms be used for a
biological assessment; however, several states use
200 to 300 organisms or more. Agencies should
evaluate the level of subsampling required to meet
their objectives. The level of taxonomic
identification should be specified in the study
design and is determined by the study objectives.
Identification to the species level gives the most
accurate information on pollution tolerances and
sensitivities, though some metrics or analytical
techniques might require identification only to the
order, family, or genus. Verification of
-------
Chapters
Table 3-3. Scoring criteria for the core metrics as determined by the 25th percentile of the metric
values from the Middle Rockies-Central Ecoregion, Wyoming.
: Metric
Stream
Class
Score
EPT Taxa
% Plecoptera
% Ephemeroptera
% Chironomidae
Predator Taxa
% Scrapers
MHBI
BC!
CTQD
Shannon H
% Multivoltine
% Univoltine
% Collector-Filterers
Range of Aggregate Score
Elevation > 6^500 ft
5
>14
>7%
__
<36%
>5
>5%
<4.3
>74
<99
>3.5
<48%
>40%
•' "','3- "'. ;
14-8
7%-4%
„
36%-68%
5-3
5%-3%
4.3-4.8
74-42
99-112
3.5-1.8
48%-57%
40%-20%
1
<8
<4%
>68%
<3
<3%
>4.8
<42
>112
<1.8
>57%
<20%
11-55
Elevation < 6,500 ft
5
>18
>22%
>6%
<12%
>7
>8%
<3.7
>79
'
<2.6%
3
18-10
22%-11%
6%-4%
12%-39%
7-4
8%-5%
3.7-4.7
79-46
2.6-23.2
1 ' '•'
<9
<11%
<4%
>39%
<4
<5%
>4.7
<46
__
__
>23.2%
9-45
taxonomic identifications is critical and can be
accomplished by (1) comparing specimens with a
reference specimen collection or (2) sending
specimens to taxonomic experts familiar with the
group in question.
Benthic macroinvertebrates can be collected
actively or passively. Two of the more
commonly used active methods use a square-
meter kicknet or a long-handle D-frame. The
former is typically used at sites that are
considered to be in higher-gradient (riffle-
prevalent) streams (Plafkin et al., 1989); the latter
is used primarily in coastal plains streams and is
standardized as the 20-jab method (USEPA,
1997). In both of these methods, organisms are
dislodged from their substrate by the sampler and
captured in a net. Passive collection approaches
include the Hester-Dendy multiplate sampler and
rock baskets. These are considered artificial
substrates. They are placed in the stream or
stream bottom and left for a standardized amount
of time. Upon retrieval, the invertebrates are
removed from the sampling units in the
laboratory. For further information on sampling
methods, see Klemm et al. (1990).
-------
Biological Monitoring
Chapters
Table 3-4. Scoring criteria for the metrics as determined by the 25th percentile of the metric values for
the two aggregated subecoregions for Florida streams.
Metric
# of Total Taxa
Stream
Class
Score
EPT Index
# Crustacea + Mollusca Taxa
% Dominant Taxon
% Diptera
% Crustacea + Mollusca
Florida Index
% Filterers
% Shredders
Range of Aggregate Score
Panhandle
5
k31
:>7
_
*18
i12
3
16-30
4-6
-.
<:20
s38
—
0-8
0-6
kO-9
1
0-15
0-3
__
>20
>38
0-8
0-6
0-9
7-29
Peninsula
5
*27
—
_.
.-
:>?
*8
__
3
14-26
:>4
>4
^37
<:32
:>16
4-6
4-7
^13
1,
0-13
0-3
0-3
>37
>32
0-15
0-3
0-3
0-12
9-33
3.7.2 Fish Sampling
Fish surveys should yield a representative sample
of the species present at all habitats within a
sampling reach that is representative of the
stream. Sampling reaches should ensure that
generally comparable habitats will exist at each
station. 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
(Klemmetal., 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.
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 also be avoided because these
areas will have habitat characteristics more typical
of the larger waterbody (Karr et al., 1986).
Sampling station lengths range from 100 to 200
meters for small streams and 500 to 1000 meters
for rivers. Some agencies identify their sampling
reach by measuring.a length of stream that is 20
to 40 times the stream width.
Fish are generally identified to the species or
subspecies level. For biological assessments of
the entire assemblage, the gear and methods used
-------
Chapter 3
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). The Index of Biotic
Integrity (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).
Length and Weight 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.
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 of fish 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) arid Weatherley (1972).
Fish External Anomalies
The physical appearance of fish 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.
Periphyton
Of the three biological assemblages discussed in
this chapter, periphyton is perhaps the least used,
though the information potential can be dramatic
as well as cost-effective. Laboratory analysis of
species composition is labor-intensive. Because
species within a genus can display varying
tolerances to a disturbance, diatoms must be
identified to species. Rosen (1995) estimates an
average of 2 hours per sample to identify 500
organisms to species, with processing time
decreasing as taxonomic expertise is gained.
Periphyton are a community of organisms that
adhere to and form a surface coating on stones,
plants, and other submerged objects. These can
take the form of soft algae, algal or filamentous
mats, or diatoms. As the primary producers in
-------
Biological Monitoring
Chapter 3
the stream ecosystem, their importance to the
food web cannot be overstated. The advantages
for using the periphyton assemblage in a
bioassessment program are many:
• They have rapid reproduction rates and short
life cycles and thus respond quickly to
perturbation, which makes them valuable
indicators of short-term impacts.
• Because they are primary producers and
ubiquitous in all waters, they are directly
affected by water quality.
* Periphyton sampling is rapid and requires few
personnel, and results are easily quantified.
• 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 are known for many
species or assemblages of diatoms.
• Periphyton are sensitive to many abiotic
factors that might not be detectable in the
insect and fish assemblages.
The state of Kentucky has developed a Diatom
Bioassessment Index (DBI), currently used in
water quality assessments (Kentucky Department
of Environmental Protection, 1993). Metrics use
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, which are used to determine
aquatic life use.
Network design refers to the array, or network,
of sampling sites selected for a monitoring
program. It usually takes one of two forms;
* Probabilistic design: Network that
includes sampling sites selected randomly
to provide an unbiased assessment of the
condition of the waterbody at a scale above
the individual site or stream; can address
questions at multiple scales,
• Targeted design: Network that includes
sampling sites selected based on known
existing problems, knowledge of upcoming
events in the watershed, or a surrounding
area that will adversely affect the waterbody
such as development or deforestation; or
installation of BMPs or habitat restoration
that is intended to improve waterbody
quality; provides assessments of individual
sites or reaches.'
An integrated design combines these two
approaches and incorporates multiple sampling
scales and monitoring objectives.
For diatoms, Montana (Bahls, 1993) uses a
diversity index, a similarity index, and a siltation
index. Three other metrics—dominant phylum,
indicator taxa, and number of genera—are used
for soft-bodied algae to support the diatom
assessment.
3.8 BlOMONITORING PROGRAM DESIGN
The design of a biomonitoring program (similar
to other types of monitoring programs) will
depend ultimately on the goals and objectives of
the program. Several of the objectives identified
-------
Chapter 3
in Chapter 2 of this document can be directly
addressed using a properly designed
biomonitoring program. These objectives may
differ in spatial and temporal scales, therefore
requiring different monitoring designs as reflected
in differences in the site selection process,
number of sites sampled, and time and frequency
of sampling. The sampling design used in
nonpoint source biological monitoring consists of
one of three types of network designs depending
on the objectives of the programs—probabilistic,
targeted, or integrated design. Objectives that are
site-specific, such as determining whether
biological impairment exists at a given site, are
addressed using a targeted monitoring design
(Table 3-5). Objectives that address questions of
large-scale status and trends, for example, require
a probabilistic design. For many nonpoint source
objectives (see Chapter 2), an integrated network
monitoring design is most appropriate.
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. The framework
focuses on river and stream resources at its higher
resolutions, but could be modified for other
waterbody types such as lakes and wetlands. In
its simplest form, the nested hierarchy consists of
five levels:
• Ecoregions
• Watersheds/subwatersheds
• Valley segments
• Habitat complexes (e.g., stream reach)
• Habitat units (e.g., riffle)
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. Appropriately
designed probabilistic sampling can provide
results such as the percentage of waterbodies in a
geographic area that are impaired (status), or, if
the sampling is repeated at regular intervals, an
assessment of the trends in the percentage of
impaired waters. Probabilistic site selection is
most appropriate for an unbiased estimate of the
status and temporal behavior of waterbodies on a
large geographic scale.
Assessments of waterbodies and subsequent
monitoring often occur at the watershed scale,
within which both targeted and probabilistic sites
could be selected. A probabilistic design would
yield information on the watershed scale as well
as on the site- or stream-specific scale; these
locations might or might not be impaired. The
targeted design would ensure that known problem
sites or sites of special interest are evaluated and
their response over time is assessed.
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. It is at this scale that the
effectiveness of specific pollution controls, BMP
installation/implementation, natural resource
management activities, or physical habitat
restoration can be monitored.
Since a target population is recognized to consist
of groups that each have internal homogeneity
(relative to other groups), it can be stratified to
minimize within-group variance and maximize
-------
Biological Monitoring
Chapter 3
Table 3-5. Comparison of probabilistic 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 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.
Stratified random sampling requires
prior knowledge for delineating the
strata (MacDonald et al., 1991).
Targeted
Design
Systematic sampling along a stream
or river can be 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 restoration
projects 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.
Systematic sampling can result in
biased results if there is a systematic
variation in the sampled population.
-------
Chapter 3
•m
among-group variance (Gilbert, 1987). Table 3-6
summarizes a waterbody stratification hierarchy
for streams and rivers, lakes, reservoirs,
estuaries, wetlands, and ground water. With the
exception Of estuaries, the highest-level strata
would be ecoregions and subecoregions.
Biogeographic provinces (e.g., Virginian and
Louisianian Provinces used in the Environmental
Monitoring and Assessment Program (EMAP), as
described by Weisberg et al. (1993)) are more
appropriate as the highest stratification level for
estuaries because of the relatively large size of
their watersheds and the fact that they are
influenced directly by marine processes.
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, poststratification 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.
3.8. f Process of Randomized Sampling
Site Selection
Probabilistic sampling designs require the random
selection of sampling sites within the basic design
(e.g., simple random, stratified random,
multistage; Chapter 4). Three major steps are
involved in selecting sampling sites using a
probabilistic design:
(1) Identify the level of site classification.
Monitoring program objectives will
dictate the geographic extent to which
monitoring is to be done, for instance
statewide, county-wide, within an
ecoregion, or within a watershed. This
level of site classification should be
identified initially.
(2) Stratify the site classes. A sampling
design appropriate to the monitoring
objectives must be selected. For
probabilistic designs, simple random
sampling is not usually the optimal
method. It can produce clusters of
sampling sites that might not be
representative of the larger scale area of
interest (e.g., Hurlburt, 1984).
Therefore, some sort of stratification is
preferred for ensuring a dispersed
distribution of site locations.
Stratification can begin at an ecoregion
site classification level and proceed to
more specific levels of resolution as
necessary to meet project objectives
(Table 3-6). If there are clear clusters of
differing land use among watersheds, the
watersheds may be further stratified to
ensure inclusion of an even distribution of
land use types (e.g., subwatersheds
having different levels of urban
development). Waterbodies can be
further stratified by section or segments.
For instance, streams can be stratified by
stream order (first, second, third, etc.),
size of drainage area, or specific sections
of a bay or lake.
(3) Select sampling sites. Sampling units or
sites within each stratum (or other
delineation as dictated by the general
design) are randomly selected. This
approach provides a basis for making
-------
Biological Monitoring
Chapters
£
a
s
.a>
!c
,2
ys
S
:>>
.0
CD
1
CD
CO
CD
JO
0
.2
^
J5
"D
C
D
E
o
Wetlands '
0
0)
'C
.53
Reservoirs '"•''
u
(0
^
J3
TJ
ro In
Eo
>
w
c •<->
o w
JJ3 w
J2 <"
3 'c
O M^
Q. 0
o
CD
tn
c
CO
h-
Transect
upland or deep water
boundaries
T-,
e
*fQ
0
^ •«
,CD CO
CO JQ
L.
CD
1 .5 I 1 I
•o
CD
CO 8 JQ
CD *CD 2
.-:>*- c
_ ^"15 | -g w
O u co ~ .£ Jb <^
75
CZ r^\ ^-*,
.0 _g 3
2 75.52-
CD E .1±
Q. (0 C
O CO -D
C
O
*D)
i§
CD"
c
~
CD
^» ^^
Wetland system type
(marine, estuarine, it
lacustrine, palustrine;
O
In
Q.
CO
*- .»
TO CD
8 c
CD ._
3 Q.
Ecoregion
c
_o
'CD
CD
8
uu
c
o
'TO
£
o
o
LJJ
•0
CD
JC
£2
CD
1
JC
"o
Jd
t—
l-i
Watershed
recharge, discharge <
rt?
2 '
CD
I
Size/surface area
of reservoir (km2)
[watershed
area/basin surface
area]
CO £P
CD c
col
CD ^
CO 315
^^
CD
.23
m
1
CD C
I**
-5- 2: CD •%?
Q. CD ™ ^ -P^
•& C §-T-, '5
^ ^^ CO fli O"
CD r~ tO
§• £ -S § "55
< 3- .2 8 3
CD g5
^ ._
V- "O
CO o
W o
Class (based on vegf
type; substrate and fl
regime; hydroperiod)
~O j^^
"ffi ^,
CD "HT
|8
> CO
Hydrology (water
level fluctuation/
drawdown;
retention time
stratification)
i "H"
>> § •-
.2 <= £ 2
!3 ^ ^E •- 1
l_
CD
111-
C^ "5 5- CO
^
"co S -o
'i— C CD
CD CD •*-J
CO .£ j_- "O f ^
J— "O i« ' — »•;
CD 2 ° £
oj ^J, E £2 CD
"S"^ 2 i 2
Flooding regime
water chemistry
soil type
75 £
•o Q-
•~ CD
"^ T3 -^
0) - £•
(D C 'p
c 'to ~
O CO CO
N JO tn
Characteristic
water quality
(natural
conditions)
o
'w :i? ^~,
te §_ g
'o cr co j2
S ^ ll
x: ro ro §
o g £- 8
tz
CD
E
CD
CD
tn tn
^ 3
>, ° ° ^
-^ CD CD Q
.9 .§
-------
Chapter 3
CD
'H
8
^^
CO
Q)
CD
1
O)
!
|
J
«
C
(
o
0
: . ' ' £
• r
t>
5
01
CO
ta
n
;!
•T3
.0"
2
eservoi
OL
o
CO
j£
CO
•o
c
Streams i
Rivers
c •<->
o «
!!
o •*-
Q. 0
CD
O
N
-^
Q.
CD
Q
co
cu •£ >,
c: £0 co
N — ^ "
i..i?-ill
3 c J -i <» "« .2 ^
g.§ 1 § li I | |
-ii>i;J5-P-.-c: co-a
^^
i • - a> if ^ -2
O^ N CO UJ .— ^T
^** fli * ^ •*— ' O
o O *- ^ C CO ir
| g.5 c?-§ grg
S it co > co co E
rt
2.
3
2
a.
CD
^.
"ZQ -^
'£ c
^ fe 55 ~
O » Jjr ^
in — " Q. *
~~"-§ S-
till
yj "i-j ~- C
•§ ^ fe -2
co .S S..E
S
5
CO
"33
£
co
.a
CO
a.
CD
Q
CD i
S gl
til
CD 3 O
Q & Q.
"co
15
CO
x:
<3
O
'E
•35
s
1o
.a
3
CO
-~. S
]§ s
CD ^ra
to o
•§.a
co E
•CD
S la
2 ^
to 2
co E
to
Microhabit
CO
3
cc c
° £
CD
N-
O5
_J
CD
4_f
CD
C
CO
O
O
CO
en
— :
CO
CD
CD
0)
33
*-_
15
"CD
c
o
,
n
33
^~
5
3
Q
S
C33
"CD
r
CD
0
5
CD
J
3
.- O3
rog
) ^~
> ro
? CD
0)
-------
Biological Monitoring
Chapters
general statements about the condition of the
entire stratum, including sites not sampled.
3.8.2 Targeted Site Selection
In a targeted site selection design, sites are
selected based on the location of known or
suspected perturbations (stressors), planned point
source controls, or BMPs in the geographic area
of concern. NPDES permits, urban stormwater
sites, timber harvest areas, rangeland, row crop
fanning, construction sites, and Superfund sites
are all 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 might also be chosen
as targeted sites.
3.8.3 Integrated Network Design
Integrated network design consists of integrating
multiple monitoring subprograms to effectively
meet various monitoring objectives and improve
the applicability of data. An emerging biological
monitoring program in Prince George's County,
Maryland, is used here as a useful example
demonstrating the design components reviewed
above (see box on next page).
Prince George's County is interested in assessing
the status and trends of biological stream
resources in the county with known confidence.
Assessment levels include county-wide,
watershed-wide, and stream-specific. Biological
assessment, based on sampling benthic
macroinvertebrates, is used as the indicator of
stream condition. Judgment of impairment or
nonimpairment results from comparison to
reference conditions (see Section 3.4). Two
major components of this program are volunteer
monitoring of selected sites and non-volunteer
monitoring of probability-based and targeted sites.
The volunteer sites serve for public education and
some initial screening of stream conditions.
Those sites monitored by the non-volunteer
program are intended to provide unbiased
estimates of biological status of streams
throughout the county, trends in their condition,
problem identification, evaluation of management
activities (e.g., restoration, BMP installation,
chemical controls, and altered land use practices),
and data for eventual establishment of cause-and-
effect associations.
Three types of sites are monitored in this program
to address the county's multiple goals: targeted
sites, reference sites, and probability sites. Each
site type addresses specific questions on stream
status in Prince George's County. Targeted sites
are sampled semiannually during two program
index periods—spring and fall. Annual sampling
allows estimation of intra- and interannual
variation in the measured variables and indices,
and it allows estimation of trends after several
years of monitoring. After the first 2 or 3 years
of monitoring, the data will be reevaluated to
determine whether sampling at longer intervals
(for example, every other year) is sufficient to
detect trends. Data from targeted sites can
document the decline or recovery of streams
subject to specific stresses and will allow
assessment of restoration and mitigation efforts.
A set of 15 to 20 reference sites (having over 50
percent forest cover) are monitored semiannually
as are the targeted sites. These data allow
estimation of annual variation and trends in the
biological characteristics of the reference sites.
These estimates are critical for determining the
biological status of test sites.
Forty-one watersheds have been identified to
address status and trends for the county. Site
-------
Chapter 3
Case Study
Development of an Integrated Biological
Monitoring and Assessment Program
in Prince; George's County, Maryland
Prince George's County, Maryland, immediately east of Washington, DC, is developing a biological
monitoring program to assess the status and trends in ecological condition and the physical habitat
quality in county streams. Program goals include:
• Document and monitor the biological status and trends of county streams,
• Integrate data from biological, chemical, and physical monitoring programs to make a
comprehensive assessment of the county's stream resources.
• Use biological monitoring, data to identify and characterize impairment to the ecological system.
• Further public education in environmental problems through a component of the biological
. monitoring program tailored for layperson involvement.
• Evaluate the effectiveness of environmental management and mitigation activities.
The program design includes both probabilistic and targeted elements that will allow specific questions
to be addressed at three spatial scales—county-wide, watershed-wide, and stream-specific.
Approximately 100 sampling locations have been selected for the initial year of monitoring (1995). In
two separate sampling periods (early spring and fall), approximately 50 probability sites, 20 known
problem sites, 20 reference sites, and another 15 sites will be used for either confirmation of volunteer
monitoring results or quality control samples.
The sampling units are stream segments between confluences. Segments are sampled at accessible
points. Sampling is two-stage: for the first stage, watersheds are selected randomly so that one-fifth of
the watersheds in the county are sampled in any one year and all watersheds will be sampled in the
fifth year of the program. The second stage is random selection of stream segments within
watersheds, stratified by stream order, so that sampling effort is allocated optimally among three
stream orders.
Used by permission of Watershed Protection Branch, Department of Environmental Resources, Prince George's Gauntv
Maryland. '
selection is in two stages. In the first stage, a set
of watersheds are selected randomly. In the
second stage, stream segments within the selected
watersheds are chosen at random for sampling.
In each year of the monitoring program, a set of 6
to 10 watersheds are selected (depending on size)
and approximately 45 stream segments of those
watersheds are sampled. After 5 years, all
watersheds in the county will have been sampled,
and in the sixth year the program will return to
the originally sampled watersheds. An estimate
of status can be made for each watershed every 5
years. The status of streams county-wide can be
estimated from the first year on. The first year's
estimate will be based on a small sample of
watersheds and segments and will have greater
uncertainty than the estimates developed in later
years.
-------
Biological Monitoring
Chapter 3
Prior knowledge of land use in the county was
applied to stratify watersheds and sites.
Urbanization is known to affect stream
hydrology, water quality, habitat, and biota, and
the northern watersheds of the county are urban
and suburban, being close to Washington, DC.
Therefore, Prince George's County was divided
(stratified) into northern and southern watersheds
so that in any given year, an equal number of
watersheds would be selected in the more urban
north and in the more rural southern parts.
3.9 MONITORING TRENDS IN BIOLOGICAL
CONDITION
Two separate factors affect our ability to
distinguish trends over time in biological
monitoring. The first factor, common to any
trend analysis, are those sources of variability
inherent in the measurements obtained from the
monitoring sites. A significant challenge is
distinguishing random changes in biological
monitoring data over time from actual trends.
Section 4.4 outlines statistical considerations
regarding this issue, and Figures 4-4 to 4-7
illustrate several common types of data patterns
that might be obtained over time in a water
quality monitoring program. One of the solutions
to tin's challenge is to have either a long time
series of data or a sufficient number of
monitoring sites (probabilistic, targeted, or
integrated design) so that the data can be
statistically evaluated.
A second factor affecting the ability to detect
trends in a biomonitoring program is the observed
relationship between reference site data and
monitoring site data over time. This factor is
somewhat unique to biomonitoring programs
because a biological assessment at any given time
is dependent on data from two sources—the
population of reference sites and the monitoring
sites. Thus, an accurate interpretation of trends
in a biological monitoring nonpoint source
program is accomplished, in part, by using a
comparative analysis of reference site and
monitoring site data. Figure 3-7 illustrates some
possible trends that could be observed in a
biological monitoring program. These types of
data can be analyzed in a variety of ways as
described in Section 4.4 and in Green (1993) and
Smith et al. (1988).
Accurate characterization of trends in biological
data over time depends on the degree to which
sources of variability in the data are defined and
characterized. In general, there are two major
sources of variability—natural and experimental.
Natural sources of variation include seasonal
effects such as species life cycles, natural
disturbances such as floods and fires, and
microhabitat differences among sites or over time.
Experimental sources of variability include
collection method and gear variation. The
previous discussion of biological assessment
methods in this chapter addresses many of the ,
common natural and experimental sources of
variability. All field collection methods should be
documented and standardized in approved SOPs.
The use of a well-defined and appropriate field
collection method, for example, reduces
experimental sources of variability. Incorporation
of a population of ecoregional reference sites into
a biological monitoring design serves as a control
for natural sources of variability over time, in
addition to providing a sound data assessment,
framework. An apparently downward trend for
certain sites in watersheds over time, for
example, may be open to interpretation if the
same trend is observed in reference sites (Figure
3-7). Repeated sampling during the same index
period over time also provides some control for
natural sources of variability.
Although natural sources of variability are not
often amenable to characterization, experimental
-------
Chapter 3
50
^-40
& 30
E
I 20
0)
•§ 10
I
0
sO
^r
<$?
rfi
1977 1978 1979 1980 1982 1983 1984 1985 1986 19871988 1989 1990 1991
YEAR
Figure 3-7. Some trends that might be observed during the course of a biological monitoring program.
or method-sources of variation can and should be
defined. Factors such as precision and sensitivity
of the assessment scores are achieved by
collecting and analyzing multiple samples from
the same site using consistent procedures and by
sampling and analyzing samples from multiple
sites that are similar, particularly ecoregional
reference sites. One of the results of this method
characterization process is the ability to define the
statistical power of a sampling and bioas'sessment
method. Statistical power is the degree to which
a Type 2 error has a given probability of
occurrence. Put simply, this is the probability of
assessing' a site as unimpaired when in fact it is
impaired; or concluding that there is no trend or
change in water quality over time when in fact
there has been a change.
Defining the statistical power of a given
bioassessment method allows one to rigorously
determine the number of sites necessary in a
monitoring program to detect a given level of
impairment, or a certain trend over time, with a
known degree of confidence. A power analysis
achieves this objective by constructing an
empirical relationship between the number of sites
or measurements observed and the resultant
difference in assessment score detected between
the reference and test sites. A bioassessment
method that has substantial intra-site variability
will have reduced statistical power resulting in a
greater number of sampling sites to distinguish a
given level of impairment. Similarly, a very
heterogeneous population of ecoregional
reference sites, with widely ranging
bioassessment scores, will also yield reduced
statistical power.
-------
Biological Monitoring
Chapter 3
An example of power analysis is shown for the
Delaware coastal plain streams in which the
benthic macroinvertebrate community was the
biomonitoring indicator used to detect status and
trends of water quality (Maxted and Dickey 1993;
Figure 3-8). The power analysis graph shows a ,
steep decline in the number of monitoring sites
necessary as percent difference in assessment
score (between reference and monitoring sites)
decreases. In other words, if one is interested in
detecting a relatively small difference in
assessment score between the reference and
monitoring sites or if, alternatively, one wants to
detect a relatively small change in assessment
score over time with a high degree of confidence,
a greater number of monitoring sites within a
Determining the Appropriate Sampling Effort: An Example Using DNREC Data
from the Mid-Atlantic Coastal Plain Ecoregion
Most environmental monitoring programs are designed to avoid detecting a problem or
noncompliance that does not really exist. These false positives are called Type I errors. It
is traditional to accept a 5 percent probability of false positives as occurring (see Section
4.1 for further discussion). The probability of false negatives (Type II error, failing to detect
a problem that does exist) is evaluated through power analysis. A power analysis provides
an estimate of the number of measurements (sample size) required to detect a change for
a given significance level (usually, « = 0.05), with the power typically set at 80 percent.
Power analysis requires prior knowledge of, or rational assumptions on, the statistical
properties of the data, in particular, the nature of the variability associated with them. Two
types of variance are used in power analysis: variance of total bioassessment scores
among all reference sites and variance among total bioassessment scores, within a site.
Maxted and Dickey (1993), as part of their nonpoint source biological monitoring program,
produced replicated bioassessment data from 23 sites. Total bioassessment scores were
developed for each using an RBP - type approach for normalization of six metrics: (1) taxa
richness, (2) EPT index, (3) percent EPT abundance, (4) percent Chironomidae abundance,
(5) percent dominant family, and (6) family biotic index.
The sampling error takes into account the natural variability among multiple sites;
measurement error is the variability observed among multiple samples at the same site,
that is, it tells us how well the site is being characterized. The power analysis estimates the
number of samples required for a given percent difference in total bioassessment score.
Using the sampling error, it estimated that in order to detect a 20 percent difference in the
index value (between a population of reference sites and a population of test sites) a
minimum of eight reference and eight test sites (subjected to NPS pollution) should be
sampled (solid line in accompanying figure). Power analysis using the measurement error
indicates that three repeated measurements at each site are necessary to detect a 20
percent difference in total bioassessment score (broken line in the accompanying figure).
'••••••••••••••••ii^HHi^BM^BBBB^Bgl^'Igl^'
Figure 3-8. Sample power analysis of a bioassessment method.
-------
Chapter 3
CO
4— »
c
CD
ro
0)
CO
-------
Biological Monitoring
Chapter 3
stratum (i.e., stream order, urban versus
agricultural) will need to be sampled.
The Prince George's County case study reviewed
earlier illustrates how power analysis was used to
determine the number of monitoring sites needed
to obtain meaningful biomonitoring data. A
design consideration was that impairment, as
defined by a 50 percent reduction of the reference
condition, be detected with 80 percent probability
of detection, and 95 percent confidence that
observed differences are significant. Power
analysis (Fiugre 3-8) revealed that a sample of
two sites on a stream class could be used to detect
a difference of 50 percent using bioassessment
procedures in coastal plain streams. Assessment
of a single watershed can be done by sampling at
least two sites of each stream order present in the
watershed. Most of the 41 watersheds in this
county are third-order coastal plain streams.
Therefore, an average of 6 sites per watershed, or
approximately 246 sites, should be sampled for an
assessment of all watersheds. This represents
approximately 25 percent of the total population
of stream segments in the county.
The above considerations led to the target
sampling design of 25 percent of stream
segments. Because county-wide assessment is one
of the goals of this program, selection probability
was kept at 25 percent. The sampling rule used
in this program is at least 25 percent of stream
segments in each stream order are selected in
each watershed. For example, one segment is
selected if there are one to four segments of a
given order in the watershed, two segments are
selected if there are five to eight, and so on. The
probability that a watershed will be included in
sampling thus varies slightly among watersheds,
and this probability is used as a weighting factor
in county-wide assessment and estimation. Given
an annual sampling effort of approximately 50
probability sites per year, a 5-year rotation of
sampling will allow assessment of all watersheds
over the 5-year period.
Repeated sampling of monitoring and reference
sites over tune, along with adequate
characterization of bioassessment method
precision, can yield significant management
information as demonstrated by the Ohio EPA
bioassessment program. Since 1977, Ohio EPA
(1992) has used assessments of the benthic
macroinvertebrate assemblage to document
changes in the biological condition of a
waterbody. Figure 3-7 illustrates annual results
of biological assessments using the Invertebrate
Community Index (ICI) (Ohio EPA, 1992) over a
14-year period in the Cuyahoga River. A 95
percent confidence interval of ±4 ICI units was
determined based on analysis of intra- and inter-
site variance in ecoregional reference scores. The
graph demonstrates several features, including a
general improvement in water quality over time.
Furthermore, certain apparent changes in score
between 1977 and 1980 are in fact illusory since
the confidence intervals overlap. Thus, actual
trends can be statistically differentiated from
random changes in the data over time.
3.10 OVERVIEW OF SOME STATE PROGRAMS
Biological monitoring programs of five states are
highlighted to illustrate technical components of
biological monitoring programs, which are
summarized for nonpoint source evaluations
in Table 3-7. The Delaware Department of
Natural Resources and Environmental
Conservation (DNREC) led the Mid-Atlantic
Coastal Streams (MACS) workgroup in adapting
the RBPs to low-gradient streams. The MACS
workgroup consists of technical staff from
biomonitoring programs of Delaware, Maryland,
Virginia, and North Carolina (USEPA, 1997).
They were able to determine that the most
appropriate method for obtaining a representative
-------
Chapter 3
Table 3-7. Summary of the primary technical issues related to biological monitoring for nonpoint source
evaluations.
Biomonitoring for Nonpoint-source Evaluations
Sampling Area/
Size
Sampling is conducted over a stream reach, such that a composite of different
habitats or different parts of a habitat is created. Natural biological
heterogeneity is accounted for by sampling a relatively large area. Fish
sampling may extend from 100 yards to 20 or 30 times the stream width.
Benthic sampling may extend over several riffles or a composite of habitats
taken from a 100-yard length of stream.
Replication
Replication at a site is important to evaluate within-site variability. However,
several samples taken at a single site would be pseudoreplication if used to
evaluate effects of impacts, such as nonpoint source pollution. Sites from
different streams and watersheds are considered replicates to assess status or
condition of regions or watershed basins. Monitoring can be accomplished
with single large samples that sufficiently represent the stream reach under
study.
Sampling Gear
Gear can be as rigorous and quantitative as a program deems necessary.
However, gear must efficiently sample the targeted assemblage and specified
habitat and be maintained in good operational condition. Electrofishing is the
preferred gear for fish assemblages. Most state agencies have selected a kick
seine or D-frame, and artificial substrates are used for periphyton.
Biological
Index Period
The investigator must find a compromise between selecting a sampling period
that is representative of the biological community and selecting one that
reflects the worst-case conditions of pollutant stress. Seasonality is an
important consideration because the taxonomic and functional feeding group
compositions change naturally throughout the year in response to emergence
and reproductive cycles. The optimal biological index period will vary
throughout the United States. Some states, such as Florida, have more than
one index period for sampling.
sample of benthic macroinvertebrates from low-
gradient, sandy-bottomed, coastal plain streams
was a multihabitat approach. The approach uses
a standard D-frame net and samples the most
dominant habitat types from a 100-meter reach in
proportion to their frequency. The Delaware
DNREC samples approximately 300 sites on an
annual basis using this approach (Table 3-8). The
Florida Department of Environmental Protection
has adopted this method for its nonpoint source
monitoring program (Table 3-9) and has
established reference conditions. The State of
Montana, Department of Health and Environ-
mental Sciences, uses a different approach for its
higher-gradient streams, which are generally
cobble-bottomed (Table 3-10). It has adapted a
traveling kick method for macroinvertebrates and
is developing reference conditions. The health of
-------
Biological Monitoring
Chapter 3
Table 3-8. Selected biomonitoring program components, Delaware Department of Natural Resources
and Environmental Conservation (DNREC).
Macroinvertebrates
Habitat Selection
Sample Gear/
Preservation
Sampling Method
Subsampling and
Enumeration
Taxonomic Level
QA procedures
Habitat
Assessment
Comments
Stable habitats that are 5% of the 100-meter assessment area. Habitats
include snags, submerged macrophytes, banks/root systems, and riffles.
Sampled in proportion to representation.
Standard aquatic D-frame dip net (0.3-meter width, 700- to 900-micron mesh),
sieve bucket (600-micron mesh), 70% ethanol, storage containers (1- to 2-
liter).
20-jab method; a single jab consists of thrusting the net into a productive
habitat for a distance of approximately 1 meter. 20 jabs composited across
habitats (6.2 m2). Samples are cleaned by running stream water through the
net, then transferring to a sieve bucket for further cleaning. Transferred to a
storage container and preserved in 70% ethanol.
Preserved samples returned to the lab for processing. Subsampling to 100-
organism level.
Currently at family level. Investigating efficacy of doing genus-level
identifications.
Same as RBPs (Plafkin et al., 1989); habitat assessments are evaluated by
all investigators while reviewing the slides and field notes. Field investigators
must have proper training.
Field data sheet: 7 parameters numerically scored (1-20) similar to RBPs;
documents other physical and water quality data.
Standard operating procedures in draft form and prepared by Mid-Atlantic
Coastal Streams Workgroup.
the fish assemblage is the primary biological
monitoring indicator for the North Dakota
Department of Environmental Health (Table
3-11), which is planning to further develop its
monitoring program to include benthic
macroinvertebrates. The State of Vermont,
Department of Environmental Conservation, does
three types of sampling (Table 3-12)—fish,
benthic macroinvertebrates from riffles in high-
gradient streams, and benthic macroinvertebrates
from multihabitats in low-gradient streams.
-------
Chapter 3
Table 3-9. Selected, biomonitoring program components, Florida Department of Environmental
Protection (Florida DEP).
Macroinvertebrates
Habitat Selection
Sample Gear/
Preservative
Sampling Method
Subsampling and
Enumeration
Taxonomic Level
QA Procedures
Habitat
Assessment
Comments
Presence or absence of major productive habitats at each sampling
location is established during preliminary reconnaissance. Habitats
include: riffles, snags, aquatic vegetation, leaf packs, undercut banks/root
systems, leaf mat, rocky outcrops, muck/silt, sand. Major habitats
sampled equally; Group of minor habitats treated as a single major habitat
Standard D frame dip net (0.3 meter width 600 micron mesh), wide mouth
jars, formalin.
20 jab dip net sample, composite sample across habitats. Individual jabs
are approximately 0.5 m making a total composite of 3 m2
Entire sample in gridded pan (5X3 cm grids), randomly select grids (1/72
of sample), remove contents, sort into taxonomic groups, continue until a
minimum of 100 organism are counted; a grid's entire contents must be
sorted.
Lowest taxonomic level (genus or species)
Replicate sampling for 10% of samples collected on an annual basis have
not been implemented but are planned. Resorting of 1 0% of samples:
Field crew undergo periodic training.
Field data sheet, 7 visually-estimated habitat parameters, weighted
equally. Physical/Chemical characterization field data sheet
Stream classification factors for establishing reference conditions based on
Ecoregion and subecoregion. Site selection factors: Availability of least
impaired and reference sites, specific monitoring issues, accessibility and
safety, compatibility of habitat. Standard operating procedures and report
describing state-wide nonpoint source program prepared by Florida DEP
and support contractors. ' • ;
-------
Biological Monitoring
Chapter 3
Table 3-10. Selected biomonitoring program components, Montana Department of Health and
Environmental Sciences (Montana DHES).
Macroinvertebrates
Region of State
Site Selection
Sample Gear
Sampling Method
Subsampling and
Enumeration
Taxonomic Level
QA Procedures
Habitat
Assessment
Comments
Entire
Riffles
12-inch D-frame net
Travelling kick across riffles, 1-2 diagonal collections (time and distance
recorded)
300-organism subsample in laboratory
Genus/species
Replication on selected sites and projects
Follows RBP habitat assessment approach
Lab processing done by outside contractors
-------
Chapter?
Table 3-11. Selected biomonitoring program components, North Dakota Department of Environmental
Health (North Dakota DEH).
Macroinvertebrates
Fish
Region of State
Site Selection
Sample Gear
Sampling Method
Subsampling and
Enumeration
Taxonomic Level
QA Procedures
Habitat
Assessment
Comments
Not developed as of current date
Planned to be started in 1997
Red River
25 sites to date; randomized
selection, but dictated by access
logistics
Electrofishing; shore-based longline
Minimum of 100 meters; 20 times
river width on wide rivers
Not applicable
Lowest positive taxon
10 percent repeat sampling
Based on RBPs
In development
-------
Biological Monitoring
Chapter 3
Table 3-12. Selected biomonitoring program components, Vermont Department of Environmental
Conservation (Vermont DEC).
Macroinvertebrates Fish
Region of State
Site Selection
Sample Gear
Sampling
Method
Sub-sampling
and Enumeration
Taxonomic Level
QA Procedures
Entire state;
high gradient
Riffles
Rectangular kick net
18 in x 8 in
500-fj.m mesh
2-min timed
composite; 30 sec in each of
2 points in slow area; 2
points in fast area
Preserve in field; subsample
gridded tray 1/4 sample and
min 300 animals
Specified in protocols; .
family-genus-species,
depending on order
All samples archived;
replicate samples always
collected; two people check
pick; two people trained in
each taxonomic order
Entire state;
low gradient
Woody debris
Macrophytes
Boulders
Rectangular kick net
18 in x 8 in
500-fj.m mesh
2-min sample; all
substrate materials are
hand scrubbed
Preserve in field
subsample gridded tray
(technique allows
density estimate for
site)
Protocols list taxonomic
level and key to be
used
All samples archived;
replicate samples
always collected; two
people check pick; two
people trained in each
taxonomic order
Entire state
Representative of
reach; mix of riffle,
run, pool
Electro-shocking
1-3 upstream
passes into
blocknet or vertical
drop instream
Total count
Species
Same person
conducts collection
and taxonomy
-------
Chapter 3
Table 3-12. (continued)
Habitat
Assessment
Comments
=====
Includes substrate
composition,
embeddedness, periphyton
cover type, canopy cover,
immediate riparian zone info
ave. stream velocity, depth
Also record temperature,
pH, alkalinity, condition and
characterization of site,
elevation, DO; also will look
at stream low flow
characteristics and surficial
and bedrock geology and
land use cover to help
differentiate sites as
"ecotype"; set biocriteria for
individual metrics
=======================
t\*r*fM Mf,^*^
Includes substrate
composition,
embeddedness,
periphyton cover type,
canopy cover,
immediate riparian
zone information,
average stream
velocity, depth
'
r/sn
Qualitative or
quantitative
transect method;
depth, velocity,
substrate
Has modified the
Index of Biotic
Integrity to fit
Vermont's wadable
streams
=============
-------
Biological Monitoring
Chapters
•i
i
§
ti
o tsf
OT w
i i
So
o
3 S
u.
• C
CO II — *?=
O 1 TS •£
'
5
8
s
•C
«
.o
m
T3 l
CO
co
0)
XX
XX
X
XX X
X
X
XX
X
X
X
X
X
X
XX
X
X
XX
XX
X
X
X
XXXXX
<< g <" -S-
I 1 ^ 1
-------
Chapter 3
&£
85
_o
I
x
X
X
XX
x
XX
XX
x
X
x x
x
X
x
xx
X
CD
•4=
CO
CO
OJ
-------
Biological Monitorinc
Q
—
c
c
p
CO
0)
JD
£
^^^^^BHJJJ^^^^MB^^^^^^HBI^^^^^MMii^^^^^^Bi^^^^^^^^H^^^^^B
| _^^^^^^^^^^^^^_^^^^_^^__^^^__
c
o
O
tn
3
'c 5
I i
o c:
88
E <=
§ c
•5 c
it
II
J|
•" •'
K
||
^ i
o
•Z
c
ll
C
•*•
S
^
o £
i j
C.
en
i
i
c
- .«"
1
1
X X X
X XX
XX X
XX X
X X
X X
XX X
X X X X X
'o
CO
(O
8
s
o
S~~
'I1
o
S
II
Q
o
8
ra
;>
_c
II
O
o
§.
8
o
o
o
H
n
m
2
3
^
~CO
>^
"c
p
o
O
ll
<
1
o
O)
S
Metric Ca
U)
ra
cu
"w
'E
o
E
ra
u>
E
•§
3
=
ra
c
o
0)
E
'c
CD
a.
ra
ra
y)
m
en
X
o
j}
o
2
>v
.a
•a
CD
M
CD
en
O)
"Metric su
It
en
CO
c
0
•o
c
ra
0?
oo
CO
c
ra
CO
W
CO
o>
— ^
"ro
"CD
^
ii
^
oo
C3>
^«
^
D.
UJ
g
O
00
m
c:
o
c:
ra
CD
T3
C
CD
cn
I
^_^
CD
00
CO
^
"ra
0)
1
E
o
M—
C
CD
.*;
.2
Chapter 3
-------
Chapters
Table 3-14. Examples of metric suites used for analysis of macroinvertebrate assemblaaes
1.
2.
3.
4.
5.
6.
7.
8.
9.
Alternative
Benthic Metrics
Total No. Taxa
% Change in Total Taxa
Richness
No. EPT Taxa .
No. Mayfly Taxa
No. Caddisfly Taxa
No. Stonefly Taxa
Missing Taxa (EPT)
No. Diptera Taxa
No. ofChironomidae
No. Intolerant Snail and
Mussel Species
Ratio EPT/Chironomidae
Abundance
Indicator Assemlage Index
% EPT Taxa
% Mayfly Composition
% Caddisfly Composition
% Tribe Tanytarsini
% Other Diptera and
Noninsect Composition
% Tolerant Organisms
% Corbicula Composition
% Oligochaete Composition
Ratio Hydropsychidae/
Tricoptera
% Ind. Dominant Taxon
% Ind. Two Dominant Taxa
Five Dominant Taxa in
Common
Common Taxa Index
Mrtrir in rnp rno RBP(dy
Category (a) (fej (c) |0 OR WA
A X X X X X X
A XXX
B X X XXX
B X
B X-
B
B X ,
B X
B .XX
B
B XXX
B XXX
B X
B X
B X
B X
B X
B X
B
B
B X X
A X XXX
A
A XX X
A X
B-IBI
(e)
X
X
X
X
X
X
X
X
-------
Biological Monitoring
Chapter 3
Table 3-14. (continued)
10,
11.
12.
13.
14,
15,
16.
17.
18,
19.
20.
21.
Alternative
Benthlc Metrics
Indicator Groups
% Ind. Omnivores and
Scavengers
% Ind. Collector Gatherers
and Filterers
% Ind. Filterers
% Ind. Grazers and Scrapers
Ratio Scrapers/Filterer
Collectors
Ratio Scrapers/(Scrapers +
FiHerer Collectors)
% Ind. Strict Predators
Raiio Shredders/Total Ind.
(% shredders)
% Similarity Functional
Feeding Groups (QSI)
Total Abundance
Pinkham-Pearson Community
Similarity Index
Community Loss Index
Jaccard Similarity Index
Quantitative Similarity Index
(Taxa)
Hilsenhoff Biotic Index
Chandler Biotic Score
Shannon-Werner Diversity
Index
Equitability
Index of Community Integrity
Metric ICI
Category (a)
B
D
D
D
D
D
D
D
D
D
A
A
A
A
A
B
B
A
A
A
RRP CRP RBP(d)
RBP RBP
(b) (c) ID OR WA
X X
X X
X
XXX
X
XXX
X X
X
X
X X
X
X X
X XXX
X
X
X
X
B-IBI
(e)
X
X
X
X
Metric Categories: A = Community structure, B = Taxonomic composition, C = Individual condition, D = Biological processes
a; Invertebrate Community Index, Ohio EPA (1987).
b: Rapid Bioassessment Protocols, Barbour et al. (1992) revised from Plafkin et al. (1989).
c: Rapid Bioassessment Protocols, Shackelford (1988).
d: Rapid Bioassessment Protocols, Hayslip (1993); ID = Idaho, OR = Oregon, WA = Washington.
(Note: these metrics in ID, OR, and WA are currently under evaluation)
et Benthic Index of Biotic Integrity, Kerans et al. (1992).
-------
4. DATA ANALYSIS
Data analysis begins in the monitoring program
design phase. Those responsible for monitoring
should identify the goals and objectives for
monitoring and the methods to be used for
analyzing the collected data. Monitoring
objectives should be specific statements of
measurable results to be achieved within a stated
time period (Ponce, 1980b). Chapter 2 provides
an overview of commonly encountered
monitoring objectives. Once goals and objectives
have been clearly established, data analysis
approaches can be explored.
Typical data analysis procedures usually begin
with screening and graphical methods, followed
by evaluating statistical assumptions, computing
summary statistics, and comparing groups of data.
The analyst should take care in addressing the
issues identified in the information expectations
report (Section 2.2). By selecting and applying
suitable methods, the data analyst responsible for
evaluating the data can prevent the "data
rich-information poor syndrome" (Ward 1996;
WardetaL, 1986).
This chapter provides detailed information on the
statistical analysis of environmental monitoring
data. The first section of the chapter is intended
for both the manager and data analyst. Its goal is
to acquaint the reader with key concepts and
issues related to data analysis. This section also
provides recommendations for selecting statistical
procedures for routine analyses and can be used
to guide the reader in selecting additional portions
of,the chapter for more in-depth reading.
4.1 INTRODUCTION
4.1.1 Estimation and Hypothesis Testing
Instead of presenting every observation collected,
the data analyst usually summarizes major
characteristics with a few descriptive statistics.
Descriptive statistics include any characteristic
designed to summarize an important feature of a
data set or sample (Freund, 1973). The reader
should note that a sample in this context refers to
a group of observations selected from the target
population. In the case of water quality
monitoring, descriptive statistics of samples are
used almost invariably to formulate conclusions or
statistical inferences regarding populations
(MacDonaldetal., 1991; Mendenhall, 1971;
Remington and Schork, 1970; Sokal and Rohlf,
1981). A point estimate is a single number
representing the descriptive statistic that is
computed from the sample or group of
observations (Freund, 1973). For example, the
mean total suspended solids concentration during
baseflow is 35 mg/L. Point estimates such as the
mean (as in this example), median, mode, or
geometric mean from a sample describe the
central tendency or location of the sample. The
standard deviation and interquartile range could
likewise be used as point estimates of spread or
variability.
The use of point estimates is warranted in some
cases, but in nonpoint source analyses point
estimates of central tendency should be coupled
with an interval estimate because of the large
spatial and temporal variability of nonpoint source
pollution (Freund, 1973). For example, the
sample mean and standard deviation could be
used to report that the mean total suspended solids
concentration during baseflow is 35 + 10 mg/L
using a 95 percent confidence interval. Stated in
other words, there is a 95 percent chance that the
actual mean baseflow concentration is between 25
and 45 mg/L. There is a 5 percent chance that
the mean baseflow concentration is outside this
range. The confidence interval is a function of
the variability of the data, the number of
observations, and the probability (e.g., 95
percent) selected by the data analyst. This sort of
estimation can be useful in developing baseline
information, developing or verifying models, or
-------
Data Analysis
.Chapter 4
determining the load of a single nonpoint source
runoff event.
Evaluating the effectiveness of controls and
changing environmental conditions is one of the
key monitoring program objectives described in
Chapter 2. In addition to summarizing key
statistics that describe the central tendency and
spread of water quality variables and biological.
metrics, statistical analysis usually involves
hypothesis testing. Two common types of
hypothesis testing done in environmental
monitoring are step changes and monotonic
trends. Step changes are typically evaluated when
comparing at least two different sample
populations such as an impacted site and a
reference site or when comparing one sample
population to an action level. Step changes can
also be evaluated when comparing samples
collected during different time periods.
Monotonic trends (e.g., consistently increasing or
decreasing concentrations) are typically evaluated
when the analyst is investigating long-term
gradual changes over time.
The null hypothesis (H0) is the root of hypothesis
testing. Traditionally, null hypotheses are
statements of no change, no effect, or no
difference. For example, the flow-averaged mean
total suspended solids concentration after BMP
implementation is equal to the flow-averaged
mean total suspended solids
concentration before BMP
implementation. The
alternative hypothesis (HJ is
counter to the null
hypothesis, traditionally being
statements of change, effect,
or difference. Upon rejecting
H01 Ha would be accepted.
Regardless of the statistical
test selected for analyzing the
data, the analyst must select
the significance level of the
test. That is, the analyst must
determine what error level is acceptable. There
are two types of errors in hypothesis testing:
Type I: The null hypothesis (H0) is rejected when
H0 is really true.
Type II: The null hypothesis (H0) is accepted
when H0 is really false.
Table 4-1 depicts these errors, with the magnitude
of Type I errors represented by a and the
magnitude of Type II errors represented by p.
The probability of making a Type I error is equal
to the significance level (a) of the test and is
selected by the data analyst. In most cases,
managers or analysts define 1-a to be in the range
of 0.90 to 0.99 (e.g., a confidence level of 90 to
99 percent), although there have been
environmental applications where 1-a has been
set to 0.80. Selecting a 95 percent confidence
level implies that the analyst will incorrectly
reject the H0 (i.e., a false positive) 5 percent of
the time.
Type II error depends on the significance level,
sample size, and variability, and which alternative
hypothesis is true. The power of a test (1-P) is
defined as the probability of correctly rejecting H0
when H0 is false. In general, for a fixed sample
size, a and P vary inversely. For a fixed value of
a, p can be reduced by increasing the sample size
Table 4-1. Errors in hypothesis testing.
Decision
Accept H0
Reject H0
State of affairs in the population
H0 is True
1-a
(Confidence level)
a
(Significance level)
(Type I error)
H0 is False
3
(Type II error)
1-3
(Power)
-------
Chapter 4
(Remington and Schork, 1970). Figure 4-1
illustrates this relationship. Suppose this interest
is in testing whether there is a significant
difference between the means from two
independent random samples. As the difference in
the two sample means increases (as indicated on
the x-axis), the probability of rejecting H0, the
power, increases. If the real difference between
the two sample means is zero, the probability of
rejecting H0 is equal to the significance level, a.
Figure 4-1A shows the general relationship
between a and p if cc is changed. Figure 4-IB
shows the relationship between a and p if the
sample size is increased.
0.0
Increasing Difference Between
the Mean of Two Random Samples
A) Increasing Significance Level from o^ to
Increasing Difference Between
the Mean of Two Random Samples
B) Increasing Sample Size from n^ to
Figure 4-1. Comparison of a and p.
-------
Data Analysis
Chapter 4
4.1.2 Characteristics of Environmental
Data
The selected statistical method must match the
type of environmental data collected and the
decisions to be made. Although summarizing the
mean annual dissolved oxygen concentration
along an impaired stream might provide an
indication of habitat quality, evaluating the
minimum dissolved oxygen during summer
months over the same time period might have a
greater impact on subsequent management
decisions since that is when critical conditions
often occur. Environmental managers and data
analysts must collectively determine which
statistical methods will result in the most useful
information for decision makers.
The selection of appropriate statistical methods
must be based on the attributes of the data
(Harcum, 1990). Two main types of attributes
important to environmental monitoring are data
record limitations and statistical characteristics.
Common data- record limitations include missing
values, changing sampling frequencies over time,
different numbers of samples during different
sampling periods, measurement uncertainty,
censored data (e.g., "less-thans"), small sample
sizes, and outliers. Data limitations are, for the
most part, human-induced attributes that often
result in less reliable observations and less
information for a given data set. The presence of
data limitations also increases the complexity in
applying standard statistical methods (and using
commercially available software).
Common statistical characteristics include location
(central tendency), variability (scale or spread),
distribution shape, seasonality, and serial
correlation. Table 4-2 presents a variety of
methods for characterizing data that are helpful in
providing a general understanding of water
quality data and selecting appropriate statistical
methods. Cross-references for each method are
provided in the last column in Table 4-2.
4.1.3 Recommendations for Selecting
Statistical Methods
The statistical methods discussed in this manual
include parametric and nonparametric procedures.
Parametric procedures assume that the data being
analyzed have a specific distribution (usually
normal), and they are appropriate when the
underlying distribution is known (or is assumed
with confidence). For data with an unknown
distribution, nonparametric methods should be
used since these methods do not require that the
data have a defined distribution.
Nonparametric methods can directly handle
special data commonly found in the nonpoint
source area, such as censored data or outliers.
Censored data are those observations without an
exact numerical value, such as a value of less than
10 jig/L (< 10 /xg/L) or not-detected (ND).
Censored data often appear in laboratory reports
when the concentration being analyzed is lower
than the detection limit or higher than the
allowable range for a particular type of laboratory
equipment or procedure (Dakins et al., 1996;
Gilliom and Helsel, 1986). Censored data can
cause problems in parametric methods because
these methods often require that all data have
numerical values. In this case, nonparametric
methods can be used because they often deal with
the ranking of the data, not the data themselves.
For example, for data "below the detection
limit," any value that is less than the smallest
value of all the data being analyzed can be
assigned. This assignment does not affect the
ranking of the data even though the exact value of
the "below the detection limit" is unknown.
Nonparametric procedures are also less affected
by outliers (Spooner, 1994a).
On the other hand, nonparametric procedures are
not as powerful as their parametric counterparts
when the assumptions of the parametric procedure
are met. Thus, when the underlying distributions
of the data being analyzed are known or can be
-------
Chapter 4
Table 4-2. Methods for characterizing data.
Data Characteristic
Central tendency
Spread
Distribution shape
Seasonal variation
Serial correlation
Key to Method Type:
Method
Sample mean
Sample median
Sample geometric mean
Boxplot '.
Sample standard deviation
Interquartile range
Sample geometric standard deviation
Range, maximum-minimum
Interquartile range
Boxplot
Histogram
Percentiles
Sample skewness
Sample kurtosis
Shapiro-Wilk test
Time series plots
Seasonal boxplot
ANOVA
Kruskal-Wallis test
Sample autocorrelation
Spearman's rho
P = Parametric, N = Nonparametric, G = Graphical
Method
Type
P
N
P
G
P
N
P
P,N
N
G
G
N
P
P
N
G
G
P
N
P
N
Section
4.2.1
4.2.1
4.2.1
4.3
4.2.2
4.2.2
4.2.2
4.2.2
4.2.2
4.3
4.3
4.2.2
4.4.1
4.4.1
4.4.1
4.3
4.3
4.6
4.6.1
4.9.2
4.9.2
Adapted from Ward et al., 1990.
transformed to the form in which standard theory
can be applied, parametric methods might be
preferred. As a matter of fact, to improve the
analytical power, nonparametric methods are
often modified to include more assumptions and
requirements. This makes the nonparametric
methods more powerful, and the difference
between nonparametric and parametric methods
becomes smaller (Hipel, 1988). For example, the
hypotheses associated with the Mann-Whitney test
(for comparing two independent random samples)
vary depending on which assumptions are valid.
The remainder of this section provides
recommendations for selecting statistical methods
that can be applied on a routine basis for
evaluating the average, changing, and extreme
conditions of environmental variables (Table 4-3,
adapted from Ward et al., 1990). In some
instances, more appropriate methods might be
available depending on the specific information
needs. For routine analyses, both parametric and
nonparametric methods are recommended.
Nonparametric procedures are recommended
together with parametric procedures since
nonparametric procedures tend to be resilient to
characteristics commonly found in nonpoint
-------
Data Analysis
Chapter 4
(O
'co
"ro
ro
15
TJ
CD
"S
2
L-
*2
C/J
73
0
JC
"5
able 4-3.
tr
"S
2
(Q
C
O
c
z
*t
•H
"S
V
2
(0
Q.
11
0^
•o
0
**•»
0>
!E
0}
,«
*w
£
.2
*w
o
Q.
Estimation
«
lc
Q.
2
o
_
fli
to
z
c
.2
Informa
uS *«
fli
j««^ C^
CM" "co P ^
• Q5 J3 •
LO -JS "* CO
. ••— - ' C C CN ^-^
(/) IO -»-« ^^> CO A /X <4-*
o^w T~ s- o ^o co
£•*-• -^ (A ^i* Q} C -
w g*ci>3-cg^-.12
1 5 1 ! i f 1 1 1
ii jjuiii
|2Q.ZgQ.ZfQ.Z
CD
•C CO ^ '> '•N — - .22 '"p •-•• '-»=
S^E-o-oS- S..£= S-CD
co1
^J^
JO
^_^^
cvj' Q.
J2 CD
_O CO
Q. CD
o .E
CQ 1-
co
f~
g
73
r"
8
CD
O)
co
CD
--^ ro
0 T3
Q. C
° &
CO -^
• [__, •—
o ro
0 O
C S
5 ro
ro 0
iB J2.
.0) 15 ^
co ^2 T-
o oj °^
*= ^ ^r
•4— • 1 %^--'
CO C j_i
0 C CO
r ro 0
Q. Z
0
o
0
CO
f^~ To
near regression (4.
en (Seasonal Kend
ator(4.9.1)a
Jj w E
. — . . — .u=
Jb 5- 0
— «
co ^
^d* ^i^
S"§
_ o.
Is
-° gj
To
0 t >-.
•-P o co
f^ ^-* '
d. 2 ^~
S <=L ^
Q. C. *— '
i Test for equality of
11.3)
I Confidence limits o
11.3)
I Tolerance intervals
Q. *t Z •*£ Z
•Jr
">;
o
0
3
cr
0
fero
§^=
'•e a
i»
2 o
Q. 'co
z §
9n2
x:
^— *
^
o^
Q_ '^
CO ~
0 0
CO CO
0 ^
.i ^
CO
c
o
T3
8
0
S
CO
cu
CO
In
0)
CD
E
£
CO
c
o
CO
CD
CO
C
o
£2
CD
S *"L
•"-_ CD
"co £
"m ^^"
"S
°2 c
^ OJ
c .-^
dapted froi
f seasons
< IT
-------
Chapter 4
source monitoring data (Berryman et ail., 1988;
Gilliom and Helsel, 1986; Harcum et al., 1992;
Harris et al., 1987; Helsel and Hirsch, 1995;
Hirsch et al., 1982; van Belle and Hughes, 1984;
Lettenmaier, 1988). However, the data analyst
must be aware that violating assumptions
associated with parametric or nonparametric tests
can lead to incorrect conclusions about the
collected data.
Average conditions
What is the quality of water? What were the
phosphorus loadings from the last storm? To
answer these types of questions the data analyst is
typically faced with describing the average
conditions. Measures of central tendency and
spread are the most common measures of average
conditions. As suggested earlier, using the mean,
geometrkrmean, or median is recommended for
summarizing the central tendency and the
standard deviation, geometric standard deviation,
and interquartile range are recommended
measures of spread or dispersion. Each
parameter* (mean, median, etc.) is a useful point
estimate; however, no information on the
parameter's accuracy is given. Therefore, it is
also recommended that point estimates of central
tendency be reported with confidence limits.
The selection of'the mean (and standard
deviation) versus the median (and interquartile
range) should be based on the objective and type
of data. The mean and standard deviation are
sensitive to a few large observations. This is
particularly true for the small sample sizes and
skewed data that are common in nonpoint source
monitoring. If the goal is to estimate pollutant
loadings, an average concentration would be
appropriate (Helsel and Hirsch, 1995). In
general, parametric and nonparametric parameters
are acceptable when the data are symmetrically
distributed. Notwithstanding the pollutant loading
example above, data that are not symmetrically
distributed (skewed) should typically be
summarized with the median and interquartile
range. The geometric mean and standard
deviation are most appropriate when the data
typically range over a couple orders Of
magnitude. The presentation of geometric means
is also called for in some regulations such as those
for coliform bacteria. In many cases, simple
graphical displays such as time series or box-and-
whiskers,plots will convey more information than
tables of numerical results.
Changing conditions
One of the most frequently asked questions
related to the evaluation of monitoring data is
whether conditions have improved or degraded.
The data collected for evaluating changes will
typically come as (1) two or more sets of random
samples or (2) a time series at a single station. In
the first case, the analyst will test for a shift or
step change. This would be typical for data
collected from a nested paired and paired
watershed design. Or when performing a
biological assessment, for example, the goal
might be to determine whether there is a
significant difference (i.e., a step change) in the
biological metric between the reference and test
(targeted) sites.
The Mann-Whitney test is recommended for
comparing two random samples when the
distribution of the data is unknown or sufficiently
nonnormal. The Student's t test can be used
when the data are normally distributed. It has
been demonstrated that the Student's t test can be
successfully applied when the data are not
normally distributed and might be more powerful
under selected circumstances (Montgomery and
Loftis, 1987), but that approach is not
recommended here. The Kruskal-Wallis test (an
extension of the Mann-Whitney test) is
recommended for when there are three or more
random samples. For example, numerous
biological surveys are initiated by collecting data
during the spring, summer, and fall. The
-------
Data Analysis
Chapter 4
hypothesis might be to determine whether there is
a significant difference in key biological indices
between the different seasons (index periods). An
analysis of variance could be used if the data were
normally distributed. Applying the Mann-
Whitney or Student's t test to each pair of random
samples is not appropriate.
A special case of random sampling is when the
random samples from one population (e.g., the
upstream location) are paired with random
samples from the second population (e.g., the
downstream location). This situation is referred
to as paired or matched sampling. The Wilcoxon
signed rank test is recommended for paired
samples. The paired t test can be used if the data
are normally distributed.
In the second case we commonly test for
monotonic or gradual changes at a single station.
In this case, observations are typically taken on a
regular basis (e.g., weekly, monthly, quarterly).
The seasonal Kendall test is recommended for
hypothesis testing. Linear regression might also
be used but is generally discouraged. If the data
do not have seasonal cycles, the Mann-Kendall
test could be used.
Determining only the existence of a change is
sometimes not sufficient for decision makers. It
is also necessary to estimate the magnitude of the
change. The seasonal Hodges-Lehman estimator
is recommended for estimating the magnitude
when comparing two random samples. The
seasonal Kendall slope estimator is recommended
when estimating the magnitude of monotonic
trends. The difference in means and the Hodges-
Lehman estimator are recommended for changes
between two independent random samples, and
the Sen slope estimator is recommended for
estimating the magnitude of changes when
seasonality is not present.
Extreme values
The most effective means for summarizing
extreme values is to compute the proportion (or
frequency) of observations exceeding some
threshold value. This can be accomplished by
plotting a time series with the threshold value or
dividing the number of excursions by the total
number of observations. A common analysis
would be to compare the proportion of excursions
from one year or station to the proportion of
excursions from another year or station. A test
for equality of proportions can be performed, or
the confidence limits on proportions can be
compared.
The evaluation of extreme values related to
nonpoint source monitoring and other rain-
induced impacts (e.g., combined sewer overflows
(CSOs)) may require greater care. For example,
when evaluating the number of overflows in a
year or comparing storms, it is important to make
sure that the data are comparable (similar rainfall,
antecedent conditions, etc.). This may result in
selecting portions of data sets for analysis.
4.1.4 Data Stratification
Lumping measurements over a period of time has
limited use in water quality evaluations unless the
period of time is defined in more specific terms
and is directly related to the source of the
identified problem. This is particularly true when
comparing the effectiveness of management
measures. If the implemented management
measure is designed to reduce pollutant loadings
during storm events, lumping baseflow and storm
event data together for analysis makes little sense
and might mask the effectiveness of the
management measure.
In urban areas the time periods should be set to
correspond to the pollutant of concern and urban
activities. Depending on the monitoring
objectives, it might be necessary to consider
-------
Chapter 4
periods of activity and nonactivity. If phosphorus
is the pollutant of concern, periods that
correspond to lawn maintenance activities and
spring flush should be considered. If sediment is
the problem, periods that correspond to the
construction season should be considered. For
irrigated agriculture, two periods should be
established to correspond to irrigation and
nonirrigation time.
In nonirrigated agricultural settings the periods
selected should conform to the normal agricultural
management pattern of the watershed. These
periods should be based on amount of surface
covered, precipitation patterns, and the timing of
land and/or water management activities. By
defining time periods, the analyst can evaluate a
hypothesis regarding whether significant
differences in nitrogen and phosphorus losses
occur during different agricultural seasons.
Alberts et al. (1978) used this concept to examine
seasonal losses of nitrogen and phosphorus in
Missouri during three periods:
• Fertilizer, seedbed, and establishment period
(March-June).
• Reproduction and maturation period (July-
October).
• Residue period (November-February).
Once temporal stratification has been completed,
and if sufficient data are available, the water
quality variable being examined could be
categorized by initiation/transport mechanisms.
In a sediment-related problem, for example, three
categories were devised (Davenport, 1984b) to
relate the principal detachment process of
sediment particles:
(1) Baseflow (no rainfall or overland runoff to
the stream). This category consists of non-
precipitation-induced flow and is considered
as the normal day-to-day flow (Viessman et
al., 1977). Sediment concentrations are
dependent on available material in the channel
network and the carrying capacity of the
flow.
(2) Rainfall and snowmelt runoff. This
category consists of runoff events where the
sediment concentrations are dependent on
flowing water detachment or reentrainment of
previously detached soil particles, together
with sufficient overland flow to transport
them to the stream network.
(3) Event. This category consists of rainfall-
runoff events where the sediment
concentrations are dependent on the
detachment of soil particles due to the impact
of raindrops and flowing water detachment or
reentrainment of previously detached soil
particles, together with overland flow to
transport them to the stream network.
Data categorized by detachment category can then
be examined in terms of resource management
systems implemented to control the various types .
of detachment. It should be noted that data
stratification results in smaller data sets. These
new data sets must be checked for normality
before performing any statistical analyses on
them. It is also important to note that due to the
smaller data set size the differences between data
sets must be more pronounced to be significant.
4.1.5 Recommended Reading List and
Available Software
Recommended reading list
Over the last 20 years, considerable effort by
researchers and practitioners has gone into the
development of improved statistical methods for
analyzing environmental data. Nonetheless, there
is probably no single reference that fully covers
all of the issues that the data analyst must
consider when selecting methods for analyzing
environmental data. The following list provides a
summary of selected references that provide more
-------
Data Analysis:
Chapter 4
details about a wider variety of issues. These
references are strongly recommended for those
who need a more in-depth discussion than that
provided in this chapter.
Chambers, J.M., W.S. Cleveland, B. Kleiner,
and P.A. Tukey. 1983. Graphical Methods for
Data Analysis. Duxbury Press, Boston, 395 pp.
Conover, W.J. 1980. Practical Nonparametric
Statistics, 2nd ed. Wiley, New York, 493 pp.
Gilbert, R.O. 1987. Statistical Methods for
Environmental Pollution Monitoring. Van
Nostrand Reinhold Company, New York, 320 pp.
Helsel, D.R. and R.M. Hirsch. 1995. Statistical
Methods in Water Resources. Elsevier,
Amsterdam, 529 pp.
Snedecor, G.W. and W.G. Cochran. 1980.
Statistical Methods, 7th ed. The Iowa State
University Press, Ames, Iowa, 507 pp.
Ward, R.C., J.C. Loftis, and G.B. McBride.
1990. Design of Water Quality Monitoring
Systems. Van Nostrand Reinhold Company, New
York, 231 pp.
Available software
Many statistical methods have been computerized
in easy-to-use software that is available for use on
personal computers. Inclusion or exclusion in
this section does not imply an endorsement or
lack thereof by the U.S. Environmental Protection
Agency. Commercial off-the-shelf software that
covers a wide range of statistical and graphical
support includes SAS, Statistica, Statgraphics,
Systat, Data Desk (Macintosh only), BMDP, and
JMP. Numerous spreadsheets, database
management packages, and graphics programs
can also be used to perform many of the needed
analyses. In addition, the following programs,
written specifically for environmental analyses,
are available:
SCOUT: A Data Analysis Program, EPA, NTIS
Order Number PB93-505303.
WQHYDRO (WATER
QUALITY/HYDROLOGY
GRAPHICS/ANALYSIS SYSTEM), Eric R.
Aroner, Environmental Engineer, P.O. Box
18149, Portland, OR 97218.
WQSTAT, Jim C. Loftis, Department of
Chemical and Bioresource Engineering, Colorado
State University, Fort Collins, CO 80524.
4.2 SUMMARY (DESCRIPTIVE) STATISTICS
4.2.1 Point Estimation
Central tendency
The central tendency of a data set is the most
important and widely used statistic (Gaugush,
1986; Ponce, 1980a). The mean, median, and
mode are three common measures of central
tendency. The arithmetic mean (x) is the sum of
the individual observations (x{) divided by the
number of observations (n):
1 «
*=-£*, (4-D
The median (P 50) is the middle value when all
observations are ordered by magnitude (xl <, x2 ...
<, xn). When there is an even number of
observations, the median is the arithmetic mean of
the two middle observations:
P ='
.50
when n is odd
when n is even
(4-2)
-------
Chapter 4
The mode is the most frequently occurring value
in the set of observations. Comparison of these
measures of central tendency reveals that the
mean is sensitive to extreme values, whereas the
median is not (Helsel and Hirsch, 1995;
Remington and Schork, 1970). When the data
are symmetrically distributed, the mean and
median are comparable. In the case of nonpoint
source pollution where storm events generate very
large pollutant loadings, it is clear that the event
mean and median may be very different. It is
important that the data analyst consider the
ramifications of relying on just one of these
statistics when reporting results.
Other measures of central tendency include the
midrange, geometric mean (GMX), harmonic
mean (HMX), and weighted mean (Remington and
Schork, 1970). The midrange is the arithmetic
mean of the smallest and largest values and is
influenced by extreme values. The geometric
mean can be computed by
(4-3)
where ln(x) and exp(x) represent the natural log
and exponential of the quantity x. It is the mean
of the logarithms, transformed back to its original
units. If the log-transformed data (i.e., v; = In Jtj)
are symmetric, GMX is a an unbiased estimate of
the median (Helsel and Hirsch, 1995; Gaugush,
1986). It is common to report the GMX for
coliform data. It has also become common
practice to estimate the HMX flow for performing
chronic risk assessments. It is computed as the
reciprocal of the mean of the reciprocals using the
following formula:
The weighted mean is a mean for which all
observations do not have equal importance. For
example, a common application of weighted
means is the use of flow-weighted means for
water quality variables measured during a storm
event or when comparing water quality between
two stream systems with different volumes of
water flowing through them. The weight can be
based on the portion of the population that the
observation represents, either spatially or
temporally (Gilbert, 1987). This may occur when
the monitoring program has used a stratified
sampling strategy and the strata have different
sample sizes. In general, a weighted mean is
computed where each observation is accorded its
own weight (Wj):
(4-5)
Weighted _ ,-=i
mean r,
Summarizing storm event data
Three approaches for summarizing storm event
data, which are applications of the weighted mean
described above, are the flow-weighted mean
concentration (FWMC), the time-weighted mean
concentration (TWMC), and the event mean
concentration (EMC). The FWMC and TWMC
are calculated as (USEPA, 1990)
FWMC = .
(4-6)
HM =
" 1
E —
(4-4)
TWMC =
1=1
(4-7)
-------
Data Analysis
Chapter 4
where
'TP -- r
concentration of the z'Ul sample;
time period for which the Ith sample is
used to characterize the concentration;
and
instantaneous discharge at the time of the
i* sample.
The numerator of Equation 4-6 is equal to the
total loading. The EMC can be estimated with
the following equation and is similar to the
TWMC except for end effects:
EMC=
(4-8)
Figure 4-2 presents a summary of hte rainfall,
runoff, and total nitrogen data collected from a
storm event in Florida. Runoff (1,780 ft3) from
this 0.2-inch storm lasted for approximately 2.4
hours. The total runoff volume and precipitation
depth can be computed by integrating the
representative curves in Figure 4-2 or directly
from the data. The nitrogen concentrations are
typical of a "first flush" in which the concentra-
tions are higher during the early part of the
runoff. Tables 4-4 and 4-5 present the raw
nitrogen values from Figure 4-2 together with the
example calculations for computing the FWMC
and EMC, respectively.
The first column in Table 4-4 is the time since the
beginning of the storm. The fourth column is the
time interval, Tj, represented by each sample.
For example, the first entry, T{, of 540 seconds is
computed as (0.24 hours - 0.09 hours) times 3600
seconds/hour. The value of 0.24 is halfway
between 0.20 and 0.28 hours. Selecting the
halfway point between 0.20 and 0.28 hours
centers the water quality observation in the time
period being evaluated. The second entry, T2, of
306 seconds is computed as (0.325 hours - 0.24
hours) times 3,600 seconds/hour. The value of
0.7
0.6
0.5
0.4
°-3
0.2
0.1
0.0
TOTAL PRECIPITATION = 0.2 INCHES
TOTAL RUNOFF = 1,780 CU. FT.
FLOW-WEIGHTED MEAN
CONCENTRATION = 1.33 HIGH.
n_ri
STORM DURATION (HOURS)
' PRECIPITATION (IN/HR)
• FLOW (CFS) «• TOTAL NITROGEN (MG/L)
• 1.5
2.5
2.0 ^
I
cr
•- 1.0
o
I
0.5
0.0
Figure 4-2. Precipitation, runoff, total nitrogen, and total phosphorus from a single storm event in Florida.
-------
.Chapter 4
Table 4-4. Total nitrogen (TN) runoff concentrations for a single storm event in Florida.
Time
(hr)
0.09
0.20
0.28
0.37
0.45
0.53
0.62
0.68
0.77
0.85
0.97
1.13
1.30
2.41
FWMC
Q,
Flow
(cfs)
0.00
0.14
0.30
0.30
0.30
0.38
0.50
0.53
0.68
0.58
0.44
0.24
0.13
0.00
= 2,768
ct
TN
(mg/L)
—
2.44
2.21
2.18
0.97
0.93
1.19
1.85
1.64
1.30
0.94
0.97
1.08
23/2,080.44 = 1
T,
lot
(sec)
.
540
306
306
288
306
270
270
306
360
504
594
4302
Sum
.33 mg/L
TA
(tt3)
.
75.60
91.80
91.80
86.40
116.28
135.00
143.10
208.08
208.80
221.76
142.56
: 559.26
2,080.44
CfTA
(rttg-ft3/L)
184.46
202.88
200.12
83.81
108.14
160.65
264.74
341.25
271.44
208.45
138.28
604.00
2,768.23
0.325 is halfway between 0.28 and 0.37 hours.
The value of 0.24 is halfway between 0.20 and
0.28 hours. The fifth column is equal to flow
(column 2) multiplied by the time interval
(column 4). For example, the entry of 75.60 ft3
is equal to 0.14 cfs times 540 seconds. The sum
of the fifth column is equal to the denominator of
Equation 4-6. The sixth column is equal to the
volume (column 5) multiplied by the nitrogen
concentration (column 3). For example, the entry
of 184.46 mg-ft3/L is equal to 75.60 ft3 times 2.44
mg/L. The sum of this column is equal to the
total nitrogen loading for the storm (and the
numerator in Equation 4-6). Using conversions,
the total nitrogen loading for this storm is 78.4
grams. As shown in Table 4-4, the FWMC is
equal to 1.33 mg/L. Because different analysts
use different conventions for analyzing storms, it
is important that the analyst exercise care when
comparing the storm summaries computed by
different analysts.
Table 4-5 demonstrates the use of Equation 4-8
with the same storm event presented in Figure 4-2
and Table 4-4. The first three columns of Table
4-5 are the same as Table 4-4. The next four
columns correspond to intermediate calculations
needed for Equation 4-8. For example, the
values of 0.11, 0.000, 0.342, and 0.14 in the first
data row are computed from 0.20-0.09, 0.00 x
0.000, 0.14 x 2.44, and 0.00 + 0.14, respec-
tively. The last two columns correspond to
intermediate calculations for the numerator and
denominator of Equation 4-8, respectively.
Finally, the EMC can be calculated as
0.6722/0.4981 or 1.35 mg/L, as shown in Table
4-5.
-------
Data Analysis
Chapter 4
Table 4-5. Total nitrogen (TN) runoff concentrations for a single storm event in Florida and example
calculations for the EMC.
Time
(hr)
0.09
0.20
0.28
0.37
0.45
0.53
0.62
0.68
0.77
0.85
0.97
1.13
1.30
2.41
The event
Flow
(cfs)
0.00
0.14
0.30
0.30
0.30
0.38
0.50
0.53
0.68
0.58
0.44
0.24
0.13
0.00
mean
TN
(mg/L)
0.00
2.44
2.21
2.18
0.97
0.93
1.19
1.85
1.64
1.30
0.94
0.97
1.08
concentration
Tw
-I,
0.11
0.08
0.09
0.08
0.08
0.09
0.06
0.09
0.08
0.12
0.16
0.17
1.11
(EMC) =
Ci*0,
0.000
0.342
0.663
0.654
0.291
0.353
0.595
0.981
1.115
0.754
0.414
0.233
0.140
CM-
CM
0.342
0.663
0.654
0.291
0.353
0.595
0.981
1.115
0.754
0.414
0.233
0.140
0.000
0.6722/0.4981 = 1.35
Q,+
Qi+i
0.14
0.44
0.60
0.60
0.68
0.88
1.03
1.21
1.26
1.02
0.68
0.37
0.13
Sum
mg/L
Num.
0.0188
0.0402
0.0593
0.0378
0,0258
0.0427
0.0473
0.0943
0.0748
0.0701
0.0517
0.0317
0.0779
• 0.6722
Den.
0.0077
0.0176
0.0270
0.0240
0.0272
0.0396
0.0309
0.0545
0.0504
0.0612
0.0544
0.0315
0.0722
0.4981
Loading rates
Converting data into a loading rate is a very
common practice in nonpoint source evaluations.
Computing loading rates results in factoring out
activities that are related to the data collection or
generation process. The most common
conversions are related to time period (kg/yr),
unit area (kg/ha), or a combination of unit area
and time period (kg/ha/month). The other major
type of conversion is related to parameter
generation or transport factors such as rainfall and
runoff; examples are kilograms per centimeter of
precipitation or kilograms per cubic liter of
streamflow.
Examples of raw data and normalized data are
provided in Tables 4-6 and 4-7, respectively. The
watershed is 20 ha and has three consecutive
years of pre- and post-implementation sediment
loading, precipitation, and runoff data. Review
of Table 4-7 indicates that there has been a 20
percent reduction in sediment generated per
centimeter of rainfall and a 22 percent reduction
in annual loading. This indicates that sediment
loading, adjusted for runoff and total
precipitation, has decreased. A more detailed
frequency analysis would be required to test for
statistical significance. It might also be useful to
consider other issues such as rainfall intensity.
Summarizing data with censored observations
Observations reported as less-than or nondetect
are often troublesome for many statistical
procedures. Quite simply, it is difficult to
compute the mean (or any number of other
statistics) when one or more of the values is
-------
Chapter 4
Table 4-6. Raw data by time period.
1971-1973
Total sediment loading
Total precipitation
Total runoff
48 kg
120 cm
15 L3
1974: Implementation of terraces and conservation
tillage
1975-1977
Total sediment loading
Total precipitation
Total runoff
45 kg
180 cm
18 L3
Table 4-7. Loadings rate data.
1971-1973
Average annual loading
Average annual loading
Average annual loading
12
0.10
1.07
1974: Implementation of terraces and
tillage
1975-1977
Average annual loading
Average annual loading
Average annual loading
15
0.08
0.83
kg/year
kg/cm/year
kg/L3/year
conservation
kg/year
kg/cm/year
kg/L3/year
reported as less than the detection limit. Some
authors have recommended not censoring the data
(Dakins et al., 1996; Porter et al., 1988), but this
concept has not been adopted too often in
practice. One approach is to substitute one-half
the detection limit for the censored observations.
This practice is discouraged by Helsel and Hirsch
(1995), Although it is widely used due to quick
implementation in spreadsheet
software.
Gilbert (1987) describes the
trimmed mean and the
Winsorized mean for use when
there are censored data in the
data set. The trimmed mean is
a useful estimator of the mean
when the data are symmetrically
distributed and it is necessary to
guard against erroneous data or
when censored observations are
present (Gilbert, 1987). The
trimmed mean is equal to the
arithmetic mean after equal
proportions of the smallest and
largest observations have been
dropped from the analysis.
Research has suggested that for
symmetric distributions, no
more than 50 percent of all data
should be dropped (Hoaglin et
al., 1983). If the data are not
symmetric, no more than 30
percent of all data should be
dropped (Mosteller and Rourke,
1973). In all cases, the
percentage of observations
trimmed should be reported.
The Winsorized mean can be
computed by estimating the
mean after substituting an equal
proportion of the smallest
observations with the next
largest observation and the largest observations
with the next smallest observation. Two final
approaches for estimating summary statistics with
censored data include maximum likelihood
estimation (Cohen, 1959) and probability plotting
procedures (Travis and Land, 1990). Helsel and
Hirsch (1995) describe these methods and their
shortcomings, particularly with small sample .
sizes. Helsel and Cohn (1988) provide
-------
Data Analysis
Chapter 4
approaches estimating summary statistics when
there are multiple censoring levels in the same
data set.
Dispersion
Measures of dispersion or measures of variation
describe the extent to which the data are spread
out from the central tendency (Freund, 1973).
The measures of dispersion described in this
manual are the range, variance, standard
deviation, and interquartile range. The variance
(and standard deviation) are acceptable measures
of dispersion when the data are normally
distributed or can be transformed into normally
distributed data. Even more so than the mean,
the variance can be influenced by a few outliers.
The interquartile range is a stable estimate of
dispersion.
The range of a set of observations is simply the
difference between the largest and smallest values
and should be considered only as a rough estimate
of dispersion due to its dependence on extreme
values (Gaugush, 1986; Ponce, 1980a; Remington
and Schork, 1970).
The variance (s2) is given by the following:
(4-9)
n-1
The standard deviation (s) is the square root of
the variance. For observations that come from a
normal distribution, about 68 percent of the
observations are within + one standard deviation
of the mean (Figure 4-3A). Figure 4-3B
demonstrates the effect of changing the mean and
variance for a normal distribution.
In cases where it is necessary to compare standard
deviations for samples with different means, a
measure of relative variation is needed. The
variation in a population can also be measured
using the coefficient of variation (CV) and is
defined as:
CV= six
(4-10)
Since CVis unitless, it does not matter what units
(e.g., mg/L, /ig/L) are used, making qualitative
comparisons of different studies easier. In Figure
4-3B, the CVs for the two normal distributions are
nearly the same (0.25 and 0.236). The CV can
also be used to compare the dispersions of two or
more data sets that are measured in different
units. It is recommended that analysts use the
above equation for computing CV although some
analysts commonly multiply the above result by
100.
The interquartile range is a robust alternative
(i.e., it changes little in the presence of outliers)
to the standard deviation (Gaugush, 1986; Helsel
and Hirsch, 1995). It is the difference between
the observation at the upper quartile, <23 (P75),
and the observation at the lower quartile, <2i
(P.25). The upper quartile is the observation value
for which 75 percent of the observation values are
lower, and the lower quartile is the value for
which 25 percent of the observation values are
lower.
To compute a quartile, the data must be ordered
from smallest to largest observation. Then
compute p(n+l) where p corresponds to the
quartile (as a fraction), either 0.25 or 0.75, and n
is the number of observations. Consider the
following example of 10 observations that have .
been ordered from low to high:
<0.10, 0.11, 0.16, 0.51, 0.59, 0.68, 0.79, 0.85,
0.98, 3.00
For n equal to 10, the lower and upper quartile
are equal to the 2.75th (0.25 x 11) and 8.25th
-------
Chapter 4
0.40 -
0.35-
g 0.30 ,
g 0.25 •
£ 0.20 -
< 0.15-
m
g 0.10-
a.
0.05-
0.00-
—t
S X
: _y
/ \
68.3% of all
observations
fall within ± 1.0
standard deviation
of the mean
* -3-2 -1 0 1
K
I
1 ^-~| j
234
55
%
Q
8
g
CO
LU
Q
t
CQ
g
OL
A) Normal distribution with a zero mean and unit variance.
0.40
0.35 • •
0.30 • .
0.25 - -
0.20 • •
0.15 ••
0.10 •-
0.05 - -
0.00 --
Mean = 4.0
Variance = 1.0
CV = 0.25
Mean = 6.0
Variance ~ 2.0
CV = 0.236
0
2468
B) Comparison of two normal distributions.
=1—
10
12
0.40
Lognormal Distribution
- positive skew
- leptokurtic
Normal Distribution
- symmetric
- mesokurtic
Uniform Distribution
- symmetric
- platykurtic
2 4 6 8 10 12 14 16
C) Comparison of symmetric and skewed distributions.
-------
Data "Analysis
Chapter 4
(0.75 x 11) ordered observation. Using the data
from above, 2, is equal to 0.11 + 0.75 x
(0.16-0.11) or 0.1475 and Q3 is equal to 0.85 +
0.25 x (0.98-0.85) or 0.8825. Similar to the CV,
the coeffi-cient of quartile variation (V) can be
used to compare different data sets:
(4-11)
Skewness and Kurtosis
Skewness (y) is a measure of distribution
symmetry and is given by the following formula:
Y =
n
(«-!)(«-2)
E^
(4-12)
Figure 4-3C is a comparison of a lognormal
distribution (positively skewed) and two
symmetric distributions. The kurtosis (k) of a
distribution describes its peakedness relative to
the length and size of its tails (Remington and
Schork, 1970). It has been argued, however, that
kurtosis measures tail heaviness, not the
peakedness of a distribution (SAS Institute, Inc.,
1985a). The normal distribution is considered to
have intermediate kurtosis (mesokurtic). Flat
distributions with short tails have low kurtosis
(platykurtic), whereas distributions with sharp
peaks and long tails have high kurtosis
(leptokurtic). These types of distributions are also
shown in Figure 4-3C. Kurtosis can be estimated
with the following equation:
k =
(4-13)
4.2.2 Interval Estimation
In practice, the real mean and standard deviation
of the target population are never known. We
take random samples from the target population,
compute the mean from the random samples, and
infer the target population mean. Since we
cannot sample all of the waterbody, some error
will always be associated with the estimate. To
report the reliability of estimated statistics, it is
recommended that the confidence interval also be
computed. This section describes procedures for
estimating the confidence interval for the mean,
standard deviation, median, and quartiles.
Mean
For large sample sizes or samples that are
normally distributed, a symmetric confidence
interval for the mean is appropriate. This is
because the distribution of the sample mean will
approach a normal distribution even if the data
from which the mean is estimated are not
normally distributed. The Student's t statistic
fo/2,n-i)is use
-------
mean split of 0.0265 and a standard deviation of
0.0040. Determine the 95 and 99 percent
confidence intervals for the population mean, /n.
Solution:
For the 95 and 99 percent confidence intervals,
a/2 is equal to 0.025 and 0.005, respectively.
There are 53 degrees of freedom. The t value is
then estimated by interpolation between the values
for 50 and 60 degrees of freedom (Table D2)
using the columns a = 0.025 and a = 0.005,
respectively. We obtained t values of 2.0061 and
2.6726. .
The 95 percent confidence interval about the
mean can then be estimated as
x-t
.025,53
V £ 0.0265-2.0061 \/0.0042/54
p. < 0.0265+2.0061 ^0.0042/54
0.0254 < u. < 0.0276
There is a 95 percent chance that the population
mean, /*, will fall between 0.0254 and 0.0276.
The 99 percent confidence interval about the
mean can then be estimated as
fi ;> 0.0265 -2.67261/0.0042/54
u < 0.0265 + 2.6726 V'o.0042/54
0.0250 < u. < 0.0280
There is a 99 percent chance that the population
mean, /*, will fall between 0.0250 and 0.0280.
Note that to have a higher confidence (99 versus
95 percent), a bigger interval is required.
Standard deviation
The confidence interval for the standard deviation
of a normal distribution for small sample size can
be estimated as (Freund, 1973)
< a
l-a/2
(4-15)
where %2 is the chi-square distribution. Values of
X2 can be found in Table D3. Note that since the
%2 is not symmetric, the above inequality requires
a different chi-square value for each end of the
confidence interval, i.e., values for a/2 and (1-
a/2). For large samples the following formula
may be used (Freund, 1973):
a <
1 +-
"a/2
1 --
a/2
(4-16)-
Note that the confidence interval for the variance
can be obtained by squaring the confidence
interval for the standard deviation (Remington
and Schork, 1970).
Median and Quartiies
Although several approaches exist to estimate
confidence intervals for any percentile, many rely
on assuming a normal or lognormal distribution.
The approach presented here (Conover, 1980) for
more than 20 observations does not rely on these
assumptions. Conover (1980) also provides a
procedure for smaller sample sizes. To calculate
the confidence interval corresponding to the
median, lower quartile, or upper quartile, the
following procedure is used.
-------
Data Analysis
Chapter 4
1. Order the data from smallest to largest
observation such that
where ;cp corresponds to the median, lower
quartile, or upper quartile.
2. Compute the values of r" and s* as
r =
0.5
where Z^ is selected from Table Dl.
3. Round r' and s" up to the next highest integers
r and s. The 1-a lower and upper confidence
limits for xp are xt and xs, respectively.
Problem:
Compute the 90 percent confidence interval for
the median using the 25 observations presented
below.
0.08, 0.09, 0.10, 0.23, 0.29, 0.32, 0.38, 0.48,
0.49, 0.61, 0.62, 0.62, 0.68, 0.70, 0.72, 0.75,
0.76, 0.77, 0.80, 0.83, 0.84, 0.87, 0.96, 0.98,
LOO
Solution:
Note that the data have already been ordered and
the median is equal to 0.68.
r* and s* can then be computed as follows:
= 25 x 0.5 - 1 .645 (25 x 0.5 x 0.5)°'s =8.4
= 25 x 0.5 + 1 .645 (25 x 0.5 x 0.5)°'s - 16.6
r and s are therefore 9 and 17, respectively.
From the above listing, x9 and xn can be
estimated as 0.49 and 0.76 mg/L, respectively.
4.3 GRAPHICAL DATA DISPLAY
Graphical data display is an important aspect of
data analysis. Gaugush (1986) recommends
beginning an analysis with a graphical display of
data. This is an excellent approach, though in
this document graphical displays are discussed
after Section 4.2, Summary Statistics, so that
basic terminology is provided first.
Based on an inspection of the data, the analyst
should be able to make a qualitative assessment of
seasonally, variance homogeneity, distributions,
data gaps, unusual sampling patterns, the
presence of censored data, and a general
characterization of the available data, All of
these features might have an influence on the type
of statistical analyses to be performed. By using
graphical methods to examine the data, the data
analyst can more appropriately select statistical
methods. The reader is cautioned, however, that
visual inspection of the results cannot be used to
group data into the categories before and after
BMP implementation. This decision must be
made based on the analyst's knowledge of the
system.
Figures 4-4 to 4-7 illustrate various graphical
displays of dissolved oxygen (DO) data for a
monitoring station in the Delaware River at Reedy
Island, Delaware. Each figure reveals different
features of the data. The DO time series plot
(Figure 4-4) demonstrates a seasonal nature to the
data. In this case, the time series includes data
from a 10-year time span. Similar plots can also
be made over shorter time periods such as
intensive data collection efforts during a storm
event. In the case of a storm event, the
investigator may plot precipitation and runoff
volume together with pollutant concentrations (see
Figure 4-2). It is also apparent from Figure 4-4
-------
Chapter 4
I
Q
UI
1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990
Figure 4-4. Dissolved oxygen concentrations from 1980 through 1989 for the Delaware River at Reedy
Island, Delaware, using a time series plot.
that data are collected more frequently in the
summer months. Inspection of the raw data show
that DO was typically sampled twice a month
during the summer, once a month during the
spring and autumn months, and less often during
the winter months. It is also clear that since the
summer of 1984, the DO has no't dropped below
5.0 mg/L.
Figures 4-5 and 4-6 are a DO histogram and
stem-and-leaf plot, respectively. In Figure 4-5,
the height of the bar indicates the number of
observations falling within a certain DO range.
For example there are 15 observations between
7.5 and 8.0 mg/L. The stem-and-leaf plot
(Figure 4-6) displays the raw data instead of a
bar. The values on the left side of the vertical
axis indicates the DO concentration in a whole
number (e.g., 111 represents 11 mg/L). The
values on the right side of the vertical axis
indicate the DO concentration to the tenths of a
mg/L. Thus 11114566 indicates that there is one
value of 11.1 mg/L, one value of 11.4 mg/L, one
value of 11.5 mg/L, and two values of 11.6
mg/L. These figures demonstrate that most of the
observations fall between 6.0 and 10.0 mg/L.
Typically, the analyst would select the histogram
for less technical audiences and the stem-and-leaf
plot for technical audiences.
Figure 4-7 is a boxplot. For each month along
the horizontal axis, the box indicates the middle
50 percent of the data (which corresponds to the
interquartile range). The lower and upper ends of
the box represent the 25th and 75th percentiles (P 25
and P75), respectively. The horizontal line inside
the box represents the median. The whiskers
extending from the box represent the range of the
remaining observations. In this case, the
whiskers extend to the minimum and maximum
observations for a given month. Some software
packages use different rules for creating the
whiskers (Chambers et al., 1983), and the analyst
should be aware of such differences when mixing
-------
Data Analysis
Chapter 4
30
DISSOLVED OXYGEN (MG/L)
Figure 4-5. Dissolved oxygen concentrations from 1980 through 1989 for the Delaware River at Reedy
Island, Delaware, using a histogram.
4
5
6
7
8
9
10
11
12
13
35689
02344455577789
000012223233334444445555555667778888899
000011111112222233333344455555566677788889
000012223333444555566677799999
00001222223345556667777899
0011112788
14566
00148 ' 4\ 3 = 4.3 MG/L
Figure 4-6. Stem-and-leaf plot of dissolved oxygen concentrations from 1980 through 1989 for the
Delaware River at Reedy Island, Delaware.
-------
Chapter 4
14 r-
12
E 10
& 8
O
a
UJ 6
O
^
MAR APR MAY JUN JUL AUG SEP OCT NOV
Figure 4-7. Boxplots of dissolved oxygen concentrations by month from 1980 through 1989 for the
Delaware River at Reedy Island, Delaware.
and matching analyses from different software
packages. Some software packages plot
observations that exceed P75 (or are less than P25)
by more than 1.5 times the interquartile range as
individual points, which is perhaps a more
desirable approach than others. Depending on
how far the observations exceed this range,
different symbols may be displayed.
The expected seasonal nature of DO is strongly
depicted in Figure 4-7, confirming the suspicions
developed from visual inspection of Figure 4-4.
This figure also allows the analyst to evaluate
how much variability there is in the data. It may
be interesting to note, for example', that in
November the lower and upper 25 percent of the
data (represented by the whiskers) are drastically
different lengths while the whiskers (and the box)
for August appear symmetric. In this case, DO
was plotted as a function of month. Similar plots
as a function of year could also have been made
with these data. Alternatively, the analyst may
compare data by station. Figure 4-8 is a boxplot
of sulfate concentrations. Stations 16 and 17 are
roughly 20 miles downstream from Stations 14
and 15. Based on visual inspection, it appears
that the sulfate concentration increases at the
downstream stations; however, a statistical test is
required. In this case, the stream receives
significant irrigation return flows between the
upstream and downstream stations, which might
be the cause of the increased sulfate
concentrations.
In other cases, it might be helpful to plot water
quality data as a function of other explanatory
variables such as flow. Figure 4-9 is a log-log
plot of total suspended solids measured at a storm
sewer in Denver, Colorado, as a function of
instantaneous flow. Depending on the nature of
the source loading, the correlation between
pollutant concentrations and flow could be
positive (as in Figure 4-9) or negative, or no
correlation might exist. Typically, a negative
-------
Data Analysis
Chapter 4
500
450
400
350
3- 300
g
•C ner\
e. 250
< 200
w 150
100
50
0
_^
1
i —
4
1
5
STATION
c
1
>
6
1
7
Figure 4-8. Boxplot of sulfate concentrations from 1993 and 1994 for the Rio Grande near El Paso,
Texas.
10000
tn
8
I
«>
1000
100
1000
Figure 4-9. Bivariate scatter plot of total suspended solids and flow at 36th Street storm sewer in Denver,
Colorado.
-------
Chapter 4
correlation (decreasing concentrations with
increasing flows) is indicative of constant
pollutant sources (e.g., traditional point sources)
while a positive correlation (increasing
concentrations with increasing flows) is indicative
of nonpoint source loadings. It is critically
important that the analyst know what is going on
in the field before jumping to any conclusion
about the meaning of concentration and flow
correlations.
Figure 4-10 is a scatter plot of orthophosphate for
several stations along the Delaware River. In
.'addition to the seasonal cycles during each year,
some unusually high values that exceed 0.2 mg/L
as phosphorus on September 23, 1991, can be
observed. In this case, one potential cause might
be unit conversions. The data were stored as
milligrams per liter of phosphorus; however,
another common set of units for orthophosphate is
milligrams per liter of phosphate. If one were to
multiply the data collected on September 23,
1991, by one-third (approximate conversion from
phosphate to phosphorus), the data would fall in
line with the rest of the observations. Ideally, the
analyst would go back to the original data to
determine what type of error occurred and
perform corrective action before proceeding with
the statistical analysis. These types of errors also
occur while converting data from parts per
million to parts per billion, converting from wet-
weight to dry-weight basis, normalizing for
organic carbon, and so forth. It might also be
helpful to plot this orthophosphate data as a
function of suspended solids for corroborative
evidence. Data visualization is a good method for
picking out gross errors; however, it cannot be
relied on for more subtle errors. The likelihood
of correcting data errors decreases significantly
with time.
4.4 EVALUATION OF TEST ASSUMPTIONS
One of the basic criteria for selecting between
parametric tests is whether the data being
analyzed have a specific distribution (usually
0.35
0.00
1989
1990
1991
1992
1993
1994
Figure 4-10. Time series plot of dissolved orthophosphate from 1989 through 1994 for portions of the'
Delaware River.
-------
Data Analysis
Chapter 4
normal). For data with unknown distributions,
nonparametric methods should be used since these
methods do not require that the data have a
defined distribution. In addition, numerous tests
require that the observations be independent (that
is, randomly collected) and that the variances of
the populations being compared be equal or of
known ratio (Ponce, 1980a).
This section describes tests that can be used to.
determine whether a data set satisfies some of the
assumptions and requirements of statistical tests.
Analysts are referred to statistics texts such as
Snedecor and Cochran (1980) for further
information regarding test assumptions.
4.4.1 Tests of Normality
There are a variety of methods for evaluating
normality that range from graphical methods to
statistical tests. If the sample data set does not
pass the normality tests, there are several options
including data transformation. Data
transformation can (Gaugush, 1986):
• Straighten (linearize) a nonlinear relationship
between two variables.
• Reduce skew (achieve symmetry) in a data set
for a single value.
• Stabilize variance (create constant variance)
for a particular variance across two or more
data sets.
Log transformations are the most common in
water quality and hydrologic variables (Gaugush,
1986; Ponce, 1980a; Spooner et al., 1986;
USEPA, 1983a) because these data typically have
a positive skew. The reader is encouraged to
study the examples of log transformations
presented by Ponce (1980a) and USEPA (1983a).
Additional information regarding other
transformations such as Box-Cox transformations
is provided by Snedecor and Cochran (1980).
The transformed data should also be tested for
normality before proceeding with further
statistical analyses (Spooner et al., 1986).
Graphical Methods
Examining boxplots can be useful in developing a
qualitative opinion regarding normality. Another
graphical approach is to prepare probability plots.
The cumulative frequency can be plotted on
normal probability graph paper. If the graphics
software does not provide for probability plots,
the following method can be used. First, sort the
data from low to high. For each observation,
compute a plotting position using
= 1-0.375
PI ~ n+0.25
(4-17)
Helsel and Hirsch (1995) identify several other
formulas that could be used for plotting position,
but note that this approach is the most appropriate
for comparing data to normal distributions in
probability plots. The plotting positions are then
converted to normal quantiles (Zp) using Table
Dl.
Consider, for example, the sulfate data from
Station 16 (see Figure 4-8). Table 4-8 presents
the 42 observations ordered from low to high.
For i equal to l,pl is equal to (1-0.375)742.25 or
0.0148. Using Table Dl, it is necessary to look
up p. equal to 1.0-0.0148 or 0.9852. The
corresponding Zp forp equal to 0.9852 is 2.176.
Therefore, the corrresponding Zp for/? equal to
0.0148 is -2.176. The same procedure is
followed for the remaining observations. Sulfate
concentrations are then plotted as a function of
the normal quantile as shown in Figure 4-11 A.
The straight line in Figure 4-11A corresponds to
the theoretical shape of the normal distribution
with a mean and standard deviation equal to those
computed from the raw sulfate data. If the data
were normally distributed, the data would tend to
-------
Chapter 4
Table 4-8. Calculation of plotting position for the sulfate data from Station 16 in Figure 4-8
Ordered
Obs. Num
Quantile
(0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
Sulfate
(mg/LJ
A
150
150
160
170
170
180
i90
200
200
200
200
200
200
210
210
210
210
210
210
220
220
Plotting
Position
0.0148
0.0385
0.0621
0.0858
0.1095
0.1331
0.1568
0.1805
0.2041
0.2278
0.2515
0.2751
0.2988
0.3225
0.3462
0.3698
0.3935
0.4172
0.4408
0.4645
0.4882
Normal
Quantile
2,
-2.176
-1.769
-1.537
-1.367
-1.229
-1.112
-1.008
-0.914
-0.827
-0.746
-0.670
-0.597
-0.528
-0.461
-0.396
-0.332
-0.270
-0.209
-0.149
-0.089
-0.030
Ordered
Obs. Num
(/)
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
Sulfate
(mg/L)
220
220
240
240
240
250
260
270
290
300
300
310
310
320
330
360
380
400
420
430
460
Plotting
Position
Pi
0.5118
0.5355
0.5592
0.5828
0.6065
0.6302
0.6538
0.6775
0.7012
0.7249
0.7485
0.7722
0.7959
0.8195
0.8432
0.8669
0.8905 '
0.9142
0.9379
0.9615
0.9852
Normal
ZD
0.030
0.089
0.149
0.209
0.270
0.332
0.396
0.461
0.528
0.597
0.670
0.746
0.827
0.914
1.008
1.112
1.229
1.367
1.537
1.769
2.176
fall along the straight line. Clearly, the data do
not fit a normal distribution, but are more typical
of a positively skewed data set. As an alternative,
the data can be log-transformed and the same
analysis performed. In this case, the log-
transformed data are less skewed (Figure 4-1 IB).
The conclusion from this analysis that the data are
not normal. Visually, it is difficult to determine
whether the data are lognormally distributed.
Skewness
The approach used in testing for normality using
skewness (Equation 4-12) is that a nonnormal
distribution may be skewed, whereas a normal
distribution is not skewed. If there are more than
150 observations and the data are normally
distributed, the confidence limits on skewness
from a normal distribution are given by (Salas et
al., 1980)
-Z.
I-a/2
\
\
(4-18)
where Z is from Table Dl. If the estimated
skewness exceeds this range, the data are not
normally distributed. Typically, the sample size
is much smaller than 150 and the estimated
skewness should be compared to the values in
Table 4-9. If the absolute value of the estimated
-------
Data Analysis:
Chapter 4
Both Remington and Schork (1970) and the SAS
Institute (1985a) caution that the test for skewness
is only a partial indicator of normality. With
small samples (less than 25), the test is
particularly unreliable. That is, because of the
small sample size, very large departures from
normality are required before statistical tests will
reject the null hypothesis of normality. Cochran
(1977) proposed a general rule for determining
how large n must be (i.e., n in the equation
below) to allow safe use of the normal
approximation in computing confidence limits for
the mean. This rule is used most effectively for
distributions with positive skewness, which are
most common for environmental data.
n1 >
25 Yj
(4-19)
where
Y, =-7
ns
(4-20)
Applying these equations to the data summarized
in Table 4-10 yields a YI of 0.99, and therefore
more than 25 (=25 x 0.992) samples are needed.
The example data set contains 42 samples.
Therefore, there are sufficient data to allow safe
use of the normal approximation in computing
confidence limits for the mean.
Kurtosis
The test for kurtosis is similar to the test for
skewness since it measures only one attribute of
normality and requires large samples for
meaningful results. Remington and Schork
(1970) recommend the following equation to
evaluate kurtosis:
(4-21)
For any normally distributed population, £j would
be 0.7979. Table 4-11 presents lower and upper
limits for k^
If the calculated value of fcj falls outside the
values in Table 4-11 for the selected level of
confidence, there is evidence of non-normal
kurtosis. Using the same example data, &, can be
computed as 0.80 and 0.82 for the raw and log-
Table 4-11. Values of kurtosis test for normality for small sample sizes.
a«
n Lower
11 0.6675
21 0.6950
31 0.7110
41 0.7216
51 0.7291
61 0.7347
71 0.7393
= 0.02
Upper
0.9359
0.9001
0.8827
0.8722
0.8648
0.8592
0.8549
',,
Lower
0.7153
0.7304
0.7404
0.7470
0.7518
0.7554
0.7583
a« 0,10
Upper
0.9073
0.8768
0.8625
0.8540
0.8481
0.8434
0.8403
After Rpmtnnton and Schork 1970
-------
Chapter 4
transformed data, respectively. From this
analysis, it is concluded that the raw and the log-
transformed data have a kurtosis that is consistent
with a normal'distribution since k, is between the
range of 0.7470 to 0.8540 for a equal to 0.10.
Shapiro-Wilk latest
The Shapiro-Wilk Wtest can be used to test the
distribution of a data set for sample sizes of less
than 2,000 (SAS Institute, Inc., 1990). This test
uses the W statistic, which is "the ratio of the best
estimator of the variance to the usual corrected
sum of squares estimator of the variance" (SAS
Institute, Inc., 1990). The null hypothesis for this
test is that the data set is a random sample from a
normal distribution. Values of W are greater than
zero and less than or equal to one. The null
hypothesis is rejected with small values. For
sample sizes greater than 2,000, the Kolmogorov
D statistic may be used (SAS Institute, Inc.,
1990).
Anderson and McLean (1974) recommend the
Shapiro-Wilk Wtest for normality and note that it
is superior to the Kolmogorov-Smirnov and chi-
squared tests in detecting non-normality over
sample sizes ranging from 10 to 50. The
following procedure for using the test is adapted
from Anderson and McLean (19,74) and Gilbert
(1987):
1. Order the n observations as xr < x2 ^ ... < xa.
2. Compute d = (n-l)s2.
3. Compute .Ar, If n is even, k = nil. Ifnis
odd, k = (n-l)/2.
4. Compute
W = —
d
(4-22)
where the values of a; appear in Table D4. The
value xn.i+] is equal to xn when i is equal to 1 and
xn-k+i when i is equal to k.
5. Reject H0 (of normality) at the a significance
level if W is less than the quantile given in
Table D5.
Table 4-12 presents the sulfate data from Station
16 in Figure 4-8 in a format ready for analysis.
The results for step 2 can be computed from the
statistics in Table 4-10. Since there are 42
observations, k is equal to 21. The first column
in Table 4-12 indicates the value of i for each row
in the table. The second column corresponds to
the values of a; from Table D4. (Note that the
values in Table D4 are for an.i+, and are exactly
the same as a,.) The third and fourth column, x{
and xn_i+l, represent the raw sulfate data. The
third column represents the first half of the
observations, and the fourth column represents
the last half of the data in reverse order (e.g., 460
is the largest sample observation). The fifth and
sixth columns correspond to the log-transformed
data from columns 3 and 4. For example,
log(150) is equal to 5.01. The last two columns
provide intermediate calculations associated with
Equation 4-22 (i.e., a-j(xn.-l+l - *,)) for the raw and
log-transformed data, respectively.
Summing the last two columns results in
Completing the summation specified in Equation
4-22. The W statistic may now be computed
using Equation 4-22 to yield 0.88 and 0.89 for the
raw and log-transformed data, respectively.
From Table D5, the quantile for 42 observations
(95 percent confidence level) is 0.942. As a
result, it can be concluded that the raw data and
the log-transformed data are normally distributed.
4.4.2 Tests of Equal Variance
When performing hypothesis tests of two samples
using parametric procedures, it is typically
necessary to make sure that the two data sets have
-------
Data Analysis
Chapter 4
Table 4-12. Example analysis of the Shapiro-Wilk Wtesi using the sulfate data from Station 16 in
/
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
a,
0.3917
0.2701
0.2345
0.2085
0.1874
0.1694
0.1535
0.1392
0.1259
0.1136
0.1020
0.0909
0.0804
0.0701
0.0602
0.0506
0.0411
0.0318
0.0227
0.0136
0.0045
Xt
150
150
160
170
170
180
190
200
200
200
200
200
200
210
210
210
210
210
210
220
220
XnM
460
430
420
400
380
360
330
320
310
310
300
300
290
270
260
250
240
240
240
220
220
log
xf
5.01
5.01
5.08
5.14
5.14
5.19
5.25
5.30
5.30
5.30
5.30
5.30
5.30
5.35
5.35
5.35
5.35
5.35
5.35
5.39
5.39
log
Intermediate
xn.Hl Calculations
6.13
6.06
6.04
5.99
5.94
5.89
5.80
5.77
5.74
5.74
5.70
5.70
5.67
5.60
5.56
5.52
5.48
5.48
5.48
5.39
5.39
SUM
121.43
75.63
60.97
47.96
39.35
30.49
21.49
16.70
13.85
12.50
10.20
9.09
7.24
4.21
3.01
2.02
1.23
0.95
0.68
0.00
. 0.00
479.00
0.44
0.28
0.23
0.18
0.15
0.12
0.08
0.07
0.06
0.05
0.04
0.04
0.03
0.02
0.01
0.01
0.01
0.00
0.00
0.00
0.00
1.81
the same variance. Testing for equal variances
between two populations can be done by
evaluating the ratio of the two sample variances
(Ft) with the following equation:
(4-23)
where
The null hypothesis in this test is that the variance
ratio is equal to 1, and the alternative hypothesis
is that the ratio is not equal to 1. The ratio is
compared to a critical value from the F
distribution (Table D6) that is based on the
sample sizes («a and nb) and the selected level of
significance (a). Since the numerator is selected
to be the variance with the larger value, it is
necessary to look at only one critical value even
though a two-sided test is being used.
For the sulfate data from Stations 16 and 17 in
Figure 4-8, F, can be computed as ,
6,342.9/5,536.3 or 1.15 with 41 (42-1) and 10
(11-1) degrees of freedom. Using Table D6, the
critical F value (for a two-sided 95 percent
confidence level test where a/2 is equal to 0.025)
is approximately 3.25. Therefore, the null
-------
Chapter 4
hypothesis is accepted and it is concluded that the
variances of the sulfate data from Stations 16 and
17 are the same.
+ 1
(4-25)
4.4.3 Tests of Randomness
Another type of hypothesis testing involves time
series at a single station. The DO data plotted in
Figure 4-4 are one example. An approach to
evaluate randomness is to compute the total
number of runs («) above and below the median
(Freund, 1973). A run is a string of values all
above or all below the median. A string of one
value is acceptable. In this test, the median is
determined, all values are placed in chronological
order, and each value is assigned an "a" if it is
above the median and a "b" if it is below the
median. For example, the following is a set of
data in chronological order:
5, 5, 6, 9, 13, 12, 2, 3, 2, 8, 14, 13, 11, 20, 4,
6,9,1,7,11,12.
The median for this set of values («=21) is 8.
The series of values in terms of "a" and "b" is
b, b, b, a, a, a, b, b, b, omit, a, a, a, a, b, b, a,
b, b, a, a
The .number of runs (M) in the example 'data set is
8. Note that in this test all values equal to the
median are omitted. Also, the number of values
above («,) and below (n^) must each be 10 or
more to allow use of the following statistics. For
«i and «2 less than 10, special tables are required
(Freund, 1973). The test statistic (derived from
the normal distribution) is:
z =
(4-24)
where
and
a =
(4-26)
Applying these equations to the above example
data,
= 200X10)
10+10
^
2 (10) (10) [2(10)-(10) -(10) -(10)]
= 2.1764
Z =
2.1764
(10+10)2 (10+10-1)
= -1.38
With a equal to 0.05 in a two-tailed test, the Z
values (for a/2) are 1.96 and -1.96 (Table Dl).
Since -1.38 falls within this range, the null
hypothesis that the sample is random is accepted.
4.5 EVALUATION OF ONE OR Two INDEPENDENT
RANDOM SAMPLES
The data collected for evaluating changes will
typically come as (1) two or more sets of random
samples or (2) a time series at a single station. In
the first case, the analyst will test for a shift or
step change (e.g., a significant difference between
conditions before and after treatment). This
might be typical for data collected from two
stations along a stream segment. Or, when
performing a biological assessment, for example,
the goal might be to determine whether there is a
-------
Data Analysis
Chapter 4
significant difference (i.e., a step change)
between biological metrics for data collected at
randomly selected reference and test (targeted)
sites. It is also possible to compare a single
random sample to a particular value. This might
be die case when comparing data to a standard or
reference condition. This section describes
common approaches for comparing one or two
independent random samples. Comparing more
than two independent random samples or time
series is discussed later.
Depending on the objective, it is appropriate to
select a one- or two-sided test. For example, if
the analyst knows that TSS would only decrease
as a result of BMP implementation or is interested
only if the TSS decreases, a one-sided test can be
formulated. Alternatively, if the analyst does not
know whether TSS will go up or down, a two-
sided test is necessary. If the analyst simply
wants to compare two random samples to decide
if they are significantly different, a two-sided test
can be used. Appropriate uses of a one-sided test
include testing for decreased sediment or nutrient
loads after implementing a flood control dam or
best management practice, or comparing a
suspected contaminated site to an upstream or
control site. Typical null hypotheses (H0) and
alternative hypotheses (H^ for one- and two-sided
tests are provided below:
One-sided test
H0: TSS (postimplementation) :> TSS (pre-
implementation)
H,: TSS (postimplementation) < TSS (pre-
implementation)
Two-sidedJest
H0: TSS (postimplementation) = TSS (pre-
implementation)
H,: TSS (postimplementation) * TSS (pre-
implementation)
Selecting a one-sided test instead of a two-
sided test results in an increased power for
the same significance level (Winer, 1971). That
is, if the conditions are appropriate, a
corresponding one-sided test is more desirable
than a two-sided test given the same level of
significance (a) and sample size. The manager
and analyst should take great care in selecting
one- or two-sided tests.
4.5.1 Tests for One Sample or Paired
Data
Suppose the analyst is interested in evaluating
compliance with a water quality standard or
reference condition, e.g., a target determined
from a load allocation or a percent substrate
embeddedness less than the amount that hinders
fisheries. In these situations the analyst might
collect a random sample and compare it to a
reference value. The Student's t and the
Wilcoxon Signed Ranks tests are the two most
appropriate tests when evaluating one independent
random sample. The sign test can also be used,
but it is generally limited to random samples that
cannot be transformed into a symmetric
distribution.
In addition, the analyst might be interested in
determining whether a water quality variable
increased between two sites located along a
stream. In this situation the analyst might collect
two random samples with matched or paired
Tests for One Sample or Paired Data
Test9
Additional
Assumptions
Student's t {paired t)
Wilcoxon Signed Franks
Sign '
Normal distribution
Symmetric distribution
None
* The standard forms of these tests require
independent random samples.
-------
Chapter 4
observations. Paired observations are a series of
data collected as pairs at a given time or location.
For example, if BOD5 is sampled at two stream
locations at a regular time interval, the result is a
pair of BOD5 observations for each time period.
The same statistical tests used for one independent
sample can be used to compare paired
observations. The tests are adjusted by
computing and analyzing the difference between
the paired observations. The associated t test is
referred to as the paired t test.
Student's t test
The participants in the Highland Silver Lake
RCWP project (Jamieson, 1986) formulated a null
hypothesis that a BMP would not reduce the post-
implementation mean TSS concentrations to less
than 25 mg/L. (Presumably, the participants
hoped that the mean TSS concentration would be
less than 25 mg/L so that HQ could be rejected.)
A formalized statement of the null and alternative
hypotheses using a one-sided test would be:
H0
Ha
£ 25 mg/L
< 25 mg/L
In this case it is assumed that the mean TSS
concentration is a good measure of central
tendency and is the best measure for evaluation.
It is also assumed that any change in TSS mean
concentration is due to the BMP alone. Ha is
stated such that a one-sided test can be applied
because there is concern specifically about
whether the postimplementation mean TSS
concentration is lower than 25 mg/L since this
might have been the target in a load allocation.
The Student's t test statistic (t) with n-\ degrees of
freedom (df) can be used if the data are
independent and normally distributed:
t=
sl\fn
(4-27)
_ Modification for Paired t Test _
The sample mean, x, and standard deviation,
S, in Equation 4-27 refer to the mean and
standard deviation of the differenced data
The differenced data must be normally
distributed.
The number of degrees of freedom is equal
to the number of paired observations minus
one. •
where jti would be equal to the hypothesized
value, 25 mg/L in this case. Assuming a one-
sided test is used, the critical value for t would be
obtained from Table D2 with n-1 degrees of
freedom and a significance level of a. If a two-
sided test were used (H0: /JL = 25 mg/L;
Ha: /x, # 25 mg/L), a value corresponding to a
significance level of a/2 would be obtained from
Table D2.
The TSS data from the Highland Silver Lake
RCWP project (Table 4-13) are from May 21,
1981, through October 31, 1984. The period
after April 1, 1983, is the postimplementation
period. Before testing H0 with a statistical test,
the data must be inspected and the assumptions of
randomness and normality must be tested. These
tests are performed on the preimplementation and
postimplementation data sets although only the
postimplementation data in the current example
are used. Using the SAS Univariate procedure
(SAS Institute, Inc., 1985a), summary statistics
and graphical presentations can be generated for
the two data sets (Figures 4-12 and 4-13),.
The values for skewness (0.82) and kurtosis
(- 0.42) indicate positive skew and low kurtosis in
the pre-BMP sample distribution. The Shapiro-
Wilk W statistic (0.893) and associated probability
-------
Data Analysis
Chapter 4
i
£ S
s |
§ §
M M
g *
H flJ
*3 JJ
M (0
0 H
g
I
t£
w vo uj n co en +
jq rH H +
° i
wrar-oicooo + +
Q) O fl ^ ^* VO *D * +
§ w t .
M a! i
$! * +
O rH H *
+
•K
WHHrHrHM r-t +
5 + *
§ >, + + o
H +
•H *
A3 +
CO -K +
.Q +
0 *
J-! +
ft * H
o o o r- H I-H + '
IO ^D IO • H H rH
H g *
H S
*
+ OJ
^-, Cn If) O O U) rH
••a* cn m m H <"
N •"">
tw -f J3
0 H- + nJ
Q ui in H
"ra ^
r-Hi-Hr-o ooo
O *CT»OO OOO
vo o ro • H
*
+ o
OJ 0) 0] C H
jj u *H ni o — —
en C to oj — E co >i
S: (0 o S B A ^1
•H AJ II II S +
uWM>wuwP.!30.0.0i ^
fiDtnc\TiicoiDcoiDcocoro £
rOeoHC-r-O O JJ
CO CN-CO^tN C04-1 HU)>-|
CO vu'tn- -ni r-oroH
H HO r- ooJocoinooaPi
fa JOOJOOH-H
2 ^
^ W OO^rH e^DLn^rOOIiHrH
oi >n n^.'SnJjJ S
rH 0) OJ £ II C rci e co
RJ C! § 0) -H O
•H (0 T3 !
w
C!
w
jj £ oo o ro in
-------
Chapter 4
EH
H
CO
EH d)
rt3 H
Q 3
K, ^
s; a)
o o
M O
fiC ft
§ (d
ft jd
H -H
ft D
CQ
i
CO
o
ft
CO
CO
I
8
II
0)
rH
id
h
**
m in r~ H en
H
3
0
4J O O3 W 0 CN
to ro ro ro ^ o\a of 0\° bi o d)
o tn o in o fl i ^
o r» in tN id n o
H CK; ex S
M e>j ro t*- -^ o oo^<
rHCn 'CJO OOO
• en n -4* ...
CO • F- rH O OOO
I-H t cs m
CQ (Dm £
4-1 U -H td 0
tn a w O
CQ O O ^ rH
> tQ || — % C (ti
g
XI +
0 + *
fH , H- *
*
rH +
p + £ o r*- ro
y * + to _p . . .
jo +H jjOoiDm
S + t c} vo I»D c**
* (t)
U
^i rH r- r*. r-
0) H • • •
ft dJ VO VD VjO
* U
• • O3 4-1 H rH H
S
in m 3 H M ro
^ ' r"|
tt)
n
•y
S
U £ ro o ro
G W § . . .
. 0) 4J O ro o ro
& g m«m
Q} O
M r) rH r- r- ro
fc d) rH • • •
ft 0) vo *i) ro
£ + * a H
rH
gi H- 4J H rH CN
O §
vo ro
Xi U H
(d 4J H rH CN
I
1 S
4J d) vr> [^ o
•^f CO 3 rH
4H M + >, "«
QJ CN CN o r- a
h3O o CN>i>Ol£) -H
4J
^^rOrOCNCMHrHO rH
4J g
CQ
o5
CO
"ro
c
.0
S
c
(U
(U
Q.
E
-*-j
05
0
Q.
CO
5
£
D)
LL
-------
Data Analysis
Chapter 4
Table 4-13. Hiahland Silver Lake TSS data for site 1.
Preimplementation Postimplementation
Date TSS(itig/L) Date TSS (mg/L)
5/21/81
6/18/81
7/30/81
9/3/81
10/6/81
11/5/81
12/8/81
4/8/82
4/27/82
5/25/82
6/22/82
7/20/82
9/20/82
10/26/82
1 1/23/82
12/2/82
Overall:
Preimplementation:
Postimplementation:
11
21
13
12
30
37
20
60
60
20
20
16
48
35
25
42
n = 31
n = 15
4/12/83
5/10/83
6/7/83
7/27/83
10/5/83
11/18/83
12/29/83
1/25/84
2/20/84
4/11/84
5/15/84
7/17/84
8/21/84
9/26/84
10/31/84
mean = 24.77 s =
mean = 29.38 s =
mean = 19. 87 s =
14
6
30
7
32
14
32
10
42
22
40
12
10
16
11
14.93 median
16.15 median
12.17 median
= 20
= 23
= 14
(0.063) show that the null hypothesis (that the
sample is normally distributed) can be rejected
with 93.7 percent confidence. In other words,
there is only a 6.3 percent chance that a lower W
value could be obtained if the sample were indeed
taken from a normal distribution. Hence, the
assumption of a normal distribution is rejected
and the alternative hypothesis that the distribution
is non-normal is accepted.
In the post-BMP sample distribution, the values
for skewness (0.70) and kurtosis (-0.99) again
indicate positive skew and low kurtosis. The
Shapiro-Wilk W statistic (0.88) and associated
probability (0.044) show that the null hypothesis
(that the sample is from a normal distribution) can
be rejected with 95.6 percent confidence. Also
rejected is the assumption of a normal distribution
for the post-BMP data set.
Taking the logarithm (base 10) of each data point
for the pre-BMP and post-BMP data sets, the SAS
Uriivariate procedure is run to see if the
assumption of normality would be appropriate for
the log-transformed data set. The output plots
and statistics are shown in Figures 4-14 and 4-15.
Note that the skewness (0.10) is much less
pronounced, but the kurtosis (-1.09) is more
negative for the transformed pre-BMP data set.
The higher Wstatistic (0.951) and associated
probability (0.493) indicate that the null
hypothesis that the transformed data are'normally
distributed should be accepted.
For the log-transformed post-BMP data, the
skewness (0.072) is also reduced and the kurtosis
-------
Chapter 4
vo vo ro co en
H H
01
O
4-i CN cn rH H H
0) CN O3 CN H H
43 CO CO H CO CO
co Cn VD 05 co t- r>
fl) -H in vo VD r- r^
E W
S H H H H H
X H ^ ro CN H
W H H
CQ
O
4J ro H ro CN ro
M en co <* rH o
fl) ro H cn ^ H
S rH 01 ro o o
hi O O rH
* • * tH r-l
rH H tH
g
CO H rH rH in ro ro
|2j in in in •* cn en
rtj rH rH H CO rO fl
K CO 00 CO t- rH tH
EH t"^ c^- r> vo ^t* "3*
r~ E** t- o o o
0 H rt H H H rf
' ' fl)
rH 0\<> oV oV> 0\° C.V oV
H p --^ cninooinH
H TJ ^ cn OT cn H
CO 0) II
U 4H
i< O fl)
<3 &4 -—
Q U)
0) tU
O nt -H in co o ^ cn inrHo
rH -H 4J iH-^Ororo OtHrH
E-j H c cocnvocoH vocoo
H > p t-^vo-cNo r-..
K -H O • . iH > « -OH
H g H tH H H 0
t^ M TJ CJ
A< (0 rO fl) rH -H
S S CX S 0 S
0\o oV o\° 0\o 0\o CJ1 S 0)
S ot--incN (Oroo
PQ ,H 0!_OI S
g!
rHOOO^OrOOHOOCN
ojovorot^o oom
in in o in m • ...
• o . co o o o o o
CN 0 1 0 O
w w n — ^ ctJ
rl fl)4J^COt>" D^^CTl"
g SSWWfUHSSWlS
IJ —^
E o
• • CN 4J O O
+ fl 0
* • fl) rH
O
+ rH rH in
+ + jy ,_(
0. g „
4J +
O * H JJ CN
* * t + I
& * U
•H * [>
43 * r>
0 *
o! H
rH * +
E * E co o ro in
H + CQ 3
O * H -PUcoinHC^
+ a;
* o
+ JH H ro ro ro ro
+ CD rH ...-
J 0. fl) v, vo vo vo
r CJ
g
O
Ln in 3 vo o ^ ^
^ 0 rH O Ol O3 CN
cO •* co ro H
tH iH [> ^ VO CN CO
fl) in in vo vo
43 tH iH H H
<"
H .
u E co o ro in
fl w 3 ....
fl) jJ O ro o vo OJ
g, g •* "> "> «>
0) o
Ct) fl) rH • • . .
ij + ——-k + a, ^ co vo vo vo
O
rH
m + * + o
u
0) ro cn ^ H
3 0 H CT> CN
rH' H CN t-- H
> ro CN ro r-
H > rH •
H H
=JtCNOICNO3^tiHHO]
£ ro in co o
tH [Q p •
I 4J U VO CN CO in
* fl H iH C3
* fl)
+ o o
rH r) rH ro ro ro ro
(D rH • *
>i CM fl) VD VO VO VD
xi a
CCi U rH rH H H
fl) J5
+ • g
E U
•U fl) ro tH ro CN
CO 3 cn co ^ rH
+ rH ro iH cn ^
t|-j CN >, cOHcnroo
(UCOOD^-COO CO pj OOH'
t-^COCN^OOOH-^ -H ..(T_J
p -P H H rH
0}lj,St_j2t2ij!Ij2 ^
wnrnni-ir-li-li-in J
CO
0)
CO
"ro
*o
c~
.Q
.2
C
0
£
Q)
"o.
E
-------
Data Analysis
Chapter 4
8
CO
S
u3 3
W 'O
01
1 S
g S
M pa
S ***
g
M
S
I
w
Q
ro
01
0
4J H
to n
0) rH
J3 P-
01 tJl t*'
H
x o
01
§
w in
0) H
S co
5 r-
A c-
o
cn cn
ro ro
CM CN
vo vo
H H
— . cn in
<• cn en
in r-
in in
H H
in in
o o
in in
H H
ti* ro
H
cn
o
in
CO
o
vo cn
rO rH
in ro
O CO
rH H
VO CO
H 0
O O
cn H
CO H
CN
VO
H
T3
so
o in
in CN
H ro
CO VO
. H
O 1
(U CO
U -H
(3 O
"ft H
§ 3
H i-l
P* ro
in ro
ro 01
C- P~
CN 0
O 0
Std Dev
Skewness
H
vo
o
o
CO
rH
m
CO
p-
o
in
H
in
H
CO
P-*
o
a
•H
O
£
P-
o
rH
CO
8
*
vo
ro
1
cn
cn
vo
H
in
rH
cn
ro
H
O
H
H
in
CO
p-
p-
o
H
ro
vo
o
o
0
pj
X
•o
CO
in
cn
IS
6
CO
cn
o
in
CO
o
0)
1
«
i-H
O
O
O
O
IT
A
a
H
ro
p-
P-
I
I
in H
H
in
o
in
o
H
O , * + + 3
JJ * + + ^
•H H- QJ
rH * 3
•H * rH
XI + td
td + * + o >
O *
•K
rH +
0)
rH
XI
^
u E
S 4J O
a1 a>
OJ O
ft ft rH
ft (U rH
ft 0)
JJ + * -f O
o
rH
&— + ti
0 §
" + * * 8
(U
rH
rt
^
=«: 01 CN H H H CN, -=J* H H
H CO p
I -P U
* OJ
+ O CJ
H ft rH
(1) rH
>i ft flJ
XI U
MH
(d 4J
... o
E O
jj
CO 3
+ rH
, (d
ra ^ H >
(U -3OrHoo^*oino moo -H
EvDin^rocNHocncop- rH
CO
P-
vo
CO
^
ro
rH
in
H
in
o
Ul
H
o
o
t-
VD
t-H
01
H
^
Ol
H
ro
ro
ro
p-
rH
ro
cn
ro
H
0
H
P-
VD
r^.
VD
H
H
m
H
00
P-
O
ro
ro
cn
r-
VD
H
VO
O
Ol
0
VO
H
P-
VD
VD
P*
VD
H
ro
01
CN
ro
H
0
o
p-
VD
rH
H
CO
H
cn
0
H
ro
ro
H
P^
VD
H
00
cn
0
in
CO
o
0
0
o
i-H
P-
VO
H
cn
OJ
ro
VD
H
ro
ro
p-
p-
VD
H
H
01
H
^
H
ro
ro
in
ro
ro
H
CN
CO
CN
H
VD
H
H
P~
VD
CN
ro
ro
H
CN
t-H
CO
S3
T3
0
£=
CD
"c
E
0)
Q.
E
o
Q.
"g
C
^
to
C
co
D)
O
4
O)
L.
-------
Chapter.^
(-1.23) is more negative than for the raw data set.
The W statistic (0.939) and associated probability
(0.367) indicate that the null hypothesis that the
transformed data are normally distributed should
be accepted. In fact, there is a 63.3 percent
probability that a lower W statistic could be
obtained if the sample is from a normal
distribution.
To test the randomness of the data sets, the test
described in Section 4.4.3 can be used. Since the
test requires only the number of runs and the
number of values above and below the median, it
does not matter whether the raw data or
transformed data are used. Using the raw data in
Table 4-13, the number of runs for the pre-
implementation data set is 6 while the number for
the postimplementation data set is 9. The
resulting z statistics (from Equation 4-24) for the
preimplementation and postimplementation data
sets are 1.5526 and 0.8971, respectively. These
values are compared to a critical value of 1.96
(using a/2 = 0.025) from Table Dl and the null
hypothesis is accepted. Both samples are random.
Once the data sets are randomly sampled and
normally distributed (after log-transformations),
the one-sample hypothesis test using the log-,
transformed post-BMP data set can be performed.
As shown in Figure 4-15, the mean of the log-
transformed post-BMP data set is 1.21969 and the
standard deviation is 0.273571. The log of the
hypothesized value (25 mg/L) is 1.3979. Note
mat it is recommended that these values be
rounded to the correct number of significant digits
when reporting the results. The t statistic
(Equation 4-27) is used to determine whether the
post-BMP mean TSS concentration is less than 25
mg/L. '
t =
1.21929 - 1.3979
0.273571 /
= -2.53
The schematic representation of this test is shown
in Figure 4-16A, where the critical t value
(-1.761) for the one-sided test (df = 14, a =
0.05) is taken from Table D2. The computed t
statistic falls to the left of the critical value/so the
null hypothesis is rejected. In turn, the
alternative hypothesis that the post-BMP mean
TSS concentration is less than 25 mg/L is
accepted.
Alternatively, had the participants in the Highland
Silver Lake RCWP project selected a two-sided
test where H0 and Ha are given as
H
= 25 mg/L
* 25 mg/L
a two-sided t test would be appropriate. The
critical t value for the two-sided test from Table
D2 (df = 14, a/2 = 0.025) would be + 2.145.
In this case, the computed t statistic (-2.52) still
falls outside this range and it is concluded that the
post-BMP mean TSS concentration is less than
25 mg/L. Notice how the rejection region
(shaded portion) in Figure 4-16B differs from
Figure 4-16A. The total shaded area in the two
curves is the same (i.e., 5 percent); however, it is
in one piece in Figure 4-16A and is split into two
parts in Figure 4-16B.
The power of this test can be evaluated using the
noncentral t distribution with respect to various
alternative hypotheses. The noncentral T statistic
with n-l degrees of freedom is given by
s/\/n sl\fn
(4-28)
where A •= jt, - /*0, the difference between the
real and hypothesized mean. The noncentrality
parameter (5) is given by
o =
(4-29)
Values of 6 are given in Table D7 for a one-sided
noncentral t distribution. Continuing with the
-------
Data Analysis;
Chapter 4
Reject H0
Accept H0
tclit =-1.761
A) One-sided f-test with 14 degrees of freedom and a equal to 0.5.
Reject H0
Reject H0
tcrit = -2.145
= 2.145
B) Two-sided t-test with 14 degrees of freedom and a equal to 0.5.
Figure 4-16. One- and two-sided t test for post-BMP mean TSS concentration.
-------
Chapter 4
current example, it is possible to develop a power
curve that indicates the trade-offs between Type I
and II errors. (Background discussion on power
curves is provided in Section 4.1.1.) From Table
D7 (df = 14, a = 0.05), one value of 6 is
obtained for each level of P (Table 4-14). In
Table 4-14, power is computed as 1-p and A is
obtained by rearranging Equation 4-29 and using
s equal to 0.273571 and n equal to 15. Note that
A, referred to as the minimum detectable
difference, is in log-transformed units.
Power can be plotted as a function of the
minimum detectable difference (see Figure 4-17).
The dotted line indicates an approximate
extrapolation back to a when the minimum
detectable difference is equal to zero. Using the
log-transformed postimplementation data, A is
equal to 0.178 (= 1.3979 - 1.21969).
Interpolating from Table 4-14 or Figure 4-17
yields that there is a 77 percent probability (i.e.,
power = 0.77) that a significant difference would
be detected (i.e., reject H0) if the difference
between the estimated mean and true mean using
log-transformed data were 0.178. For A less than
0.027, there is only a 10 percent chance of
Table 4-14. Evaluation of power using the post-
implementation TSS data.
Power
(1-P)
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.80
0.90
0.95
0.99
P
0.90
0.80
0.70
0.60
0.50
0.40
0.30
0.20
0.10
0.05
0.01
8
0.38
0.84
1.18
1.46
1.73
2.00
2.28
2.62
3.08
3.46
4.18
A
0.027
0.059
0.083
0.103
0.122
0.141
0.161
0.185
0.218
0.244
0.295
detecting a significant difference, whereas for A
greater than 0.3 there is almost a 100 percent
chance of detecting a significant difference.
Wilcoxon Signed Ranks test
Alternatively, if the log (or some other)
transformation did not result in normally
1.01
o
o.
0.8
0.6
0.4
0.2
0.0
0.00
0.05
0.10 0.15 0.20
MINIMUM DETECTABLE DIFFERENCE
0.25
0.30
Figure 4-17. Evaluation of power using the log-transformed postimplementation TSS data.
-------
Pata Analysis;
Chapter 4
distributed data, the analyst could consider the
Wilcoxon Signed Ranks test. Although less
restrictive than the t test, this test requires that the
data are independent and come from a symmetric
distribution. As the name implies, a symmetric
distribution is one in which the distribution of
data above the midpoint is a mirror image of the
data distribution below the midpoint. (The
normal distribution is a special case of a sym-
metric distribution.) When the data distribution is
symmetric, the mean and median coincide and
therefore inferences about the median are also
valid for the mean (Conover, 1980). For this
presentation, the median concentration is
evaluated rather than the mean using the
following hypotheses:
H0:
25
0
Ha: 25-P. 50 > 0
or H0: 25-P. 50
The test statistic, T, is normally distributed and is
given by Conover (1980) as
T =
1*1
0.5
(4-30)
where dj is equal to the difference between the
hypothesized value (25 mg/L) and the actual data
and the rank is assigned a negative value if 4 is
negative. El-Shaarawi and Damsleth (1988)
provide a modified version of the Wilcoxon
Signed Ranks test for use with serially correlated
data.
Modification for Paired Data
d, corresponds to the difference
between paired observations
From the previous example, it is already known
that the raw postimplementation data are
lognormal and thus not symmetric. Therefore,
the log-transformed data are analyzed since it has
already been determined that the log-transformed
observations are symmetric as well as indepen-
dent. Table 4-15 shows the calculations used to
evaluate the log-transformed post-implementation
data set. For convenience the data are sorted
from smallest to largest observation. The dif-
ference, d{, is computed as log(25) - log(TSSi).
For example, the first entry is equal to log(25) -
log(6) or 0.620. Since the log-transformed data
were symmetric, d{ will also be symmetric. The
fourth column is the absolute value of the dif-
ference, \d-\. The last two columns are the rank
and rank-squared of \d\ where the rank is
assigned a negative value if d{ is negative. T is
equal to 76/(1238.5)°-5 or 2.16. Since 2.16 is
greater than 1.645 (which is obtained from Table
Dl using a = 0.05), the null hypothesis is
rejected and it is concluded that the median
concentration is less than 25 mg/L. Had the raw
data that are not symmetric been incorrectly used,
T would have been equal to 1.54 and the null
hypothesis would have been incorrectly accepted.
Sign test
Suppose that the postimplementation data could
not be transformed into a symmetric distribution.
By using the sign test, the symmetric distribution
assumption can be relaxed (i.e., it is not
required). In this case, the appropriate
hypotheses for a one-sided test are
H0:
H:
P(-)
P(-)
where P(+) is defined as the probability of an
observation's being greater than the hypothesized
value (in this case 25 mg/L). As stated, H0
implies that 50 percent or more of the population
is greater than or equal to the hypothesized value.
-------
Chapter 4
Table 4-15. Nonparametric evaluation of postimplementation data using the Wiicoxon Signed Ranks
test.
TSS
(mg/L)
6
7
10
10
11
12
14
14
16
22
30
32
32
40
42
Log(TSS)
0.778
0.845
1.000
1.000
1.041
1.079
1.146
1.146
1.204
1.342
1.477
1.505
1.505
1.602
1.623
di=Log{25)
-Log(TSSi)
0.620
0.553
0.398
0.398
0.357
0.319
0.252
0.252
0.194
0.056
-0.079
-0.107
-0.107
-0.204
-0.225
rank rank
[dt[ [d,
d,)2
0.620 15 225
0.553 14 196
0.398 12.5 156.25
0.398 12.5 156.25
0.357 11 121
0.319 ' 10 100
0.252 8.5 72.25
0.252 8.5 72.25
0.194 5 25
0.056
1 1
0.079 -2 4
0.107 -3.5 12.25
0.107 -3.5 12.25
0.204 -6 36
0.225 -7 49
SUM = 76 1238.5
1 Assign the negative of the rank if d, is negative.
Modification for Paired Data
The comparison is made between
the paired observations rather
than with a hypothesized vatue.
than 20, use Table D8 with/? equal to 0.5 and n
equal to the number of " + " and "-" (ties- are
excluded). Find the table entry, y, that
approximately equals a, rejecting H0 if T <, y. If
n is greater than 20, y can be computed as
y =
(4-31)
By comparing each observation from the random
sample to the hypothesized value, the data set is
converted into a series of " + ," "-," and ties.
The test statistic, T, is equal to the number of
" +." The more " + " that result from the
comparisons, the more H0 is supported.
Using the raw postimplementation data, T is equal
to 5 and n is equal to 15. There are no ties. In
this one-sided test, small values of T indicate that
"-" are more probable. For sample sizes less
were Za is obtained from Table Dl. For
example, if a is equal to 0.05 in a one-sided test,
Zoos is equal to -1.645. Using the example data,
a y equal to 4 (a=0.0592) is obtained from Table
D7. T is greater than 4, so H0 is accepted.
Had the hypotheses been stated in the other
direction (i.e., H0: P(+) <, P(-); Ha: P(+) >
P(-)), H0 would be rejected if T :> n - y. Had this
been a two-sided test, the rejection region would
-------
Data Analysis
Chapter 4
be for T£ y or T £ n-y wherey is obtained from
Table D8 or Equation 4-31 using a/2.
Table 4-16 presents paired observations for BOD5
collected at two locations from the same stream.
In this case, the hypothesis that there is no
difference in BOD5 concentrations between the
two locations with a a = 0.10 is being tested:
Hypotheses
H0:
H.: P(+) * P(-)
Description
BODS concentrations at the
two locations are the same.
BOD5 concentrations at
location 1 tends to be larger
or smaller than the BODS
concentration at location 2.
In this case, a two-sided test is appropriate where
P(+) indicates the probability that an observation
from location 1 is greater than an observation
from location 2. The fourth column indicates
whether the BOD5 concentration at location 1 is
larger (+), smaller (-), or equal to (tie) the BOD5
concentration at location 2. In this analysis there
are 8 " + " and a total of 13 observation pairs
without ties. From Table D8 with a/2 = 0.05
and n = 13, y = 3 (a = 0.0461) is obtained. H0
is accepted since 3^8^ (13-3).
Comparison of example results
In this case, the Student's t test and Wilcoxon
Signed Ranks test give the same conclusion. It is
proposed that the results from the t test are more
appropriate for this example since all of the
assumptions of the parametric test were met. Had
the assumptions not been met, the results from the
Wilcoxon Signed Ranks test would have been
more appropriate. That is, if all assumptions are
met, parametric procedures are more powerful
than their nonparametric alternative. The sign
test, while not incorrect, was not a good choice
for the example data because the distributional
assumptions were met and more powerful tests
could be applied. Applying the Wilcoxon Signed
Ranks test to data that are not symmetric results
in a level of significance (a) that is somewhat
lower than what is specified, whereas applying
Table 4-16. Sign test for comparing paired BOD5 concentrations.
Day
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Cone, at Location 1
(mg/L)
29
22
10
26
12
32
23
11
32
27
28
23
18
35
20
Cone, at Location 2 Sign of
(mg/ll) Difference
19 +
20 +
5 +
24 +
15
24 +
25 - .
23
32 tie
30
20 +
16 +
33
25 +
20 tie
-------
Chapter 4
the t test to data that are not normally distributed
results in an a that is much larger than specified
(Helsel and Hirsch, 1995).
4.5.2 Two-sample Tests
In many instances, paired observations are not a
practical or appropriate sampling methodology.
Instead, two random samples are collected. The
pre- and postimplementation data in Table 4-13
from the Highland Silver Lake RCWP are one
example. The Student's t test for two samples .
and the Mann-Whitney test are the most
appropriate tests for these types of data.
Two-sample t test
Suppose that a comparison of the pre- and post-
implementation TSS data sets is desired to see if
the BMPs have had an effect on TSS levels in
Highland Silver Lake. Remembering the
assumptions made earlier about using the mean
TSS concentration as a good measure of central
tendency and assuming that any change in TSS
mean concentration is due to the BMP alone, the
pre- and postimplementation data sets can be used
in a one-sided hypothesis:
H0: TSS (Post) ^ TSS (Pre) or
H0: TSS (Post) - TSS (Pre) ;> 0
Ha: TSS (Post) < TSS (Pre) or
Ha: TSS (Post) - TSS (Pre) < 0
H0: TSS (Post) = TSS (Pre) or
H0: TSS (Post) - TSS (Pre) = 0
Ha: TSS (Post) * TSS (Pre) or
Ha: TSS (Post) - TSS (Pre) * 0
With confidence that the BMP would have only
an effect of reducing TSS concentrations, H0 is
tested using a one-sided t test. Both the pre-
implementation and postimplementation data sets
are random samples and normal when log-
transformed. However, the two-sample t test also
requires that the variances of the two populations
be equal (Gaugush, 1986). Since a major effect
of many nonpoint source control practices is to
reduce the occurrence of large loading events, it
is very likely that these practices will have an
effect on the variance of nonpoint source loads.
Thus, an F test is performed to evaluate variance
homogeneity before proceeding with the t test
even though the t test is robust with respect to
moderate departures from homogeneous variance
(Winer, 1971).
Since the log-transformed data (Figures 4-14 and
4-15) are being used, the variance of the
transformed data must also be used in the F test.
The resulting F statistic is computed from
Equation 4-23:
Note that in this case the H0 that the
post-implementation TSS is greater than
' or equal to the preimplementation TSS
concentration is tested with an Ha that
postimplementation TSS is lower. The
results from this analysis will be
interpreted as simply indicating whether
the BMPs worked. This could also
have been set up as a two-sided test
where H0 and Ha would be
Tests for Two independent Random Samples
Test8
Key Assumptions
Two-Sample t
Mann-Whitney
Both data sets must be
normally distributed
Data sets should equal
variances'3
None
a The standard form of these tests requires independent
random samples,
fa The variance homogeneity assumption can be relaxed
(see Table 4-17),
-------
Data Analysis
Chapter 4
F, = 0.075/0.057 - 1.32
The variances are substituted into Equation 4-23
so that the F statistic is greater than unity to
account for the organization of Table D6. The
critical F value from Table D6 (fn = 14,/d = 15,
a/2 = 0.025) is 2.89. The value 1.32 is
compared to 2.89, and the null hypothesis of
equal variance is accepted.
Satisfied that the data meet all of the assumptions
required of the two-sample hypothesis test, H0
(TSS (Post) z TSS (Pre)) is now tested. The two-
sample t statistic with n, +%-2 degrees of freedom
is (Remington and Schork, 1970)
t =
\
(4-32)
n. n.
where sp is the pooled standard deviation, which
is defined by
0.5
(4-33)
= (1.407-1.21969)-0
= 2.034
0.2562
J-+J-
16 15
Comparing this t statistic in a one-tailed test to the
t value from Table D2 (a = 0.05, df = n, + n2 -
2 = 29), it is found that the 2.034 exceeds the
table value of 1.6991. Therefore, the null
hypothesis is rejected and it is concluded that the
postimplementation mean log-transformed TSS
concentration is lower than the preimplementation
level (i.e., the BMPs worked given earlier
assumptions). Note that if a two-tailed test had
been used, the null hypothesis would have been
accepted since the corresponding t value from
Table D2 is 2.0452. Remington and Schork
(1970) give test statistics for other cases in which
the difference between means is being tested.
These cases and corresponding equations are
given in Table 4-17. In particular, note Case #3,
which allows for unequal variances.
The power of this test can be estimated using the
noncentrality parameter (Larsen and Marx,
1981): .
The difference quantity (A0) can be any value, but
in this case it is set to zero. A0 can be set to a
non-zero value to test whether the difference
between the two data sets is greater than a
selected value. Using the transformed data for
preimplementation (n, = 16, s,2 = 0.057087,
3j == 1.407) and postimplementation conditions
(/J2 = 15, S2Z = 0.074812, Ii = 1.21969), sp is
calculated as
_ 0.057087(16-1) +0.074812(15-1)
S* [ 16+15-2
= 0.2562
and the t statistic is calculated as
0.5
8 =
Xl~X2
V2 a^
1
"l
1
«2
(4-34)
where a is approximated with the pooled standard
deviation. Using the data in this example,
8 =
1.407-1.21969
= 1.438
0.2562 J —+ —
16 15
From Table D7 (df = 29, a = 0.05), a p
approximately equal to 0.60 is obtained, so the
power is equal to 0.40. Had the difference in
-------
Chapter'4
Table 4-17. Summary of parametric tests used to evaluate difference between means (Remington and
Schork, 1970).
Case I: Difference between means when variances are; known
(test statistic is standard normal distribution}
Null
Hypothesis
Test Statistic
Assumptions
= A,,
(O?/BI
Independent, random samples of size
and «2 from two normally distributed
populations.
Case 2: Difference between means when variances are unknown
but equal (test statistic is Student's t distribution
with %4-«f2 degrees of freedom)
Ho
Test Statistic
Assumptions
= A^
t =
- A
Independent, random samples of size
and «2 from two normally distributed
populations with equal variances
Ua$e 3; Difference between means when variances are known and
unequal (test statistic is approximately Student's /; see below
for degrees of freedom)
H0
Test Statistic
Assumptions
= A
df =
Independent, random samples of size
and nz from two normally distributed
populations with unknown and
presumably unequal variances
Hj + 1 «2 + 1
df = df * - 2
Case 4: Pairing— the mean difference (test statistic is
Student's t distribution wife, n-l degrees of freedom)
H0
Test Statistic
Assumptions
= A
- A
PI
Random sample of size n paired
differences from a normally distributed
populations of differences
-------
Data Analysisj
Chapter 4
means (of the log-transformed data) been larger
(e.g., 0.30), 6 would be 2.31 and the power
would be equal to 73 percent.
Mann-Whitney (Wilcoxon's rank sum) test
The Mann-Whitney test can also be used to
compare two independent random samples. This
test is very flexible since there are no assumptions
about the distribution of either sample or whether
the distributions have to be the same (Helsel and
Hirsch, 1995). Wilcoxon (1945) first introduced
this test for equal-sized samples. Mann and
Whitney (1947) modified the original Wilcoxon's
test to apply it to different sample sizes. This test
tests whether one data set tends to have larger
observations than the other. Example two- and
one-sided hypotheses are as follows:
Two-sided
H0: Prob [TSS (Post) > TSS (Pre)] = 0.5
Description: The probability that the post-
implementation TSS is larger than the pre-
implementation TSS is equal to 50 percent.
H,: Prob [TSS (Post) > TSS (Pre)] + 0.5
Description: The postimplementation TSS is
larger or smaller than the preimplementation
TSS.
One-sided
H0: Prob [TSS (Post) > TSS (Pre)] > 0.5
Description: The probability that the post-
implementation TSS is larger than the pre-
implementation TSS is equal to or greater
than 50 percent.
Ha: Prob [TSS (Post) > TSS (Pre)] < 0.5
Description: The postimplementation TSS is
smaller than the preimplementation TSS.
If the distributions of the two samples are similar
except for location (i.e., similar spread and
skew), Ha can be refined to imply that the median
concentration from one sample is "greater than,"
"less than," or "not equal to" the median
concentration from the second sample. To
achieve this greater detail in Ha, transformations
such as logs can be used.
Table 4-48 shows the intermediate calculations
using the same TSS data presented earlier. First,
all observations from the pre- and post-
implementation are sorted together and ranks
assigned. Note that ties are assigned the average
rank. The test statistic is equal to the sum of the
ranks for the group with the smaller number of
observations—in this case, the postimplementation
data set.
Tables of Mann-Whitney test statistics (e.g.,
Conover, 1980) may be consulted to determine
whether to reject H0 for small sample sizes. If n:
and «? are greater than or equal to 10
observations, the test statistic can be computed
from the following equation (Conover, 1980):
T - n.
N+l
(4-35)
N(N-1)
where
«: = number of observations in sample with
fewer observations (e.g., post-
implementation) ;
«2 = number of observations in sample with
more observations (e.g., pre-
implementation) ;
N = «! + «2!
-------
Chapter 4
Table 4-18 Nonparametric evaluation of post-implementation data using the Mann-Whitney test.
Rank
1
2
3.5
3.5
5.5
5.5
7.5
7.5
9
10.5
10.5
12.5
12.5
15
15
15
Pre-
ImpJement.
TSS (mg/L)
-
-
-
-
11
-
12
-
13
•
-
16
-
20
20
20
Post-
implement.
TSS (mg/L)
6
7
10
10
-
11
-
12
-
14
14
-
16
-
-
-
Rank
17
18
19
20.5
20.5
22.5
22.5
24
25
26
27.5
27.5
29
30.5
30.5
Pre-
Impfement.
TSS (mg/L)
21
_
25
30
_
.
_
35
37
-
42
-
48
60
60
Post-
Implement.
TSS (mg/L)
22
_
_
30
32
32
_
_
40
42
_
_
«
Sum of ranks for post-implementation, 7 = 193.5
Sum of all
ranks squared, I/
?i2= 10,409.5
T = sum of ranks for sample with fewer
observations; and
RI = rank for the zth ordered observation used
in both samples.
This equation is appropriate for situations when
there are many ties. Applying this equation yields
193.5-:
31+1
N
15*16
31(31-1)
10409.5 -
15*16(31+1)2
4(31-1)
Tt is normally distributed, and Table Dl can be
used to determine the appropriate quantile. Since
the test was one-sided and a is equal to 0.05, the
appropriate quantile from Table Dl is -1.645. T,
is less than -1.645, and therefore the null
hypothesis is rejected. The post-implementation
TSS concentrations are significantly less than the
pre-implementation TSS concentrations. Had a
two-sided test been used, the appropriate quantile
from Table Dl would have been -1.96 and the H0
would have been accepted. In this case, the two-
sample t test and the Mann-Whitney test result in
the same conclusion.
4.5.3 Magnitude of Differences
So far, Section 4.5 has described statistical tests
for comparing one and two random samples for
significant differences. A question remains:
How big is the difference? For data that are
normally distributed, the difference can be
computed as the difference between the two
sample means. The confidence interval (CI) for
-------
Data Analysis
Chapter 4
die differences can be computed under the equal
variance scenario as (Winer, 1971): '
CI =
(4-36)
(4-37)
where df is from Table 4-17 (Case 3).
Helsel and Hirsch (1995) recommend that a
Hodges-Lehmann estimator (A) be used if the data
have been transformed for testing or if the data
are not normally distributed. The Hodges-
Lehmann estimator (Hodges and Lehmann, 1963)
can be used as a nonparametric estimator of the
difference between the two samples. To compute
the Hodges-Lehman estimate, the analyst
computes the difference between all n, and nz
observations. Using the TSS data used earlier,
there are 16»15 or 240 differences to compute.
The Hodges-Lehmann estimator is the median of
these differences or 8 mg/L. This estimator is
preferred to the difference between the medians
of the random samples (Helsel and Hirsch, 1995).
For sample sizes larger man 10, the upper and
lower confidence intervals for A can be estimated:
n1M2(/i1+n2+l)
(4-38)
+ 1
n,«2 -R,
(4-39)
where R, and Ru correspond to the 1th and udl
ranked difference. The 95 percent confidence
interval for the difference between the pre- and
post-implementation data would be computed as
16-15-1.96,
If the standard deviations were not similar, the CI R,--
would be
16-15(16+15+1)
•=70.4«70
Ru = 16-15 - 70.4 +1 = 170.6 « 171
Therefore, the confidence interval on the median
difference is equaHo the 70* and 171st ranked
difference or -1 ^A <; 19.
4.6 COMPARISON OF MORE THAN Two
INDEPENDENT RANDOM SAMPLES
The analysis of variance (ANOVA) and Kruskal-
Wallis are extensions of the two-sample t and
Mann-Whitney tests, respectively, and can be
used for analyzing more than two independent
random samples. Unlike the t test described
earlier, the ANOVA can have more than one
factor or explanatory variable. In the Highland
Silver Lake RCWP project example used in
Section 4.5, one factor described whether the data
were collected before or after implementation of a
BMP. In the example that will be analyzed in this
section, trout population, there are two factors.
One factor is based on the stream from which the
trout were collected; the other factor is based on
the region from which the trout were collected.
The Kruskal-Wallis test accommodates only one
factor, whereas the Friedman test can be used for
two factors. In addition to applying one of the
above tests to determine whether one of the
samples is significantly different from the others,
it is also necessary to do postevaluations to
determine which of the samples is different. This
section recommends Tukey's method to analyze
the raw or rank-transformed data only if one of
the previous tests (ANOVA, rank-transformed
-------
Chapter 4
ANOVA, Kruskal-Wallis, or Friedman) indicates
a significant difference between groups. The
reader is cautioned that when performing an
ANOVA using standard software, the ANOVA
test used must match the data.
4.6.1 One-Factor Comparisons
ANOVA
The ANOVA for one factor is a procedure for
comparing the mean value from each group with
the overall mean. H0 is typically stated that there
are no differences between the group means,
whereas Ha states that at least one group's mean is
significantly different from the overall mean or
TT . ,, — • ii =r
Ha: At least one group mean is different.
The basic assumptions made in using an ANOVA
are as follows (Remington and Schork, 1970):
• Each sample is a random sample from the
corresponding population, and observations
from different populations are independent.
• The measurement variable is normally
distributed in each of the k groups.
• The groups have the same variance
(homoscedasticity).
The variation (or total noise) in the data can be
split into the treatment sum of squares (SST) and
the errors sum of squares (SSE) (see Equation
4-40) (Helsel and Hirsch, 1995) where
k — number of groups,
«j = number of observations in the/1 group,
*y = Ith observation in the/1 group,
x = overall mean, and
mean of the/1 group.
x, =
This notation is also used in Table 4-19, which
indicates each observation, group sample size,
group sample mean, and group true mean. Note
that sample sizes for the different groups need not
be the same. The reader should compare the
notation in Table 4-19 to that used in Equation
4-40.
The observations (xy) within each group are
assumed normally distributed about the mean, ^
and variance, a2. The variance is the same for all
classes, but the mean can vary among classes.
The overall mean is denoted as fj., and the
corresponding linear model is expressed as
(Snedecor and Cochran, 1980)
[ / = 1 ,.,.n.i j = l,...k; e.. = AT(0,02)]
(4-41)
This fixed effects model shows that each observed
value is the sum of an overall mean (/*), a
treatment or class deviation (Oj), and a random
element (ey) from a normally distributed
population with a zero mean and a standard
deviation equal to a. The model is referred to as
"fixed" because the «j, while unknown, are
constant for a group. The random element
represents variations due to such factors as unit-
to-unit variation hi treatment effect, measurement
errors, or individual characteristics of the unit
(Snedecor and Cochran, 1980). To detect a
significant difference, the variation within the
group (i.e., 6y) must be sufficiently smaller than
the variation between groups.
Total sum of squares -
SST
k
j=l /=!
+ SSE
EiX--*/
(4-40)
-------
Data Analysis
Chapter 4
Factor Level
1
X21
Xn1
1
Sample size: n.
Sample mean: xi
2 ...
X12
X22
Xn 2
n2
X2
k s s
X1k
X2k
Xn k
k
nk
\
Tlie ANOVA test statistic, F, is based on a ratio
of the treatment mean squares (MST) and error
mean squares (MSB):
(4-42)
where
MST =
SST
(4-43)
(4-44)
and N is the total number of observations.
An F value of 1 represents the condition where H0
is true, and large F values indicate differences
among the /ij. Snedecor and Cochran (1980) note
that die F test is more affected by nonnormality
and heterogeneity of variances when sample sizes,
«j, are not equal.
Table 4-20 presents a common format for the
results from a one-factor ANOVA analysis
generated by typical software. The first column
identifies which portion of the linear model is
being displayed and corresponds to the top
portion of Equation 4-40. The second column
presents the sum of squares for each source of
variation, the third column presents the degrees of
freedom, and the fourth column presents the
treatment and error mean squares (Equations 4-43
and 4-44). F is calculated using Equation 4-42.
The p value corresponds to the significance level
associated with the computed F. The "F crit"
corresponds to the critical value from Table D6
using k-1 and N-k degrees of freedom and a
selected a. Note that some software packages do
not present "F crit." If the p value is less than
the selected a, H0 is rejected because at least one
of the groups has a different mean.
As an example one-factor ANOVA, consider the
situation where the trout populations of three
streams are measured by the multiple-step Zippin
-------
Chapter 4
Table 4-20. Gommon one-way ANOVA output format.
Source of
Variation
Between
Groups
(Treatment)
Within
Groups
(Error)
Total
SS
SST
SSE
SST + SSE
df
k-1
N-k
N-1
MS F p-value F Criteria
MST = SST/(k-1) MST/MSE p F value for
selected a
' MSE = SSE/(N-k)
approach for electrofishing at five randomly
selected sites in the Coastal Plain region (Platts et
al., 1983). The data from this monitoring effort
are shown in Table 4-21.
Using the one-factor ANOVA procedure from a
standard spreadsheet, trout population as a
function of stream was modeled to test the null
hypothesis that stream has no effect on trout
population (i.e., the treatment effect is zero). The
results of this test are shown in Table 4-22. Note
that the F value of 6.332 is equal to MST
(92.867) divided by MSE (14.667). The p value
is 0.013. The critical value from Table D6 with 2
and 12 degrees of freedom and « = 0.05 is
3.885. H0 is rejected since at least one of the
stream's trout populations has a different mean.
Since H0 is rejected, it is appropriate to continue
with postevaluations to determine which group
has a different mean. Had H0 not been rejected,
postevaluations would be meaningless and
inappropriate.
One approach (Least Significant Difference) to
determining which of the means is different is to
compare each pair of means. To do a
Stream
Black Creek
Site Trout
1 60
2 65
3 64
4 63
5 58
^ 62.0
Blue Creek
Population (Pounds/ACre/Year- Year
49
60
54 ,
58
57
55.6
Red Creek
Class 2)
50
56
51
60
52
538
-------
Data Analysis j
Chaptjer 4
Table 4-22. One-way ANOVA of stream trout data from the coastal plain region using stream as the
Source of
Variation
Between
Groups
(Treatment)
Within
Groups
(Error)
Total
SS
185.733
176.000
361.733
df
2
12
14
MS F j>vaUie F Criteria
92.867 6.332 . 0.013 3.885
14.667
pairwise comparison, the standard error of the
difference between two means is calculated as
(Snedecor and Cochran, 1980)
SD ~
\
(4-45)
" "
with N-k degrees of freedom. Using the data
from Table 4-22, SD is equal to 2.422 with 12
degrees of freedom. For a/2 equal to 0.025 (and
df = 12), the value of t from Table D2 is 2.1788.
Therefore, if any pair of means exceeds a
difference of 2.422 x 2.1788 or 5.3, the
difference is significant. The mean trout
populations for Black, Blue, and Red Creeks are
62, 55.6, and 53.8 pounds/acre/year,
respectively. The trout population hi Black Creek
is significantly higher than the trout population in
Blue Creek or Red Creek. Note that a pairwise
comparison was made between the three groups
(i.e., three pairwise comparisons) with a = 0.05;
therefore, the overall error rate is 1-(1-0.05)3, or
about 14 percent. Other approaches for multiple
comparisons are discussed in Section 4.6.4.
Kruskal-Wallis test
The Kruskal-Wallis test is an extension of the
Mann-Whitney test described earlier. This test
can be used when there are several independent
samples that do not have the same distribution. In
this case, H0 and Ha are as follows:
H0: All k groups have identical distributions.
Ha: At least one of the groups tends to yield
larger observations than at least one other
group.
If the distributions of all groups are similar except
for location (i.e., similar spread and skew), Ha
can be refined to imply that the median
concentration from one group is different from
the median concentration from at least one other
group. To achieve this greater detail in Ha,
transformations such as logs can be used.
Again consider the notation used in Table 4-19
where there are k groups and each group has «j
observations. AT is the total number of
observations. To compute the Kruskal-Wallis
statistic, the following steps (Conover, 1980) can
be used:
-------
Chapter 4
• Rank all of the data from lowest to highest,
assigning the average of ranks to ties. The
rank pf observation x% is denoted as
Compute Rj for all k random samples using
for j = 1,2,...,*
/=!
(4-46)
Compute the test statistic, T:
_k -2
(2
j
(4-47)
where
N-l
E£ *(*,
(N+l)2
4
(4-48)
For k = 3, all «j are 5 or less, and there are no
ties, special tables should be used to determine
the rejection region for 7" (see Conover, 1980). If
these criteria do not apply, Table D3 with/7 =
1-a and k-1 degrees of freedom should be used.
If the computed T statistic from Equation 4-47 is
greater than the value obtained from the table, H0
is rejected.
Table 4-23 presents the rank of the trout
population data used in the previous example; Rj
for each group has already been computed.
Applying Equation 4-48 with the individual ranks
from Table 4-23 and N = 15, S2- is equal to
19.82. Substituting S1, N = 15, n} = 5 (for ally)
into Equation 4-47 along with the R} summarized
in Table 4-23, T is equal to 7.21. From Table D3
with a = 0.05 and 2 degrees of freedom, the
critical value is 5.991. H0 is rejected. Had there
been no ties, the exact critical value would be
5.66 (Conover, 1980).
Since H0 has been rejected, it is acceptable to do a
multiple comparisons evaluation. One approach
is to compare the ranks from each pairwise
group. The groups i and/ are different if the
following inequality is satisfied (Conover, 1980):
> t
1 -(a/2)
(4-49)
In this example, all n} are equal to 5 and the
above equation can be reduced to
Table 4-23. Rank of trout population from streams in the coastal plain region
Stream
Site
1
2
3
4
5
Ri
Black Creek
11
15
14
13
8.5
61.5
Blue Creek
Rank
1
11
5
8.5
7
32.5
Red Creek
2
6
3
11
4
26
-------
Data Analysis
Chapter 4
> 2.1788 19.82
15-3
or
> 23.08
where t is obtained from Table D2 with 15-3
degrees of freedom. By comparing the above
result with R} in Table 4-23, it can be concluded
that the trout population in Black Creek is
significantly greater than the trout population in
Blue Creek or Red Creek.
4.6.2 Two-Factor Comparisons
ANOVA
In a two-way ANOVA the variation due to two
factors is quantified. One factor cannot be a
subset of the other factor. Subsetted factors are
referred to as nested factors, a subject that is not
considered here. The reader is referred to
Gaugush (1986) and Snedecor and Cochran
(1980) for more thorough discussions regarding
factorial experiments and hierarchical arrange-
ments for fixed effects models. In this section,
Equation 4-41 is extended to include a second
factor (Helsel and Hirsch, 1995; Snedecor and
Cochran, 1980),
xi,k =
(4-50)
where i = 1, ..., a\j = 1, .... b; and A: = 1, ...,
n. The number of levels in factors A and B are
represented by a and b, respectively. There are
aKb treatment groups. The number of replicates
is equal to n and is constant across all treatment
levels i and7. That is, .there are the same number
of observations for each unique combination of
factors A and B. In this case, each observed
value is the sum of an overall mean Ox,), the
influence of the Ith category of factor A
(ty), the influence of the/1 category of
factor B (Pj), the interaction effect
between factors'A and B ((ap)jj), and a
; residual error (eijk). If (ap^y is equal to
zero, there is no interaction. No interaction
means that a change in factor B has the same
impact on jtijk regardless of factor A (and vice
versa).
H0 is that all treatment groups have the same
mean, whereas Ha indicates that at least one
treatment group mean has a different mean. The
two assumptions made using this model (Equation
4-50) is that (1) the effects are additive, and
(2) the residuals are independent, random
variables normally distributed with a zero mean
and constant variance across all treatment groups
(Snedecor and Cochran, 1980).
Helsel and Hirsch (1995) caution the practitioner
that when evaluating data with unequal numbers
of observations some smaller statistical packages
incorrectly apply the balanced equations (equal
number of observations) presented here to
unbalanced data sets (unequal number of
observations) without notice. Packages such as
SAS and Minitab provide options for analyzing
unbalanced data sets. Two-way ANOVA can be
performed for two cases, one in which there is no
interaction between the two variables and one in
which there is an interaction between the two
variables. The sum of squares for factor A (55/4),
factor B (SSB), and the interaction between A and
B (SSI) for a balanced data set including
interaction can be computed using Equations 51
through 55 (Helsel and Hirsch, 1995).
Table 4-24 is an ANOVA table that incorporates
the above equations into the second column,
presents the degrees of freedom in column three,
and provides the equations for the mean squared
error terms and F statistics in the fourth and fifth
columns.
-------
Chapter 4
" "
SSA =
1=1
bn
a b n
SSB =
j=i an
a b n
Xijk
abn
where
SSI = Total SS - SSA - SSB - SSE
(4-53)
and
a b n a b \ 2~t
^ = EEEfr«*)a -^^u=1
1a b n
/ j f j / j %f,
. _ ^^^^ijk, ' = 1 ^' = 1 fc=1
i=l ;'=! i=l
(A cA\
(4-54)
Table 4-24. Common two-way ANOVA output format,
Source of
Variation
Factor A
Factor B
Interaction
, (Factor AxFactor
B)
Error
Total
SS
SSA
SSB
SSI
SSE
Total SS
df
a-1
b-1
(b-1)
ab(n-1)
abn-1
MS
MSA = SSA/
(a-1)
MSB = SSB/
(b-1)
MSI = SSI/
MSE = SSE/
p- F
F value criteria
MSA/MSE p F value for
selected a
MSB/MSE p F value for
selected a
MSI/MSE p F value for
selected a
-------
Data Analysis
Chapter 4
To demonstrate this procedure, the two-way
ANOVA procedure is applied to the data in Table
4-25, This data set includes the trout population
data from three streams and three regions
(i.e., a = 6 = 3). This test could reflect, for
example, the hunch that regional effects on trout
population differ across streams (e.g., perhaps the
streams are impacted differently by point and
nonpoint sources). In this experimental design,
factor A is the region and factor B is the stream.
Using standard statistical software, Table 4-26
presents the results of the two-way ANOVA
calculations. The;? values for the region, stream,
and region x stream factors are l.OxlO'10, 0.001,
and 0.1458, respectively. Using a = 0.05, H0 is
rejected; there is a significant difference between
treatment group means due to region and stream.
The interaction of region and stream is not
significant at the 95 percent confidence level.
Based on this analysis, it is acceptable to perform
a multiple comparisons analysis for regions and
streams.
This ANOVA discussion is simple in many
respects. For example, a balanced data set and a
fixed effects model were analyzed. In situations
Stream
Black Creek
Blue Creek
Red Creek
Region
Mountain
Piedmont
Coastal Plain
Mountain
Piedmont
Coastal Plain
Mountain
Piedmont
Coastal Plain
1
75
68
60
70
64
49
68
62
50
Site
234
Trout Population
(Pounds/Acre/Year - Year Class 2)
70 65 72
72 70 70
65 64 63
76 69 67
66 60 69
60 54 58
70 63 65
66 58 69
56 51 60
5
68
67
58
74
62
57
70
67
52
Table 4-26 Two-way ANOVA of trout population data using an interaction term.
Source of
Variation
Region
Stream
Region x Stream
Error
ss
1213.73
219.73
94.93
468.80
1997.20
df
2
2
4
36
44
MS
606.87
109.87
23.73
13.02 ,
F
46.60
8.44
1.82
p-value
1.0E-10
0.0010
0.1458
Fcrlt
(CF0.05)
3.26
3.26
2.63
-------
Chapter 4
where multiple variables are examined, a
balanced data set is not likely to be feasible or
economical. A key limitation of the fixed effects
model is that inferences cannot be made beyond
the groups being tested. In the trout population
example, only statements about the three streams
and three regions analyzed can be made. Nothing
about a fourth stream or region can be inferred.
If the three streams had been randomly selected
from across the state with the intent of
determining whether there was a spatial difference
in trout population, the stream factor would have
been a random factor rather than a fixed factor,
and the calculation of the F statistics would be
different. If both factors were random, the F
statistics would use the mean squares for
interaction (MSI) rather than the MSB as the
denominator. If there were a mixture of fixed
and random factors, the F statistic for the fixed
factor would be computed with the MSI and the
random factor would be computed with the MSB
in the denominator (Helsel and Hirsch, 1995).
Ranked transformed ANOVA
To perform the ANOVA described in Section
4.6.2, the data in each treatment group must be
normally distributed with a constant variance. If
the data do not meet this requirement, it is
possible to use transformations of the data such as
logarithms to convert the data to a normal
distribution with constant variance. The use of
logarithms implies that the influences of each
factor are multiplicative in the original units
(Helsel and Hirsch, 1995; Snedecor and Cochran,
1980). Alternatively, the data can be rank-
transformed (i.e., a rank from 1 to Wean be
assigned to the data) and a two-way ANOVA can
be performed on the ranks. Rejection of H0 using
an ANOVA on the rank-transformed data
indicates that the medians differ between
treatment groups. Helsel and Hirsch (1995) state
that "rank transformation results in tests which
are more robust to non-normality, and resistant to
outliers and non-constant variance, than is
ANOVA without transformations."
4.6.3 Matched Data
Collecting paired data to mask or block out
unwanted noise due to meteorological or
geographical differences is a common practice
when comparing "before" and "after" data.
Comparing just two groups was described in
Section 4.5.1. Comparing matched data with
more than two groups is described here. In this
case, the objective is to compare one factor
(referred to as the treatment) while blocking out
the other factor (referred to as the block).
The linear model for this analysis is (Helsel and
Hirsch, 1995)
*v = V- +-<*j +P, + £« (4-56)
where y = 1, ..., k and i = 1, ..., n. In this case,
each observed value is the sum of an overall mean
<», the influence of they* group effect (KJ), the
influence of the z"1 block effect (P;), and a residual
error (ey). In addition to the two-way ANOVA
without replication and the Friedman test
described here, Helsel and Hirsch (1995) also
describe the median polish and the median
aligned-ranks ANOVA.
Two-way ANOVA without replication
In the ANOVA model, e^ is assumed to be
normally distributed. The sums of squares for the
two-way ANOVA without replication are
computed using Equations 57 through 60 (Helsel
and Hirsch, 1995). Table 4-27 presents a
common format for a two-way ANOVA without
replication. Removing the block effect from the
calculation of the SSE results in a higher F
statistic, thus improving the detection of
significant differences between groups. H0 is
rejected if the computed F is greater than the
-------
Data Analysis
Chapter 4
"•
E
SST =
(4-57)
v
E
SSB =
kn
(4-58)
= Total SS - SST - SSB
(4-59)
where
fe n
k n
Total SS = £ E (*// ~ T
(4-60)
Source of
Variation
Treatment
Block
Error
SS
SST
SSB
SSE
Total SS
df
k-1
n-1
(k-1) x (n-1)
kn-1
MS
MST = SST/
(k-1)
MSB = SSB/
(n-1)
MSE = SSE/
F
F p»value Criteria
MST/MSE p F value for
selected a
-------
Chapter 4
critical F value from Table D6 with (£-1) and
(&-l)Oz-l) degrees of freedom.
Friedman test
The Friedman test is the most common
nonparametric test for randomized complete block
designs. It is an extension of the sign test (Helsel
and Hirsch, 1995). H0 is that the median value of
the k groups are identical, whereas Ha states that
at least one median is different. To compute the
test statistic, the following steps are used:
• Rank the data in each block from 1 to k.
• Compute the, average rank for each group
Compute xf using the following formula,
which accounts for ties:
•xf =
12 n
k(k+l) -
., n k
I V^ V^ . ..•> j
£
n(k-\
,0-7))
(4-61)
where % equals the number of ties of the extent j
in block i. For k+n < 9, exact tables should be
used (see Helsel and Hirsch, 1995). Otherwise,
H0 is rejected if xf greater than or equal to the
critical F value from Table D6 with (k-T) and
(n-T)(k-T) degrees of freedom and/? = 1-cc.
4.6,4 Multiple Comparisons
All of the hypothesis tests featured to this point
allow the analyst to determine whether at least
one treatment results in a mean or median that is
significantly different from that which results
from the other treatments. It does not indicate
which treatment is different or whether there are
multiple differences. Multiple comparisons
should be done only if the analyses performed
under Section 4.6.1, 4.6.2, or 4.6.3 indicate a
significant difference.
Two key features distinguish multiple
comparisons: (1) whether a is based on a
pairwise or overall comparison and (2) whether
the test is a multiple-stage test (MST) or a
simultaneous inference method (SIM). An
important distinction should be made about
whether a pairwise or overall a is used. The a
level indicates the probability of making an
incorrect comparison. Helsel and Hirsch (1995)
cite an example of a one-factor analysis with six
groups (in which there are 15 pairwise
comparisons). If a = 0.05, the potential for
making at least one error is equal to !-(!-. 05)15 or
0.54, a 54 percent chance of making one error.
MSTs are valid for groups with constant sample
size, whereas SIMs are valid for equal and
unequal sample sizes.
k + l
For these reasons, Helsel and Hirsch
(1995) recommend using Tukey's
method, which uses an overall a and
is a SIM. Other tests include the
Bonferroni t tests, Duncan's multiple
range test, Gabriel's multiple-
comparison procedure, the Ryan-Einot-Gabriel-
Welsch (REGW) multiple F test, the REGW
multiple range test, Scheffe's multiple-comparison
procedure, and the Waller-Duncan yfc-ratio test.
The reader should consult statistics texts (e.g.,
Snedecor and Cochran, 1980) to learn more about
these procedures, with preference given to
Tukey's method for equal or unequal sample sizes
and the REGW tests when the sample sizes are
equal. If a nonparametric analysis was
performed, the most appropriate approach is to
rank-transform the data and apply a test based on
the above discussion.
Tukey's method indicates that the mean between
two groups can be considered different if (Helsel
and Hirsch, 1995)
*/ -*y
^n-n
\
MSE
n +n
2n.n
(4-62)
-------
Data Analysis
Chapter 4
where
q —
-------
Chapter 4
the jc's and y's to reduce the overall variance.
For example, a nonpoint source application might
be to examine the effect of different BMP
implementation programs on several water quality
parameters.
Analysts are encouraged to read the detailed
discussion of regression in statistics texts such as
Snedecor and Cochran (1980), Cochran (1977),
and Srivastava and Khatri (1979) for a more
complete discussion of this important statistical
procedure.
4.7.2 Simple Linear Regression
The simplest form of regression is to consider just
one dependent variable and one independent
variable using
V = Po
where y is the dependent variable, x is the
independent variable, and P0 and P, are numerical
constants representing the y-intercept and slope,
respectively. Helsel and Hirsch (1995)
summarize the key assumptions regarding
application of linear regression (Table 4-28). The
uses of a regression analysis should not be
extended beyond those supported by the
assumptions that are met. Note that the normality
assumption (assumption 5) can be relaxed when
testing hypotheses and estimating confidence
intervals if the sample size is relatively large.
The first step in applying linear regression is to
examine the data to see if linear regression makes
sense—that is, to use a bivariate scatter plot to see
if the points approximate a straight line. If they
fall in a straight line, linear regression makes
sense; if they do not, data transformation might
be needed, or perhaps a nonlinear relationship
should be used.
To illustrate the use of linear regression, the data
in Table 4-29, which are a subset of calibration
data for a plot-size runoff sampler (Dressing et
al., 1987), can be used. In this data set the
sampling percentage (split) was measured for a
range of flow rates. The scatter plot in Figure
4-18 shows that linear regression can be applied
to the data.
Presuming that the data are representative
(assumption 2 in Table 4-28), the next step is to
develop the regression line using the method of
least squares, which minimizes the sum of the
squares of the vertical deviations from the points
to the line (Freund, 1973). To determine the
values of P0 and P, in Equation 4-64, the
following equations can be used (Helsel and
Hirsch, 1995):
(4-64) Pj = -f =
S,.
ss
C*,0'/) - nxy
(4-65)
Po = y ~ P,*
(4-66)
For the data in Table 4-29, the above equations
were used to compute a slope of -0.0119 and an
intercept of 3.1317. Thus, the linear model for
predicting split versus flow rate is
Split = 3.1317- 0.0119 • Flow rate
Assumption evaluation
The top section of Table 4-30 provides the same
information along with additional characteristics
about the PO and p, that were computed using
standard spreadsheet software. Before looking at
these additional characteristics, the analyst must
make sure that P0 and p, make sense. In this
case, perhaps the best approach is to plot the
regression line with the raw data as shown in
Figure 4-18. The bottom portion of Table 4-30
contains the predicted split (data for the
-------
Data Analysis
Chapter 4
Table 4-28. Assumptions necessary for the purposes of linear regression..
Assumption
(1) Model form is
correct: y is linearly
related to x
(2) Data used to fit
the model are
representative of data
of interest
(3) Variance of the
residuals is constant
and does not depend
on x or anything else
(4) The residuals are
independent
(5) The residuals are
normally distributed
Purpose
Predict y
given x
/
/
Predict y and a
variance for the
prediction
/
/
/
Obtain best
linear unbiased
estimator of y
/
/
/
/
Test hypotheses,
estimate confidence
or prediction intervals
/
/
/
S
S
/ indicates that assumption is required.
Source: Helsel and Hirsch, 1995.
regression line in Figure 4-18) for each flow rate
as well as the residual, e-t, defined as y-} - ft.
Residuals plotted as a function of predicted values
of y and time, and normal probability plots of
residuals, are the most effective approaches to ,
evaluate the last three assumptions listed in Table
4-28, respectively. As shown in Case A of
Figure 4-19, the plot of residuals versus predicted
values of y or time should appear to be a uniform
band of points around 0 (Ponce, 1980a). The
analyst should look for two types of patterns when
evaluating assumption 3 from Table 4-28 (e.g.,
constant variance). The first is a pattern of
increasing or decreasing variance with predicted
values of y, as depicted in Case B of Figure 4-19.
The second is a pattern (e.g., a trend, a curved
line) of the residual with predicted values of y.
Both characteristics are usually assessed based on
a review of the residual plots and professional
judgment alone. The analyst may also need to
examine variables other than predicted values of y
to fully evaluate assumption 3.
Independence of residuals (assumption 4 from
Table 4-28) can be evaluated by examining
residuals plotted'as a function of time. The
analyst should look for the same patterns as
before. As an alternative for evaluating
independence, the analyst can also plot the z'th
residual, eh as a function of the (z'-l)th residual,
-------
Chapter 4
Table 4-29. Runoff sampler calibration data.
X
Flow Rate
(gpm)
52.1
19.2
4.8
4.9
35.2
44.4
13.2
25.8
n = 15 Ix
X
Ix2
Ixy
Y
Split
(%)
2.65
3.12
3.05
2.86
2.72
2.70
3.04
2.83
433.30
28.89
= 15,940.33
1,166.93
X
Flow Rate
(gpm)
17.6
37.6
41.4
40.1
47.4
35.7
13.9
Iy = 41.81
y = . 2.79
Iy2 = 117.25
Y
Split
(%>
2.84
2.60
2.54
2.58
2.49
2.60
3.19
Sxy = -40.817533
SS
SS
x = 3423.73733
y = 0.70929333
2.0
10
20 30 40
FLOW RATE (gpm)
50
60
Figyre 4-18. Split versus flow rate.
-------
Data Analysis
Chapter. 4
Table 4-30. Regression analysis of runoff sampler calibration data.
Intercept (J30)
Flow Rate (0,)
Regression
Residual
Total
Coefficients
3.1317
-0.0119
df
1
13
14
Flow Rate
(gpm)
52.10
19.20
4.80
4.90
35.20
44.40
13.20
25.80
17.60
37.60
41.40
40.10
47.40
35.70
13.90
Standard
Error
0.072914
0.002237
ss
0.486623
0.222670
0.709293
Split
(%)
2.65
3.12
3.05
2.86
2.72
2.70
3.04
2.83
2.84
2.60
2.54
2.58
2.49
2.60
3.19
{Statistic
42.950756
-5.330126
MS
0.486623
0.017128
Predicted
Sptit
2.5106
2.9028
3.0745
3.0733
2.7121
2.6024
2.9743
2.8241
2.9219
2.6835
2.6382
2.6536
2.5666
2.7061
2.9660
p Value
2.14E-15
0.00014
F
28.410248
Residual
epyrVf
0.1394
0.2172
-0.0245
-0.2133
0.0079
0.0976
0.0657
0.0059
-0.0819
-0.0835
-0.0982
-0.0736
-0.0766
-0.1061
0.2240
Lower Upper
95% 95%
2.97420 3.28924
-0.01675 -0.00709
Significance
F
0.0001366
reviewing any residual plot: If there are more
points in a certain section of the residual plot, the
residuals might not appear to be a uniform band
of points around 0 (as suggested in Case A of
Figure 4-19); instead, that section might have a
somewhat wider band (Helsel and Hirsch, 1995).
This is an expected result.
The normality of residuals can be assessed by
examining a probability plot. Two problems with
non-normal residuals are the loss of power in
subsequent hypothesis tests and increased
prediction intervals together with the impression
of symmetry (Helsel and Hirsch, 1995).
Figure 4-20 displays all three of these plots for
the split data analyzed from Table 4-29. From
Figure 4-20, A and B, the split residuals appear to
be independent of predicted values of y and time
as well as having a constant variance. The
regression meets assumptions 3 and 4 listed in
Table 4-28. In this analysis, testing for residual
independence is important since the testing
apparatus was calibrated initially. The pumps or
other equipment could have differed in
performance over time, which in turn would
affect the results. Figure 4-20C, the probability
plot, suggests that the data might not rigorously
follow the normality assumption, although by
inspection any normality violation is believed to
-------
Chapter 4
10-1
-1CM
Predicted Y
-10-1
Case B
Predicted Y
Figure 4-19. Plot of residuals versus predicted values.
(Source: Ponce, 1980a)
be relatively minor. To check, the Shapiro-Wilk
W statistic (see Section 4.4.1) is computed as
0.935. Comparing 0.935 to the test statistic (with
j7=0.95, n=15) from Table D5, 0.98, the split
residuals can be accepted as being normally
distributed. (Note that accepting H0 in this case
might be due to small sample size and resulting
lack of power.) Had this analysis violated any of
these assumptions, using a different regression
technique, transforming the data, or adding
additional variables to the regression would have
to be considered. Alternatively, the use of the
regression results could be limited to those
identified in Table 4-28 as restricted by the
assumptions met.
Model evaluation
To determine how well the regression line fits
the data, several things can be evaluated:
• Evaluate the proportion of variation in y
explained by the model.
• Test whether P0 is zero.
• Test whether pi is zero.
• Compute the confidence interval for P0.
• Compute the confidence interval for pt.
The coefficient of determination, R2, can be
used to evaluate what proportion of the
variation can be explained by the model
(Gaugush, 1986). R2 can be computed as
(Helsel and Hirsch, 1995)
R2 =
where
ss.
SSE
SS
xy
(4-67)
(4-68)
1=1
and
SSE =
(4-69)
1=1
The residual, eh is defined as yt - y,. S^ and SSX
can be computed from Equation 4-65. Values for
R2 range between 0 and 1, with 1 representing the
case where all observed y values are on the
regression line. The correlation coefficient, r,
measures the strength of linear relationships
(Freund, 1973) and is computed as the square
root of R2. The sign of r should be the same as
the sign of the slope. It ranges from -1 to 1, with
the extreme values representing the strongest
association and 0 representing no correlation.
-------
Data Analysis
Chapter 4
025 T
ooo
-025
-H 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
2,4 25
2.6 2.7 2.8 2.9
PREDICTED VALUES OF SPLIT (%)
3.0 3.1
A) Split residuals as function of predicted values of split.
0.25 -r
ooo
-02S-) 1 H-
H 1 1-
TIME
-I 1 1 1 1
B) Split residuals as function of time.
35
s
0,25 n
000
-0,25
-2,0 -1,5 -1.0
-0,5 0.0 0.5
NORMAL QUANTILES
C) Probability plot of split residuals.
Figure 4-20. Plot of split residuals.
-------
chapter 4
Using the split data from above, the sum of
.residuals-squared (SSE) is equal to 0.2227; thus,
R2 is equal to 1 - (0.2227/0.7093) = 0.686, or
68.6 percent of the variance is explained by the
model. (The 0.7093 is from Table 4-29.) The
overall model can also be evaluated with the F
statistic (28.41), which is computed in Table
4-30. The F statistic is a measure of the
variability in the data set that is explained by the
regression equation in comparison to the
variability that is not explained by the regression
equation. Since thep value of 0.0001366 is less
than 0.05, the overall model is significant at the :
95 percent confidence level.
Are Po and pt significantly different from zero?
The standard error for P0 and P[ in the top portion
of Table 4-30 can be calculated as (Helsel and
Hirsch, 1995)
n SS
(4-70)
(4-71)
where
1 "
1 V^ 2
^~£e'
(4-72)
The value s is equal to the standard error of the
regression (which is the same as the standard
deviation of the residuals). The corresponding t
statistics (with n - 2 degrees of freedom) for P0
and p, are then equal to P0 and PJ divided by their
respective standard error. The t statistic can then
be compared to values from the t distribution to
determine whether P0 or P, are significantly
different from zero. In mis case P0 and P[ are
both significantly different from zero based on
inspection of their associated p values in Table
4-30. The overall model can also be evaluated
with the F statistic computed in the middle portion
of Table 4-30. This portion of Table 4-30 has the
same format as the ANOVA tables described in
the previous section. The values in this table are
computed using the equations summarized in
Table 4-31. Verification of the results in Table
4-30 is left to the reader.
The confidence intervals for P0 and P, can be
computed using the following formulas (Helsel
and Hirsch, 1995):
(4-73)
(4-74)
where ta/2^_2 is from Table D2. The lower and
upper 95 percent confidence limits for P0 and PJ
are provided in the top portion of Table 4-30,
from which ra/2)I1_2 was obtained as 2.1604.
The correlation coefficient, r, calculated from
sample data, is an estimate of the corresponding
population parameter, p, referred to as the
population correlation coefficient. Establishing a
confidence interval for p requires that x also be a
normally distributed random variable (Freund,
1973). The Shapiro-Wilk W statistic for x (the
flow rate data in Table 4-29) is 0.931.
Comparing 0.931 to the test statistic of 0.98,
obtained earlier, the data can be accepted as
normally distributed. Using Table D9
(Remington and Schork, 1970) the 95 percent and
99 percent confidence limits for p can be obtained
knowing n and r. For the data in Table 4-29, n is
15 and r is -0.828. So, the 99 percent confidence
limits from Table D9 are approximately -0.95 to
-0.50.
A t test can also be used to test H0 that p is zero.
The t statistic (with n-2 degrees of freedom) for
this test is (Freund, 1973)
-------
Data Analysis
Chapter 4
Table 4-31. Common ANOVA output format for linear regression.
Significance
Source of Variation SS df MS F F
Regression
Residual
Total
SSR = (Sxy)2ISSx 1 MSR = SSR/1 MSR/MSE p
SSE n-2 MSE = SSE/(n-2)
SSR + SSE n-1
(4-75)
For the above data t would be -5.33. From Table
D2 the two-sided t value for 95 percent signifi-
cance (df = 13) is -2.1604. Therefore, H0
(p — 0) is rejected and Ha that p is not zero is
accepted.
The Fisher Z transformation can be used to test
H0 of p equal to values other than zero (Freund,
1973). For this test, r is changed into a Z value
using (Freund, 1973)
(4-76)
1-r
Freund (1973) provides a table of Z values to
simplify this procedure. The test statistic is
(Freund, 1973)
where pt corresponds to the Z value for the
nonzero value of p being tested for. For
illustration, H0 that p is equal to -0.8 for the
regression performed can be tested using the data
from Table 4-29. Equation 4-76 yields -1.1827
for r = -0.828 and -1.0986 for r = -0.8.
Substituting these values in Equation 4-77 yields
z = (-1.1827+1.0986)^15-3 = -0.2913
The two-sided z statistic at 95 percent significance
(Table Dl) is -1.96, H0 is accepted, p = -0.8.
A confidence interval for p can be determined by
calculating an interval for /*z, and then
retransforming the confidence interval from Z
values to p (Freund, 1973). The formula for the
confidence interval for ^z is (Freund, 1973)
Z--
"afi
n-3
"a/2
n-3
(4-78)
Again using the sample data, the 95 percent
confidence interval for /x.z becomes
-1.1827-
1.96
<; -1.1827 +
1.96
(4.77) -1.7485 ^ nz ^ -0.6169
Solving for p,
Lo\verlimit _ _^ 74g5 _ \_
°f 9 ' 2 ( 1-p
Upperlimit = _Q
°f P '
1 +p
1 -p
-------
Chapter'4
-0.94 < p < -0.55
Using the regression line
The most obvious use of the regression line is to
predict y values for selected values of x. For
example, using the regression equation
Split = 3.1317- 0.0119x Flow Rate,
the split for any flow rate can be estimated. (It is
not good practice, however, to predict values
beyond the range of test conditions.) For a flow
rate of 10 gpm, the predicted split is 3.01 percent;
for a flow rate of 50 gpm, the predicted split is
2.53 percent.
Since in most cases the regression line will not fit
the data perfectly, the uncertainty associated with
the predicted values should be quantified. The
regression line can be used either to establish the
confidence interval for the population mean of y
or to determine the prediction interval for a single
value of y. The limits for the single value of y are
wider than the corresponding limits on the mean
of y (Remington and Schork, 1970) because .
single observations vary more than means.
The equation for the confidence interval for the
population mean y at x = x0 is (Helsel and
Hirsch, 1995)
\
SS
(4-80)
ss
(4-79)
This interval is most narrow at x and widens as x0
moves farther fromx By calculating the interval
at each point along the regression line, a curve
such as the dashed line in Figure 4-21 for the
example data can be plotted. The equation for the
prediction interval for individual values ofy at
x = x0 is (Helsel and Hirsch, 1995)
Figure 4-21 also shows this interval for the
example data.
One of the simplest (in theory) nonpoint source
control applications of linear regression is the
regression of a water quality indicator against an
implementation indicator. For example, flow-
adjusted total suspended solids (TSS)
concentration could be regressed against a
sediment control variable such as the total
combined erosion rate of all cropland for which
delivery to the stream is likely to be 50 percent or
greater. A significant negative slope would
suggest (but not prove) that water quality has
improved because of implementation of sediment
control practices.
Another possible use of simple linear regression is
to model a water quality parameter versus time.
In this application a significant slope would
indicate change over time. The sign of the slope
would indicate either improvement or degradation
depending on the parameter used. For nonpoint
source studies, a simple regression versus time
will most likely be confounded by the variability
in precipitation and flows. Thus, considerable
data manipulation (transformations, stratification,
etc.) might be required before regression analysis
can be successfully applied. In these cases, it
might be more appropriate to apply one of the
alternatives to regression described by Helsel and
Hirsch (1995).
In many cases water quality parameters are
regressed against flow. This is particularly
relevant in nonpoint source studies. In analysis of
covariance, regressions against flow are often
performed prior to an ANOVA (Spooner et al.,
1985). One of the implicit goals of nonpoint
source control is to change the relationship
-------
Data Analysis;
Chapter 4
34 -,
32
30
g 28
fe 2,6
2.4
2,2
20
95 PERCENT CONFIDENCE
INTERVAL FOR MEAN.
RESPONSE
95 PERCENT CONFIDENCE
INTERVAL ON INDIVIDUAL
ESTIMATES OF SPLIT
10
20 30
FLOW RATE (GPM)
40
50
60
Figure 4-21. Plot of split versus flow rate with confidence limits for mean response and individual
estimates.
between flow and pollutant concentration. This
will be discussed in greater detail under analysis
of covariance.
In paired watershed studies, measured parameters
from paired samples are often regressed against
each other to compare the watersheds. These
regression lines can be compared over time to test
for the impact of nonpoint source control efforts
(Spooner et al., 1985). This will be discussed in
greater detail under analysis of covariance.
4.7.3 Nonlinear Regression and
Transformations
The discussion of nonlinear or curvilinear
regression is limited to cases where the nonlinear
relationship can be transformed into a linear
relationship for which simple linear regression
can be performed. Data inspection should
indicate to the analyst the nature of the
relationship between the dependent and
independent variables. Possible curvilinear
relationships include exponential curves (semi-
log), power functions (log-log), and parabolas,
among others (Freund, 1973).
Nonlinear regression (as discussed here) involves
transformation to linear equations, followed by .
simple linear regression. Helsel and Hirsch
(1995) provide a detailed discussion on
transformations using the "bulging rule"
described by Mosteller and Tukey (1977), which
can be used to select appropriate transformations.
Crawford et al. (1983) list the numerous
regression models most often applied by the U.S.
Geological Survey for flow-adjusting
concentrations. The selection of which
transformation to use is ultimately based on an
inspection of the residuals and whether the
assumptions described earlier are met. Typical
transformations include x2, x2, Inx, llx, x°-5, etc.
-------
Chaptier4
When the residuals do not exhibit constant
variance (heteroscedasticity), one of several
common transformations should be used.
Logarithmic transformations are used when the
standard deviation in the original scale is
proportional to the mean of y. Square root
transformations are used when the variance is
proportional to the mean of y. In many instances,
the right transformation will "fix" the nonlinear
and heteroscedastic problem. With data that are
percentages or proportions (between the values of
0 and 1), the variances at 0 and 1 are small. The
arcsin of the square root of the individual values
is a common transformation that helps spread out
the values near 0 and 1 to increase their variance
(Snedecor and Cochran, 1980).
There are several disadvantages when applying
transformations to regression applications. The
most important issue is that the regression line
and confidence intervals are symmetric in the
transformed form of the variables. When these
lines are transformed back to their normal units,
the lines will no longer be symmetrical. The
most notable time in hydrology when this creates
a problem is when estimating mass loading. To
estimate the mass, the means for short time
periods are regressed and summed to estimate the
total mass over a longer period. This approach is
acceptable if no transformations are used—the
analyst is summing the means. However, if a log
transformation is used, summing the mass over
the back-transformed values results in summing
the median, which will result in an estimate that is
biased low for the total mass (Helsel and Hirsch,
1995).
As an example of nonlinear regression, consider a
common relationship that is used to describe load
(L) as a function of discharge (<2):
ln(Q)
(4-82)
L = aQ
Taking the logarithms of both sides yields
(4-81)
which has the same form as Equation 4-64,
introduced at the beginning of this section, where
ln(L) corresponds to y, ln(a) corresponds to PO, b
corresponds to p,, and ln(0 corresponds to x.
By taking the logarithms of both sides, the
nonlinear problem has been reduced to a simple
linear model. The only additional step that the
analyst must perform is to convert L and Q to
ln(L) and ln(0 before using standard software.
The analyst should be aware that all of the
confidence limits are in transformed units; when
they are plotted in normal units, the confidence
intervals will not be symmetric.
Figure 4-22 demonstrates how transforming the
data may improve the regression analysis. In
Figure 4-22A, sulfate concentrations (in
milligrams per liter) are plotted as a function of
stream flow (in cubic feet per second). The
apparent downward trend is typical of a stream
dilution effect; however, the trend is clearly
nonlinear. The trend line plotted in this figure, as
well as the residuals plotted in Figure 4-22C,
demonstrate that a linear model would tend to
over- and underestimate sulfate concentrations
depending on the flow. Figure 4-22B displays the
same data after computing the logarithms (base
10) of the sulfate and flow data. A trend line
fitted to these data and the residual plot (Figure
4-22D) clearly demonstrate that applying linear
regression after log transformation would be
appropriate for these data.
4.7.4 Multiple Regression
Multiple regression is applied to quantify a
relationship between a dependent variable and
more than one independent variable (Gaugush,
1986). The assumptions made for simple linear
regression also apply to multiple regression
(Ponce, 1980a). The method of least squares is
also used to determine the best multiple
-------
Data Analysis
Chapter 4
•5 2.20
A '
B
-1.0 -0.5
0.0 0.5
Log[FIOW)
1.0 1.5
II
49
0.11
0.10
a 0.00 ..
3
-0.10
3
* .
*
C '
10
Flow, cd
-1.0 -O.B 0.0 0.5 1.0 1.S
Log|Flow)
Figure 4-22. Comparison of regression analyses using raw and log-transformed data.
regression line. The general linear model to '
consider is (Ponce, 1980a)
The corresponding normal equations are
presented below (Ponce, 1980a).
= Po + PI*I + P2X2 * • • • + Pn^n + e After solving for the P,, ..., Pn, P0canbe
(4-83) calculated from (Ponce, 1980a):
,) P
(4'84)
p,
. (4-85)
: + (E v,) P2 + (E-32) P3 + - - - - (E
(E*,*.) P, + (E v.) P2 + (E vj P
P. - E
<4-87)
-------
Chapter 4
Po =y -.Mi - M2 -
(4-88)
Ponce (1980a) presents a hand-computed example
of multiple regression using three independent
variables. The reader is encouraged to follow
through that example to develop an understanding
of multiple regression before using computerized
procedures. Gaugush (1986) states that multiple
regression with two independent variables can be
performed using textbook formulas, but that
matrix algebra is required for broader appli-
cations. Winer (1971) provides a matrix algebra
approach to multiple regression, but the discus-
sion is complicated and probably not critical to
appropriate use of multiple regression techniques
(especially when the analyst consults a statis-
tician).
Gaugush (1986) also provides an example of
multiple regression in which the SAS procedure
GLM (SAS Institute, Inc., 1985b) is used. This
example relates pollutant level to three indepen-
dent variables—distance from source, tempera-
ture, and discharge. An interpretation of the SAS
output is also provided.
Key points made in the examples above include:
• An F test indicates the significance of the
regression.
• The coefficient of multiple determination (ft2),
which is calculated as in simple linear regres-
sion, shows the proportion of variation in y
explained by the model.
• Computerized output such as that from SAS
can be used to refine the model for subsequent
runs.
As a further note regarding use of SAS, the
RSQUARE procedure (SAS Institute, Inc.,
1985b) can be used in an exploratory fashion to
perform all possible multiple regressions for
subsets of independent variables, listing the
models in decreasing order of R2 magnitude.
Thus, the model with the largest R2 value will be
listed first. The STEPWISE procedure allows
five approaches to stepwise regression for users
who wish to determine which variables should be
included in a regression model (SAS Institute,
Inc., 1985b). However, this procedure is not
guaranteed to identify the model with the largest
R2. Other computer software packages, such as
SPSS (Statistical Package for the Social Sciences),
can also be used for multiple regression
(Ingwersen, 1980).
The following discussion ofR2, taken largely
from a technical nonpoint source newsletter
(Spooner, 1984), emphasizes proper interpretation
of R2 values.
The purpose of regressing a response variable (y)
on one or more independent variables (x) is to
"explain" some of the variation observed in the
measured values in y. The F tests for each
individual x variable can be used to determine
whether they are individually important to the
regression on y. R2 is a. measure of the fraction of
variation in y explained by the linear regression
on*!, x2, ..., xn variables in the model.
Specifically, R2 is the fraction of the sum of
squares (SS) of the deviations of y from its mean
that is attributed to the regression. R2 values
range from 0 (model useless) to 1 (model
perfect)(Equation 4-87).
H0 that
can be tested using the F statistic to determine
whether the regression model explains any of the
variation in Y. The F statistic is
(n-k-l) R2/(k-l)(l-R2) with (jfc-1) and (n-k-l)
degrees of freedom. It should be noted that (Jfc-l)
is the degrees of freedom for the regression
-------
Data Analysis
Chapter 4
model SS and (n-k-l) is the degrees of freedom
for the error SS.
•
-------
Chapter 4'
Within this procedure the MTEST statement can
be used to test hypotheses regarding the
multivariate regression model. F values are
calculated for the following procedures (SAS
Institute, Inc., 1985b):
• Wilks' lambda
• Pillai's trace
• Hotelling-Lawley trace
• Roy's maximum root
4.8 ANALYSIS OF COVARIANCE
Suppose an analyst is interested in evaluating
BMPs by comparing data collected from a paired
watershed design. Data are collected from two
watersheds during two periods—calibration and
treatment. During calibration, neither watershed
has a BMP in place, while during the later period,
one of the two watersheds has a BMP installed.
A natural extension of the regression techniques
described in Section 4.7 is to compare regression
equations between the treatment watershed and
the control watershed, with one regression
equation developed during the calibration phase
and the second regression equation developed
during the treatment phase. The analysis of
covariance (ANCOVA), a procedure that
combines features of ANOVA and regression, can
be used to evaluate this situation. ANCOVA can
also be used to test for differences in the average
value for a dependent variable (e.g., sediment
concentration) between the levels of a group
variable (e.g., seasons or years) after adjusting
for an independent variable (e.g., flow or
upstream concentration).
A typical ANCOVA model in which the slopes
and intercepts for the two groups are suspected to
be different can be represented as (Helsel and
Hirsch, 1995)
y =
(4-92)
where Z is a binary variable that is equal to 0 or 1
depending on which group x and y are from. For
example, Z could be 0 during calibration and 1
during treatment of a paired watershed analysis.
In this case, P0 and P0 + p2 are the intercepts
during the calibration and treatment periods,
respectively, p, and P, + P3 are the slopes during
the calibration and treatment periods,
respectively. If P2 is nonzero and p3 is zero, the
regression produced by Equation 4-92 would be a
pair of parallel lines (Figure 4-23A). If p2 and P3
are nonzero, the regression produced by Equation
4-92 would be a pair of lines like those presented
in Figure 4-23B.
The remainder of this discussion follows an
analysis performed for field runoff (cm) during
the conversion from conventional to conservation
tillage in Vermont (USEPA, 1993c). Two
watersheds were monitored during a calibration
period during which 49 (n() paired observations
of runoff were made. Figure 4-24A is a bivariate
log-log plot of storm runoff for the treatment
watershed as a function of storm runoff for the
control watershed. Based on an inspection of this
plot, it seems reasonable to perform the analyses
using log-transformed (base 10) data.
A regression analysis was performed on these
data to determine whether there was a significant
relationship between the watersheds, whether
enough data had been collected during
calibration, and whether the residual errors were
smaller than the expected BMP effect. A
summary of the regression ANOVA is provided
in Table 4-32 (with «, = 49, SSy = 148.441, SSX
= 70.933, and Sxy = 78.463). (Equations 4-65
through 4-67 and Table 4-31 can be used to hand-
check the table entries.) The/? value associated
with the resulting F statistic indicates that the
model explains a significant proportion of the
variation.
To determine whether enough calibration data
have been collected, the ratio of the MSB to the
-------
Data Analysis
; Chapter.4
VARIABLE X
A) Variable y versus x for two periods (constant slope).
VARIABLE X
B) Variable y versus x for two periods (variable slope).
Figure 4-23. Comparison of regression equations for data from two periods.
-------
Chapter,4
10°
. 10
r
§ E1(f
10=
— calibration pertod
Log Y= 0.549+0.789(LogX) •* j
r2 = 0.69
10° 10* id3 162 id1
Control (West) Field
Storm Runoff (cm)
Calibration Period Regression
10"
• — calibration period
* ••••.calitrationperiod
LogY = 0.603 + O.SO(LogX)
r* = 0.33
Id3
Control (West) Field
Storm Runoff (cm)
10°
Treatment and Calibration Period Regression
Figure 4-24. Storm runoff from calibration and treatment periods in Vermont. (Source: EPA, 1993c).
-------
Data Analysis
Chapter 4
Table 4-32. ANOVA for regression of treatment watershed runoff on control watershed runoff during
calibration.
Source of Variation
Regression
Residual
Total
SS
86.792
61.649
148.441
df
1
47
48
MS
86.79
1.31
Significance
F F
66.17 0.0001
smallest worthwhile difference (d) can be
compared using the following formula (EPA,
1993c):
MSB
F\l+-
(4-93)
where nt and 722 are the number of observations in
the calibration and treatment periods, respec-
tively, and F is from Table D6 with 1 and «i + «2
- 3 degrees of freedom. If the treatment period
has not been initiated, assume that HI = n^.
Using the example data where x of the log-
transformed data is -2.518, the number of
observations necessary to detect a 20 percent
change can be estimated. The left side of the
above equation would be equal to 1.317(0.2 x
-2.518)* or 5.2. With nl=n2 = 49 and F =
3.94 (p = 0.95, 1 and 95 df), the right side of the
above equation can be evaluated as 6.0. Since the
left side of the equation is less than the right side,
there would be enough samples to detect a 20
percent change hi discharge. Equation 4-79 can
be used to determine the confidence bands for the
regression equation, which allow determining the
level of change needed to have a significant
treatment effect.
Once the treatment period data have been
collected, the same type of regression analysis is
performed. Following this step, the significance
of an overall regression (which combines
calibration and treatment data) can be evaluated
and the difference between the individual slopes
and intercepts can be evaluated. Continuing with
the example, a summary of the regression
ANOVA for the treatment period is provided in
Table 4-33 (with^ = 114, SSy = 135.0,
SSX = 227.43, and Sxy = 101.32). The/? value
associated with the resulting F statistic indicates
that the model explains a significant proportion of
the variation.
The ANCOVA can be performed by combining
the results from Tables 4-32 and 4-33. Table
4-34 demonstrates the general format for
performing ANCOVA hand calculations. Note
that S indicates summation of terms. This
approach is applied to the example data with the
results presented in Table 4-35. Table 4-36
presents the same calculations performed with
SAS. (An appropriate SAS program is provided
below.) The ANCOVA indicates that the overall
treatment and calibration regressions were
significantly different and that the slopes and
intercepts of the equations were also different.
The difference in slopes is evident from Figure
4-24B. The small differences between the
calculations in Tables 4-35 and 4-36 are due to
rounding errors. If there are not significant
-------
Chapter'4.
Table 4-33. ANOVA for regression of treatment watershed runoff on control watershed runoff during
treatment.
Source of Variation
Regression
Residual
Total
SS
45.13
89.87
135.00
df
1
112
113
Significance
MS F F
45.13 56.25 0.0001
0.80
Table 4-34. ANCOVA for comparing regression lines.
Source df
Within
Calibration nr1
Treatment n2-1
Slopes n^+r\2-2.
Intercepts n.,+n2-i
Ssx
Eq.
4-65
Eq.
4-65
Z
sxy
Eq-
4-65
Eq.
4-65
Z
combined
Ssy
Eq.
4-68
Eq.
4-68
Z
Slope
data
ft
S^/SS,
s*/ssx
Error:
Sxy/SS,
difference:
df
n,-2
n2-2
Z
1
1
r\i+nz-2
SS (res.)
SSy-(Sxy)2/SSx
SSy-(Sxy)2/SSx
Z
SSy-(Sxy)2/SSx
Slope SS-Error SS
Comb. SS- Slope SS
MS F
SS/df
SS/df
SS/df
SS/df
SS/df MS/Error MS
SS/df MS/SlopeMS
-------
Data Analysis
Chapter 4
Table 4-35. ANCOVA for comparing regression lines from calibration and treatment (hand
Source df
Within
Calibration 48
Treatment 113
Slopes 161
Intercepts 162
' Significant at p =
Ss,
70.933
227.430
298.363
311.671
0.001
0.05
Sxy SSy P,
78.463 148.441 1.106
101.315 135.00 0.445
Error:
179.778 283.441 0.603
Slope difference:
178.762 283.492
df
47
112
159
160
1
1
161
SS (res,)
61.650
89.866
151.516
175.116
23.600
5.8453
180.961
MS
1.3117
0.8024
0.9529
1.0945
23.600
5.8453
F
-
24.77*
5.34t
Table 4-36. ANCOVA for comparing regression lines from calibration and treatment (computerized
Source of Variation
Model
Error
Overall
Intercept
Slope
df
3
159
1
1
1
MS
43.99
0.95
103.09
5.47
23.42
F Significance F
46.17
108.18
5.74
24.58
0.001
0.0001
0.0178
0.0001
-------
Chapter 4
SAS Program to Compare Regression Lines
PROC GLM;
CLASS PERIOD; . ^
MODEL LOGFLOW2 = LOGFLOW1- PERIOD
LQGFLOW1*P£RIQD;
RUN;
/* LQGFL0W1»tog-transformed data from control
watershed */
1* LQGFLOW2 = fog-transformed data from
treatment watershed */
I* PERIOD = indicator for whether paired data were
from the calibration or treatment period */
differences between the slopes, all of the periods
can be represented by a common slope and the
relationship between y and x is constant over the
tested period.
4.9 EVALUATION OF TIME SERIES
In nonpoint source data analysis, we often want to
know whether there is a tendency for a pollutant
concentration to increase or decrease over"time.
If such a tendency exists, we say there is a trend.
Trend analysis is often used to determine whether
the implementation of a BMP actually reduces the
pollutants in a stream, or whether the
development of an urban area is causing the
deterioration of water quality downstream, as well
as maintaining a status of ambient water quality
conditions. A trend can be visually examined by
plotting the observed data versus time. A
statistical test is required to analyze the trend.
This section describes statistical procedures for
detecting and evaluating monotonic (continuously
nonincreasing or nondecreasing) trends in a single
time series (e.g., 10 years of monthly TSS at a
single station) and presents several methods for
evaluating temporal correlation.
The first issue to consider is when a monotonic
trend test should be used. The most important
factor before beginning the analysis is to assess
whether any interventions or activities led to the
hypothesis that a shift in water quality might have
occurred. For example, suppose a BMP to
reduce sediment loadings was installed during the
course of the monitoring program. A shift in TSS
concentration (hopefully downward) after BMP
installation would be expected. In this case, it is
more appropriate to divide the data into "before"
and "after" groups and analyze the data using the
two independent random sample procedures
described in Section 4.5. On the other hand, if a
series of BMPs are being implemented across a
watershed over several years and monitoring is
being performed in a downstream estuary, the
changes would be expected to be gradual. In this
case a monotonic trend test might be more
appropriate. If there is no hypothesis to naturally
divide the data, it is also best to use a monotonic
trend test. Concentration data should not be used
to determine data groupings for the purposes of
developing hypotheses or selecting between a
two-sample or monotonic trend test.
The second issue to consider is the case where
sampling was interrupted for several years in the
middle of a 10-year monitoring effort. It is
suggested that if the data gap is greater than one-
third of the total data record, it is better to use a
two-sample test (Helsel and Hirsch, 1995). A
similar issue to consider is the case where several
data records will be examined, but they have
different starting and stopping points. Helsel and
Hirsch (1995) suggest that the analyst divide the
data record into three periods of equal length; if
any third of the record has more than 20 percent
missing values, that record should not be used.
The final issue to consider is whether to account
for exogenous variables (e.g., flow, temperature,
rainfall) before testing for trends. A common
example is the approach used by the USGS to
account for flow variability in its National Stream
Quality Accounting Network (NASQAN) stations.
In USGS analyses, water quality variable
concentrations are adjusted to account for flow.
-------
Data Analysis
Chapter 4
The flow-adjusted concentrations are then
evaluated for trends. These adjustments can be
made using simple linear regression analyses, as
discussed in Section 4.7 or the nonparametric
procedures (e.g., locally weighted scatter plot
smoothing) discussed by Helsel and Hirsch
(1995). The purpose of adjusting the data for an
exogenous variable is to reduce the background
noise so that the detection of time trends is more
powerful.
There are several methods to detect monotonic
trends in time series. Regression analyses have
already been discussed in Section 4.7. To apply
linear regression where time is the independent
variable, all of the assumptions listed in Table
4-28 are necessary. If these assumptions are met,
linear regression is an acceptable approach.
Significant trends are declared when the slope
term, pt, is significantly different from zero.
Multivariate regression procedures that model the
water quality variable as a function of an
exogenous variable (e.g., flow) and time
simultaneously can also be used to detect trends if
the regression assumptions are met. When
evaluating several data sets for a single report,
these assumptions are rarely met for all of the
data sets. In these cases, nonparametric
procedures are recommended. This is not to say
that data transformations for nonparametric tests
are not desirable, as will be discussed later.
Since simple linear and multivariate regression
have been discussed, this section is limited to
discussing the Mann-Kendall t and the Seasonal
Kendall tests. Both are nonparametric.
Following the monotonic trend discussion,
procedures for computing the autocorrelation
coefficient and Spearman's rho are provided.
These procedures are useful for evaluating
whether the data are truly independent, one of the
fundamental assumptions in the procedures
described next. If the data are serially correlated,
it is possible to systematically sample from the
data set, to group the data into time periods and
use a summary statistic (e.g., time- or volume-
weighted mean or median), or to use more
advanced time series analysis procedures (Helsel
and Hirsch, 1995) to analyze these data.
4.9.1 Monotonic Trends
Regression
Refer to Section 4.7 for a discussion on simple
linear and multivariate regression.
Mann-Kendall T Test
The Mann-Kendall T test analyzes the sign of the
difference between later-measured data and the
earlier-measured data. Each later-measured
datum is compared to all data measured earlier.
This approach results in a total of n(n-l)/2
possible pairs of data, where n is the total number
of observations in the time series. The
Mann-Kendall T test assumptions include the
typical requirements that the data be independent
and that one value can be declared larger than,
smaller than, or equal to another value. The third
assumption is similar to the regression
requirements that the residuals must have a
constant variance, but no distribution
requirements are necessary.
The usual hypotheses for a Mann-Kendall T test is
whether y tends to increase or decrease with time
(Helsel and Hirsch, 1995):
Mann-Kendall T Test Assumptions
» The random variables y,, y2r..., y1f..., y},..-,
yn are mutually independent
* The measurement scale of the data is at
least ordinal (i.e., yj can be declared as <, >f
The data are identically distributed with only
a shift in the central location if there is a
trend >
-------
Chapter',^
Two-sided test
H0: Prob lyj > yj = 0.5
Ha: Prob fy > yj * 0.5
One-sided test
H0: Prob fy > yj = 0.5
Ha: Prob fy > yj > 0.5
where % > t{ •
(two-sided test)
where tj > t-t
(one-sided test,
increasing trend)
The next step is to compute the difference
between the later-measured value and all
earlier-measured values, (yryd, where j > i and
assign the integer value of 1, 0, -1 to positive
differences, no differences, and negative
differences, respectively. The test statistic, S, is
then computed as the sum of the integers:
n-l
sign(y.-
(4-94)
where sign(») is equal to 1, 0, or -1 as indicated
above. This task is most easily accomplished
assuming the data are ordered in increasing time
order. When S is a large positive number,
later-measured values tend to be larger than
earlier-measured values and there might be an
upward trend. When S is a large negative
number, later-measured values tend to be smaller
than earlier-measured values and there might be a
downward trend. When the absolute value of S is
small, there might be no trend. The test statistic,
T, can be computed as
T =
(4-95)
which has a range of -1 to 1 and is analogous to
the correlation coefficient in regression analyses.
Computing S or T becomes tedious when n is
large. Gilbert (1987) provides a FORTRAN
program to alleviate the computation effort. S
and T are invariant to transformations such as logs
(i.e., 5 and T will be the same value whether the
raw or log-transformed data are used).
For sample sizes greater than 10, the large sample
approximation can be used to compute a test
statistic that can be compared to a normal
distribution using the following equation:
z = i
where
S-l
S+l
ifS>0
ifS<0
(4-96)
n
18
5 N
/or w/^e« //zere are no ties or ,
(4-97)
18
(4-98)
for when there are ties, where Zs is zero if S is
zero and tt is equal to the number of ties of extent
/. Zs is compared to the critical z value from
Table D1. For a two-sided 95 percent confidence
level, the critical z value would be +1.96. If Zs
is not contained within this range, reject H0. See
Helsel and Hirsch (1995) for sample sizes of 10
or less. To determine ti} consider the following
20 observations that are in ascending order:
<1, <1, <1,4, 4, 6, 6, 8, 8, 10, 11, 11, 11
11, 16, 19,20,22,32,45
In this example there are seven ties of extent 1
(i.e., no ties), three ties of extent 2 (4, 4, 6, 6, 8,
8), one tie of extent 3 (< 1,
-------
Data Analysis
Chapter 4
Table 4-37. Annual total rainfall for 21 years.
Table 4-37 presents a
list of annual rainfall
for 21 years. Table
4-38 presents the
intermediate calcula-
tions for computing S.
The top portion of
Table 4-38 is a table
of the differences
Vj - y{, for example
y2-yi = 13.2.
Observations ys
through yl6 were
omitted from Table
4-38 for presen-tation
purposes. The bottom ^^^^^^^—^^^^
portion of Table 4-38
presents the interme-
diate calculations for signfy - )>i). Summing these
values (including those not presented in this table)
yields a value of 12. Since there were no ties, Zs
= (12-l)/(1096.7)°-s or 0.33,,H0 is accepted-
there is no trend hi the rainfall data.
Had there been a significant trend in the data, the
Sen slope estimator could be estimated as (Helsel
and Hirsch, 1995)
Year
1
2
3
4
5
6
7
8
9
10
11
Rainfall (in.)
40.2
53.4
43.5
37.7
50.2
38.7
47.8
39.5
44.9
41.7
36.4
Year
12
13
14
15
16
17
18
19
20
21
Rainfall (in.)
51.2
54.3
41.5
44.8
46.7
51.8
49.5
34.1
33.2
53.7
6. = m ediam
(4-99)
\XJ-*'
for all i < j and i = 1, 2, ..., n-l andy = 2, 3,
.... n; hi other words, computing the slope for all
pairs of data that were used to compute S. The
median of these slopes is the Sen slope estimator.
Using the rainfall data as an example, the slope
between y4 andy2 is equal to (37.7-53.4)7(4-2) or
-7.9. Had there been a significant trend, this
process would have been carried out for the
remaining pairs of observations and the median
slope selected as the Sen slope estimator.
As might be expected, any linear slope estimator
is a poor choice when the apparent slope is
exponential. In Section 4.7.3, transformations to
reduce the analysis to a linear problem were
discussed. These same approaches are also
appropriate here. So while it does not matter for
computing 5 or T that the trend be linear,
transforming the data prior to computing the slope
estimator might be useful. For example, if the
data were transformed using natural logs, the
percentage change from.year to year in the above
example would be estimated as (epi -1) x 100
(Helsel and Hirsch, 1995).
Seasonal Kendall test
In the nonpoint source area, many data follow
seasonal patterns. The decision to use a seasonal
Kendall test (Hirsch et-al., 1982) can usually be
made by examining boxplots by season. The test
statistic is computed by performing a Mann-
Kendall calculation for each season and then
combining the results for each season. That is, if
sampling is monthly, January observations are
compared only to other January observations, etc.
Thus 5k is computed as the sum of the S from
each season (Helsel and Hirsch, 1995):
S,= YS, (4-100)
-------
Table 4-38. Analysis of rainfall data using Mann-Kendall t test
Yi
y2
YS
y4
ys
• Ye
' y?
. Yi?
. yis
Yl9
• ,Y2o
y2i
Yi
y2
: YS
y4
. ys
Y6
YV
Yl7
Yis
Yi9
y20
y2i
yj YI y2
y, 40.2 53.4
40.2 13.2
53.4
43.5
37.7
50.2
38.7
47.8
51.8
49.5
34.1
33.2
53.7
40.2 53.4
40.2 1
53.4
43.5
37.7
50.2
38.7
47.8
51.8
49.5
34.1
33.2
53.7
Ys Y4 Ys
43.5 37.7 50.2
3.3 -2.5 10.0
-9.9 -15.7 -3.2
-5.8 6.7
12.5
43.5 37.7 50.2
1-1 1
-1 -1 -1
-1 1
1
Ye
38.7
-1.5
-14.7
-4.8
1.0
-11.5
38.7
-1
-1
-1
1
_j
y? ••-
47.8 ...
7.6 ...
-5.6 ...
4.3 ...
10.1 ...
-2.4 ...
9.1 ...
47.8 ...
1 '....
-1 ...
-...1-..-:..
1 ...
-1 ...
1 ...
y.n
51.8
11.6
-1.6
8.3
14.1
1.6
13.1
4.0
51.8
1
-1
1
1
1
1
1
Yl8
49.5
9.3
-3.9
6.0
11.8
-0.7
10.8
1.7
-2.3
49.5
1
-1
1
1
-1
1
1
-1
Yl9
34.1
-6.1
-19.3
-9.4
-3.6
-16.1
-4.6
-13.7
-17.7
-15..4
34.1
-1
-1
-1
-1
-1
-1
-1
-1
-1
Y20
33.2
-7.0
-20.2
-10.3
-4.5
-17.0
-5.5
-14.6
-18.6
-16.3
-0.9
33.2
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
y2i
53.7
13.5
0.3
10.2
16.0
3.5
15.0
5.9
1.9
4.2
19.6
20.5
53.7
1
1
1
1
1
1
1
1
1
1
1
where S-t is S from the i* season and m is the
number of seasons. Zsk is estimated as
or Zsk is zero if Sk is zero and
Sk
ifS.>Q
ifS,<0
\
( = 1 15
(4-102)
(4-lOi)
Sk
where n( is the number of observations in the Ith
season.
-------
Data Analysis
Chapter4
4.9.2 Correlation Coefficients
Spearman's rho
Spearman's rho test is used to detect whether
there is a correlation between paired data.
Spearman's rho is computed as (Conover, 1980)
p =
n+l
n+l
(4-103)
where R(») represents the rank of the observation
and n is the number of observations. If there are
ties, Equation 4-104 may be used.
The resulting value of p is then compared to
critical values in Table D10. Spearman's rho can
be used in the same manner as the T statistic
computed in Section 4.9.1. Spearman's rho can
also be used to evaluate serial correlation by
setting >»s = .xi+k to determine the lag-k
autocorrelation. For k = 1, the first observation
is compared to the second observation, the second
observation to the third observation, and so on.
Using die rainfall data, Table 4-39 presents the
intermediate calculations for Spearman's rho for
k = 1. Notice that 3^ = xl+l and that there are
only 20 observations in this analysis. The third
and fourth represent the ranks of x-t and yi?
respectively. The remaining three columns are
intermediate calculations for the numerator of the
above equation. Finally, p is equal to -1267
[(20(202-1)/12] or -0.19. Assuming a two-sided
hypothesis, the critical value from Table D10
(with n = 20 and a = 0.05) is ±0.4451; the
rainfall data are not correlated at lag-1. This
result cannot be compared with the previous
example. In the previous example the correlation
between annual rainfall and time was evaluated.
In this example, "this year's annual rainfall" is
compared to "next year's annual rainfall."
Autocorrelation coefficient
The analyst may also use the correlation
coefficient, r. Salas et al. (1980) provided the
formula for the lag-& autocorrelation coefficient
as:
n-k
t=l
n-k
(4-105)
Anderson (1941) gave the limit
_/,_i\0.5
n-k
(4-106)
for the 95 percent probability levels for the lag-fc
autocorrelation coefficient where n is the sample
size.
- n
n+l
Ew2-«
n+l
i 0.5
(4-104)'
-------
Chapter^
Table 4-39. Analysis of rainfall data using Spearman's rho
Xi
40.2
53.4
43.5
37.7
50.2
38.7
47.8
39.5
44.9
41.7
36.4
51.2
54.3
41.5
44.8
46.7
51.8
49.5
34.1
33.2
33.2
y,
53.4
43.5
37.7
50.2
38.7
47.8
39.5
44.9
41.7
36.4
51.2
54.3
41.5
44.8
46.7
51.8
49.5
34.1
33.2
53.7
-
R(Xj)
7
19
10
4
16
5
14
6
12
9
3
17
20
8
11
13
18
15
2
1
• -
R(y.)
18
9
4
15
5
13
6
11
8
3
16
20
7
10
12
17
14
2
1
19
-
RM-
-3.5
8.5
-0.5
-6.5
5.5
-5.5
3.5
-4.5
1.5
-1.5
-7.5
6.5
9.5
-2.5
0.5
2.5
7.5
4.5
-8.5
-9.5
-
RM-
7.5
-1.5
-6.5
4.5
-5.5
2.5
-4.5
0.5
-2.5
-7.5
5.5
9.5
-3.5
-0.5
1.5
6.5
3.5
-8.5
-9.5
8.5
-
Sum
Numer.
-26.25
-12.75
3.25
-29.25
-30.25
-13.75
-15.75
-2.25
-3.75
11.25
-41.25
61.75
-33.25
1.25
0.75
16.25
26.25
-38.25
80.75
-80.75
-126.00
4.10 MULTIVARIATE ANALYSES
There are several multivariate procedures in
addition to the multivariate regression discussed
in 4.7.4. Mathematical descriptions of these
procedures are beyond the scope of this guidance,
but researchers should consult a statistician to
assess the opportunities for using these
procedures. In general, the multivariate
procedures described in this section have not
found wide usage in day-to-day applications.
With the current availability of computerized
statistical procedures .(e.g., SAS, SPSS), it is
possible to perform multivariate analyses with
ease, requiring of the researcher only that he or
she understands and meets the assumptions of the
particular test and knows how to interpret
correctly the results of the test. It is extremely
important that a qualified statistician be consulted
regarding the assumptions involved and the
appropriate interpretation of test results. Without
such precautions, our current computer
technology will only facilitate the proliferation of
misguided analyses and misinterpreted results.
The multivariate analyses described briefly in this
guidance include canonical correlation, cluster
analysis, principal components and factor
analysis, and discriminant analysis. These
procedures were selected for discussion based on
the work of Gaugush (1986), which should be
-------
Data Analysis
Chapter 4
reviewed in addition to the detailed discussions
provided in statistics texts for a better
understanding of these multivariate analyses.
4.10.1 Canonical Correlation
Canonical correlation is a technique for analyzing
the relationship between two sets of variables,
with each set able to contain several variables
(SAS Institute, Inc., 1985b). It follows that
simple and multiple correlation are special cases
of canonical correlation in which one or both sets
of variables contain only one variable (SAS
Institute, Inc., 1985b).
Gaugush (1986) states that "[c]anonical
correlation is used to identify and estimate a
linear function (called a canonical variate) of one
set of variables that is maximally correlated with
a linear function of a second set of variables."
The SAS CANCORR procedure (SAS Institute,
Inc., 1985b) finds as many canonical variates as
there are variables in the smaller set of variables.
The first and subsequent canonical variates are
uncorrelated, with the first having the highest
correlation coefficient, followed by the second-
highest correlation coefficient for the second
canonical variate, etc. It should be noted that
"the first canonical correlation is at least as large
as the multiple correlation between any variable
and the opposite set of variables" (SAS Institute,
Inc., 1985b).
Gaugush (1986) notes that the information
resulting from canonical correlation is largely
descriptive and therefore the procedure has not
been used as much as other multivariate
procedures that support hypothesis testing and/or
prediction.
Gaugush (1986) promotes the use of canonical
correlation to, for example, "describe the strength
of a relationship between a linear combination of
nutrient variables and a linear combination of
biomass-related variables." The strength of such
a relationship is estimated by the canonical
correlation coefficient.
Another use of canonical correlation is in
determining how many "common elements" are
contained within two sets of variables (Gaugush,
1986). The percent overlapping variance (i.e.,
the squared canonical correlation coefficient) can
be used to indicate the relative importance of each
canonical variate (Gaugush, 1986).
To use canonical correlation in hypothesis testing,
it is important that the assumption of multivariate
normality is satisfied (Gaugush, 1986). Snedecor
and Cochran (1980) discuss the multivariate
normal distribution briefly and state its. property
that "any variable has a linear regression on the
other variables (or on any subset of the other
variables), with deviations that are normally
distributed." Gaugush (1986) notes that the
assumption of multivariate normality is often
satisfied by "creating data distributions that are
approximately normal."
To satisfy the assumptions of canonical
correlation, Gaugush (1986) recommends:
• Use transformations if needed to create
roughly symmetric univariate data
distributions.
• Carefully examine the validity of outliers and
run analyses with and without outliers to
document their impact on the correlations.
• Transform data if necessary to create linear
relationships among the variables in each set
of variables.
Finally, Gaugush (1986) gives an example
application of canonical correlation using the SAS
CANCORR procedure described above.
-------
Chapter. 4
4.10.2 Cluster Analysis
Cluster analysis is a classification method for
placing "objects into groups or clusters suggested
by the data, not defined a priori, such that objects
in a given cluster tend to be similar to each other
in some sense, and objects in different clusters
tend to be dissimilar" (SAS Institute, Inc.,
1985b). SAS offers several clustering options
under the CLUSTER procedure (SAS Institute,
Inc., 1985b). It is important to recognize that
numerous methods come under the heading of
cluster analysis and these methods will give
different results. The types of cluster analysis
include the following (SAS Institute, Inc., 1985b):
• Disjoint clusters, which place each object in
one and only one cluster.
• Hierarchical clusters, in which one cluster
may be contained entirely within another
cluster, but for which no other kind of
overlap is allowed.
• Overlapping clusters with or without
constraints placed on the number of objects
that belong to two clusters.
• Fuzzy clusters, which are defined by a
probability of membership of each object in
each cluster. (These can be disjoint,
hierarchical, or overlapping.)
Example analyses include the following:
• Gaugush (1986) used Ward's method of
cluster analysis to group reservoirs based on
similarity in log total phosphorus
concentration, log total nitrogen
concentration, log Secchi disk depth, and log
chlorophyll a concentration.
• Kimball (1986) used cluster analysis to group
wells based on mean nitrate, well depth,
maximum nitrate, coefficient of variation of
nitrate, and variance of nitrate. Mean nitrate
and coefficient of variation of nitrate yielded
the most information. A major conclusion
made from this investigation of wells in South
Dakota was that "classification of ground
water sample locations by geologic
environment and depth is crucial to
understanding the system."
4.10.3 Principal Components and Factor
Analysis
Principal component analysis (PCA) is a
multivariate procedure for examining
relationships among several quantitative variables
(SAS Institute, Inc., 1985b). PCA is used with
factor analysis to "create a relatively small
number of new variables (called 'factors') from a
larger number of original variables" (Gaugush,
1986). The primary use of these procedures is
exploratory analysis; that is, hypothesis testing is
not normally performed (SAS Institute Inc
1985b).
Gaugush (1986) notes that PCA is usually
performed before factor analysis. Principal
components are linear combinations of the
original variables. The first principal component
explains the most variability associated with the
data, while the second principal component
explains the second-most variability associated
with the data and is not correlated to the first
principal component. As an example, Gaugush
(1986) describes how PCA can be used to develop
a trophic state index from biological, nutrient,
and physical data. It is sometimes helpful to
prepare a scatter plot of the data using the first
two principal components for exploratory
analysis.
Factor analysis is then used to enhance the
scientific interpretation of the principal
components developed. Factor analysis can then
be used to redefine the factors (i.e., the linear
functions of one or more of the original variables)
-------
Data Analysis j
Chapter 4
so that they can be interpreted in more scientific
terms. That is, factor analysis can be used to
reshape a principal component such that the
factors match more closely a researcher's intuitive
(or research-based) model of the relationships
among the variables.
Although hypothesis testing is not normally
performed on the results of PCA and factor
analysis, Gaugush (1986) recommends that data
distributions be approximately symmetric with no
outliers. As in other cases, data transformations
might be needed to meet these recommendations.
Because of problems of scale, Gaugush (1986)
recommends that PCA and factor analysis be
based on the correlation matrix unless the
variables are all of approximately the same
magnitude. In cases where the variables are of
the same magnitude, the covariance matrix can be
used.
This discussion of PCA and factor analysis is
intended only to familiarize the water quality
researcher with the general use of these
techniques. Gaugush (1986) goes several steps
farther in describing these procedures, including
an illustrative example. SAS gives a fairly
detailed mathematical description of PCA and
factor analysis (SAS Institute, Inc., 1985b) and
offers procedures for performing both
(PRINCOMP and FACTOR procedures).
4.10.4 Discriminant Analysis
Discriminant analysis resembles regression
analysis, but with a major difference in mat the
dependent variable in discriminant analysis is
categorical, whereas the dependent variable in
regression analysis is often continuous (Gaugush,
1986). An example application of discriminant
analysis might be to predict the presence or
absence of brook trout based on pH and
aluminum concentration. Researchers are
encouraged to follow the descriptions of
discriminant analysis offered by SAS (SAS
Institute, Inc., 1985b) and Gaugush (1986) before
using the procedure. The following are some of
the uses for discriminant analysis (SAS Institute,
Inc., 1985b):
e To find a mathematical rule (or "discriminant
function") for predicting to which class an
observation belongs, given data for the
independent quantitative variables.
• To find linear combinations of the
independent quantitative variables that best
reveal the differences between the classes.
• To find a subset of the independent
quantitative variables that best shows the
differences between the classes.
Discriminant analysis requires prior knowledge of
all classes (e.g.; a sample), whereas cluster
analysis has no such requirement (SAS Institute,
Inc., 1985b). In fact, cluster analysis is used to
define the classes. Gaugush (1986) also cautions
that outliers can adversely affect the results of
discriminant analysis and that the predictor
variables should follow a multivariate normal
distribution within each group, with variance-
covariance matrices that are constant across
groups. There is, however, at least one
procedure (NEIGHBOR procedure) that can be
used for non-normal data (SAS Institute, Inc.,
1985b).
4.11 EXTREME EVENTS
One of the key characteristics that separate
environmental, and in particular nonpoint source-
influenced data, is the presence of extreme
events. The majority of nonpoint source pollution
entering streams occurs during runoff from
precipitation events. This section presents an
approach for estimating annual precipitation and
storm events, describes the approach used by
EPA's DESCON model for estimating design
flows, and concludes with statistical methods
-------
Chapter ;4
appropriate for evaluating water quality extreme
events. Earlier sections describe methods for
summarizing average conditions and determining
changes. This section also describes methods for
evaluating extreme conditions in water quality
variables. This is important for evaluating
standard violations or evaluating peak
concentrations to determine if a BMP was
effective.
4.11.1 Rainfall Analyses
Annual precipitation
Chow (1951) presents a method for computing
annual precipitation for a variety of return
periods. This method is outlined below assuming
that the annual rainfall is available for n years.
• Compute the mean and standard deviation for
the n years of data. Also compute the
coefficient of variation (CV).
• Use CV to estimate the log-probability
frequency factor, K, for a given return period
(Table 4-40).
• Compute the annual precipitation (X^) for
different return periods using Equation 4-107.
(4-107)
For the rainfall data presented in Table 4-37, x
and CVare equal to 44.5 inches and 0.15,
respectively. From Table 4-40, the value of K
corresponding to a 2 year return period is -0.09.
Substituting this value into the above equation
yields Xc equal to 44.5(1+(0.15)(-0.09)) or 43.9
inches. The 100 year annual precipitation would
be equal to 44.5(1+(0.15)(2.70)) = 62.5 inches.
The adequacy of the record length can be
evaluated using (Mockus, 1960):
Y = (4.3(mog
10
(4-108)
where 7 is the minimum record length in years, t
is the Student's t quantile (Table D2) at the 90%
level with 7-6 degrees of freedom, and R is the
ratio of the 100 year event to the 2 year event.
To solve the above equation, an iterative
approach is necessary. Using an initial guess of Y
equal to 15 years, t is equal to 1.8331, while R is
equal to 62.5/43.9 or 1.42. Substituting these
values into the above equation yields Y =
[(4.3)(1.8331)(. 1534)]2+6 or 7.5. Adjusting our
guess of Y to 9 years, t is equal to 2.3534 and Y
= [(4.3)(2.3534)(. 1534)]2+6 or 8.4 years (which
is close enough to our initial guess). Since the
actual length of record is 21 years, our 100 year
return annual precipitation estimate of 62.5 inches
can be expected to be reasonable.
Storm return period
The method developed by Hershfield (1961) is the
most usually applied method in the field today and
is commonly referred to as "TP40." The method
is based on interpolating the design storm from
four figures (Figures 4-25 through 4-28) and
applying the following equation (Weiss, 1962):
/ = 0.0256(C-A)x+O.OOQ256[(D-C)-(B-A)]xy
+ O.Ol(B-A)y+A
(4-109)
where / is the rainfall amount (in inches); A is the
2-year, 1-hour rainfall (in inches) interpolated
from Figure 4-25; B is the 2-year, 24-hour
rainfall (in inches) interpolated from Figure 4-26;
C is the 100-year, 1-hour rainfall (in inches)
interpolated from Figure 4-27; and D is the 100-
year, 24-hour rainfall (in inches) interpolated
from Figure 4-28. The return period, jc, and
duration, v, are taken, from Table 4-41 and 4-42,
respectively.
-------
Data Analysis
Chapter 4
Table 4-40. Theoretical log-probability frequency factors.
Return Period (years)
1.01
2
Probability (%> equal
cs
0.0
0.5
1.0
1.139
1.4
1.5
2.0
3.0
4.0
99
-2.33
-1.98
-1.68
-1.61
-1.49
-1.45
-1.28
-1.04
-0.90
50
0.0
-0.09
-0.15
-0.16
-0.19
-0.20
-0.24
-0.28
-0.29
5
to or greater than
" 20
0.84
0.80
0.75
0.73
0.69
0.68
0.61
0.51
0.42
20
the given
5
1.64
1.77
1.85
1.86
1.88
1.89
1.89
1.85
1.78
100
variate
1
2.33
2.70
3.03
3.11
3.26
3.31
3.52
3.78
3.91
cv
0.0
0.166
0.324
0.363
0.436
0.462
0.596
0.818
1.000
Source: Chow, 1951
Table 4-41. Linearized rainfall frequency variate for equation 4-109.
I Return Period
(in years)
Linearized
1
-6.93
2
0
5
9.2
10
16.1
25
25.3
50
32.1
100
39.1
Source: Weiss. 1962
Table 4-42. Linearized rainfall duration variate for equation 4-109.
Duration (hours)
Linearized Variate
(y)
Duration (hours)
Linearized Variate
(y)
0.17
-37
2
17.6
.033
-24
3
28.8
0.5
-15.6
6
49.9
0.67
-9.4
12
73.4
1
0
24
100.0
Source: Weiss, 1962
-------
Chapter 4
CO
O)
03
•8
I
I
.
03
-------
Data Analysis
Chapter 4
U-
-------
Chapter,4.
CO
._
CD
JQ
I
O
CO
Q)
CD
Q)
O
O
O
O
-------
Data Analysis
Chapter 4,
OO
O)
to
•*-*
-------
Cha'pter'4
Suppose the analyst is interested in estimating the
1-year, 2-hour storm in El Paso, Texas. From
Figures 4-25 through 4-28, A, B, C, and D are
estimated as 0.8, 1.5, 2.0, and 4.0, respectively.
From Table 4-41, x is equal to -6.93 and from
Table 4-42, y is equal to 17.6. Substituting these
values into Equation 4-109 yields a 1-year, 2-hour
storm equal to 0.7 inches.
4.11.2 Design Flows
This section describes the computational steps
employed by DFLOW and DESCON for each of
the three types of design flows considered and has
been extracted and adapted from Rossman (1990).
It begins with the extreme value design flow,
since this type of design flow also serves as a
starting point in computing the biologically-based
design flow.
Extreme value design flow (low flows)
The extreme value design flow is computed from
the sample of lowest m-day average flows for
each year of record, where "m" is the user-
supplied flow averaging period. Established
practice uses arithmetic averaging to calculate
these m-day average flows. A log Pearson Type
III probability distribution is fitted to the sample
of annual minimum m-day flows. The design
flow is the value from the distribution whose
probability of not being exceeded is 1/7?, where R
is the user-supplied return period. The procedure
is modified slightly to accommodate situations
where some annual low flows are zero.
STEP 1. Initialize each element of a vector X of
daily flow values to UNKNOWN (i.e., a very
large number such as Ix 1020).
STEP 2. Read in daily flow values from the
retrieved STORET flow file into X, where X(l)
corresponds to the first day of record. (Note:
February 29th of leap years is ignored.)
STEP 3. Create m-day running arithmetic
averages from the daily flows in X, and replace
the daily flows of X with these values. The
running average ofX(i), X(i+l), ..., X(i+m-l) is
placed mX(i).
STEP 4. Find the lowest m-day running average
value for each water year recorded in X (where a
water year begins on April 1) and store the
resulting values in vector Y. Let NY denote the
number of entries in Y.
STEP 5. Let N be the number of non-zero entries
in 7. Assume that these F-values are a sample
drawn from a log Pearson Type III probability
distribution. The design flow is the value from
this distribution whose probability of not being
exceeded is l/R, where R is the user-supplied
return period. Use the following procedure to
find the design flow:
STEP 5a, Find the mean (17), standard deviation
(S), and skewness coefficient (G) of the natural
logarithms of the non-zero entries in Y.
STEP 5b. Let FO be the fraction of entries in Y
that are zero:
FO = (NY -N)/NY
(4-110)
Let P be the cumulative probability corresponding
to the user-supplied return period of R years,
adjusted for the presence of zero-flow years:
P = (l/R - F0)f(l - FO)
(4-111)
In other words, if FO is the probability of having
a year with zero stream flow, and l/R is the
allowed probability of a year with an excursion
below the design flow, then P is the
corresponding excursion probability in years with
non-zero flows.
STEP 5c. Let Z be the standard normal deviate
corresponding to cumulative probability P. Z can
-------
Data Analysis <
Chapljer 4
be computed using the following formula (Joiner
and Rosenblatt, 1971):
running average of X(i), X(i+l), ...,X(i+m-l) is
placed in X(i) and is computed as follows:
Z = 4.91(P°-14-(1-P)0-14)
(4-112)
STEP 5d. Compute the gamma deviate, K,
corresponding to the standard normal deviate Z
and skewness G using the Wilson-Hilferty
transformation (Loucks et al., 1981):
K = (2/g) ((1 + GZ/6 -G2/36)3
(4-113)
STEP 5e. Compute the design flow as
cxp(i7 +KS) (4-114)
Biologically-based design flow
Biologically-based design flows are computed by
starting with a trial design flow, then counting
how often this flow is not exceeded by m-day
average flows in the historical record. (In
contrast with the traditional method of computing
extreme value design flows, the m-day flow
averages are harmonic means, not arithmetic
ones. This count is compared to the allowed
number of such occurrences, and the trial design
flow is adjusted accordingly. The specific
computational steps involved are as follows:
STEP 1. Initialize each element of a vector X of
daily flow values to UNKNOWN (i.e., a very
large number such as Ix 1020).
STEP 2. Read in daily flow values from the
retrieved STORET flow file into X, where X(l)
corresponds to the first day of record. (Note:
February 29th of leap years is ignored.)
STEP 3. Create w-day running harmonic
averages from the daily flows in X, and replace
the daily flows of X with these values. The
Define B(j) as l/X(i+/-l) if X(i+j-l > 0, and 0
otherwise, fory = 1 to m. Let DSUM be the sum
of B(J) for j = I to m and mO be the number of
B(f) values that equal 0. Then replace X(i) with
= (m-mO)/DSUM*(m-mO)/m.
Note that this procedure takes into account the
possibility of zero flows when forming a
harmonic average.
STEP 4. Compute an extreme value m-day
average trial design flow (DFLOW) using the
biologically-based average number of years
between flow excursions (R) as the return period.
STEP 5. Compute the allowed number of flow
excursions, A, (i.e., the number of distinct m-day
average flows allowed to be below the design
flow) over the NDAYS of stream flow record: A
= NDAYS/365/fl.
STEP 6. Use the procedure described below to
compute the number of biologically-based flow
excursions resulting under the trial design flow
DFLOW. Because the trial flow was computed as
an extreme value flow, the resulting number of
biologically-based excursions will most likely be
larger than the allowed number, A. If it is not;
then keep increasing the trial design flow by some
fixed increment until the resulting number of
excursions exceeds A.
STEP 7. Use the Method of False Position
(Carnahan et al., 1969) to successively refine the
estimate of the biologically-based design flow as
follows:
STEP 7a. Set lower and upper bounds on the
design flow with their corresponding excursion
counts:
FL = 0; XL = 0.
-------
FU = DFLOW; XU = number of excursions
under DFLOW.
STEP 7b. Check on convergence of the bounds.
If FU - PL is within 0.5 percent of PL, then end
with DFLOW = FU. If XL is within 0.5 percent
of A, then end with DFLOW = PL. If XU is
within 0.5 percent of A, then end with DFLOW
= FU. Otherwise proceed to the next step.
STEP 7c. Interpolate between the bounds to find
a new trial design flow, FT:
FT = FL + (FU-FL)(A-XL)/(
(4-115)
and compute the number of excursions (XT)
occurring for this flow (see procedure described
below).
STEP 7d. Update the bounds based on the value
of XT: If XT z-A, then set FL = FT and XL =
XT. Otherwise set FU = FT and XU = XT.
Then return to the convergence check of step 7b.
The process used to count the number of flow
excursions for a given design flow proceeds in
two phases. The first phase identifies all
excursion periods in the period of record. An
excursion period is a sequence of consecutive
days where each day belongs to an m-day running
average flow that is below the given design flow.
Recall that "m" is the flow averaging period set
by the user. Phase two groups these excursion
periods into excursion clusters and counts up the
total number of excursions occurring within all
clusters. An excursion cluster consists of all
excursion periods falling within a prescribed
length of time from the start of the first period in
the cluster (120 days is the default cluster length).
The number of excursions counted per cluster is
subject to an upper limit whose default value is 5.
Before describing the detailed procedures for each
of these phases a simple numerical example will
be used to illustrate the method. Suppose that the
design flow under consideration is 100 cfs and
that the period of record yields a sequence of 4-
day running average flows as detailed in Box 1.
The first flow excursion period for this record
consists of the 4-day averages occurring on days
1,2 and 3. Thus the period extends from day 1 to
day 6 (days 4, 5 and 6 belong to the averaging
period that begins on day 3). There are two other
excursion periods consisting of days 13 to 18 and
513 to 548. Under the default clustering
parameters, there are 2 excursion clusters; cluster
1 contains periods 1 and 2, and cluster 2 contains
period 3. The number of excursions in each
cluster is detailed in Box 2.
Note that the number of excursions in each period
equals the period length divided by the averaging
period. The nominal number of excursions in
cluster 2 is 9, and since this exceeds the limit of
5, only 5 are counted. The total number of
excursions for the design flow of 100 cfs in this
example is 3 + 5 = 8.
The detailed procedure for counting biologically-
based flow excursions under a specified design
flow is as follows:
PHASE 1
Pl(0 = day which begins excursion period z,
P2(z) = day which ends excursion period i,
XP(z') = number of excursions in period i,
XKLrnax = maximum cluster length (e.g., 120
days).
t = current day of record.
STEP 1. Set i = 0, P2(0) = 0, and t = 1.
STEP 2. If the m-day running average beginning
on day t is greater or equal to the specified design
flow then proceed to Step 5.
-------
Data Analysis
Chapter 4
Day
1
2
3
4-12
13
14
15
16-512
4-Day Average Flow
(cfs)
34
65
25
> 100
57
34
26
> 100
Day
513-545
546-end
4-Day Average Flow
(cfe)
< 100
> 100
Boxl
Cluster
1
2
Period
1
2
3
Start Day
4
13
513
Length
(days)
6
6
36
No, of
Excursions
in Period
6/4 = L5
6/4 = L5
36/4 = 9,0
No* of
Excursions
in Cluster
3,0
5.0
Box 2
STEP 3. If the current day t is more than a day
beyond the end of the current excursion period
(t > P2(f) + 1), or if the length of the current
excursion period equals XKLmax then begin a
new excursion period by setting:
PKO = *
P2(i) = m - 1
XP(i) = 0.
STEP 4. Update the ending day of the current
excursion period and the excursion count for this
period:
P2(i) = P2(0 + 1
XP(/) = (P2(0 - Pl(0) / m.
STEP 5. Proceed to the next day of record (t = t
+ 1). If not at the end of the record then return
to Step 2. Otherwise proceed to phase 2.
PHASE 2
Define:
i = current excursion period,
k = current excursion cluster,
Kl = day of record which begins cluster k,
XK(&) = number of excursions in cluster k,
Xkmax = maximum number of excursions
counted per cluster (e.g., 5),
STEP 1. Set i = 1, k = 0, and Kl = a large
negative number.
-------
STEP 2. If the length of the current cluster is
greater than the maximum length (i.e., P2(z) - Kl
> XKLmax) then begin a new cluster with
excursion period z, i.e.,
k = k+ 1
Kl = Pl(fc)
XK(#) = 0.
STEP 3. Update the excursion count for the
current cluster,
= minimum(XK(A;) + XP(0, XKmax).
STEP 4. Proceed to the next excursion period
(z = z + 1) and return to Step 2. If no more
excursion periods remain, then total up the
number of excursions in each cluster
(XK(1) +XK(2) + ... + XK(£)) to determine
the total number of excursions.
4.11.3 Frequency of Extreme Events
This section describes methods for evaluating
extreme conditions in water quality variables.
This is an important consideration for evaluating
standard violations or evaluating peak
concentrations to. determine if a BMP was
effective. Gilbert (1987) presents an approach for
evaluating proportions. The method is based on
computing the number of observations exceeding
a threshold value X,.. The proportion of
observations, p, exceeding Xc can be computed as
p = uln
(4-116)
where u is the number of observations exceeding
Xc and n is the number of observations. For n <
30, Table Dll can be used to develop
nonparametric 90th or 95* percentile confidence
.limits. For n > 30, Equations 4-117 and 4-118
may be used. The lower limit is equal to 0 if u is
0 and the upper limit is 1 if the u is equal to n.
If np and n(l-p) are greater than 5 (some authors
suggest a value of 10), then Gilbert (1987)
suggests that the normal approximation can be
used to compute the upper and lower limits with
the following equation:
P ± z.
\Pd-p)
(4-119)
The confidence intervals can be used to evaluate
one-sample hypotheses such as
U0:p = 0.10
Lower
limit
n+z:
l-K/2
(a-0.5)-("-°-5>2+-
(4-117)
Upper
limit
n+Z,
(u+0.5)-
(M+O.S)2
(4-118)
-------
Data Analysis!
Chapter 4
If the 95 percent confidence intervals include
0.10, we accept the null hypothesis. Otherwise
the null hypothesis is rejected.
An evaluation of proportions can also be used to
determine the necessary sample size to ensure that
q percent of the population is less than the largest
randomly sampled observation. This approach
provided by Conover (1980) is demonstrated with
the next example.
Example:
Determine die number of random samples that
would be required to ensure with a 95 percent
probability (a=0.05) that 90 percent of the
population is less than the largest observation.
Solution:
Enter Table Dl 1 with q equal to 0.9 and 1-cc
equal to 0.95 and directly read a sample size of
29. Therefore, it would require 29 samples to
ensure that the largest observation is greater than
90 percent of the population.
Application of this example is similar to quality
control processes. In this case, once 29 samples
have been collected, the upper bound is set equal
to the largest observation. From then on, we
would expect that only 10 percent of the future
samples would exceed the upper bound with 95
percent confidence. If more than 10 percent of
future observations exceeded the upper bound, we
would infer that some change has occurred (Ward
etal., 1990).
It is also possible to compare the proportions PI
and p2 between two samples with sample sizes
equal to /*, and n2. For example, it may be
appropriate to compare the percent of standard
violations from before and after. In this case, the
null and two-sided alternative hypothesis are
H0: pi = p^
Moore and McCabe (1989) provide the test
statistics as
...
(4-120)
where sf and p are given by
p (l-p) —+—
P =
(4-121)
(4-122)
Moore and McCabe (1989) suggest that nj>,
«,(!-/?), n^p, and «2(1 -p) all be greater than or
equal to 5 for application. If the absolute value of
z is greater than the associated normal deviate
(e.g., 1.96 for a two-sided test with a equal to
0.05), then H0 is rejected.
-------
5. QUALITY ASSURANCE AND QUALITY CONTROL
5.1 INTRODUCTION
Quality assurance (QA) and quality control (QC)
are commonly thought of as procedures used in
the laboratory to ensure that all analytical
measurements made are accurate. Yet QA and
QC extend beyond the laboratory and are
essential components of all phases and all
activities within each phase of a nonpoint source
(NFS) monitoring project. This section defines
QA and QC, discusses their value in NFS
monitoring programs, and explains EPA's policy
on these topics. The following sections provide
detailed information and recent references for
planning and ensuring quality data and
deliverables that can be used to support specific
decisions involving nonpoint source pollution.
5.1.1 Definitions of Quality Assurance
and Quality Control
Quality assurance:
An integrated system of management procedures
and activities used to verify that the quality
control system is operating within acceptable
limits and to evaluate the quality of data (Taylor,
1993; USEPA, 1994c).
Quality control:
A system of technical procedures and activities
developed and implemented to produce
measurements of requisite quality (Taylor, 1993;
USEPA, 1994c).
QC procedures include the collection and analysis
of blank, duplicate, and spiked samples and
standard reference materials to ensure the
integrity of analyses and regular inspection of
equipment to ensure it is operating properly. 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 5-1 lists some common activities
that fall under the headings of QA and 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.
5.1.2 Importance of QA/QC Programs
Although the value of a QA/QC program might
seem questionable while a project is under way,
its value should be quite clear after a project is
completed. If the objectives of the project were
used to design an appropriate data collection and
analysis plan, all QA/QC procedures were
followed for all project activities, and accurate
and complete records were kept throughout the
project, the data and information collected from
the project will be adequate to support a choice
from among alternative courses of action. In
Addition, the course of action chosen will be
defensible based on the data and information
collected. Development and implementation of a
QA/QC program can require up to 10 to 20
percent of project resources (Cross-Smiecinski
and Stetzenback, 1994), but this cost can be
recaptured in lower overall costs due to the
project's being well planned and executed.
Likely problems are anticipated and accounted for
before they arise, eliminating the need to spend
countless hours and dollars resampling,
reanalyzing data, or mentally reconstructing
portions of the project to determine where an
error was introduced. 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
(Ericksonet al., 1991).
-------
QA/QC
Chapter 5
Table 5-1. Common QA and QC activities.
QA Activities
Organization of project into component parts
Assignment of roles and responsibilities to project staff
Use of statistics to determine the number of 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
Audits of field and laboratory operations
Maintenance of accurate and complete records of all project activities
Personnel training to ensure consistency of sample collection techniques and equipment
use
QC Activities
Collection of duplicate samples for analysis
Analysis of blank and spike samples
Replicate sample analysis
Regular inspection and calibration of analytical equipment
Regular inspection of reagents and water for contamination
Regular inspection of refrigerators, ovens, etc. for proper operation
Adapted from Drouseetal., 1986, and Erickson et al., 1991.
This chapter discusses many elements and aspects
of QA/QC programs that do not differ
significantly from one type of program to
another—for instance, from a point source permit
compliance sampling program to an NFS best
management practice effectiveness monitoring
program. Therefore, much of the following
discussion is not specific to NFS projects. This
does not, however, mean that a well-designed and
well-implemented QA/QC program is not
necessary for an NFS project. It is hoped that the
following discussion will convey to the reader the
importance of QA and QC to the success of every
project involving the collection and analysis of
environmental data.
5.1.3 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
methodologies, 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 insufficient for
decision making. QA/QC practices should be
used as an integral part of the development,
design, and implementation of an NFS monitoring
project to minimize or eliminate these problems
(Erikson et al., 1991; Pritt and Raese, 1992;
USEPA, 1994d).
-------
CHapter-5
EPA's mandatory agency-wide Quality System
policy 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,
1984c ). This policy is now based on the quality
system standard developed by the American
Society of Quality Control (ASQC, 1994). Each
office or laboratory is required to specify the
quality levels that data must meet to be acceptable
and 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 and QC operations applied to
environmental programs (USEPA, 1994d). In
addition, each non-EPA organization must have a
well-documented Quality Assurance Project Plan
(QAPP) that covers each monitoring or
measurement activity associated with a project
(Erickson etal., 1991; USEPA, 1983c, 1994).
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). This information is used to
design the program to meet these objectives.
Developing a QAPP prior to undertaking the NPS
monitoring project also establishes the boundaries
of the project, in terms of the time allotted to it
and the decisions that can realistically be made
from the data and information that will be
collected.
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. 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 the work 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, 1994c).
5.2 DATA QUALITY OBJECTIVES (DQOs)
Before collecting environmental data in support of
an NPS project, it is important to determine the
type, quantity, and quality of data needed to meet
the project objectives and support a specific
decision based on the results of the project. Not
doing so creates the risk of expending too much
effort on data collection (i.e., more data are
collected than necessary), not expending enough
effort on data collection (i.e., more data are
necessary than were collected), or expending the
wrong effort (i.e., the wrong data were
-------
QA/OC
Chgpte'r 5
collected). Proper planning and execution of a
data collection effort can prevent these problems.
EPA has developed the Data Quality Objectives
Process as a flexible planning tool that should be
used to prepare for a data collection activity. The
information compiled in this effort is then used to
develop the QAPP (USEPA, 1994e).
5.2,1 The Data Quality Objectives
Process
The Data Quality Objectives (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, 1994e).
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 diem. 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.
The DQO process consists of seven steps,
described below. The process is iterative. As
one step of the process is completed, its outputs
might lead to reconsideration of previous steps.
The previous steps should then be repeated.
Optimization of the design (the last step) should
begin only when all previous steps have been
completed. When the optimization step is
reached, as at any time during the DQO process,
it might be necessary to reconsider earlier steps
(i.e., to reiterate part or all of the process) to
determine the optimum design.
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 for the Data Quality Objectives Process
(USEPA, 1994e), from which the following
information was taken. This reference contains a
case study example of the DQO process. A
computer program, Data Quality Objectives
Decision Error Feasibility Trials (EPA QA/G-
4D), 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.
(Contact EPA's Quality Assurance Management
Staff, 202 260-9464).
(1) State the problem
In this first step the problem to be studied is
described concisely. 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.
-------
Chapter 5
QA7QC:
• 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.
(2) Identify the decision
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" that will link the principal
study question to one or more possible actions
that should solve the problem. Possible options
include take no action, take action, or modify an
action. A decision statement might be phrased as
follows: Determine whether [or which] NFS
impacts require taking [one of the alternative
actions]. For example, if the question to be
addressed is "Are nutrients from agricultural
runoff contributing to the growth of algal mats in
the river?" and the alternative actions are
"require vegetation buffers along streams" or
"take no action," the decision statement is
"Determine whether nutrients from agricultural
runoff are contributing to algal growth and
require regulation." 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.
(3) Identify the inputs to the decision
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.
(4) Define the study boundaries
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 parameter, algae in the 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
(since 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)
-------
QA/QC
Chapters
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.
(5) Develop a decision rule
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 the mean concentration of
contaminant X in the water downstream from
farm Y exceeds 0.5 yug/L, then vegetation will be
planted; 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.
(6) Specify limits on decision errors
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 no impact when
there is [a false positive, or type I error], or
deciding there is an impact when there is none
[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.
-------
Chapters
• 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 point above and
below the gray region that reflect the tolerable
probability for the occurrence of decision
errors.
(7) Optimize the design
Evaluate information from the previous steps and
generate alternative data collection designs. 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. 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 t 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.
5.2.2 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
(Ericksonetal., 1991; USEPA, 1994e). The
-------
QA/QC
Chapter 5
QAPP presents the policies, organization, and
objectives of the data collection effort and
explains how particular QA and 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, 1994e). 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, 1994e).
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, 1994c, 1994e).
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, 1994e).
5.3 ELEMENTS OF A QUALITY ASSURANCE
PROJECT PLAN
QAPPs must be prepared according to guidance
provided in EPA Requirements for Quality
Assurance Project Plans for Environmental Data
Objectives (USEPA, 1994c). EPA requires that
four types of elements be discussed in a Quality
Assurance Project Plan (QAPP). These elements
are listed in Table 5-2 and discussed briefly
below. (For complete descriptions and
requirements, be sure to see USEPA (1994c)).
Additional information on the contents of a QAPP
is contained in Drbuse 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 described below should always be
addressed in the QAPP, unless otherwise directed
by the overseeing or sponsoring EPA
organization(s). The types, quantity, and quality
of environmental data.collected for each project
could be quite different. As noted in USEPA
(1994c), "the content and level of detail in each
QAPP will vary according to the nature of the
work being performed and the intended use of the
data." 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 work can begin, and it should
be reviewed annually (for multiyear projects) and
updated and reapproved as often as necessary
during the project.
5.3.1 Group A: Project Management
These elements cover basic project management,
including project history and objectives, roles and
responsibilities of participants, and other factors
to ensure that the project has a defined goal
understood by all the participants and that all
planning activities have been documented.
Al Title and Approval Sheet
Provide the title of the plan; name of
organization(s) implementing the project; and
names, titles, and signatures of the appropriate
approving officials and their approval dates.
A2 Table of Contents
List sections, figures, tables, references, and
appendices. If document control format is
-------
Chapters
Table 5-2. Elements required in an EPA Quality Assurance Project Plan. (USEPA, 1994b)
QAPP Element
A1 Title and Approval Sheet
A2 Table of Contents
A3 Distribution List
A4 Project/Task Organization
A5 Problem Definition/Background
A6 Project/Task Description
A7 Quality Objectives and Criteria for Measurement Data
A8 Project Narrative (ORD only)
A9 Special Training Requirements/Certification
B1 Sampling Process Design
B2 Sampling Methods Requirements
B3 Sampling Handling and Custody Requirements
B4 Analytical Methods Requirements
. B5 Quality Control Requirements
B6 Instrument/Equipment Testing, Inspection, Maintenance Requirements
B7 Instrument Calibration and Frequency
B8 Inspection/Acceptance Requirements for Supplies and Consumables
B9 Data Acquisition Requirements (Nondirect Measurements)
B10 Data Management
C1 Assessments and Response Action
C2 Reports to Management
D1 Data Review, Validation, and Verification Requirements
D2 Validation and Verification Methods
D3 Reconciliation and User Requirements
required, see Cross-Smiecinski and Stetzenback
(1994) and USEPA (1994e).
A3 Distribution List
List all individuals and organizations who will
receive copies of the approved QAPP and
subsequent revisions.
A4 Project/Task Organization
Discuss the specific roles and responsibilities of
all individuals or organizations participating in the
project.
A flow chart or box diagram is useful for
depicting project organization and responsibilities
(Figure 5-1). Using the diagram, explain the
rationale for the organization (e.g., to maximize
-------
QA/QC
Chapter 5
1
£
W
ID
O
<
<
S
<
MH
U
g
§
S S.
a o.
S CO
G)
Q
U tt
2- a
< §•
a«
CO
I«
So &
a S-
g 3>
5
T3 *j fli
« J3 S
\ J3 ^M .*-
V *11
'
r r ~P
/
/ g 1
li
u
1 "
*1
S
<
u
H
W
fc
CO
U
^
S
3
00
CO
ii
o
0
pa
S
(U
«
i
t
a
3
s
1
<
U
II
0*
c
u
t>0
8
2
Wt
CO
>>
la
s =
CT (/j
V&
>»
60
_O
"o
•a
P-»
K
i
i
i
i
i
i
i
i
i
i
i
i
i
.5 ti a
^^ ^ & S
o ,5 O
IS
;
1
'
;
•
o
jg
UJ_
JO
Q.
•s
0)
'§"
0.
Q>
O
CO
cr
CO
t:
CO
o
.o
"5
_N
CO
»
_
Q.
CO
§
CT
u.
-------
.ChaptenS
the interaction of site and task leaders). This
section provides details on the division of the
pfoject into teams, support teams, review
committees, and other groups and identifies the
persons and entities that will be involved in the
project. All members of each project team should
be listed along with their affiliations with
participating organizations. The program
manager, managers or coordinators of any
specific tasks, directors of technical tasks to be
conducted, and any organizations or agencies that
will be involved in the project should be
identified. Also identify the specific roles and
responsibilities (such as field sampling, laboratory
analyses, and report preparation) that will be
conducted by each person and organization
involved in the project.
A5 Problem Definition/Background
, State the problem to be solved or the decision to
be made and describe its history for this particular
project.
A6 Project/Task Description
Describe the work to be performed
(measurements to be made, applicable quality
standards, any special personnel or equipment
requirements, assessment tools needed, records
and reports needed) and the schedule for its
implementation.
A7 Quality Objectives and Criteria for
Measurement Data
The DQO process will provide this information,
or state the project quality objectives and
measurement performance criteria that are
necessary to support the management decision(s)
to be made based on the result(s) of the project.
State quality objectives in terms of project
requirements, preferably in quantitative terms,
rather than in terms of analytical or sampling
method capabilities. Then, with the quality
objectives stated, select the appropriate methods
to achieve the requirements (Cross-Smiecinski
and Stetzenback, 1994). The quality of data
should be expressed in terms of precision,
accuracy, comparability, representativeness, and
completeness (defined below). A table of quality
objectives, like that in Figure 5-2, is helpful.
Definitions of data quality terms
Precision (reproducibility)
(a) Precision is a measure of mutual agreement
among individual measurements of the same
property. The coefficient of variation (CV),
also known as the percent relative standard
deviation (RSD), is used to express precision
(Ericksonetal., 1991).
CV = (-Er)lOO
x
where
s = sample standard deviation and
x = arithmetic mean.
(b) Precision is an expression of mutual
agreement of multiple measurement values of
the same property conducted under prescribed
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):
RPD =
where
2 (x. -
(100)
-------
QA/QC
Chapters
Parameter
Particulate NO3/SO4
SSI8
47 mm TF/PCb
N03
47 mm TF/PC
SO2
47 mm TF/PC
Meteorological
Wind speed
Wind direction
Dew point
Solar radiation
Ambient temperature
Units
ug/m3
ug/m3
(jg/m3
ug/m3
m/s
deg
0°C
watts/m2
°C
Expected
Range
10-1000
10-1000
1 to 25
1 to 75
Oto75
0 to 360
-30 to 70
-
-20 to 50
Accuracy
10%
10%
20%
20%
2%
2%.
2%
-
1°C
Precision
20%
20%
20%
20%
2%
2%
5%
-
2°C
Completeness
70%
90%
90%
90%
90%
90%
90%
-
90%
* PM,S Size Selective inlet High Volume Sampler
* Teflon/Polycarbonate Filter
Figure 5-2. Sample quality assurance objectives. (Erickson et al., 1991).
jc2 =
analyte concentration of first duplicate
and
analyte concentration of second
duplicate.
Accuracy (bias)
(a) Accuracy 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
{100 [(X-T)/T]} unless spiking materials are
used and percent recovery is calculated
(Erickson etal., 1991).
(b) Accuracy is the correctness of the value
obtained from analysis of a sample. It is
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):
%R = —- (100)
where
A = spiked sample result;
B = sample result; and
C = spike added.
-------
Chapter/5,
Comparability
(a) Comparability is defined as the confidence
with which one data set can be compared to
. another (Erickson et al., 1991).
(b) Comparability is the quality that makes data
obtained from one study comparable to data
from other studies. 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
(a) Representativeness can be defined both
qualitatively and quantitatively; it depends on
the experimental design and choice of
sampling methods. The desired degree of
representativeness is important in planning for
the collection of samples and the subsequent
uses of the data. 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 etal., 1991).
(b) Representativeness is a measure of how
representative the data obtained for each
parameter is compared with the value the
same parameter has within the population
being measured. Since 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, 1993).
Completeness
(a) 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).
(b) Completeness is the amount of valid data
obtained from the measurement system (field
and laboratory) versus the amount of data
expected from the system. 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
% C = 100] —
n
where
= 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).
A8 Project Narrative
This is a narrative description of work to be
performed that will demonstrate to technical or
QA reviewers that the project or task will achieve
its quality objectives. See USEPA (1994e) for
complete details of what should be included in a
project narrative.
A9 Special Training Requirements/Certification
If personnel will require any specialized training
or certification to successfully complete the
-------
QA/QC
Chapter 5
project, discuss how this training will be obtained
and documented.
A10 Documentation and Records
Itemize all of the information and records (e.g.,
raw data, field logs, instrument printouts, results
of calibration and QC checks, analytical
laboratory case narratives) that must be included
in a data report package, and describe the desired
report format and final disposition of records and
documents.
5.3.2 Group B: Measurements and
Acquisition
The Project/Task Description element (A6)
contains a summary of this information, which
should be provided in detail in this section.
Methods that have been well documented and are
available to all participants can merely be cited;
for those not well documented, detailed copies of
the methods and/or Standard Operating
Procedures (SOPs) must be provided in the
QAPP.
Bl Sampling Process Design (Experimental
Design)
Explain the experimental design or data collection
design, including types and numbers of samples
required, sampling locations and frequencies,
sampling screening criteria (if applicable), sample
matrices, measurement parameters of interest, and
the rationale for the design. As with all
information contained in a QAPP, recording
information such as the reasoning behind
decisions will make the data more defensible in
the future. Statistics can play an important part in
determining the sampling strategy. Therefore,
record all statistical procedures that will be used
to determine the sampling strategy. Two basic
sampling decisions that must be made are the '
types and numbers of quality control samples to
be collected (Keith, 1988). See USEPA (1994e)
for additional details on what to include in this
element of the QAPP.
B2 Sampling Methods Requirements
Identify and describe all procedures for collecting
samples for each sampling method, as well as
what should be done when a sampling or
measuring failure occurs and who is responsible
for taking corrective action. Other aspects
pertinent to sampling, such as record keeping,
sample storage, and transport to laboratories,
should also be described in this section (Cross-
Smiecinski and Stetzenback, 1994).
B3 Sample Handling and Custody Requirements
Describe all aspects of sample handling and
custody. Sample custody is a documentation of
where and with whom samples are at all times
from the moment they are collected in the field to
when they are analyzed in the laboratory. A
sample is considered to be under custody if (1) it
is in your actual possession; (2) it is in your view,
after being in your physical possession; (3) it was
in your physical possession and then you locked it
up to prevent tampering; or (4) it is in a
designated and identified secure area (Air
National Guard, 1993). Special tracking
procedures called "chaih-of-custody" procedures
are used whenever samples are collected for use
in an enforcement action or when demonstrating
compliance with a regulatory requirement (e.g.,
NPDES). Chain-of-custody forms should be
printed on multipart carbonless paper for tracking
custody and should have, at a minimum, space for
recording date, time, name of person accepting
samples, sample numbers, and remarks (Figure
5-3). Copies of the form must be completed in
the field, and signed by the fieldteam when they
transfer custody of the samples to the shipper.
Upon receipt in the laboratory, the laboratory
signs the remaining copies, indicating they have
accepted custody of the samples. Each time the
form is signed, the person signing the form
-------
Chapter 5
CHAIN-OF-CUSTODY RECORD
PROJ NO
PROJECT NAME
SAMPLERS (Signature)
DATE
TIME
DESCRIPTION
Relinquisl ed by: (Signature)
Print Name:
Relinquished by: (Signature)
Date
Date
Print Name:
^ffasmimmammmm
mnam
Time
Time
No.
of
Containers
Received by: (Sign.)
Print Name •
Received by: (Sign.)
TAG
NUMBERS
PARAMETERS
Relinquished by (Sign.)
Date
Time
Print Name
Date
Print Name
B^HJ^nB^^^^^nBHMMBHBBB
^"•^^^^^^^^^^^^^^^^^^^^^^^^^^•••^^••I^^^M
Time
REMARKS
Received fay (Sign.)
Print Name
Remarks
^^^^^^^^^^^^^^^"'''"^^'"^^^•"•^^'••^^•••••^^•^••••••M^^BI^^^MB^^^^^^^^BBBMHIMJmfia
Figure 5-3. Sample custody chart. (After Gross-Smiecinski and Stetzenback, 1994)
retains the bottom copy and passes the remaining
copies along with the samples. The laboratory
should return at least one copy of the completed
chain-of-custody record to the client, or proceed
as directed in the QAPP.
B4 Analytical Methods Requirements
Describe the analytical methods and equipment
required for both field and laboratory activities,
waste disposal requirements, and specific
performance requirements, as well as what should
be done when a failure in the analytical system
occurs and who is responsible for taking the
corrective action. Also include information on
any supporting methods or documents used to
collect field1 or laboratory data. For instance, if
identifications of benthic invertebrates are made,
include information on the source(s) used to
verify identifications; if the amount of riparian
vegetation cover is estimated, describe the method
used to arrive at the estimate.
-------
QA/QC
Chapter 5
Analytical methods: Describe the methods that
will be used for the project. If the methods to be
used arc published (e.g., by the U.S. Geological
Survey, EPA, or ASTM) it is sufficient to
indicate what methods will be used and where
descriptions of them can be found. If the best
methods to be used cannot be completely
ascertained until some samples have been
analyzed, indicate the order of preference for use
of the methods. Any modifications to published
or standard methods or variations of them must be
documented, and the variations must be verified
as providing data of acceptable quality.
Method validation: Method validation accounts
for and documents, at a minimum, the following
characteristics: known and possible interferences;
method precision; method accuracy, bias, and
recovery; method detection level, and method
comparability to superseded methods, if any (Pritt
and Raese, 1992). All methods chosen for use in
the project must be validated.
Generally, laboratories with their own QA/QC
procedures will be used for sample analyses. The
methods to be used in the laboratory must be
acceptable to project managers. All potential
laboratory facilities to be used in the project
should be extensively evaluated before their
selection and throughout their participation in the
project.
B5 Quality Control Requirements
Identify the QC procedures (types, frequency, and
control limits of QC checks) needed for each
sampling, analysis, or measurement technique.
(They might have to be modified to suit each
project.) Also state what corrective action is
required when control limits are exceeded. Data
collected as part of field sampling and laboratory
measurements must be verified as accurate.
Thus, some samples are taken or measurements
made to check for accuracy rather than to collect
additional data. Specify what means will be used
to check the accuracy of samples and
measurements. Field blanks, duplicate samples,
replicate samples, spiked samples, and spiked
blanks are commonly used methods. Describe
precisely how these control samples will be
prepared for analysis.
Standard reference materials (SRMs) should be
used periodically in any measurement system to
monitor' for changes to the system that might go
unnoticed. SRMs should be used when a
measurement change is noted to verify that the
change is not due to a change in the measurement
system. The optimum frequency of use of SRMs
and also of replicates of actual test samples
depends on the integrity of the measurement
system and the magnitude of the errors involved
when the system ceases to give predictable
results. All measurements from 'last-known-in-
control sample to first-known-out-of-control
sample are suspect, so the length of the period
between these two samples must be calibrated to
be appropriate to the measurements being made
(Taylor, 1993).
B6 Instrument/Equipment Testing, Inspection,
and Maintenance Requirements
This section should include descriptions of the
types of preventive maintenance for equipment
that will be used to ensure that research schedules
are adhered to and project objectives are
completed on schedule. The section should
include the following: a schedule of preventive
maintenance, an inventory of critical spare parts
and supplies, maintenance contract information,
location of important manuals and instructions,
record keeping requirements, and training of
instrument and equipment operators (Cross-
Smiecinski and Stetzenback, 1994). Some aspects
of training can be considered a part of preventive
maintenance. Describe in this section general
safety precautions that will be part of project
operations. Examples include materials handling,
transportation of chemicals, hazardous waste
-------
.Chapters
disposal procedures, emergency procedures,
standard safety operations, chemical hygiene,
hazard communication, hazardous waste
management, waste disposal, location of safety
equipment, tour of facilities, and annual classes in
cardiopulmonary resuscitation and standard first
aid (Pritt and Raese, 1992).
B7 Instrument Calibration and Frequency
Describe the procedures used for equipment
calibration, the frequency of calibration of each
piece of equipment, and the results .of calibration
procedures. Record any problems encountered
and corrective actions taken. This section should
identify each tool, gauge, instrument, or other
sampling, measuring, and test equipment used for
data collection activities for which quality must be
controlled and which must be calibrated to
maintain performance within specified limits.
B8 Inspection/Acceptance Requirements for
Supplies and Consumables
Supplies and consumables to be used in the
project must be inspected and accepted, according
to specified criteria, for use in the project.
Identify who will perform the inspections and
how they will be conducted.
B9 Data Acquisition Requirements (Nondirect
Measurement)
Data obtained from noninstrument sources such as
computer databases, spreadsheets, and programs
and literature files need to be identified and
acceptance criteria established for the use of the
data. Also discuss any limitations resulting from
uncertainty in the quality of the data and the
impact of adding more error to the results.
BIO Data Management
This section should describe all aspects of data
management, from their generation in the field or
laboratory to final use or storage. Discuss the
control mechanisms (and provide examples of
forms or checklists) for detecting and correcting
errors and for preventing loss of data during data
reduction. This discussion should also include all
data handling equipment and procedures that will
be used to process, compile, and analyze the data
(hardware and software).
5.3.3 Group C: Assessment/Oversight
The purpose of these elements is to ensure that
the QAPP will be implemented as prescribed;
they describe the activities for assessing the
effectiveness of the implementation of the QAPP
and its associated QA/QC program.
Cl Assessments and Response Actions
Assessments can include a variety of activities,
such as surveillence, peer review, management
systems review, technical systems audit, or
performance evaluation. Audits are assessments
of the extent to which QA procedures and QC .
activities are being adhered to. They may be
performed by an internal (i.e., within the project
structure) but independent audit team or by an
external audit team. Audits may be performed
before, during, and/or after the project is
performed. Audit frequency, intensity, and type
should be determined, and the audit(s) should be
scheduled as part of the overall program QA
effort. This section of the QAPP should describe
the audits to be performed and the process and
procedures for responding to problems raised
during audits (Cross-Smiecinski and Stetzenback,
1994).
This section should also describe actions to be
taken if and when unexpected problems arise
during the course of the study. Problems that can
be foreseen, such as running low on commonly
used laboratory supplies, should be addressed as
SOPs. Many problems, however, are
encountered so infrequently or are unpredictable
-------
QA/QC
Chapter 5
enough that SOPs will not be prepared for them.
Special or emergency procedures address these
types of problems. It is difficult to address
unanticipated problems before they arise, but the
QAPP sliould specify who is responsible for
handling problems that arise from different
aspects of the project (e.g., field sampling,
laboratory analysis, audits). It is helpful to
categorize problems based on their impact on the
project (e.g., critical, important, noncritical,
unimportant) and to specify the type of corrective
action necessary based on the problem's category.
A critical problem, for instance, would be one
that would affect obtaining data of the necessary
quality or quantity. If a critical problem arises, a
critical-problem response by project staff would
be required. This should be specified in the
QAPP.
C2 Reports to Management
This section specifies the type and frequency of
reports to be prepared and submitted to project
management, as well as the chain of responsibility
for ensuring that reports are prepared and
submitted. The preparer of the reports and
recipients of each report should be identified.
Any required report contents and format should
also be specified.
5.3.4 Group D: Data Validation and
Usability
After the data collection has been completed, the
data must be examined to determine whether they
conform to the specified criteria and will satisfy
project objectives.
DJ Data Review, Validation, and Verification
Requirements
The requirements used to review and accept,
reject, or qualify data should be identified,
including any project-specific calculations or
algorithms.
D2 Validation and Verification Methods
This section should describe each of the elements
defined below in enough detail to support use of
the data for their intended purpose and for
comparability to past, present, and future studies
(Cross-Smiecinski and Stetzenback, 1994). If
computer software is used in data manipulations,
record which software is to be used. Software
that performs complex manipulations might have
to be verified before its use to ensure that it
functions properly (Cross-Smiecinski and
Stetzenback, 1994).
Data reduction: The transformation of raw data
into a more useful form, calculations.
Data verification: A routine activity conducted
by technical, laboratory, and clerical personnel on
small sets of the data to determine whether data
have been accurately quantified, recorded, and
transcribed; whether data have been collected and
analyzed in accordance with prescribed, approved
procedures; whether the data appear suitably
complete; and whether the data appear to be
reasonable and consistent, based on prior
knowledge of the research.
For example, it is a good practice to enter data
into the database twice and scan them for outlying
values. This helps to detect and eliminate
transcription errors. Range checks, internal
consistency checks, and quality assurance
evaluations should also be included for data
certification (Drouse et al., 1986).
Data validation: The process by which a sample,
measurement method, or datum is deemed useful
for a specified purpose; an independent, timely
review of a body of verified data against a
predetermined set of qualitative and quantitative
criteria to evaluate their adequacy for their
intended use.
-------
Chapters
Data reporting: Specify any special forms or
formats (e.g., tables and figures) that are to be
used, as well as who is responsible for data
reporting, due dates, etc.
D3 Reconciliation with User Requirements
The precision, accuracy, completeness,
representativenesss, and comparability of data
must be assessed using appropriate techniques.
This section should give details of the formulas,
statistical techniques, and procedures that will be
used to assess the data. The methods used to
assess the data must be in agreement with the
DQOs. The terms precision, accuracy,
completeness, representativeness, and
comparability are defined on page 5-11, and some
sample data assessment formulas are given.
The following sections provide more specific
information for preparing QAPPs with respect to
field and laboratory operations, and data and
reporting requirements.
5.4 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 and 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 and QC for
each of the phases separately. This will ensure
that no details are omitted. Table 5-3 summarizes
many important items that should be considered in
the field operations portion of a QA/QC program.
5.4.1 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 prepared beforehand, with
quality reminders included where appropriate,
will 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.
-------
QA/QC
Chapter 5;
Table 5-3. Checklist of items that should be considered in the field operations section of a QA/QC
program. ___^^==_=====^===^==================================
Field Operations
Element
Specifics
Check-
off
Responsibility
Organization Field organization chart created
Staff duties and responsibilities defined
Communication lines within and with other units
established
Project documents made available to all staff
Staff qualifications established
Field Sampling sites investigated and selected
Logistics Means of access to sampling sites determined
Sample transport and shipping procedures specified ....
Field sample handling areas selected
Chain-of-custody for samples established
Field equipment selected and supplied
Procedures for decontamination of sampling equipment
established
Monitoring Equipment installation procedures specified
Equipment Equipment maintenance and control schedules
established
Equipment maintenance manual updated and distributed
Trouble shooting and corrective action manual updated
and distributed
Quality Type(s) of control samples (blanks, duplicates, spikes,
Control analytical standards, reference materials) to be used
Samples have been determined
Frequency of control sample use has been
determined
Field Audits QA field auditor designated
Aspects of field operations that will undergo quality
assessments as part of field audits have been
determined
Acceptance criteria for compliance with SOPs and the
QAP set for field events and activities
Field audit forms, with investigations to be conducted
and data to be collected, prepared
Person(s) to review field audit records designated
-------
Chapters
Table 5-3. (continued)
Health and
Safety
SOPs
Field personnel properly trained
Proper field gear and clothing issued to. field
personnel
Sample management
Sample collection procedures . . .
Reagent preparation
Decontamination
Equipment calibration and maintenance
Corrective action
Waste disposal
Health and safety
Field measurements ....
Reagent/standard preparation ......
Equipment calibration and maintenance ...
Data reduction and validation ....
Reporting . . . .
Corrective action
Waste disposal
Health and safety
Records management ....
Project-specific records
Field operations records .
5.4.2 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).
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
-------
QA/QC
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.
5.4.3 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
and QC. All measurement equipment must be
routinely checked and calibrated to verify that it is
operating properly and generating reliable results
(Spooner, 1994), 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.
5.4.4 Sample Collection
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 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
-------
Chapter 5
operations section of a QAPP should include clear
statements of the regulatory requirements
applicable to the project (Spooner, 1994). 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 (Spooner, 1994).
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
(Spooner, 1994). Be sure these labels are printed
with waterproof ink on waterproof paper, and use
a No. 2 pencil or waterproof/solvent-resistant
marker to record information.
5.4.5 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;
Spooner, 1994). Chain-of-custody seals must be
applied to sample containers and shipping
containers.
5.4.6 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 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
-------
QA/QC
Chapter 5
samples, broken sample containers, need for
equipment spare parts, and other concerns
(Spooner, 1994).
5.5 LABORATORY OPERATIONS
Laboratory operations should be conducted with
the same attention to detail as field operations.
Often, an independent laboratory conducts sample
analyses, so QA and 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.
5.5.7 General Laboratory QA and QC
Numerous references are available on laboratory
QA/QC procedures, and one or more should be
consulted to gain an understanding of laboratory
QA and 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 NFS 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
• 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 (Table 5-4). More
detailed references on laboratory QA and QC
should be consulted for further information.
5.5.2 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
-------
Chapter. 5
Table 5-4. Checklist of items .that should be considered in the laboratory operations section of a
QA/QC program.
Element
Sample
Management
Equipment
SOPs
Records
Management
QC
Procedures
Audits
Health and
Safety
Laboratory Operations
Specifics
Sample receipt
Sample storage
Sample handling
Sample scheduling
Equipment calibration and maintenance
Sample management
Analytical methods
Sample preparation and analysis procedures ....
Reagent/standard preparation
Raw data requirements
Data reduction and validation
Precision, accuracy, and method
detection/reporting limits
Reporting
Corrective actions
Project-specific records
Laboratory operations records
Control samples
Method blanks
Matrix spikes
Matrix duplication/matrix spike duplicates . .
Laboratory audits schedule
Fire and emergency equipment
Fire and emergency equipment inspection
Health equipment (masks, gloves, ...)
Waste disposal
D.
Check-off
Responsibility
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
-------
QA/QC
Chapter,5
be described in the QAPP, including procedures
and frequency (Cross-Smiecinski and
Stetzenback, 1994).
5.5.3 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, the U.S. Environmental
Protection Agency, and the American Society for
Testing and Materials, or those published in
Standard Methods for the Analysis of Water and
Wastes (Clesceri et al., 1989). 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).
5.5.4 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).
5.5.5 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 independently certified, who conducts
the training, how the staff's 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).
5.5.6 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
-------
^Chapters
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 NFS monitoring project.
5.6 DATA AND REPORTS
It is essential during the conduct of an NFS
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.
5.6.1 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.
5.6.2 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 hi 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 et al., 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, 1994c):
• 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 must
be determined absolutely, generally with the
help of a statistician.
• 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.
-------
QA/QC
Chapter 5
• Spatial analysis of historical data can indicate
which sampling locations are most likely to
provide die 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.
5.6.3 Documentation and Record Keeping
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)
• 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
5.6,4 Report Preparation
The original project description should include a
schedule and required 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.
-------
REFERENCES
Air National Guard. 1993. Draft final remedial investigation/reasibility study work plan. Wyoming Air
National Guard 153rd Tactical Airlift Group, Cheyenne Municipal Airport, Cheyenne, Wyoming. Prepared
by Aepco, Inc. and Tetra Tech, Inc. March.
i.
Alberts, E.E., G.E. Schuman, and R.E. Burwell. 1978. Seasonal runoff losses of nitrogen and phosphorus
from Missouri Valley loess watersheds. J. Environ. Qual. 7(2):203-207.
Allen , D.M., S.K. Service and M.V. Ogburn-Matthews. 1992. Factors influencing the collection efficiency
of estuarine fishes. Trans. Am. Fish. Soc. 121(2):234-244.
Aller, L., T. Bennett, J.H. Lehr, and RJ. Petty. 1985. DRASTIC: A standardized system for evaluating
ground water pollution potential using hydrogeologic settings. EPA/600/2-85/018. U.S. Environmental
Protection Agency, Washington, DC.
Anderson, R.L. 1941. Distribution of the serial correlation coefficients. Ann. Math. Stat. 8(1):1-13.
Anderson, V.L., and R.A. McLean. 1974. Design of experiments, a realistic approach. Marcel Dekker,
Inc., New York, New York.
ASQC. 1994. Specifications and guidelines for quality systems for environmental data collection and
environmental technology programs. ANSI/ASQC E4-1994. American Society for Quality Control, ASQC
Quality Press, Wisconsin.
Bahls, L. 1993. Periphyton bioassessment methods for Montana streams. Water Quality Bureau,
Department of Health and Environmental Science, Helena, Montana.
Bailey, G.W. and T.E. Waddell. 1979. Best management practices for agriculture and silviculture: An
integrated review. In Best management practices for agriculture and silviculture. Ann Arbor Science, Ann
Arbor, Michigan.
Baker, D.B., K.A. Krieger, R.P. Richards, and J.W. Kramer. 1985. Effects of intensive agricultural land use
on regional water quality in northwestern Ohio. In Perspectives on nonpoint source pollution. Proceedings
of a National Conference, May 19-22, 1985, Kansas City, Missouri. EPA 440/5-85-001. U.S.
Environmental Protection Agency, Washington, DC.
Ball, J. 1982. Stream classification guidelines for Wisconsin. Wisconsin Department of Natural Resources
Technical Bulletin. Wisconsin Department of Natural Resources, Madison, Wisconsin.
Barbour, M.T., J.L. Plafkin, B.P. Bradley, C.G. Graves, and R.W. Wisseman. 1992. Evaluation of EPA's
rapid bioassessment benthic metrics: Metric redundancy and variability among reference stream sites.
Environ. Toxicol. Chem. ll(4):437-449.
-------
References
Barbour, M.T., and J.B. Stribling. 1991. Use of habitat assessment in evaluating the biological integrity of
stream communities. In Biological criteria: Research and regulation proceedings of a symposium. EPA-
440/5-91-005. U.S. Environmental Protection Agency, Office of Water. July.
Barbour, M.T., J.B. Stribling and J.R. Karr. 1995. Multimetric approach for establishing biocriteria and
measuring biological condition. In Biological assessment and criteria. Tools for -water resource planning
and decision making, ed. W.D. Davis and T. Simon, pp. 63-76. Lewis Publishers, Boca Raton, Florida.
Berryman, D., B. Bernard, D. Cluis and J. Haemmerli. 1988. Nonparametric tests for trend detection in
water quality time series. Water Resour. Bull. 24:545-556.
Berryman, D., B. Bobee, D. Cluis, and J. Haemmerli. 1988. Nonparametric tests for trend detection in water
quality time series. Water Resour. Bull. 24(3):545-556.
Brady, N.C. 1984. The nature and properties of soils. MacMillan Publishing Company, New York.
Brakensiek, D.L., H.B. Osborn, and W.J. Rawls. 1979. Field manual for research in agricultural hydrology.
Agriculture Handbook No. 224, U.S. Department of Agriculture, Science and Education Administration,
Washington, DC.
Brinson, M.M., B.L. Swift, R.C. Plantico, and J.S. Barclay. 1981. Riparian ecosystems: Their ecology and
status. FWS/OBS-81/17. Eastern Energy and Land Use Team, National Water Resources Analysis Group,
U.S. Fish and Wildlife Service, Kearneysville, West Virginia. September.
Brookes, A. 1990. Restoration and enhancement of engineered river channels: Some European experiences.
Reg. Rivers: Res. Manage. 5:45-56.
Brown, R.G. 1985. Effects of an urban wetland on sediment and nutrient loads in runoff. Wetlands 4:147-
158.
Brown, M.P., P. Longabucco, and M.R. Rafferty. 1986. Nonpoint source control of phosphorus - A
watershed evaluation volume 5, The eutrophication of the Cannonsville Reservoir. New York State
Department of Environmental Conservation, Bureau of Technical Services and Research, Albany, New York.
Buttle, J.M., and F. Xu. 1988. Snowmelt runoff in suburban environments. Nordic Hydrol. 19:19-40.
Canning, D.J. 1988. Construction erosion control. Washington Department of Ecology, Shorelenads and
Coastal Zone Management Program. Shorelands Technical Advisory Paper No. 3.
Carnahan, B., H.A. Luther, and J.O. Wilkes. 1969. Applied numerical methods. Wiley, New York.
CARWQCB. 1989. Nonpoint source experience in the Santa Clara Valley. California Regional Water
Quality Control Board, Santa Clara Valley Water District, Santa Clara, California.
-------
Chambers, J.M., W.S. Cleveland, B. Kleiner, and P.A. Tukey. 1983. Graphical methods for data analysis.
Duxbury Press, Boston, Massachusetts.
Chesapeake Bay Program. 1995. Chesapeake Bay: Introduction to an ecosystem. Chesapeake Bay Program,
Annapolis, Maryland.
Chow, V.T. 1954. The log-probability law and its engineering applications. Separate No. 536, 80.
American Society of Civil Engineering. November.
Clark, W.H. 1986. Rock Creek rural clean water program comprehensive water quality monitoring report
1981-1986. Idaho Department of Health and Welfare, Division of Environmental Quality, Boise, Idaho.
Clarkson, C.C., D.E. Lehnig, S.V. Plante, R.S. Taylor, and W.M. Williams. 1984. Hydrologic basis for
suspended sediment criteria. Prepared by Camp, Dresser & McKee for U.S. Environmental Protection
Agency, Washington, DC.
Clausen, J.C. 1985. The St. Albans Bay watershed RCWP: A case study of monitoring and assessment. In
Perspectives on nonpoint source pollution. Proceedings of a national conference, May 19-22, 1985, Kansas
City, Missouri. EPA 440/5-85-001, U.S. Environmental Protection Agency, Washington, DC.
Clausen, J.C. 1991. Developing a monitoring system for rural surface waters: Individual BMPs. In
Nonpoint source watershed workshop: Seminar publication. EPA/625/4-91/027. U.S. Environmental
Protection Agency, Office of Research and Development, Office of Water, Washington, DC., pp. 99-102.
September.
Clausen, J.C., Wayland, K.G., K.A. Saldi, and K. Guillard. 1993. Movement of nitrogen through an
agricultural riparian zone: 1. Field studies. Water Sci. Tech. 28(3-5):605-612.
Clesceri, L.S., A.E. Greenberg, and R.R. Trussell, eds. 1989. Standard methods for the examination of
water and wastewater. 17th ed. American Public Health Association, American Water Works Association,
and Water Pollution Control Federation, Washington, DC.
Cochran, W.G. 1977. Sampling techniques. 3rd ed. John Wiley and Sons, New York, New York.
Coffey, S. 1993. The nonpoint source manager's guide to water quality and land treatment monitoring.
North Carolina Cooperative Extension Service, North Carolina State University, Raleigh, North Carolina..
Coffey, S.W., J. Spooner, and M.D. Smolen. 1993. The nonpoint source manager's guide to water quality
and land treatment monitoring. NCSU Water Quality Group, Department of Biological and Agricultural
Engineering, North Carolina State University, Raleigh, North Carolina.
Cohen, A.C., Jr. 1959. Simplified estimators for the normal distribution when samples are singly censored
or truncated. Technometrics l(3):217-237.
-------
References
Cohen, A.N. 1990. An introduction to the ecology of the San Francisco estuary. San Francisco Estuary
Project, San Francisco, California.
Colby, B.R. 1956. Relationship of sediment discharge to stream/low. U.S.G.S. Open File Report. U.S.
Geological Survey, Reston, Virginia.
Connin, S. [USEPA, 1991] 1991. Characteristics of 'successful riparian restoration projects in the Pacific
Northwest. EPA910/9-91-033. Prepared for U.S. Environmental Protection Agency, Region 10, Water
Division, Seattle, Washington. August.
Conover, W.J. 1980. Practical nonparametric statistics, 2nd ed. Wiley, New York.
Conquest, L. L., S. C. Ralph, and R. J. Naiman. 1994. Implementation of large-scale stream monitoring
efforts: sampling design and data analysis issues. In Biological monitoring of aquatic systems, ed. S.L. Ldeb
and A. Spacie. Lewis Publishers, Boca Raton, Florida.
Cowardin, L.M., V. Carter, F.C. Golet, and E.T. LaRoe. 1979. Classification of wetlands and deep-water
habitats of the United States. U.S. Fish and Wildlife Service, Biological Services Program, Department of
the Interior, Washington, D.C. FWS/OBS-79/31.
Cowie, G.M., J.L. Cooley and A. Dutt. 1991. Use of modified benthic bioassessment protocols for
evaluation of water quality trends in Georgia. USDI/GS Project 14-08-0001-G1556 (03); ERC-06-91;
USGS/G-1556-03. Technical Completion Report, Institute of Community and Area Development,
University of Georgia, Athens, Georgia and Environmental Resources Center, Georgia Institute of
Technology, Atlanta, Georgia. July.
Crawford, C.G., J.R. Slack, and R.M. Hirsch. 1983. Nonparametric tests for trends in water-quality data
using the statistical analysis system. U.S.G.S. Open File Report 83-550. U.S. Geological Survey, Reston,
Virginia.
Cross-Smiecinski, A., and L.D. Stetzenback. 1994. Quality planning for the life science researcher:
Meeting quality assurance requirements. CRC Press, Boca Raton, Florida.
Cummins, K.W. 1994. Bioassessment and Analysis of Functional Organization of Running Water
Ecosystems. In Biological monitoring of aquatic ecosystems, ed. S.L. Loeb and A. Spacie. Lewis
Publishers, Ann Arbor, Michigan.
Cupp, C. E. 1989. Stream corridor classification forforestedlands of'Washington. Report for Washington
Forest Protection Association, Olympia, Washington.
Dakins, M.E., P.S. Porter, M. West, and S.T. Rao. 1996. Using uncensored trace-level measurements to
detect trends in ground water contaminants. Water Resour. Bull. 32(4):799-805.
Dauble, D.D., and R.H. Gray. 1980. Comparison of a small seine and a backpack electroshocker to evaluate
near shore fish populations in rivers. Prog. Fish Cult. 42:93-95.
-------
Davenport, T.E. and M.H. Kelly. 1984a. Water resource data and preliminary trend analysis for the
Highland Silver Lake monitoring and evaluation project, Madison County, Illinois. IEPA/WPC/84-030,
Illinois Environmental Protection Agency, Springfield, Illinois.
Davenport, T.E., and M.H. Kelly. 1984b. Soil erosion and sediment transport dynamics in Blue Creek
watershed, Pike County, Illinois. IEPA/WPC/83-004. Planning Section, Water Pollution Control, Illinois
Environmental Protection Agency, Springfield, Illinois.
Davis, W.S., B.D. Snyder, J.B. Stribling, and C. Stoughton. 1996. Summary of state biological assessment
programs for streams and \vadeable rivers. EPA 230-R-96-007. U.S. Environmental Protection Agency,
Office of Policy, Planning, and Evaluation, Washington, DC.
Day J.W., Jr., C.A.S. Hall, W.M. Kemp, and A. Yannez-Arancibia. 1989. Estuarine ecology. John Wiley
and Sons, Inc., New York, New York.
Dean, J.D., P.P. Jowise, and A.S. Donigian, Jr. 1984. Leaching evaluation of agricultural chemicals (leach
handbook). PB8-4-236413, U.S. Environmental Protection Agency, Athens, Georgia.
Dewey, M.R., L.E. Holland-Bartel and S.T. Zigler. 1989. Comparison offish catches with buoyant pop nets
and seines in vegetated and nonvegetated habitats. North. Am. J. Fish. Manage. 9:249-253.
Dressing, S.A., J.M. Kreglow, R.P. Maas, F.A. Koehler, W.K. Snyder, and F.J. Humenik. 1982. Best
management practices for agricultural nonpoint source control: II. Commercial fertilizer. Biological and
Agricultural Engineering Dept, North Carolina State University, Raleigh, North Carolina.
Dressing, S.A., J. Spooner, J.M. Kreglow, E.O. Beasley, and P.W. Westerman. 1987. Water and sediment
sampler for plot and field studies. J. Environ. Qual. 16(l):59-64.
Drouse, S.K., D.C. Hillman, J.L. Engles, L.W. Creelman and SJ. 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 by Lockheed
Engineering and Management Services Co., Inc., Las Vegas, Nevada, for U.S. Environmental Protection
Agency, Office of Research and Development, Environmental Monitoring Systems Laboratory, Las Vegas,
Nevada. December.
El-Shaarawi, A.H., and E. Damsleth. 1988. Parametric and nonparametric tests for dependent data. Water
Resour. Bull. 24:513-519.
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 by NSI Technology Services Corporation, Corvallis, Oregon for Corvallis
Environmental Research Laboratory, Oregon. January.
-------
References
Fisher, J.S., R.R. Perdue, M.F. Overton, M.D. Sobsey, and B.L. Sill. 1987. A comparison of-water quality
at two recreational marinas during a peak-use period. North Carolina State University, Raleigh, North
Carolina. Prepared for the National Oceanic and Atmospheric Administration.
Freund, J.E. 1973. Modern elementary statistics. Prentice-Hall, Englewood Cliffs, New Jersey.
Freund, R.J., and R.C. Littell. 1981. SASfor linear models: A guide to the ANOVA and GLMprocedures.
SAS Institute, Inc., Gary, North Carolina.
Frissell, C.A., W.J. Liss, C.E. Warren, and M.D.-Hurley. 1986. A hierarchical framework for stream habitat
classification: Viewing streams in a watershed context. Environ. Manage. 10(2): 199-214.
Gaines, A.G., Jr., and A.R. Solow. 1990. The distribution of fecal coliform bacteria in surface -waters of
the Edgartown Harbor coastal complex and management implications. Edgartown Harbor Project, Interim
Report No. 5. Edgartown Harbor Association, Inc.
GAO. 1986. The Nation's -water: Key unanswered questions about the quality of rivers and streams.
Report No. GAO/PEMD-86-6. U.S. General Accounting Office, Washington, DC.
Gaugush, R.F., ed. 1986. Statistical methods for reservoir water quality investigations. Instruction Report
E-86-2, U.S. Army Engineer Waterways Experiment Station, Vicksburg, Mississippi.
Gerritsen, J., J.S. White, E.W. Leppo, J.B. Stribling, and M.T. Barbour. 1996. Examination of the effects
of habitat quality, land use, and acidification on the macroinvertebrate communities of coastal plain streams.
CBWP-MANTA-TR-95-2. Chesapeake Bay Research and Monitoring Division, Maryland Department of
Natural Resources, Annapolis, Maryland.
Gibson, G.R., M.T. Barbour, J.B. Stribling, J. Gerritsen and J.R. Karr. 1994. Biological criteria: Technical
guidance for streams and small rivers. EPA 822-B-94-001. U.S. Environmental Protection Agency, Office
of Water, Washington, DC. September.
Gilbert, R.O. 1987. Statistical methods for environmental pollution monitoring. Van Nostrand Reinhold
Company, New York.
Gilliom, R.J., and D.R. Helsel. 1986. Estimation of distributional parameters for censored trace level water
quality data, 1, estimation techniques. Water Resour. Res. 22(2): 135-146.
Goodman, J. 1991. Evaluation of site-specific ground-water quality data. In Nonpoint source watershed
workshop: Seminar publication, U.S. Environmental Protection Agency, Office of Research and
Development, Washington, DC. Pp. 145-151.
Goodman, J., D. German, and J. Bishoff. 1996. Ground water monitoring. A guide to monitoring for
agricultural nonpoint source pollution projects. South Dakota Department of Environment and Natural
Resources, Pierre, South Dakota.
-------
Gray, J.S. 1989. Effects of environmental stress on species-rich assemblages. Biol. J. Linn. Soc. 37:19-32.
Green, R.H. 1993. Application of repeated measures designs in environmental impact and monitoring
studies. Austr. J. Ecol. 18:81-98.
Griffin, C.B. 1995. Introduction to GIS for public agencies. State University of New York, College of
Environmental Science and Forestry, Syracuse, New York. June.
Gulland,J.A. 1983. Fish stock assessment: A manual of basic methods. FAO/Wiley Series, Vol. 1. Wiley
& Sons, New York.
Gustin, M.S., G.E. Taylor, Jr., and T.L. Leonard. 1995. Atmospheric mercury concentrations above mercury
contaminated mill tailings in the Carson River Drainage Basin, NV. In D. Porcella, J. Huckabee, B.
Wheatley (eds.), Third international conference on mercury as a global pollutant, Whistler, British
Columbia, Canada, July 10-14. Pp. 217-220.
Hallberg, G.R., B.E. Hoyer, E.A. Bettis III, and R.D. Libra. 1983. Hydrogeology, water quality, and land
management in the Big Spring basin, Clayton County, Iowa. Iowa Geologic Survey Open-File Report 83-3.
Hallberg, G.R., R.D. Libra, E. A. Bettis, and B.E. Hoyer. 1984. Hydrogeologic and water quality
investigations in the Big Spring basin, Clayton County, Iowa. Iowa Geological Survey, Open File Report
84-4.
Harcum, J.B. 1990. Water-quality data analysis protocol development. Ph.D. diss., Department of
Agricultural and Chemical Engineering, Colorado State University, Fort Collins, Colorado.
Harcum, J.B., J.C. Loftis, and R.C. Ward. 1992. Selecting trend tests for water quality series with serial
correlation and missing values. Water Resour. Bull. 28(3):469-478.
Harris, J., J.C. Loftis, and R.H. Montgomery. 1987. Statistical methods for characterizing groundwater
quality. Ground Water 25(2): 185:193.
Hawkins, C.P., J.L. Kershner, P.A. Bisson, M.D. Bryant, L.M. Decker, S.V. Gregory, D.A. McCullough,
C.K. Overton, G.H. Reeves, R.J. Steedman and M.K. Young. 1993. A hierarchical approach to classifying
stream habitat features. Fisheries 18(6):3-12.
Hayes, M.L. 1983. Active fish capture methods. In Fisheries techniques, ed. L.A. Nielsen and D.L.
Johnson, pp. 123-145. American Fisheries Society, Bethesda, Maryland.
Hayslip, G.A. 1993. EPA Region 10 in-stream biological monitoring handbook (for wadable streams in the
Pacific Northwest). EPA-910-9-92-013. U.S. Environmental Protection Agency, Region 10, Environmental
Services Division, Seattle, Washington.
Heath, R.C. 1989. Basic ground-water hydrology. Water-supply Paper 2220. U.S. Geological Survey,
Washington, D.C.
-------
References
Helsel, D.R., and T.A. Cohn. 1988. Estimation of descriptive statistics for multiply-censored water-quality
data. Water Resour. Res. 24(12): 1997-2004.
Helsel, D.R., and R.M. Hirsch. 1995. Statistical methods in water resources. Elsevier. Amsterdam.
Hershfield, D.N. 1961. Rainfall frequency atlas of the United States. U.S. Weather Bureau Technical Paper
40. May.
Hipel, K.W. 1988. Nonparametric approaches to environmental impact assessment. Water Resour. Bull.
24(3).
Hirsch, R.M., and J.R. Slack. 1984. A nonparametric trend test for seasonal data with serial dependence.
Water Resour. Res. 20:727-732.
Hirsch, R.M., J.R. Slack, and R.A. Smith. 1982. Techniques of trend analysis for monthly water quality
data. Water Resour. Res. 18(1):107-121.
Hoaglin, D.C., F. Hosteller, and J.W. Tukey. 1983. Understanding robust and exploratory data analysis.
Wiley, New York.
Hodges, J.L., and E.L. Lehmann. 1963. Estimates of location based on rank tests. Ann. Math. Stat. 34:598-
611.
Horowitz, A.J., K.A. Elrick, J.A. Robbins, and R.B. Cook. 1993. The effect of mining and related activities
on the sediment-trace element geochemistry of Lake Coeur D'Alene, Idaho, USA - Part II: Subsurface
sediments. U.S. Geological Survey, Denver, Colorado. Open-File Report 93-656.
Hubert, W.A. 1983. Passive capture techniques. In Fisheries techniques, ed. L.A. Nielsen and D.L.
Johnson, pp. 95-122. American Fisheries Society, Bethesda, Maryland.
Hughes, R.M., and J.R. Gammon. 1987. Longitudinal changes in fish assemblages and water quality in the
Willamette River, Oregon. Trans. Am. Fish. Soc. 116(2):196-209.
Hurlbert, S.H. 1984. Pseudoreplication and the design of ecological field experiments. Ecol Monogr.
54(2): 187-211.
IEPA. 1979. Water quality management plan, Volume III. Illinois Environmental Protection Agency,
Springfield, Illinois.
Ingwersen, J.B. 1980. Statistical analysis using SPSS at the USDA-Fort Collins computer center. WSDG-
AD-00002. U.S. Department of Agriculture, Forest Service, Washington, DC.
ITFM. 1995a. The strategy for improving-water-quality monitoring in the United States. Final report of
the Intergovernmental Task Force on Monitoring Water Quality. Intergovernmental Task Force on
Monitoring Water Quality. February.
-------
ReferenceC
ITFM. 1995b. The strategy for improving water-quality monitoring in the United States. Technical
appendixes. Final report of the Intergovernmental Task Force on Monitoring Water Quality.
Intergovernmental Task Force on Monitoring Water Quality, Washington, DC. February.
Jamieson, C.A., ed. 1986. Summary of the 1986 RCWP data analysis workshop. U.S. Environmental
. Protection Agency, Office of Water Regulations and Standards, Washington, DC.
Jerald, A., Jr. 1983. Age determination. In Fisheries techniques, ed. L.A. Nielsen and D.L. Johnson, pp.
301-324. American Fisheries Society, Bethesda, Maryland.
Johengen, T.H., and A.M. Beeton. 1992. The effects of temporal and spatial variability on monitoring
agricultural nonppint source pollution. In The national rural clean water program symposium: 10 years of
controlling agricultural nonpoint source pollution: The RCWP experience. Seminar publication.
EPA/625/R-92/006. U.S. Environmental Protection Agency, Office of Research and Development, Office
of Water, Washington, DC., pp. 89-95. August.
Joiner, B.L., and J.R. Rosenblatt. 1971. Some properties of the range in samples from Tukey's symmetric
lambda.distributions. J. Amer. Stat. Assoc. 66:394-399.
Karr, J.R., and D.R. Dudley. 1981. Ecological perspective on water quality goals. Environ. Manage. 5:55-
68.
Karr, J.R., K.D. Fausch, P.L. Angermeier, P.R. Yant and I.J. Schlosser. 1986. Assessing biological integrity
in running waters: A method and its rationale. Special Publication 5. Illinois Natural History Survey,
Urbana, Illinois.
Kaufmann, P.R., A.T. Herlihy, and L.A. Baker. 1992. Sources of acidity in lakes and streams of the United
States. Environ. Pottut. 77(2/3): 115-122.
Keith, L.H., ed. 1988. Principles of environmental sampling. American Chemical Society, Washington
DC.
Kendall, M.G. 1975. Rank correlation methods, 4th edition. Charles Griffin, London.
Kentucky Department of Environmental Protection. 1993. Methods for assessing biological integrity of
surface waters. Division of Water, Kentucky Department of Environmental Protection, Frankfort, Kentucky.
Kerans, B.L., J.R. Karr, and S.A. Ahlstedt. 1992. Aquatic invertebrate assemblages: Spatial and temporal
differences among sampling protocols. J. North Am. Benthol. Soc. 11:377-390.
Khaleel, R., K.R. Reddy, and M.R; Overcash. L980. Transport of potential pollutants in runoff water from
land areas receiving animal waste: a review. Water Res. 14:421-436.
Kimball, C.G. 1986. 1986 annual RCWP progress report - Project 20. South Dakota Dept. of Water and
Natural Resources, Pierre, South Dakota.
-------
References
Klemm, D.J., P.A. Lewis, F. Fulk, and J.M. Lazorchak. 1990. Macroinvertebrate field and laboratory
methods for evaluating the biological integrity of'surface waters. EPA-600-4-90-030. U.S. Environmental
Protection Agency, Environmental Monitoring and Support Laboratory, Cincinnati, Ohio.
Klemm, D.J., Q.J. Stober and J.M. Lazorschak. 1992. Fish field and laboratory methods for evaluating the
biological integrity of surface waters. EPA/600/R-92/111. U.S. Environmental Protection Agency,
Environmental Monitoring and Support Laboratory, Cincinnati, Ohio. March.
Knapton, J.R., and D.A. Nimick. 1991. Quality assurance for -water-quality activities of the U.S. Geological
Survey in Montana. U.S. Geological Survey Open File Report 91-216. U.S. Geological Survey, Helena,
Montana. August.
Koterba, M.T., F.D. Wilde, and W.W. Lapham. 1995. Ground water data collection protocols and
procedures for the National Water Quality Assessment Program: Collection and documentation of water
quality samples and related data. Open File Report 95-399. U.S. Geological Survey, Reston, Virginia.
Krcglow, J.M., S.A. Dressing, R.P. Maas, F.A. Koehler, L. Christensen, W.K. Snyder, and F.J. Humenik.
1982. Best management practices for agricultural nonpoint source control: I. Animal waste. Biological and
Agricultural Engineering Dept, North Carolina State University, Raleigh, North Carolina.
Lapham, W.W., F.D. Wilde, and M.T. Korterba. 1995. Ground water data collection protocols and
procedures for the National Water Quality Assessment Program: Selection, installation, and documentation
of wells, and collection of related data. Open File Report 95-398. U.S. Geological Survey, Reston, Virginia.
Larsen, R. J., and M.L. Marx. 1981. An introduction to mathematical statistics and its applications. Prentice
Hall, Englewood Cliffs, New Jersey.
Leigh, D.S. 1994. Mercury contamination and floodplain sedimentation from former gold mines in north
Georgia. Wat. Resour. Bull. 30(4):739-748.
Leopold, L.B. 1968. Hydrology for urban land planning. Circular 559. U.S. Geological Survey, Reston,
Virginia.
Lettenmaier, D.P. 1988. Multivariate nonparametric tests for trend in water quality. Water Resour. Res.
24(3):50S-512.
Loftis, J.C., P.S. Porter, and G. Settembre. 1987. Statistical analysis of industrial wastewater monitoring
data. J. Water Pollut. Control 59(3): 145-151.
Loucks, D.P., J.R. Stedinger, and D.A. Haith. 1981. Water resource systems planning and analysis.
Prentice-Hall, Englewood Cliffs, New Jersey.
Luchetti, G., K. Thornburgh, and C. Rawson. 1987. A water quality monitoring program for major
agricultural areas of the Snohomish River basin. Tulalip Technical Report, Shohomish Co., Washington.
-------
Lyons, J. 1992. Using the index ofbiotic integrity (IBI) to measure environmental quality in warmwater
streams of Wisconsin. General Technical Report NC-149. U.S. Department of Agriculture, Forest Service,
North Central Forest Experiment Station, St. Paul, Minnesota.
Maas, R.P., J.M. Kreglow, S.A. Dressing, F.A. Koehler, L. Christensen, W.K. Snyder, and F.J. Humenik.
1982. Best management practices for agricultural nonpoint source control: III. Sediment. Biological and
Agricultural Engineering Dept, North Carolina State University, Raleigh, North Carolina.
Maas, R.P., S.A. Dressing, J. Spooner, M.D. Smolen, and F.J. Humenik. 1984. Best management practices
for agricultural nonpoint source control: IV. Pesticides. Biological and Agricultural Engineering Dept.,
North Carolina State University, Raleigh, North Carolina.
MacDonald, L.H., A.W. Smart, and R.C. Wissmar. 1991. Monitoring guidelines to evaluate effects of
forestry activities onstreams in the Pacific Northwest and Alaska. EPA/910/9-91-001. U.S. Environmental
Protection Agency, Region 10, Seattle, Washington.
Mann, H.B. 1945. Non-parametric tests against trend. Econometrica 13:245-259.
Mann, H.B., and D.R. Whitney. 1947. On a test of whether one of two random variables is stochastically
larger than the other. Ann. Math. Stat. 18:50-60. :
Marcus, J.M., and T.P. Stokes. 1985. Polynuclear aromatic hydrocarbons in oyster tissue and around three
coastal marinas. Bull. Environ. Contam. Toxicol. 35:833-844. :
Maxted, J. R. and E. L. Dickey. 1993. Statistical analysis of replicate data - Fall 1991 biological survey.
Preliminary technical report. Delaware Department of Natural Resources and Environmental Control,
Division of Water Resources. Dover, Delaware. August.
McMahon, P.J.T. 1989. The impact of marinas on water quality. Water Sci. Tech. 21(2):39-43.
Meador, M.R., T.F. Cuffney, and M.E. Gurtz. 1993. Methods for sampling fish communities as part of the
National Water Assessment Program. Open File Report 93-104. U.S. Geological Survey, Raleigh, North
Carolina.
Meals, DW. 1991. Surface water trends and land-use treatment. In Nonpoint source-watershed-workshop:
Seminar publication, U.S. Environmental Protection Agency, Office of Research and Development,
Washington, DC. Pp. 136-142.
Meals, DW. 1992. Relating land use and water quality in the St: Albans Bay watershed, Vermont. In
Seminar publication: The National Rural Clean Water Program symposium. 10 years of controlling
agricultural nonpoint source pollution: The RCWP experience. EPA/625/R-92/006. U.S. Environmental
Protection Agency, Office of Research and Development, Washington, DC. Pp. 131-143.
Mendenhall, W. 1971. Introduction to probability and statistics. Duxbury Press, Belmont, California.
Cited in MacDonald et al., 1991.
-------
References
Miller, D.L., P.M. Leonard, R.M. Hughes, J.R. Karr, P.B. Moyle, L.H. Schrader, B.A. Thompson, R.A.
Daniels, K.D. Fausch, G.A. Fitzhugh, J.R. Gammon, D.B. Halliwell, P.L. Angermeier, and DJ. Orth. 1988.
Regional applications of an index of biotic integrity for use in water resource management. Fisheries 5:12-
20.
Milliken, A., and V. Lee. 1990. Pollution impacts from recreational boating: A bibliography and summary
review. Rhode Island Sea Grant, Narragansett, Rhode Island.
Mitsch, W.J., R.J. Stevenson, J.R. Taylor, and M.A. Cardamone. 1983. Water contamination in the Ohio
River basin in Kentucky - a summary report. Systems Science Institute, University of Louisville, Kentucky.
Mockus, V. 1960. Selecting a flood-frequency method. Am. Soc. Eng. Trans. 3:48-51,54.
Montgomery, R.H., and J.C. Loftis. 1987. Applicability of the f test for detecting trends in water quality
variables. Water Resour. Bull. 23(4):653-662.
Moore, D.S., and G.P. McCabe. 1989. Introduction to the practice of statistics. W.H. Freeman and
Company, New York.
Mosteller, F., and R.E.K. Rourke. 1973. Sturdy statistics. Addison-Wesley, Reading, Massachusetts.
Mosteller, F., and J.W. Tukey. 1977. Data analysis and regression. Addison-Wesley Publishers, Menlo
Park, California.
National Research Council. 1986. Ecological knowledge and environmental problem-solving. Concepts
and case studies. National Academy Press, Washington, DC.
NCDEM. 1990. North Carolina coastal marinas: Water quality assessment. North Carolina Department
of Environment, Health, and Natural Resources, Division of Environmental Management, Raleigh, North
Carolina.
NCDEM. 1991. Coastal marinas: Field survey of contaminants and literature review. Report No. 91-03.
North Carolina Division of Environmental Management, Raleigh, North Carolina.
Netter, J., W, Wasserman, and M.H. Kutner. 1985. Applied linear statistical models. 2nd ed. Irwin
Publishers, Homewood, Illinois.
Nixon, S.W., C.A. Oviatt, and S.L. Northby. 1973. Ecology of small boat marinas. Rhode Island Sea Grant,
Narragansett, Rhode Island. -
NOAA. 1990. Estuaries of the United States. Vital statistics of a national resource base. National Oceanic
and Atmospheric Administration, Silver Spring, Maryland.
Novotny, V., and G. Chesters. 1989. Delivery of sediment and pollutants from nonpoint sources: A water
quality perspective. J. Soil Water Cons. 44:568-576.
-------
Refeiinsei:
Oak Ridge National Laboratory. 1993. Public health assessment for Carson River mercury site,
Moundhouse, Lyon County, Nevada. Preliminary report. Oak Ridge National Laboratory, Oak Ridge,
Tennessee.
Ohio EPA. 1987. Biological criteria for the protection of aquatic life: Volumes I-III. Ohio Environmental
Protection Agency, Division of Water Quality Monitoring and Assessment, Surface Water Section,
Columbus, Ohio.
Ohio EPA. 1992. Executive Summary and Vol I: Summary, Status, and Trends. In Ohio water resource
inventory. Ohio Environmental Protection Agency, Division of Surface Water, Ecological Assessment
Section, Columbus, Ohio.
Omernik, J.M. 1987. Ecoregions of the coterminous United States. Ann. Assoc. Am. Geogr. 77(1): 118-125.
Omernik, J.M. 1995. Ecoregions: A spatial framework for environmental management. In Biological
assessment and criteria: Tools for -water resource planning and decision making, ed. W.S. Davis and T.P.
Simon, pp. 49-62. Lewis Publishers, Boca Raton, Florida.
Pankratz, A. 1983. Forecasting -with univariate box-Jenkins models. Wiley and Sons, Inc., New York.
Pennsylvania RCWP Coordinating Committee. 1984. The Conestoga headwaters rural clean water
program, pn!9, 1984 annual progress report. U.S. Department of Agriculture, Soil Conservation Service,
Harrisburg, Pennsylvania.
Pitt, R., and J. McLean. 1992. Stormwater, baseflow, and snowmelt pollutant contributions from an
industrial area. Water Environment Federation 65th annual conference & exposition, surface water quality
& ecology symposia, volume VII. September 20-24, New Orleans, Louisiana.
Plafkin, J.L., M.T. Barbour, K.D. Porter, S.K. Gross and R.M. Hughes. 1989. Rapid bioassessment
protocols for use in streams and rivers: Benthic macroinvertebrates and fish. EPA/440/4-89-001. U.S.
Environmental Protection Agency, Office of Water, Washington, DC.
Platts, W.S., W.F. Megahan, and G.W. Minshall. 1983. Methods for evaluating stream, riparian, ahdbiotic
conditions. Gen. Tech. Rept. INT-138. U.S. Department of Agriculture, Forest Service, Ogden, Utah.
Ponce, S.L. 1980a. Statistical methods commonly used in water quality data analysis. WSDG Technical
Paper WSDG-TP-00001. US Department of Agriculture, Forest Service, Washington, DC.
Ponce, S.L. 1980b. Water quality monitoring programs. WSDG Technical Paper WSDG-TP-00002. U.S.
Department of Agriculture, U.S. Forest Service, Washington, DC.
Porter, P.S., R.C. Ward, and H.F. Bell. 1988. The detection limit, water quality monitoring data are plagued
with levels of chemicals that are too low to be measure precisely. Environ. Sci. Tech. 22:856-861.
-------
References
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.
Reckhow, K.H. 1979. Sampling designs for lake phosphorus budgets. In eds. Everett, L.G. and K.D.
Schmidt, Establishment of water quality monitor ing programs. Proceedings of a symposium held in San
Francisco, California, June 12-14, 1978, AWRA, Minneapolis, Minnesota.
Reckhow, K.H., K. Kepford and W.W. Hicks. 1993. Methods for the analysis of lake -water quality trends.
EPA 841-R-93-0003. U.S. Environmental Protection Agency, Washington, DC.
Remington, R.D., and M.A. Schork. 1970. Statistics with applications to the biological and health sciences.
Prentice-Hall, Englewood Cliffs, New Jersey.
t
Renard, K. G.; Foster, G. R.; Weesies, G. A.; Porter, J. P. 1991. RUSLE: Revised universal soil loss
equation. J. Soil Water Cons. 46(l):30-33.
Richards, R.P. 1986. Monte carlo studies of sampling strategies for estimating tributary loads, II: Effects
on bias and precision due to differences among watershed sizes and the transported materials monitored.
Water Quality Laboratory, Heidelberg College, Tiffin, Ohio.
Richards, C., and G.W. Minshall. 1992. Spatial and temporal trends in stream macroinvertebrate
communities: the influence of catchment disturbance. Hydrobiologia 241(3):173-184.
Rosen, B.H. 1995. Use of periphyton in the development of biocriteria. In W.S. Davis and T.P. Simon
(ed.), Biological assessment and criteria. Tools for water resource planning and decision making. Lewis
Publishers, Boca Raton, Florida. Pp. 209-215.
Rossman,L.A. 1990. DFLOWuser's manual. EPA 600/8-90/051. U.S. Environmental Protection Agency,
Office of Research and Development, Cincinnati, Ohio.
Salas, J.D., J.W. Delleur, V. Yevjevich, and W.L. Lane. 1980. Applied modeling ofhydrologic time series.
Water Resources Publications, Ft. Collins, Colorado.
SAS Institute, Inc. 1985a. SAS user's guide: Basics. Version 5 edition. SAS Institute, Inc., Gary, North
Carolina.
SAS Institute, Inc. 1985b. SAS user's guide: Statistics. Version 5 edition.SAS Institute, Inc., Gary, North
Carolina.
SAS Institute, Inc. 1990. SAS/STATuser's guide. Volume 1, CECLUS-FREQ. Version 6,4th edition. SAS
Institute, Inc., Gary, North Carolina.
Sawyer, C.M., and A.F. Golding. 1990. Marina pollution abatement. In 1990 Environmental management
for marinas conference. International Marina Institute, Wickford, Rhode Island.
-------
Refereneg!
Scalf, M.R., J.F. McNabb, WJ. Dunlap, R.L. Cosby, and J. Fryberger. 1981. Manual of ground-water
sampling procedures. NWWA/EPA Series.
SCDHEC. 1987. Heavy metals and extractable organic chemicals from the coastal toxics monitoring
network 1984-1986. South Carolina Department of Health and Environmental Control. Technical Report
No. 007-87.
Schlosser, I. J., and J.R. Karr. 1980. Determinants of water quality in agricultural watersheds. UILU-WRC-
80-0147, University of Illinois Water Resources Center, Urbana, Illinois.
SCRIP. 1996. How coal mining produces pollution in the upper Conemaugh River basin. Internet Home
Page.
!
Seabloom, R.A., G. Plews, F. Cox, and F. Kramer. 1989. The effect of sewage discharges from pleasure
craft on Puget Sound waters and shellfish quality. Washington State Department of Health, Shellfish
Section. Olympia, Washington.
Sen, P.K. 1968. Estimates of the regression coefficient based on Kendalls tau. J. Amer Stat Assoc
63:1379-1389.
Shackleford, B. 1988. Rapid bioassessments of lotic macroinvertebrate communities: Biocriteria
development. Arkansas Department of Pollution Control and Ecology, Little Rock, Arkansas.
Shapiro, S.S., and M.B. Wilk. 1965. An analysis of variance test for normality (complete samples)
Biometrika 52:591-611.
Shelley, P.E. 1979. Monitoring requirments, methods, and costs for the nationwide urban runoff program.
U.S. Environmental Protection Agency, Water Planning Division, Washington, DC.
Shelton, L.R. 1994. 'Field guide for collecting and processing stream-water samples for the National Water
Quality Assessment Program. Open File Report 94-455. U.S. Geological Survey, Reston, Virginia.
Shelton, L.R., and P.O. Capel. 1994. Guidelines for collecting and processing samples of stream bed
sediment for analysis of trace elements and organic contaminants for the National Water Quality Assessment
Program. Open File Report 94-458. U.S. Geological Survey, Reston, Virginia.
Sherwani, J.K. and D.H. Moreau. 1975. Strategies for water quality monitoring. Report No. 107, Water
Resources Research Institute of the University Raleigh, North Carolina.
Sherwood, C.R., D.A. Jay, R.B. Harvey, P. Hamilton, and C.A. Simenstad. 1990. Historical changes in the
Columbia River Estuary. Prog. Oceanog. 25:299-352.
Simons, D.B., R. Li, T.P. Ballestero, and K.G. Eggert. 1981. Workshop in sampling requirements for
monitoring and evaluation of'watershed management practices, May 20-21, 1981. U.S.EPA Environmental
Research Laboratory, Athens, Georgia.
-------
References
Singh, V.P., and M. Florentine, eds. 1996. Geographical information systems in hydrology. Kluwer
Academic Publishers, Dordrecht, Netherlands.
Skalski, J.R., and D.H. McKenzie. 1982. A design for aquatic monitoring programs. J. Environ. Manage.
14:237-251.
Smith, R.A., R.B. Alexander, M.G. Wolman. 1987. Water-quality trends in the Nation's rivers. Science
(Mar 27): 1607-1615.
Smith, F., S. Kulkami, L.E. Myers, and MJ. Messner. 1988. Evaluating and presenting quality assurance
data. In Principles of environmental sampling, ed. L.H. Keith, pp. 157-168. American Chemical Society,
Washington, DC.
Smolen, M. 1984. Water quality models. National Water Quality Evaluation Project Notes, No. 9,
Biological and Agricultural Engineering Deptartment, North Carolina State University, Raleigh, North
Carolina.
Smolen, M.D., R.P. Maas, J. Spooner, C.A. Jamieson, S.A. Dressing, L.C. Stanley, and FJ. Humenik. 1986.
NWQEP 1986 annual report, status of agricultural NFS projects - Draft. Biological and Agricultural
Engineering Dept., North Carolina State University, Raleigh, North Carolina.
Snedecor, G.W. and W.G. Cochran. 1980. Statistical methods. 7th ed. The Iowa State University Press,
Ames, Iowa.
Sokal, R.R., and F.J. Rohlf. 1981. Biometry. W.H. Fremman and Co. San Francisco, California. Cited in
MacDonaldetal., 1991.
Southerland, M., and J.B. Stribling. 1995. Status of biological criteria development and implementation.
In Biological criteria tools for water resources planning and decision making, ed. W.D. Davis and T. Simon,
pp. 79-94. Lewis Publishers, Boca Raton, Florida.
Spooner, J. 1984. Use and misuse ofr-square in regression analysis. National Water Quality Evaluation
Project Notes, Technical Supplement to No. 12, Biological and Agricultural Engineering Dept., North
Carolina State University, Raleigh, North Carolina.
Spooner, J. 1986a. Analysis of covariance, Part I. National Water Quality Evaluation Project Notes,
Technical Supplement to No. 18, Biological and Agricultural Engineering Dept., North Carolina State
University, Raleigh, North Carolina.
Spooner, J. 1986b. Analysis of covariance, Part II. National Water Quality Evaluation Project Notes,
Technical Supplement to No. 19, Biological and Agricultural Engineering Dept., North Carolina State
University, Raleigh, North Carolina.
Spooner, J. 1994. Nonpoint source water quality monitoring designs and data analysis techniques: A
review. Review draft. North Carolina Cooperative Extension Service, North Carolina State University.
-------
Spooner, J. 1994a. Comparisons between parametric and nonparametric statistical trend tests. Second
National Nonpoint Source Watershed Monitoring Conference.
Spooner, J. and R.P. Maas. 1984. Regression-basic concepts and definitions. National Water Quality
Evaluation Project Notes, Technical Supplement to No. 11, Biological and Agricultural Engineering Dept,
North Carolina State University, Raleigh, North Carolina.
Spooner, J., R.P. Maas, S.A. Dressing, M.D. Smolen, and F.J. Humenik. 1985. Appropriate designs for
documenting water quality improvements from agricultural nps control programs. In Perspectives on
nonpoint source pollution. Proceedings of a national conference, May 19-22, Kansas City Missouri EPA
440/5-85-001. Washington, DC. '
Spooner, J., C.A. Jamieson, R.P. Maas, S.A. Dressing, M.D. Smolen, and F.J. Humenik. 1986. Rural clean
water program status report on the CM&E projects 1985 - supplemental report: Analysis methods.
Biological and Agricultural Engineering Dept., North Carolina State University, Raleigh, North Carolina.
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. D.M. Nielsen and M.N. Sara, ed. Pp. 377-390. American Society for
Testing and Materials, Philadelphia, Pennsylvania.
Srivastava, M.S., and C.G. Khatri. 1979. An introduction to multivariate statistics. North Holland, New
York.
Steedman, R.J. 1988. Modification and assessment of an index of biotic integrity to quantify stream quality
in southern Ontario. Can. J. Fish. Aquat. Sci. 45:492-501.
Storch, T.A. 1986. A comparison of water quality in .Chautauqua Lake, New York: 1937-1983. In Lake and
reservoir management, volume II. Proceedings of the Fifth Annual Conference and International Symposium
on Applied Lake & Watershed Management, November 13-16, 1985, Lake Geneva, Wisconsin. North
American Lake Management Society, Merrifield, Virginia.
Strahler, A. N. 1957. Quantitative analysis of watershed geomorphology. Amer. Geophys Union Trans
38:913-920.
Taylor, J.K. 1993. Standard reference materials handbook for SRM users. NIST Special Publication 260-
100. National Institute of Standards and Technology, Standards and Reference Materials Program, U.S.
Department of Commerce, Technology Administration, Washington, DC. February.
Tchernia, P. 1980. Descriptive regional oceanography. Pergamon Marine Series, No. 3. Pergamon Press,
Great Britain.
Thomas, B.J. 1970. Sedimentation activities in the north Pacific division, In Proceedings of a seminar on
sediment transport in rivers and reservoirs. April 7-9. U.S. ACE, Hydrologic Engineering Center.
-------
References
Thornton, K.W., B.L. Kimmer, and F.E. Payne (editors). 1990. Reservoir limnology: Ecological
perspectives. John Wiley and Sons, Inc., New York, New York.
Travis, C.C., and M.L. Land. 1990. Estimating the mean of data sets with nondetectable values. Environ.
Sci. Tech. 24:961-962.
USDA. 1981. Forest management for water quality. Forest Service, Area Planning and Development.
USDA-NRCS. 1996. National handbook on-water quality monitoring. National Water Quality Handbook,
Part 600. U.S. Department of Agriculture, Natural Resources Conservation Service. December.
USDOI. (n.d). Appalachian clean stream initiative. U.S. Department of the Interior, Office of Surface
Mining Reclamation and Enforcement, Washington, DC. Leaflet.
USDOI. 1991. Riparian-wetland initiative for the 1990's. BLM/WO/GI-91/001+4340. U.S. Department
of the Interior, Bureau of Land Management, Washington, DC. September.
USEPA. 1975. Model state water monitoring program. National Water Monitoring Panel. EPA-440/9-74-
002. U.S. Environmental Protection Agency, Washington, DC.
USEPA. 1977. Nonpoint source—stream nutrient level relationships: A nationwide study. U.S.
Environmental Protection Agency, Washington, DC.
USEPA. 1978a. An assessment of major nonpoint pollution sources: relative magnitudes, cost-effective
controls, institutional constraints. Booz, Allen, and Hamilton, Inc., Bethesda, Maryland.
USEPA. I978b. Microbiological methods for monitoring the environment, water, and wastes. EPA-600/8-
78-017. U.S. Environmental Protection Agency, Washington, DC.
USEPA. 1980a. An approach to water resources evaluation of nonpoint silvicultural sources (a procedural
handbook). EPA-600/8-80-012. U.S. Environmental Protection Agency, Washington, DC.
USEPA. 1980b. Water -quality management needs assessment FY '80-'84. Draft. U.S. Environmental
Protection Agency, Water Planning Division, Washington, DC.
USEPA. 1981. Guidelines for evaluation of agricultural nonpoint source water quality projects. U.S.
Environmental Protection Agency, Interagency Taskforce, Washington, DC. April.
USEPA. 1982b. Data collection and quality assurance for the nationwide urban runoff program. U.S.
Environmental Protection Agency, Water Planning Division, Washington, DC.
USEPA. 1982c. Results of the Nationwide Urban Runoff Program. Volume II—Appendices. U.S.
Environmental Protection Agency, Water Planning Division, Washington, DC.
-------
References:
USEPA. 1983a. .Results of the Nationwide Urban Runoff Program, volume I - Final report. U.S.
Environmental Protection Agency, Water Planning Division, Washington, DC.
USEPA. 1983b. Chesapeake Bay Program: Findings and recommendations. U.S.Environmental Protection
Agency, Region 3, Philadelphia, Pennsylvania.
USEPA. 1983c. Interim guidelines and specificationsfor preparing qualityassurance 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. February.
USEPA. 1984a. Report to Congress: Nonpoint source pollution in the United States. U.S.Environmental
Protection Agency, Office of Water Program Operations, Washington, DC.
USEPA. 1984b. A probabilistic methodology for analyzing water quality effects of urban runoff on rivers
and streams, first draft. U.S. Environmental Protection Agency, Office of Water, Washington, DC.
USEPA. 1984c. Policy and program requirements to implement the mandatory quality assurance program.
EPA Order 5360.1. U.S. Environmental Protection Agency, Washington, D.C. April.
USEPA. 1987a. Handbook, ground water. EPA/625/6-87/016. U.S. Environmental Protection Agency
Washington, DC. .
USEPA. 1987. An overview of sediment quality in the United States. EPA-905/9-88-002. U.S.
Environmental Protection Agency, Region 5, Chicago, Illinois.
USEPA. 1990. Surface water monitoring program guidance. Draft. U.S. Environmental Protection
.Agency, Office of.Water Regulations and Standards, Assessment and Watershed Protection Division.
Washington, D.C. September.
USEPA. 1990a. The lake and reservoir restoration guidance manual. EPA-440/4-90-006. U.S.
Environmental Protection Agency, Office of Water, Washington, DC. August.
USEPA. 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, Washington. August.
USEPA. 1993a. Guidance specifying management measures for sources of nonpoint pollution in coastal
waters. EPA 840-B-92-002. U.S. Environmental Protection Agency, Office of Water, Washington, DC.
USEPA. 1993b. Guide to federal water quality programs and information. EPA-230-B-93-001. U.S.
Environmental Protection Agency, Washington, DC. February.
USEPA. 1993c. Paired watershed study design.. EPA 841-F-93-009. U.S. Environmental Protection
Agency, Washington, DC. September.
-------
References
USEPA. 1994a. Biological criteria: Technical guidance for streams and small rivers. EPA 822B94001.
U.S. Environmental Protection Agency, Office of Water, Washington, DC.
USEPA. 1994b. Volunteer stream monitoring: A methods manual. EPA-841-D-95-001 (Draft). U.S.
Environmental Protection Agency, Office of Wetlands, Oceans, and Watersheds, Assessment and Watershed
Protection Division, Washington, DC. December.
USEPA. 1994c. EPA requirements for quality management plans. Interim final. EPA QA/R-2. U.S.
Environmental Protection Agency, Quality Assurance Management Staff, Washington, DC.
USEPA. 1994d. EPA requirements for quality assurance project plans for environmental data operations.
Interim Final. EPA QA/R-5. U.S. Environmental Protection Agency, Quality Assurance Management Staff,
Washington, DC. August.
USEPA. 1994e. Guidance for the data quality objectives process. EPA QA/G-4. Final. U.S. Environmental
Protection Agency, Quality Assurance Management Staff, Washington, DC. September.
USEPA. 1995. National water quality inventory 1994 Report to Congress. EPA 841-R-95-005. U.S.
Environmental Protection Agency, Office of Water, Washington, DC.
USEPA. 1997. Field and laboratory methods for macroinvertebrate and habitat assessment for low gradient
non-tidal streams. Mid-Atlantic Coastal Streams Workgroup, U.S. Environmental Protection Agency,
Environmental Services Division, Region 3, Wheeling, West Virginia.
USEPA/USDOI. 1995. 1995 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.
Environmental Protection Agency, Region 3 and U.S. Department of the Interior, Office of Surface Mining,
Appalachian Regional Coordinating Center, Pittsburgh, Pennsylvania. November
USFWS. 1982. Mitigation and enhancement techniques for the upper Mississippi River system and other
large river systetns. Resource Publication 149. U.S. Department of the Interior, U.S. Fish and Wildlife
Service, Washington, DC.
USFWS. 1991. Principles and techniques of electrofishing. U.S. Fish and Wildlife Service, Fisheries
Academy, Office of Technical Fisheries Training, Kearneysville, West Virginia.
USGS. 1977. National handbook of recommended methods for water-data acquisition. Chapter 2:
Groundwater. U.S. Department of the Interior, Reston, Virginia.
USGS. 1984. National water summary 1983 -hydrologic events and issues. Water Supply Paper 2250.
U.S. Government Printing Office, Washington, DC.
USGS. 1985. National water summary 1984, hydrologic events, selected water-quality trends, and ground-
water resources. Water-Supply Paper 2275, U.S. Government Printing Office, Washington, D.C..
-------
Referiiffi—
U.S. House Committee on Public Works and Transportation. 1980. Report on implementation of the
FWPCA. House Report No. 97-71. Washington, DC.
van Belle, G., and J.P. Hughes. 1984. Nonparametric tests for trend in water quality. Water Resour. Res.
20(1):127-136.
Vermont RCWP Coordinating Committee. 1984. St. Albans Bay rural clean water program summary report
1984. U.S. Department of Agriculture and Vermont Water Resources Research Center, Burlington,
Vermont.
Vermont RCWP Coordinating Committee. 1986. St. Albans Bay rural dean water program annual progress
report 1986. U.S. Department of Agriculture and Vermont Water Resources Research Center, Burlington,
Vermont.
Viessman, W.; J.W. Knapp, G.L. Lewis, and T.E. Harbough, 1977. Introduction to hydrology. Hurpert Row,
New York, New York.
Walburg, C.H., J.F. Novotny, K.E. Jacobs, and W.D. Swink. 1983. Effects of reservoir releases on water
quality, macro-invertebrates, and fish in tailwaters: Field study results. U.S. Army Engineer Waterways
Experiment Station, Vicksburg, Mississippi.
Ward, R.C., and J.C. Loftis. 1986. Establishing statistical design criteria for water quality monitoring
systems, review and synthesis. Water Resour. Bull. 22(5):759-767.
Ward, R.C., J.C. Loftis, and G.B. McBride. 1986. The "data-rich but information-poor syndrome" in water
quality monitoring. Environ. Manag. 10(3):291-297.
Ward, R.C., J.C. Loftis, and G.B. McBride. 1990. Design of water quality monitoring systems. Van
Nostrand Reinhold Company, New York.
Washington State Department of Ecology. 1991. Stormwater management manual for the Puget Sound
basin -public review draft. Publication No. 90-73.
Weatherley, A.H. 1972. Growth and ecology of fish populations. Academic Press, New York, New York.
Weisberg, S. B. and 11 co-authors. 1993. EMAP-estuaries Virginian Province 1990 demonstration project
report. EPA 600/R-92/100. United States Environmental Protection Agency, Environmental Research
Laboratory, Narragansett, Rhode Island.
Weiss, L.L. 1962. A general relation between frequency and duration of precipitation., Monthly Weather
Rev. 87-88.
Wendt, P.H., R.F. van Dolah, M.Y. Bobo, and JJ. Manzi. 1990. The effects of a marina on certain aspects
of the biology of oysters and other benthic macrofauna in a South Carolina estuary. Unpublished draft
manuscript. South Carolina Department of Health and Environmental Control, Columbia, South Carolina.
-------
usability 5-27
validity 5-27
water quality 2-10
data management 5-17
data quality objectives 5-3
development steps 5-4
data set, balanced 4-61
data stratification 4-8
decision rule 5-6
decision statement 5-5
design flow
biologically based 4-102
extreme event 4-101
detection limit 4-15
Diatom Btoasscssmcnt Index 3-26
discriminant analysis 4-94
DQO sec data quality objectives
ccoregions 3-16
EMAP see Environmental Monitoring and Assessment Program
EMC see event mean concentration
Environmental Monitoring and Assessment Program 3-29
error
decision 5-6
gross 2-13
sampling, random 2-13
Type 14-2,4-43
Type 114-2,4-43
estuaries 1-3,1-16
characteristics 1-17
cstuarine drainage area 1-17
event mean concentration 4-11
extreme events 4-94
design flow 4-101
frequency 4-105
extreme values 4-8
factor analysis 4-93
fertilizers 1-4,1-10,1-21
field operations 5-19
Fisher Z transformation 4-72
flow alteration 1-8
flow-weighted mean concentration 4-11
fluvial drainage area 1-17
forest harvesting 1-10
Friedman test 4-52,4-63
FWMC swjlow-weightedmean concentration
geographic information systems 2-11
geology 1-21
geometric mean 4-11
see also statistic
GLM 4-77
Great Lakes 1-1,1-3,1-6,1-10
ground water 1-19
characteristics 1-20
habitat
and biological condition 3-6
assessment 3-4,3-6
quality 3-2,3-4
riparian 1-8
structure 3-4
histogram 4-21
Hodges-Lehman estimator 4-8,4-52
hydraulic residence time 1-13
hydromodification 1-8
hypothesis
alternative 4-2
null 4-2
testing 4-2
hypothesis testing 2-12,4-2
multiple comparisons 4-63
see also test
imperviousness 1-7
Intergovernmental Task Force on Monitoring
2-1
interquartile range 4-1,4-16
see also statistic
interval estimate 4-1
Invertebrate Community Index 3-38
1TFM see Intergovernmental Task Force on Monitoring
Kendall 4-8
seasonal 4-86,4-88
slope estimator 4-8
Kolmogorov-Smirnov 4-31
Kruskal-Wallis 4-7,4-52,4-56
kurtosis4-18,4-30
laboratory operations 5-24
lakes 1-13
characteristics 1-14
stratification 1-14
least significant difference 4-55
livestock
grazing 1-7
wastes 1-4
loading rate 4-14
log transformations 4-26,4-75
log-log plot 4-23
Mann-Kendall T 4-86
one-sided 4-87
two-sided 4-87
Mann-Whitney 4-5,4-7,4-47,4-50,4-56
marinas 1-11
mean, Winsorized 4-15
mercury 1-9
metrics, biological 3-11,3-20
mines, abandoned 1-10
mining 1-9,2-25
coal 1-9
ore 1-9
underwater 1-8
monitoring
biological 1-16,3-1
chemical 2-31
evaluation 2-6
ground water 1-20
objectives 2-4,2-5
physical 2-31
trend 2-24
monitoring plan
development 2-1
monitoring programs
design 2-12
design, probabilistic 2-12,2-14
design, targeted 2-12
geographic scale 2-3
purposes 2-1
responsibilities 2-28
temporal scale 2-3
-------
NASQAN see National Stream Quality Accounting Network
National Stream Quality Accounting Network 2-25
nonpoint source pollution 7-7
categories 1-1,1-6
effects of 1-4
extent of problem 1-1
ocean shoreline waters 1-3
open coastal waters 1-18
characteristics 1-19
PCA see principal component analysis
periphyton 3-25
pesticides 1-6, 1-7, 1-10, 1-21
plot of residuals 4-66
point estimate 4-1,4-10
point source pollution 1-1
pollution, natural sources 1-2
ponds 1-13
characteristics 1-14
population 5-5
port construction 1-11
power, analytical 4-5
precision 5-7 /
Prince George's County, MD 3-32,3-38
principal component analysis 4-93
QA/QC
EPA policy 5-2
importance 5-1
see also quality assurance and quality control
QAPP see quality assurance project plan
QMP see quality management plan
qualify assurance 2-31,5-7
quality assurance project plan 2-31,5-3
elements 5-8
optimizing 5-8
quality control 2-31, 5-7
quality management plan 5-3 ,
quartile4-19
range 4-16
rapid bioassessment protocols 3-1, 3-16
RBP see rapid bioassessment protocol
RCWP see Rural Clean Water Program
record keeping 5-28
reference conditions, establishment 3-16
regression
evaluation 4-69
independence of residuals 4-66
linear 4-64
multiple 4-75
multivariate 4-78
nonlinear 4-74
normality of residuals 4-68
plot of residuals 4-66
simple linear 4-65
report preparation 5-28
representativeness 5-73
reproducibility 5-77
reservoirs 1-13
characteristics 1-14
resource extraction 1-9
rivers 1-12
characteristics 1-12
rivers and stream 1-12
Rural Clean Water Program
Idaho 2-26
Vermont 2-24
safety 5-23
salts 1-6, 1-7
sample
composite 2-31
continuous 2-31
grab 2-31
integrated 2-31
mean 4-18
type 2-31
sample custody 5-14
sample handling 5-14, 5-23
sampling
cluster 2-19
cluster, two-stage 2-20
double 2-21
equipment 2-32
fish 3-24
frequency 2-13
macroinvertebrate 3-21
optimizing 2-18, 5-7
record keeping 2-32
simple random 2-14
site locations 2-25
site selection 3-29
station 2-32
stratified random 2-17
systematic 2-18
systematic stratified 2-19
time 2-21
two-stage 2-19, 2-20
two-step 2-15
SAS 4-77
scatter plot 4-25
Sen slope estimator 4-88
Shapiro-Wilk W4-31,4-38
silviculture 1-10
skewness4-18
software
data analysis 4-10
multivariate 4-91
soils 1-21
Spearman's rho 4-90
standard deviation 4-1,4-16,4-19
see also statistic
statistic
average conditions 4-7
central tendency 4-4,4-10
changing conditions 4-7
coefficient of variation 4-16
descriptive 4-1
geometric mean 4-1,4-11
interquartile range 4-1, 4-16
mean 4-1
median 4-1
mode 4-1
nonparametric 4-4
parametric 4-4
standard deviation 4-1,4-16,4-19
variability 4-4
-------
variance 4-16
weighted mean 4-11
stcm-and-lcaf plot 4-21
storm return period 4-95
storm water 1-2,1-3,1-4,1-7,1-11,2-10,4-8,4-11,4-94
agriculture 1-1
stream
characteristics 1-12
order 2-3
segmentation 1-13
temperature 1-8,1-9,1-10
streambank
destabilization 1-1,1-2,1-4
erosion 1-6,1-7
scouring 1-7
strcamsidc vegetation
removal 1-7,1-9
Student's / 2-15,4-7.4-18,4-35,4-46
paired 4-8
two-sample 4-47
test
Duncan's multiple range 4-63
equal variances 4-32
Gabriel's multiple-comparison 4-63
graphical 4-26
kurtosis 4-30
normality 4-26
one sample 4-34
one-sided 4-34
paired data 4-34
randomness 4-33
Ryan-Einot-Gabricl-Welsch multiple F4-63
sign 4-44,4-63
skcwness 4-27
two-sample 4-47
two-sided 4-34
Waller-Duncan Ar-ratio 4-63
time series plot 4-20
time-weighted mean concentration 4-11
topography 1-21
slope 1-21
trend, monotonic4-85,4-86
Tukey's method 4-63
TWMC see time-weighted mean concentration
Universal Soil Loss Equation 2-11,2-12
Revised 2-11
urban runoff 1-7
urbanization 1-7
variability
hydrologic 2-11
spatial 2-11
sec also statistic
variables
selection 2-7
surrogate measures 2-9
variance 4-16
sec also statistic
Water Erosion Prediction Project 2-11
water quality, correlation to land treatment 2-11
water resource types 1-12
watershed sampling
multiple 2-24
nested paired approach 2-22
paired approach 2-22,3-3,4-74
single 2-24
weighted mean 4-11
see also statistic
Wilcoxon signed rank 4-8,4-34,4-43,4-46
Wilcoxon's rank sum
see Mann-Whitney
[Note: Italicized page numbers indicate pages on which
definitions of terms are found.]
-------
APPENDIX A: REVIEW OF AVAILABLE MONITORING GUIDANCES
A.1 INTRODUCTION
The monitoring guidances and papers reviewed in this appendix are grouped under the following
headings:
General Monitoring Guidances and References
Guidances for Stream and River Monitoring
Guidances for Lake and Reservoir Monitoring
Guidances for Watershed Monitoring
Guidances for Ground Water Monitoring
Guidances for Biological Monitoring
Program-Specific Monitoring Guidances
The reviews have been organized primarily according to the type of waterbody or waterbody system to
winch the guidance is most applicable-streams and rivers, lakes and reservoirs, or watersheds
Guidances that are not specific to one type of waterbody are grouped under, the remaining headings.
For example, a guidance for biological monitoring in streams and rivers is listed under "Guidances for
Stream and River Monitoring," whereas a biological monitoring guidance that is not specific to
waterbody type is listed under "Guidances for Biological Monitoring." Numerous guidances are not
specific to either a waterbody type or a particular type of monitoring, and these are listed under
"General Monitoring Guidances and References." Guidances specific to programs mandated under the
Clean Water Act, such as the National Pollutant Discharge Elimination System (NPDES) program or
the National Estuary Program, are listed separately.
A complete listing of the guidances reviewed in this appendix is presented first, followed by the
individual reviews. A table at the end of the appendix indicates at a glance the aspects of monitoring
addressed m each guidance. The numbers at the top of the table correspond to the numbers, preceding
the guidance titles in this listing and in the reviews.
A.2 CATEGORICAL LISTING OF MONITORING GUIDANCES REVIEWED
A.2.1 General Monitoring Guidances and References
1. National Handbook on Water Quality Monitoring. USDA-NRCS 1996.
2. Water Quality Indicators Guide. USDA-NRCS 1997.
3. The Nonpoint Source Manager's Guide to Water Quality and Land. Treatment Monitoring
Coffey, Spooner, and Smolen 1993.
4.
Coordinated Nonpoint Source Water Quality Monitoring Program for Idaho. Idaho Department
of Health and Welfare 1990.
-------
Appendix A
5. Water Quality Monitoring for the Clean Water Partnership. A Guidance Document. Minnesota
Pollution Control Agency 1989.
6. Evaluating the Effectiveness of Forestry Best Management Practices in Meeting Water Quality
Coals or Standards. Dissmeyer 1994.
7. Designing Effective Nonpoint Source Water Quality Monitoring Programs. Maas 1989.
8. Volunteer Estuary Monitoring: A Methods Manual. USEPA 1993.
9. Volunteer Water Monitoring: A Guide for State Managers. USEPA 1990.
10. The Volunteer Monitor. The National Newsletter of Volunteer Water Quality Monitoring.
11. The Strategy for Improving Water-Quality Monitoring in the United States. Intergovernmental
Task Force on Monitoring Water Quality 1995.
12. Tlie Strategy for Improving Water-Quality Monitoring in the United States. Technical
Appendixes. Intergovernmental Task Force on Monitoring Water Quality 1995.
13. Guidance for State Water Monitoring and Wasteload Allocation Programs. USEPA 1985.
14. Guidelines for Evaluation of Agricultural Nonpoint Source Water Quality Projects. USEPA
1981.
A.2.2 Guidances for Stream and River Monitoring
15. Monitoring Guidelines to Evaluate Effects of Forestry Activities on Streams in the Pacific
Northwest and Alaska. MacDonald, Smart and Wissmar 1991.
16. Monitoring Protocols to Evaluate Water Quality Effects of Grazing Management of Western
Rangeland Streams. USEPA 1993.
17. Rapid Bioassessment Protocols for Use in Streams and Rivers: Benthic Macroinvertebrates and
Fish. USEPA 1989.
18. An Improved Biotic Index of Organic Stream Pollution. Hilsenhoff 1987.
19. Using the Index of Biotic Integrity (IBI) to Measure Environmental Quality in Warm-water
Streams of Wisconsin. Lyons 1992.
20. Evaluation Monitoring of Stream Habitat During Priority Watershed Projects. Simonson and
Lyons 1992.
-------
21. 7996 Water Body Assessment Guidance: A Stream to Standards Process. Idaho Division of
Environmental Quality 1996.
22. 1996 Beneficial Use Reconnaissance Project Workplan. Idaho Division of Environmental
Quality 1996.
23. Handbook: Stream Sampling for Waste Load Allocation Applications. USEPA 1986.
24. Evaluation Monitoring of Stream Fish Communities During Priority Watershed Projects.
Simonson and Lyons 1992.
25. Techniques for Detecting Effects of Urban and Rural Land-Use Practices on Stream-Water
Chemistry in Selected Watersheds in Texas, Minnesota, and Illinois. Walker 1993.
A 2.3 Guidances for Lake and Reservoir Monitoring
26. Technical Support Manual: Waterbody Surveys and Assessments for Conducting Use
Attainability Analyses. Volumes I-IH. USEPA 1983-1984.
27. Volunteer Lake Monitoring: A Methods Manual. USEPA 1991.
28. Statistical Methods for the Analysis of Lake Water Quality Trends. USEPA 1993.
29. Monitoring Lake and Reservoir Restoration. USEPA 1990.
A.2.4 Guidances for Watershed Monitoring
30. Monitoring Primer for Range Watersheds. Bedill and Buckhouse Draft.
31. Seminar Publication: The National Rural Clean Water Program Symposium. 10 years of
controlling agricultural nonpoint source pollution: The RCWP experience. USEPA 1992.
32. Watershed Monitoring Manual. Kansas Biological Survey 1993.
A.2.5 Guidances for Ground Water Monitoring
33. A Review of Methods for Assessing Nonpoint Source Contaminated Ground-Water Discharge to
Surface Water. USEPA 1991.
34. Ground Water Monitoring: A Guide to Monitoring for Agricultural Nonpoint Source Pollution
Projects. Goodman, German, and Bishoff 1995.
35. Response to Ground Water Contaminants Detected through Idaho's Statewide Ambient Ground
Water Quality Monitoring Program. Idaho Department of Health and Welfare 1995.
-------
Appendix A
A.2.6 Guidances for Biological Monitoring
36.
Fish Field and Laboratory Methods for Evaluating the Biological Integrity of Surface Waters.
USEPA 1993.
37. Bioaccumttlation Monitoring Guidance: Selection of Target Species and Review of Available
Bioaccwnulation Data. Volume I. USEPA 1987.
3 8. Biological Field and Laboratory Methods for Measuring the Quality of Surface Waters and
Effluents. USEPA 1973.
A.2.7 Program-specific Monitoring Guidances
39.
Watershed Monitoring and Reporting for Section 319 National Monitoring Program Projects.
USEPA 1991.
40. NPDES Storm Water Sampling Guidance Document. USEPA 1992.
41. Monitoring Guidance for the National Estuary Program. USEPA 1991.
42. Water Quality Standards Handbook. USEPA 1983.
43. Ecological Assessments of Hazardous Waste Sites: A Field and Laboratory Reference
Document. USEPA 1989.
44. Environmental Monitoring and Assessment Program: Ecological Indicators. USEPA 1990.
45. RCRA Ground-Water Monitoring Technical Enforcement Guidance Document. USEPA 1986.
46. Summary of U.S. EPA-Approved Methods, Standard Methods, and Other Guidance for 301 (h)
Monitoring Variables. USEPA 1985.
47. Statistical Analysis of Ground-Water Monitoring Data at RCRA Facilities - Interim Final
Guidance. USEPA 1989.
48. CWA Section 403: Procedural and Monitoring Guidance. USEPA 1994.
49. Methods for Collecting Benthic Invertebrate Samples as Part of the National Water Quality
Assessment Program. Cuffhey, Gurtz, and Meador 1993.
50. Methods for Collecting Algal Samples as Part of the National Water Quality Assessment
Program. Porter, Cuffhey, Gurtz, and Meador 1993.
51. Methods for Characterizing Stream Habitat as Part of the National Water Quality Assessment
Program. Meador, Hupp, Cuffhey, and Gurtz 1993.
-------
52. Guidelines for the Processing and Quality Assurance ofBenthic Invertebrate Samples Collected
as Part of the National Water Quality Assessment Program. Cuffney, Gurtz, and Meador
1993.
53. Methods for Sampling Fish Communities as a Pan of the National Water Quality Assessment
Program, Meador, Cuffhey, and Gurtz. 1993.
54. Guidelines for Studies of Contaminants in Biological Tissues for the National Water Quality
Assessment Program. Crawford and Luoma 1994.
-------
Appendix A
A3 GUIDANCE REVIEWS
A.3.1 General Monitoring Guidances and References
1. National Handbook on Water Quality Monitoring. USDA-NRCS 1996.
REFERENCE: USDA-NRCS. 1996. National Handbook on Water Quality Monitoring. U.S. Department of
Agriculture, Natural Resources Conservation Service. December.
CONTENTS: Water quality problem; Objectives; Statistical designs; Scale of study; Variable selection; Sample
type; Sampling location; Sampling frequency and duration; Station type; Sample collection and analysis; Land
use and management monitoring; Data management.
MAIN FOCUS: Primarily useful for designing a monitoring program. Thoroughly discusses all aspects of
monitoring program design (site selection, parameter selection, sampling program design) with numerous
examples and illustrations to help the reader determine what is most appropriate for particular applications.
This document describes methods for monitoring water quality responses to land use, land management activities,
and conservation practices in streams, lakes, and ground water. It is intended to be a comprehensive guidance for
water quality managers who have little experience in monitoring study design and implementation. The many
purposes that monitoring studies can serve are discussed to help water quality managers define the scope of their
monitoring programs. Examples of programs meant to serve the different purposes are included.
Specific guidance is provided on designing a monitoring study, setting up a monitoring station, and analyzing
water quality data. Worksheets are provided to facilitate rapid and complete monitoring study design. The
document contains 12 chapters, each of which covers a specific step of monitoring study design: (1) define the
water quality problem, (2) define the monitoring objectives, (3) describe statistical design, (4) determine the study
scale, (5) select water quality variables, (6) select sample type, (7) determine sampling locations, (8) determine
sampling frequency and duration,(9) design station type, (10) describe sample collection and analysis methods,
(11) describe land use monitoring, and (12) describe data management.
2.
Water Quality Indicators Guide. USDA-NRCS.
REFERENCE: USDA-NRCS. 1997. Water Quality Indicators Guide. U.S. Department of Agriculture,
Natural Resources Conservation Service.
CONTENTS: Pollution related to agriculture; Water quality field analysis; Ecology of freshwater streams;
Sediment; Nutrients; Pesticides; Animal Wastes; Salts. Appendices: Water quality procedures; Aquatic
organisms; Glossary; Conservation and best management practices; Field sheets.
MAIN FOCUS: Field analysis of water pollution. Indicators for water pollution due to sediment, nutrients,
pesticides, animal wastes, and salts are explained and field sheets are provided for tabulating water quality
conditions.
-------
The core of the Water Quality Indicators Guide is the field sheets and list of associated practices to remedy or
abate agricultural nonpoint source pollution. The field sheets are arranged in matrix format with environmental
indicators given for sediment, animal wastes, nutrients, pesticides, and salts. Each indicator is divided into
descriptions of the environment from excellent to poor, and each description is given a weighted numerical
ranking. The user matches the individual description with what is observed in the water or on the land. By
totaling the individual rankings, a score is obtained indicating the potential for agricultural nonpoint source
problems. Practices can be selected from the list to alleviate problem situations. (From document preface.)
3. The Nonpoint Source Manager's Guide to Water Quality and Land Treatment Monitoring. Coffey,
Spooner, and Smolen 1993.
REFERENCE: Coffey, S.W., J. Spooner, and M.D. Smolen. 1993. The Nonpoint Source Manager's Guide
to Land Treatment and Water Quality Monitoring. NCSU Water Quality Group, Department of Biological and
Agricultural Engineering, North Carolina State University, Raleigh, North Carolina.
CONTENTS: Overview of monitoring program; Management objectives and problem development;
'Monitoring program objectives; Monitoring program design; Data collection; Data analysis; Program
evaluation.
MAIN FOCUS: Specifically addresses monitoring to track land treatments. Distinguishes between two levels
of monitoring: Level I—basic, low-cost monitoring, and Level II—more intensive and comprehensive
monitoring, at higher cost. Water quality and land treatment monitoring parameters are discussed separately for.
each level of monitoring.
This guide discusses the objectives and design of monitoring program used to evaluate present conditions, identify
water quality problems, detect trends and impacts, and document water quality improvements associated with the
implementation of land treatments. Variations in monitoring program objectives and designs to make them
appropriate for streams, lakes, wetlands, and reservoirs are indicated. Two levels of detail, applicable to different
monitoring objectives, are discussed separately for each of three types of monitoring programs: land use and land
treatment monitoring, water quality monitoring, and loading rate monitoring. For each level of detail and each
type of monitoring, appropriate biological, habitat, physical, and chemical variables are discussed.
A chapter on the design of monitoring programs emphasizes the importance of monitoring both land treatments
and water quality to provide feedback on the impact of land management activities on water quality. Guidance on
the use of existing data in monitoring program design is provided, and estimated costs for some procedures are
included to help water quality managers estimate monitoring program budgets. Only brief discussions of data
cbllection and analysis and program evaluation are presented. While this guidance primarily addresses
agricultural land treatments, it is equally applicable to designing and implementing programs to monitor the effects
of other types of land treatments, or general status and trend monitoring programs.
-------
Appendix A
4. Coordinated Nonpoint Source Water Quality Monitoring Program for Idaho. Idaho Department of Health
and Welfare 1990.
REFERENCE: Clark, W.H. 1990. Coordinated nonpoint source water quality monitoring program for Idaho. Idaho
Department of Health and Welfare, Division of Environmental Quality, Boise, Idaho. January.
CONTENTS: Introduction; Program development; Interagency monitoring responsibilities; Surface water quality
program: Monitoring program implementation; Summary. Appendices (partial listing): Idaho monitoring sampling site
listing, suggested monitoring parameters and protocols, and a monitoring plan checklist.
MAIN FOCUS: Statewide, integrated nonpoint source monitoring program for Idaho, addressing monitoring for
agriculture, forestry, and mining.
The document serves as a guide for a coordinated statewide monitoring program established in Idaho. It describes
basin and watershed trend monitoring, beneficial use monitoring, and best management practice effectiveness
monitoring. The three main nonpoint source activities that occur in Idaho—agriculture, forestry, and mining—are
addressed by the statewide monitoring program and in the guide. For each of these activities, there is an
introduction and objectives section, a description of the current program, and a description of the recommended
program. The monitoring program described in the guide addresses trends in major river basins and watersheds,
beneficial use support status, and best management practice effectiveness. The guide also includes a listing of
appropriate variables and protocols and a checklist of major items to be included in a nonpoint source water quality
monitoring plan. [From document abstract]
5. Water Quality Monitoring for the Clean Water Partnership. A Guidance Document. Clean Water
Partnership 1989.
REFERENCE: Clean Water Partnership. 1989. Water Quality Monitoring for the Clean Water Partnership.
A Guidance Document. Minnesota Pollution Control Agency, Division of Water Quality. March.
CONTENTS: Purpose; Steps in developing a monitoring plan; Water quality parameters; Sampling site
selection; Sampling strategies; Hydrology; Quality assurance/quality control.
MAIN FOCUS: Brief discussion of each monitoring parameter covering reason for its measurement,
considerations for measuring the parameter, and sampling and analysis techniques.
This guidance covers all of the technical steps hi developing and implementing monitoring programs for the
evaluation of the effect of projects on water quality. Brief descriptions of each of the parameters to be monitored
constitute most of the document. The monitoring program described is one that meets the requirements for Clean
Water Partnership projects.
-------
6. Evaluating the Effectiveness of Forestry Best Management Practices in Meeting Water Quality Goals or
Standards. Dissmeyer 1994.
REFERENCE: Dissmeyer, G.E. 1994. Evaluating the Effectiveness of Forestry Best Management Practices
in Meeting Water Quality Goals or Standards. USDA Forest Service, Southern Region, Atlanta, Georgia.
Miscellaneous Publication 1520. June.
CONTENTS: Planning monitoring projects; Quality assurance/quality control; Statistical considerations;
Selecting the appropriate BMP effectiveness monitoring level; Monitoring methods; Deciding on BMP
effectiveness: Some case histories.
MAIN FOCUS: Discussion of four levels of monitoring for different monitoring program objectives. Focuses
on monitoring program design for determination of BMP effectiveness, management activity water quality
impact, choice of BMPs for best water quality protection, and longevity of BMP effectiveness. Discussions of
numerous monitoring protocols, with references to detailed descriptions for further information.
This manual is intended to assist managers and staff in developing water quality monitoring plans to evaluate the
effectiveness of forestry BMPs in meeting water quality goals or standards for streams, including chemical,
physical, biological, and habitat integrity. The focus is on monitoring project design and parameters and methods
selection. The methods discussed in the document are appropriate for fourth order streams and smaller.
Monitoring project design is discussed with the intention of separating the NFS impact of management activities
from other NFS impacts. The document also addresses assessments of how long BMPs remain effective and
which BMPs best protect water quality. Effectiveness monitoring is separated into four levels, depending on
program needs. Monitoring methods for chemical, physical, biological, and habitat integrity are recommended to
achieve each of the four monitoring levels. Generally, the reader is referred to the original literature or to other
documents for procedural details of the monitoring methods, and this guide serves as a manual for monitoring
program design.
7.
Designing Effective Nonpoint Source Water Quality Monitoring Programs. Maas 1989.
REFERENCE: Maas, R.P. 1989. Designing Effective Nonpoint Source Water Quality Monitoring Programs.
Prepared for the Tennessee Valley Authority. November.
CONTENTS: I. General guidance for developing nonpoint source monitoring programs: Background;
Purposes and objectives of water quality monitoring in NPS projects; Source inventories; Water sampling in
NFS control projects; NPS water quality monitoring designs; Biological monitoring; Using preliminary data to
get the most from your water quality monitoring program; Determining the minimum detectable change in water
quality required to be observable from an NPS water quality monitoring program; Matching purposes and
design of nonpoint source water quality monitoring programs; II. Case study: Oostanaula Creek, Tennessee!
MAIN FOCUS: Focuses primarily on monitoring program design for documenting the benefit of NPS control
programs; Provides details (i.e., monitoring program design, statistical analyses) about time-trend and paired
watershed designs; Includes a case study of implementing a cost-effective NPS monitoring program.
The purpose of this document is to provide guidance to those involved in NPS pollution control in designing and
carrying out successful and cost-efficient water quality monitoring programs. It is intended as a technical
-------
Appendix A
document to provide NFS monitoring professionals with the necessary resource materials to make informed
decisions regarding the design of water quality monitoring programs. It should also provide NFS program
managers with sufficient background to evaluate technical decisions on NFS monitoring strategies for their
consistency with an overall purpose and their probability of documenting water quality improvement. This
document focuses primarily on experience gamed through state and federal agricultural and urban NFS projects,
such as the Model Implementation Program, initiated in 1978, and the Rural Clean Water Program, initiated in
1980, (from author's preface)
A particularly useful application of NFS monitoring information is to document the beneficial effect of an NFS
control program on water quality. This, however, can be a particularly difficult monitoring objective to fulfill.
This document examines monitoring design characteristics that maximize the ability to achieve this objective.
8.
Volunteer Estuary Monitoring: A Methods Manual. USEPA 1993.
REFERENCE: USEPA. 1993. Volunteer Estuary Monitoring: A Methods Manual. U.S. Environmental
Protection Agency, Office of Water, Washington, DC. EPA 842-B-93-004. December.
CONTENTS: Our troubled estuaries; Setting the stage; Monitoring dissolved oxygen; Monitoring nutrients and
phytoplankton; Monitoring submerged aquatic vegetation; Monitoring bacteria; Monitoring other estuarine
conditions; Training volunteers; Presenting monitoring results; Appendices.
MAIN FOCUS: Clearly written, intended to serve a lay audience. Step-by-step descriptions of common
estuary monitoring methods.
This manual is meant to be a companion to three other EPA documents: Volunteer Water Monitoring: A Guide
for State Managers, Volunteer Lake Monitoring: A Methods Manual, and. Volunteer Stream Monitoring: A
Methods Manual. The manual reviews those water quality parameters considered most important to monitor to
determine an estuary's water quality: dissolved oxygen, bacteria, nutrients, phytoplankton, and submerged ,
aquatic vegetation. Each chapter discusses why it is important to monitor the particular parameter, the role of the
parameter in estuarine ecology, and sampling equipment for taking measurements of the parameter. Methods for
sampling the parameter are set out in easy-to-follow steps. Two introductory chapters discuss the state of the
Nation's estuaries, basic estuarine ecology, basic monitoring equipment, and gross conditions to note while
monitoring, such as temperature and shoreline condition. Other chapters discuss volunteer training, the
importance of credible data, and data presentation techniques. Supply houses are listed in one appendix, and
quality assurance is addressed in another.
9.
Volunteer Water Monitoring: A Guide for State Managers. USEPA 1990.
This guide is directed toward those contemplating setting up a volunteer monitoring program. Nearly every aspect
of creating a successful monitoring program is discussed, including attracting volunteers, keeping volunteers
interested and motivated, finding funding sources, quality assurance and quality control, and ensuring that the
collected data are put to use. The techniques and methods of monitoring are discussed in other manuals (e.g.,
Volunteer Lake Monitoring: A Methods Manual).
Descriptions of five state volunteer monitoring programs are provided: Illinois, Kentucky, New York, Ohio, and
the Chesapeake Bay. Each discussion provides details of program objectives, how volunteers were recruited and
trained, sampling protocols, data management, program administration, volunteer recognition, and expenses and
funding. State contact names, addresses, and phone numbers are provided for each program.
-------
REFERENCE: USEPA. 1990. Volunteer Water Monitoring: A Guide for State Managers. U.S.
Environmental Protection Agency, Office of Water, Washington, DC. EPA 440/4-90-010.
CONTENTS: Volunteers in water monitoring; Planning a volunteer monitoring program; Implementing a
volunteer monitoring program; Providing credible information; Costs and funding. Appendix: Descriptions of
five successful programs.
MAIN FOCUS: A guide to the administrative details of a volunteer monitoring program, including training
volunteers, funding, program design, and media relationships. Presentations of volunteer monitoring programs
in the appendix provide good examples of program implementation to meet differing needs and to deal with
different local and state requirements. State contacts are provided.
10.
Tlie Volunteer Monitor. Rhode Island Sea Grant.
REFERENCE: The Volunteer Monitor. Published by the Coastal Resources Center, the University of Rhode Island,
Rhode Island Sea Grant Program, Narragansett, Rhode Island.
CONTENTS: Example issue content: Special topic: biological monitoring; Monitoring groups need a national
association; EPA Lakes Methods manual; Wetlands field guide; Lake monitoring network; Technical tips; Doing your
own lab analysis for fecal coliform; Monitoring aquatic plants; Monitoring diseased eelgrass; Local bank sponsors
River Rescue; Third National Volunteer Monitoring Conference set.
MAIN FOCUS: Typically provides information on monitoring programs throughout the country, new publications,
materials and technical methods, contacts for equipment supplies, school programs, and information on monitoring
particular plants or animals.
The Volunteer Monitor is a newsletter published by Rhode Island Sea Grant that is devoted entirely to topics
related to volunteer monitoring. Issues contain articles on a diversity of topics, including chemical, physical, and
biological monitoring, reviews of documents related to monitoring, EPA activities, monitoring conferences,
monitoring associations, equipment and methods, data collection and analysis, public education, and technology
transfer. Information on purchasing monitoring supplies and articles of special interest, such as on methods to
monitor particular species or habitats, are regularly published.
11. The Strategy for Improving Water-Quality Monitoring in the United States. Intergovernmental Task
Force on Monitoring Water Quality 1995.
The Intergovernmental Task Force on Monitoring Water Quality was established to address a need for greater
coordination among agencies collecting water-quality information to make that information more meaningful to all
users. This is the final report of the ITFM. It describes the strategy of the ITFM for nationwide water quality
monitoring and its implementation. Highlights of the strategy and recommendations, discussed in detail in the
final report and technical appendixes, include a focus on goal-oriented monitoring and the use of indicators;
gathering and using existing information; ensuring institutional collaboration; striving for methods comparability;
improving information automation, accessibility, and utility; adhering to quality assurance and quality control
protocols; evaluating monitoring activities; identifying needs for research and development; promotion of training;
and provision of adequate funding.
-------
Appendix A
REFERENCE: ITFM. 1995. "Die Strategy for Improving Water-Quality Monitoring in the United States.
Final report of the Intergovernmental Task Force on Monitoring Water Quality. Intergovernmental Task Force
on Monitoring Water Quality. February.
CONTENTS: Major conclusions and recommendations; Summary; General intent; Background; Water quality
questions; Nationwide strategy for improving water quality monitoring; Implementation; Initial agency actions
to improve monitoring; Conclusion.
MAIN FOCUS: Recommends a strategy for nationwide water quality monitoring and technical monitoring
improvements to support sound water quality decision-making at all levels of government and in the private
sector.
12. The Strategy for Improving Water-Quality Monitoring in the United States. Technical Appendixes.
Intergovernmental Task Force on Monitoring Water Quality 1995.
This companion document to the ITFM 1995 final report provides detailed discussions of the more technical
aspects of the national monitoring program. In addition, general information on monitoring programs, such as
program objectives and design, are discussed in the context of the national program. Numerous tables related to
the technical topics discussed consolidate much information contained in many other technical monitoring
documents in a single place.
REFERENCE: ITFM. 1995. The Strategy for Improving Water-Quality Monitoring in the United States.
Teclmical Appendixes. Final report of the Intergovernmental Task Force on Monitoring Water Quality.
Intergovernmental Task Force on Monitoring Water Quality. February.
CONTENTS: Glossary; Framework for a water-quality monitoring program; Terms of reference—National
Water-quality Monitoring Council; Indicators for meeting management objectives—summary and rationale;
Indicator selection criteria; Ecoregions, reference conditions, and index calibration; Multimetric approach for
describing ecological conditions; Terms of reference—Interagency Methods and Data Comparability Board;
Data comparability and performance-based methods policy paper—comparability of data-collection methods;
Target audiences, monitoring objectives, and format considerations for reporting water-quality information;
Annotated bibliography of selected outstanding water-quality reports; Ground water quality monitoring
framework; Date elements glossary; Evaluation of a performance-based methods system approach to field and
prelaboratory methods; Performance-based methods system for biological collection methods—A framework for
examining method comparability.
MAIN FOCUS: Discussions of numerous technical points related to the national monitoring program,
including indicators, ecoregions, and multimetrics. Provides useful charts and graphs related to these topics.
-------
13. Guidance for State Water Monitoring and Wasteload Allocation Programs. USEPA 1985.
REFERENCE: U.S. EPA. 1985. Guidance for State Water Monitoring and Wasteload Allocation Programs.
U.S. Environmental Protection Agency, Office of Water Regulations and Standards, Washington, DC. EPA
440/4-85-031. October.
CONTENTS: Overview of water quality program monitoring; Monitoring for water quality-based controls;
Monitoring for compliance and enforcement; Water quality assessments; Quality assurance; Data reporting;
Total maximum daily loads and wasteload allocations.
MAIN FOCUS: Provides a general overview of state water quality monitoring responsibilities under the Clean
Water Act (pre-1987).
This guidance is oriented toward program management and does not contain extensive technical information on
monitoring programs. The guidance is intended to be used by states and EPA regional offices for developing the
monitoring and wasteload allocation portions of annual state 106 and 205(j) work programs. Monitoring and
wasteload allocation activities are defined in accordance with EPA regulations. Two principal areas are covered
by the guidance. The first is an outline of the objectives of the water monitoring program, and the second is a
description of the process for calculating total maximum daily loads (TMDLs), waste load allocations for point
sources, and load allocations for nonpoint sources of pollution.
Separate chapters of the guidance address different aspects of state programs (conducting water quality
assessments, developing water quality-based controls, and assessing compliance), and for each of these program
aspects the guidance discusses the types of data needed, who is responsible for doing the work, the methods to be
used, data reporting requirements, parameters, and uses of the data. References to technical guidance and sample
program checklists are provided in an appendix.
14. Guidelines for Evaluation of Agricultural Nonpoint Source Water Quality Projects. USEPA 1981.
REFERENCE: USEPA. 1981. Guidelines for Evaluation of Agricultural Nonpoint Source Water Quality
Projects. U.S. Environmental Protection Agency. L.R. Shuyler, EPA project officer. Prepared under the
Rural Nonpoint Source Control Water Quality Evaluation and Technical Assistance project.
CONTENTS: Evaluation procedures for nonpoint source control measures; Evaluation and sampling needs;
Streams; Lakes; Ground water; Socioeconomic evaluation.
MAIN FOCUS: The sections on streams and lakes contain an assessment of evaluation alternatives and specific
monitoring recommendations relevant to the type of receiving water, including guidance for identification of
impacted beneficial uses. The important characteristics of each receiving water type that should be monitored
are discussed.
This guidance was developed under EPA's Rural Nonpoint Source Control Water Quality Evaluation and
Technical Assistance project as a joint effort of EPA and USDA. It is intended to provide basic guidelines for
measuring water quality changes and estimating socioeconomic impacts resulting from NFS pollution control
programs. Monitoring requirements for a basic, level I monitoring program are discussed, as well as more
-------
Appendix A
intensive requirements for a detailed monitoring program. The guidance provided in this document is basic in the
sense that the parameters mat should be measured for minimum level I and level II assessments are mentioned and
discussed briefly, but no guidance for taking measurements, identifying monitoring stations, designing a
monitoring program, or analyzing results is provided. Emphasis is placed on the collection of historical data prior
to monitoring and on monitoring physical, chemical, and biological characteristics that will indicate present
condition and changes in those characteristics due to BMP implementation.
A separate section on socioeconomic evaluation of NFS pollution control programs is provided. Questions of
impacts on farmer income, overall project value with respect to alternative community needs, and choices of
alternative environmental projects are mentioned as important considerations prior to the implementation of an
NFS pollution control project. Two levels of socioeconomic evaluations are discussed. A level I evaluation
considers the effects of an NFS pollution control project on land use, crop production, income, pollution control
effectiveness, and project efficiency hi a cost/benefit sense. A level II evaluation includes the elements of a level
I evaluation as well as an estimation of community and off-farm impacts and an evaluation of alternative options.
Tables at the end of the paper summarize the recommendations presented in the text.
A.3.2 Guidances for Stream and River Monitoring
15. Monitoring Guidelines to Evaluate Effects of Forestry Activities on Streams in the Pacific Northwest and
Alaska. MacDonald, Smart, and Wissmar 1991.
REFERENCE: MacDonald, L.H., A.W. Smart, and R.C. Wissmar. 1991. Monitoring Guidelines to
Evaluate Effects of Forestry Activities on Streams in the Pacific Northwest and Alaska. U.S. Environmental
Protection Agency, Region 10, Water Division, Seattle, Washington. EPA/910/9-91-001. May.
CONTENTS: Part I: Context and structure of monitoring projects; Statistical considerations in water quality
monitoring; Principles of developing a monitoring plan and selecting the monitoring parameters; parameter
recommendations and interactions. Part II: Physical and chemical constituents; Changes in flow; sediment;
Channel characteristics; Riparian monitoring; Aquatic organisms.
MAIN FOCUS: Provides a good review of the importance of proper statistical design in a monitoring
program. Tabulates monitoring parameters according to their usefulness for monitoring different land
treatments. Specific to monitoring stream conditions in the Pacific Northwest and Alaska.
The focus of these guidelines is on monitoring water quality in streams, and it does not directly address
monitoring in lakes, reservoirs, or other downstream areas. The discussions are also limited to conditions in the
Pacific Northwest and Alaska, which reduces the scope of conditions and activities considered in the document. •
However, the information provided on monitoring objectives and monitoring parameters is generally applicable.
Information on monitoring to detect the water quality impacts of grazing, mining, and recreation is also provided
because these activities occur on or near lands where forestry activities are conducted and it can be difficult to
separate the water quality impacts of these activities from those of forestry operations.
The document has two parts. Part 1 discusses seven purposes of monitoring, legal requirements for NFS pollution
monitoring, statistical considerations in water quality monitoring, monitoring plan development, and the selection
of monitoring parameters. Monitoring parameters are recommended for different forestry-related activities (e.g.,
forest harvest, road construction). Part 2 is a comprehensive discussion of individual monitoring parameters and
is intended to facilitate the selection of the most appropriate monitoring parameters for specific monitoring
-------
objectives. This is not a technical guidance on sampling procedures or statistical analyses used for monitoring
programs, but rather a comprehensive discussion of the various elements of a monitoring program to help water
resource or forestry operations managers make informed decisions concerning monitoring programs. References
are provided to direct the reader to the appropriate technical guidance where needed.
16. Monitoring Protocols to Evaluate Water Quality Effects of Grazing Management of Western Rangeland
Streams. USEPA 1993.
REFERENCE: USEPA. 1993. Monitoring Protocols to Evaluate Water Quality Effects of Grazing
Management of Western Rangeland Streams. U.S. Environmental Protection Agency, Region 10, Water
Division, Surface Water Branch. EPA 910/R-93-017. October.
CONTENTS: Impacts of grazing on water quality and beneficial uses; Monitoring plan procedure;
Stratification, reconnaissance, and classification of rangeland riverine riparian areas;
Evaluation/recommendation of monitoring methods: Stream temperature and shade; Nutrients; Bacterial
indicators; Stream channel morphology; Streambank stability; Substrate fine sediment; Pool quality; Streamside
vegetation.
MAIN FOCUS: Describes a methodology to classify a stream and riparian vegetation prior to selecting
monitoring sites. Provides tables of monitoring parameter attributes (sampling frequency, collection time
necessary, equipment requirements, lab costs, level of expertise needed). Detailed information on
recommended methods is provided.
This document describes a monitoring system to assess the impacts of grazing on water quality in streams of the
western United States and protocols used to assess changes in water quality that result from stream restoration
projects. Protocols that are easy to use and cost-effective (i.e., have reduced sampling frequency, minimized need
for specialized equipment, and a reduced requirement for laboratory analyses) were selected. The document
focuses on monitoring attributes of the stream channel, stream bank, and streamside vegetation that are important
to the support of aquatic life and that are impacted by grazing. A discussion of the impacts of grazing on stream
ecosystems provides a basis for selecting monitoring parameters, and a procedure to develop a monitoring plan is
recommended. Methods to stratify and classify stream reaches based on stream type, dominant soils, and riparian
vegetation communities are described, and they provide a basis for selecting monitoring sites and reference areas.
Forms for recording data and a list of equipment needed for each protocol are provided.
Monitoring methods that are commonly used to assess the effects of grazing on water quality are described, and
the attributes of each are tabulated. The advantages and disadvantages of the methods are described and tabulated
for ease of comparison, and recommendations for specific protocols are made based on ease of use and cost-
effectiveness. These recommended protocols, including data collection and analysis procedures, are then
thoroughly described. The recommended protocols are stream temperature and shade, nutrients, bacterial
indicators, stream channel morphology, streambank stability, substrate fine sediment, pool quality, and streamside
vegetation. A list of references pertaining to each protocol is provided at the end of the discussion of each one.
The usefulness of the recommended protocols is not limited to monitoring the impacts of grazing, and the reader
should find the discussions of the protocols valuable if the same protocols are being considered for monitoring the
water quality effects of other land use activities. Other references should be consulted for specific guidance on
sampling and statistical analysis of data.
-------
Appendix.A
17. Rapid Bioassessment Protocols for Use in Streams and Rivers: Benthic Macroinvertebrates and Fish.
USEPA 1989.
REFERENCE: U.S. EPA. 1989. Rapid Bioassessment Protocols for Use in Streams and Rivers: Benthic
Macroinvertebrates and Fish. U.S. Environmental Protection Agency, Office of Water, Washington, DC.
EPA/440/4-89/001. May.
CONTENTS: The concept of biomonitoring; Overview of protocols and summary of components; Quality
assurance/quality control; Habitat assessment and physicochemical parameters; Benthic macroinvertebrate
btosurvey and data analysis; Fish biosurvey and data analysis; Integration of habitat, water quality, and
biosurvey data. Appendices: Guidance for use of field and laboratory data sheets; Rapid bioassessment
subsampling methods for benthic protocols; Family and species-level macroinvertebrate tolerance classifications;
Tolerance, trophic guilds, and origins of selected fish species.
MAIN FOCUS: Provides both an introduction to the concept of biomonitoring and detailed methods sections
for conducting rapid bioassessment protocols. Sample data sheets for all protocols are provided, and data
analysis and interpretation is thoroughly discussed.
This is a comprehensive technical reference for bioassessment procedures for streams and rivers. Three
macroinvertebrate protocols and two fish protocols are presented. The macroinvertebrate protocols were tested in
wadable freshwater streams, though they should be applicable to large freshwater rivers as well. They were
developed by consolidating various procedures hi use by a variety of state water quality agencies, and they
require levels of effort ranging from fairly simple to rigorous. The fish protocols were validated in freshwater
streams and large rivers and are therefore equally applicable to both. They were developed based on previous
work by other researchers and on standard fish population assessment models.
The document contains an introduction to the biomonitoring approach of detecting aquatic life impairments and
estimating their severity. Procedures for assessing the habitat where the sampling is done are explained, and the
physical and chemical parameters relevant to biological survey data interpretation are discussed. Complete
instructions for conducting benthic biosurveys and fish surveys are provided, laboratory methods and data analysis
techniques are explained, and quality assurance and quality control are addressed. Field data forms and guidance
for their use are provided.
18. An Improved Biotic Index of Organic Stream Pollution. Hilsenhoff 1987.
REFERENCE: Hilsenhoff, W.L. 1987. An Improved Biotic Index of Organic Stream Pollution. Great Lakes
Entomologist 20:31-39.
CONTENTS: Abstract; Reassignment of tolerance values; Identification; Collection and evaluation of samples;
Literature cited; Appendix 1: Tolerance values for stream arthropods.
MAIN FOCUS: Provides a discussion of identification problems specific to insect orders. Includes a detailed
discussion of proper stream arthropod collection techniques. An appendix provides revised tolerance values for
stream arthropods.
-------
Hilsenhoff introduced a biotic index for evaluating the water quality of streams in 1977 and offers improvements
to it in this paper. The initial index assigned tolerance values of 0-5 to species, but after evaluation of over 1,000
samples the index has been revised to accommodate tolerance values of 0-10 to provide greater precision.
Tolerance values of species are provided in an appendix. Some orders of arthropods are difficult to identify, and
the problems associated with those orders are discussed. A revised procedure for collecting, sorting, and
evaluating samples using the biotic index presented in the paper is provided.
19. Using the Index of Biotic Integrity (IBI) to Measure Environmental Quality in Warmwater Streams of
Wisconsin. Lyons 1992.
REFERENCE: Lyons, J. 1992. Using the Index of Biotic Integrity (IBI) to Measure Environmental Quality in
Warmwater Streams of Wisconsin. U.S. Department of Agriculture, Forest Service, North Central Forest
Experiment Station. General Technical Report NC-149.
CONTENTS: General considerations; Applying the IBI in Wisconsin warmwater streams: Collecting and
processing the field data; Analyzing the data; Interpreting IBI scores.
MAIN FOCUS: Complete discussion of data analysis. Provides Maximum Species Richness plots for data
interpretation. Applicable to similar streams in nearby states.
This paper summarizes the results of a 4-year fish collection and data analysis effort aimed at developing a version
of the IBI for warmwater Wisconsin streams. The paper is designed primarily as a "how to" manual and
therefore contains little discussion of the principles of the IBI. Discussion focuses on collection of fish samples
for analysis, and analysis and interpretation of the data. Maximum Species Richness (MSR) plots for data
interpretation are provided.
Because of the similarity in stream characteristics and fish fauna between Wisconsin and parts of adjacent states,
the Wisconsin version of the IBI described in this paper should be useful in southeastern and northeastern
Minnesota, the entire Upper Peninsula and the nordiern Lower Peninsula of Michigan, extreme northwestern
Illinois, and extreme northeastern Iowa.
20. Evaluation Monitoring of Stream Habitat During Priority Watershed Projects. Simonson and Lyons
1992.
REFERENCE: Simonson, T., and J. Lyons. 1992a. Evaluation Monitoring of Stream Habitat During
Priority Watershed Projects. Wisconsin Department of Natural Resources. May.
CONTENTS: Station summary data sheet; Station map data sheet; Station flow data sheet; Transect data sheet;
Appendix: Gear used for habitat sampling.
MAIN FOCUS: Each data element to be recorded is explained separately. Data recording forms are provided.
This paper describes all of the data elements to be recorded during evaluation phase monitoring of fish habitat for
WDNR Priority Watershed Projects. The purpose of the monitoring is to document changes in fish communities
and fish habitat that occur in streams where improved land use practices are implemented to reduce NPS
pollution. The level of detail given for each data element make this paper a manual for conducting an evaluation,
-------
Appendix A
not just a discussion of data elements. Two sets of sample data entry sheets are provided, one blank and one filled
out with example data. The gear needed to conduct an evaluation is described in an appendix, and the names and
addresses of suppliers are provided.
21. 1996 Water Body Assessment Guidance: A Stream to Standards Process. Idaho Division of
Environmental Quality 1996.
REFERENCE: Idaho Division of Environmental Quality. 1996. 1996 water body assessment guidance: A stream to
Standards process. Idaho Department of Health and Welfare, Division of Environmental Quality, Boise Idaho. August.
CONTENTS: Introduction; Water body initialization; Aquatic life beneficial uses status determinations; Recreational
beneficial uses status determinations; Water supply beneficial uses status determinations; Wildlife habitat and aesthetics
beneficial uses status determinations; Idaho water quality standards and wastewater treatment requirements narrative
criteria; Criterion evaluation process; Beneficial use attainability; Beneficial uses status determinations appeals process;
Listing water quality-limited water bodies. Appendices (partial listing): Macroinvertebrate biotic index; Habitat index;
Reconnaissance index of biotic integrity; Index of biotic integrity; Algal biotic index; Water body assessment form; Water
body assessment guidance assumptions.
MAIN FOCUS: An analytical tool for determining if a water body is or is not supporting a beneficial use.
The guidance presents the assessment method that Idaho uses to list water quality-limited water bodies and to
designate beneficial uses. The guidance incorporates a beneficial use attainability designation process for
unclassified waters. The method used by Idaho uses ecological indicators to make water quality assessments. The
document is easy to follow, owing to its flow chart type of organization. In this manner, an assessment is broken
down into many easily-answered questions.
22. 1996 Beneficial Use Reconnaissance Project Workplan. Idaho Division of Environmental Quality 1996.
REFERENCE: Idaho Division of Environmental Quality. 1996. 1996 Beneficial Use Reconnaissance Project
\vorkplan. Idaho Department of Health and Welfare, Division of Environmental Quality, Boise Idaho. May.
CONTENTS: Introduction: Beneficial uses, Purpose, Objectives, Scope, Rationale for stream selection; Methods:
Stream site selection, Core parameters, Pilot investigations to validate procedures, Rationale for parameter selection and
summary of procedures, Recommended procedure sequence for site evaluation; Quality assurance; Safety training and
certification; Data analyses and interpretation; Appendices: Streams proposed for monitoring in 1996, Field equipment
check list, Field forms. Audit forms.
MAIN FOCUS: Descriptive overview of IDDEQ's Beneficial Use Reconnaissance Project.
The workplan lists the parameters to monitor, with references for each method where detailed information on them
can be obtained, and monitoring level-of-intensity information for each parameter. The quality assurance (QA)
section summarizes the QA approach for the project and references QA manuals to be consulted for detailed
information. The workplan is modified annually to incorporate changes in methods and protocol. This workplan is
-------
to be used as a guide for training stream monitoring field crews. It describes the Beneficial Use Reconnaissance
Project, but is not a detailed "how-to" manual.
The protocols used by IDDEQ to measure water quality, beneficial use attainability, beneficial use status, and
general stream health are described in it. The protocols are meant to provide a reconnaissance level screen of
stream conditions, and as reconnaissance level protocols they are only intended to differentiate between impaired
and non-impaired streams. The protocols are meant to be applicable to any wadable stream regardless of location in
Idaho. Another objective of the protocols is to identify the principal measures that are likely to provide insight into
stream ecology, biology, and water quality, and to determine their relationships to beneficial uses. Time
constraints, staff limitations, and cost effectiveness were considered in selecting the protocols.
23. Handbook: Stream Sampling for Waste Load Allocation Applications. USEPA 1986.
REFERENCE: USEPA. 1986. Handbook:: Stream Sampling for Waste Load Allocation Applications. U.S.
Environmental Protection Agency, Office of Research and Development, Washington, DC. EPA/625/6-86/013.
September.
CONTENTS: Sampling requirements for wasteload allocation modeling; Sampling requirements for
conventional pollutants; Sampling requirements for toxic pollutants; Whole effluent approach; Example
application.
MAIN FOCUS: Summarizes data requirements for modeling applications and discusses the applicability and
advantages of numerous models. Flow charts for modeling approaches and parameter formulas are provided.
This report discusses sampling requirements in support of wasteload allocation studies in rivers and streams. Two
approaches to wasteload allocation are addressed: the chemical-specific approach and the whole effluent
approach. Numerical or analytical toxicant fate models are used to implement the chemical-specific approach.
Modeling requirements and sampling guidelines are delineated for this method.
For the whole effluent approach, the method is summarized and then instream dye study requirements are
presented. The report concludes with example applications of the chemical-specific approach for conventional and
toxic pollutants.
This guidance does not discuss equipment requirements, personnel requirements, sample collection, stream
characterization, or laboratory analytical techniques. The primary purpose of the document is to assist water
quality specialists in designing stream surveys to support modeling applications for wasteload applications. The
data collection process required to calibrate, verify, and apply models used for wasteload allocations to critical
design conditions is described.
24. Evaluation Monitoring of Stream Fish Communities During Priority Watershed Projects. Simonson and
Lyons 1992.
This paper, the companion to Simonson and Lyons' paper Evaluation Monitoring of Stream Habitat During
Priority Watershed Projects, describes the data elements to be recorded during evaluation phase monitoring of fish
communities for WDNR Priority Watershed Projects. This paper is similar in content to that paper, except that
only one set (blank) of data sheets is provided.
-------
Appendix A,
REFERENCE: Simonson, T., and J. Lyons. 1992b. Evaluation Monitoring of Stream Fish Communities
During Priority Watershed Projects. Wisconsin Department of Natural Resources. May.
CONTENTS: Station summary data sheet; Catch summary data sheet; Individual fish data sheet.
MAIN FOCUS: Each data element to be recorded is explained separately. Data recording forms are provided.
25. Techniques for Detecting Effects of Urban and Rural Land-Use Practices on Stream-Water Chemistry in
Selected Watersheds in Texas, Minnesota, and Illinois. USGS 1993.
REFERENCE: Walker, J.F. 1993. Techniques for Detecting Effects of Urban and Rural Land-Use Practices
on Stream-Water Chemistry in Selected Watersheds in Texas, Minnesota, and Illinois. U.S. Geological Survey
and Wisconsin Department of Natural Resources. USGS Open-file Report 93-130.
CONTENTS: Techniques for detecting effects of land-use practices on water chemistry; Application of
techniques to selected watersheds; Summary and conclusions.
MAIN FOCUSt Statistical techniques for the detection of effects of land-use practices on water chemistry are
applied to selected watersheds. Alternative procedures for assessing the effects of land-use practices are
compared.
There is little information available about the effectiveness of best management practices at the watershed scale.
This report presents a discussion of several parametric and nonparametric statistical techniques for detecting
changes in water-chemistry data. The use of storm load data is discussed as an alternative to using fixed-
frequency instantaneous concentration data. Statistical techniques were applied to three urban watersheds in Texas
and Minnesota and three rural watersheds in Illinois. For the urban watersheds, single- and paired-site data
collection strategies were considered. For the rural watersheds, the selected techniques were found not to be
effective at identifying changes. The use of regressions improved the ability to detect changes, (from author's
abstract)
A.3.3 Guidances for Lake and Reservoir Monitoring
26. Technical Support Manual: Waterbody Surveys and Assessments for Conducting Use Attainability
Analyses. Volumes MIL USEPA 1983-1984.
These documents contain EPA guidance to assist states in implementing the revised Water Quality Standards
Regulation that appeared in the Federal Register in November 1983. Consideration of the suitability of a
waterbody for attaining a given use is an integral part of the water quality standards review and revision process.
This guidance is intended to assist states hi determining the uses currently being achieved, the potential uses of the
waterbodies, and the causes of any impairment of the uses. A framework for determining the attainable aquatic
protection use is described, and parameters to be used to make the determinations mentioned above are provided.
Methods and approaches that can be used by states for conducting use attainability analyses are discussed.
Volume I discusses rivers and streams, Volume II discusses estuarine systems, and Volume III discusses lake
systems.
-------
REFERENCE: USEPA. 1983. Technical Support Manual: Waterbody Surveys and Assessments for
Conducting Use Attainability Analyses. Volume I. U.S. Environmental Protection Agency, Office of Water,
Washington, DC. November.
USEPA. 1984. Technical Support Manual: Waterbody Surveys and Assessments for Conducting Use
Attainability Analyses. Volume II: Estuarine systems. U.S. Environmental Protection Agency, Office of
Water, Washington, DC.
USEPA. 1984. Technical Support Manual: Waterbody Surveys and Assessments for Conducting Use
Attainability Analyses. Volume III: Lake systems. U.S. Environmental Protection Agency, Office of Water,
Washington, DC. November.
CONTENTS: Volume I: Physical evaluations; Chemical evaluations; Biological evaluations; Interpretation.
Volume II: Physical and chemical characteristics; Characteristics of plant and animal communities; Synthesis
and interpretation. Volume III: Physical and chemical characteristics; Biological characteristics; Synthesis and
interpretation.
MAIN FOCUS: A general discussion of conducting physical, chemical, and biological analyses of river and
stream, estuarine, and lake systems.
27. Volunteer Lake Monitoring: A Methods Manual. USEPA 1991.
REFERENCE: USEPA. 1991. Volunteer Lake Monitoring: A Methods Manual. U.S. Environmental
Protection Agency, Office of Water, Washington, DC. EPA 440/4-91-002. December.
CONTENTS: Focusing on a lake condition; Monitoring algae; Monitoring aquatic plants; Monitoring
dissolved oxygen; Monitoring other lake conditions; Training citizen volunteers; Presenting monitoring results.
Appendix: Scientific supply houses.
MAIN FOCUS: Specifically addresses lake monitoring. Provides step-by-step instructions for setting up and
implementing a volunteer lake monitoring program. Sample data collection forms are provided, and a
discussion on lake ecology provides background for volunteer monitors. Well-illustrated.
This manual presents specific information on volunteer lake water quality monitoring methods. It is intended for
organizers of volunteer lake monitoring programs and for the volunteers who actually sample lake conditions. It
summarizes the steps necessary to plan and manage a volunteer monitoring program, including setting general
goals, identifying the uses and users of collected data, and establishing sound quality assurance procedures. The
document concentrates special attention on three of the most common lake pollution problems: increased algal
growth, increased growth of rooted aquatic plants, and lowered or fluctuating levels of dissolved oxygen. It also
briefly discusses other lake pollution problems: sedimentation, turbidity, lake acidification, and bacteriological,
conditions. Appropriate parameters to monitor and specific steps for each selected monitoring method are
identified, and example sampling forms are provided.
-------
Appendix A
28. Statistical Methods for the Analysis of Lake Water Quality Trends. Reckhow, Kepford, and Hicks 1993.
REFERENCE: Reckhow, K.H., K. Kepford, and W.W. Hicks. 1993. Statistical Methods for the Analysis of Lake
Water Quality Trends. U.S. Environmental Protection Agency, Office of Water, Washington, DC. EPA 841-R-93-
003. December.
CONTENTS: Basic statistics and statistical concepts—Descriptive statistics; Robustness, resistance, and influence;
Hypothesis testing; Statistical methods for trend detection. Individual lake analysis—Summary statistics; Graphical
analyses; Normality; Seasonality; Independence; Trend detection in total phosphorus; Total nitrogen; Conclusions;
Regional and statewide lake analysis. Appendices—Basic descriptive statistics; Introduction to SAS macros; SAS
tables; List of SAS programs and files on disk.
MAIN FOCUS: Guides lake program managers on the application and interpretation of methods of trend detection in
lake water quality. Provides step-by-step descriptions of statistical methods, as well as graphs and figures
demonstrating analyses.
This manual is a technical supplement to The Lake and Reservoir Restoration Guidance Manual. This document
and the accompanying software in the SAS system present nonparametric statistical methods for trend assessment
in water quality, emphasizing lakes. The purpose of the document is to provide lake program managers with
guidance on the application and interpretation of methods for the detection of trends in lake water .quality. This
manual discusses basic statistical concepts and approaches hi applied statistics that are relevant to trend detection,
before introducing the procedures and tests. Numerous graphs and figures demonstrate analyses done with actual
data.
29. Monitoring Lake and Reservoir Restoration. USEPA 1990.
REFERENCE: USEPA. 1990. Monitoring Lake and Reservoir Restoration. U.S. Environmental Protection
Agency, Office of Water, Washington, DC. EPA 440/4-90-007. August.
CONTENTS: Planning die monitoring program; Monitoring methods; Watershed monitoring; In-lake
restoration techniques and monitoring; A long-term monitoring protocol; Case study: Detection of trends and
sampling strategy evaluations.
MAIN FOCUS: Three levels of watershed monitoring are discussed. Complete discussions of lake and
reservoir restoration objectives and the methods used to achieve the objectives are provided.
This manual is a technical supplement to Tfie Lake and Reservoir Restoration Guidance Manual (USEPA, 1988).
It provides guidance for both design and implementation of a monitoring program by outlining specific standards
for specific types of lake restoration and protection projects. It is intended to guide monitoring carried out under
the Clean Lakes Program hi connection with the Phase II or implementation portion of a lake restoration project.
Phase I or diagnostic/feasibility monitoring is more exploratory in nature and more generic in terms of parameters
used, and it is not directly addressed in this guidance. The primary users of this guidance are expected to be
Regional EPA Clean Lakes project officers, state and local project managers, and project sponsors and
consultants.
-------
Procedures for performing in-lake sampling, measuring stream flow, handling and preserving samples, and
analyzing data are described. Three levels of watershed monitoring are described: watershed inventories, limited
stream monitoring, and comprehensive watershed monitoring. Control techniques for four lake restoration
objectives are described. The four objectives are the control of nuisance algae, an increase in depth, the control
of nuisance plants, and the mitigation of acidic conditions. Where appropriate, monitoring parameters for both
during and after the treatments are tabulated and discussed. Formulas for the calculation of monitoring
parameters are provided. Guidance is also provided for implementing a long-term monitoring protocol.
A,3.4 Guidances for Watershed Monitoring
30- Monitoring Primer for Range Watersheds. Bedill and Buckhouse Draft.
REFERENCE: Not available.
CONTENTS: Basis and rationale for monitoring in range watersheds; Water quality in the context of range
watersheds; Some new concepts to go with the current ones; Environmental factors and range watersheds;
Management of range watersheds which directly affects water quality; Monitoring methods and measurements.
MAIN FOCUS: Provides a good introduction to monitoring terms and concepts. Although the document is
directed toward range monitoring, the terms and concepts are generally applicable to all monitoring.
This short document provides a general introduction to monitoring in the context of rangeland management.
Watersheds are defined, their characteristics and functions are described, and the rationale behind their being the
unit for rangeland management and monitoring is discussed. The focus of the document is on monitoring riparian
areas and vegetation, not upland areas or streams. A general discussion of the concepts and steps involved in
designing and implementing a rangeland monitoring program is provided. Detailed information on designing a
monitoring program, selecting monitoring parameters and protocols, sampling procedures, and data analysis is not
provided.
31. Seminar Publication: The National Rural Clean Water Program Symposium. 10 Years of Controlling
Agricultural Nonpoint Source Pollution: The RCWP Experience. USEPA 1992.
REFERENCE: USEPA. 1992. Seminar Publication: The National Rural Clean Water Program Symposium.
10 Years of Controlling Agricultural Nonpoint Source Pollution: The RCWP Experience. September 13-17,
1992. U.S. Environmental Protection Agency, Office of Research and Development, Office of Water,
Washington, PC. EPA/625/R-92/006. August. ,,..,....
CONTENTS: Water quality and land treatment monitoring; Relating water quality to land treatment; Land
treatment and operation and maintenance of BMPs; Project coordination and farmer participation; Institutional
arrangements, program administration, and project spin-offs; Information and education; Socioeconomics,
technology transfer, lessons learned; Research needs and future vision; Additional information.
MAIN FOCUS: Reports of hands-on experiences encountered during nonpoint source and watershed project
implementation. Since the RCWP had projects in states from most regions of the United States, this document
contains specific information relevant to a variety of circumstances.
-------
Appendix A
Tills symposium proceedings is intended to provide guidance for state nonpoint source programs and local
watershed projects. It is the result of 10 years of experience of the National Rural Clean Water Program, and the
papers in this document address both the successes and difficulties experienced in the 21 projects that composed
the program. The papers included in the proceedings were peer reviewed. Most of the papers address individual
projects of the RCWP, and they provide valuable insights into the variety of approaches and solutions for
addressing specific circumstances and obstacles that may be encountered during the implementation of a nonpoint
source or watershed program. In this respect the information in this document goes beyond the general "how-to"
information provided in most guidance documents.
32. Watershed Monitoring Manual. Kansas Bio logical Survey 1993.
REFERENCE: Kansas Biological Survey. 1993. Watershed monitoring manual. Ecotoxicology Program, Kansas
Biological Survey, University of Kansas, Lawrence, Kansas. July.
CONTENTS: Introduction and background information; Chemical measurements: Grab samples, In situ water quality
analyses, Local water quality analyses; Physical measurements: Habitat, Stream discharge; Biological measurements:
Macroinvertebrate, Fish, Drift sampling, Primary productivity; Biological laboratory procedures; Appendices:
Preparation of equipment and supplies; Water quality glassware and plasticware washing procedure; Glass fiber filter
preparation.
MAIN FOCUS: Detailed instructions of methods for stream monitoring.
The manual is a guide for field crews undertaking long-term watershed monitoring. The manual describes all field
procedures and local laboratory procedures to be undertaken for the measurement of chemical, physical, and
biological parameters. The manual is geared toward a specific project, but the procedures can serve as a guide for
monitoring streams for other projects. Each section (on chemical, physical, and biological measurements) is
presented as separate procedural descriptions of the various tasks to be performed. Each procedure is presented
with an objective statement, listing of necessary equipment, and detailed description of the sampling procedure to
follow. Reference sections list the literature from which the procedures are taken. [From document introduction]
A.3.5 Guidances for Ground Water Monitoring
33. A Review of Methods for Assessing Nonpoint Source Contaminated Ground-Water Discharge to Surface
Water. USEPA 1991.
REFERENCE: USEPA. 1991. A Review of Methods for Assessing Nonpoint Source Contaminated Ground-
Water Discharge to Surface Water. U.S. Environmental Protection Agency, Office of Water, Washington, DC.
EPA 570/9-91-010. April.
CONTENTS: Methods for measuring or estimating nonpoint source contaminated ground-water discharge to
surface water; The impact of nonpoint source contaminated ground-water discharge to surface water in water
quality-limited water bodies: determining total maximum daily load and waste load allocations.
MAIN FOCUS: Each method is thoroughly described, including any limitations and assumptions in its use, the
expertise needed to apply it, and data inputs and data outputs. A short evaluation of the method and relevant
references with full citations are then provided.
-------
This document is a summary of methods that have been applied to measure or estimate nonpoint-source-
contaminated ground-water discharge to surface water. An overview of methods is presented, but this guidance is
not a manual for employing the methods described. After the review of analytical methods, a separate chapter
presents an overview of the total maximum daily load assessment and waste load allocation processes and
discusses the applicability of the methods described. Some of the methods reviewed are the use of seepage
meters, geophysical techniques, numerical models, and isotope methods.
An annotated bibliography is available as a companion volume to this report. The papers that provided the
background for this guidance are referenced throughout and are abstracted in the annotated bibliography. Full
citations for each of the background papers are provided in this report.
34.
Ground Water Monitoring: A Guide to Monitoring for Agricultural Nonpoint Source Pollution Projects.
Goodman, German, and Bishoff 1995.
REFERENCE: Goodman, J., D. German, and J. Bishoff. 1995. Ground Water Monitoring: A Guide to Monitoring
for Agricultural Nonpoint Source Pollution Projects. Draft. February.
CONTENTS: Rural Clean Water Program background; Nonpoint source project process; Vadose zone monitoring.
Ground water monitoring—Monitoring project planning; Monitoring system development; Monitoring system
implementation; Quality assurance/quality control; Data management and evaluation; New developments in ground
water monitoring. Ground water/surface water monitoring.
MAIN FOCUS: Geared mainly toward hydrologic systems and geologic regimes of the glaciated regions of the upper
midwestern area of the United States. Includes detailed considerations and steps to take in nearly all of the content
areas.
This document provides a usable "how to" handbook for designing and operating a ground water monitoring
network for nonpoint source pollution control projects. It is the product of a comprehensive ground water
monitoring and evaluation project that was conducted in the Oakwood Lakes-Poinsett area of east-central South
Dakota under the Rural Clean Water Program (RCWP). This document is not intended to be an all-encompassing
guidebook, but it does document the successes and failures in the South Dakota RCWP project and other RCWP
projects that conducted ground water monitoring.
35.
Response to Ground Water Contaminants Detected through Idaho's Statewide Ambient Ground Water
Quality Monitoring Program. Idaho Department of Health and Welfare 1995.
This document provides guidance to the IDDEQ for response to ground water quality contaminant detections
reported by the Idaho Statewide Ambient Ground Water Quality Monitoring Program. The objective of the protocol
is to describe how IDDEQ can investigate a contaminant detected through statewide monitoring and to establish
consistent guidance for the development of information to be provided to IDDEQ for the determination of an
applicable response. A phased approach for characterizing the nature and extent of reported contamination is taken
in the protocol, with separate phases to investigate whether the contaminant detected is associated with a known
problem or existing project; persistent or non-persistent; isolated, localized, or regional; and whether it can be
associated with a suspected source.
-------
Appendix A
REFERENCE: Cardwell, J. 1995. Response to ground water contaminants detected through Idaho's statewide ambient
ground \vater quality monitoring program. Water Quality Monitoring Protocols - Report No. 9. Idaho Department of
Health and Welfare, Division of Environmental Quality, Monitoring and Technical Support Bureau, Boise, Idaho.
January.
CONTENTS: Introduction; Notification procedure; Protocol; Quality assurance project plans; Data management.
Appendices (partial listing): Selected ground water quality information sources listing; Selected pollutants measured and
their most commonly associated uses; Selected analytes measured and their most commonly associated uses.
MAIN FOCUS: Investigation and reporting protocols for reports of ground water contamination in Idaho.
A.3.6 Guidances for Biological Monitoring
36. Fish Field and Laboratory Methods for Evaluating the Biological Integrity of Surface Waters. USEPA
1993.
REFERENCE: USEPA. 1993. Fish Field and Laboratory Methods for Evaluating the Biological Integrity of
Surface Waters. U.S. Environmental Protection Agency, Office of Research and Development, Washington,
DC. EPA/6WR-92/111. March.
CONTENTS: Quality assurance and quality control; Safety and health; Sample collection for analysis of the
structure and function of fish communities; Specimen processing techniques; Sample analysis techniques;
Special techniques; Fish bioassessment protocols for use in streams and rivers; Family-level ichthyoplankton
index methods; Fish health and condition assessment profile methods; Guidelines for fish sampling and tissue
preparation for bioaccumulative contaminants; Fisheries bibliography.
MAIN FOCUS: Provides guidance for all methods offish collection, from netting to electrofishing, and rapid
bioassessment protocols. A sample analysis section discusses fish identification and how specimens are
measured and weighed properly for data analysis purposes. Separate sections discuss fish kill investigations and
marking and tagging techniques. The fisheries bibliography is comprehensive.
This document describes guidelines and standardized procedures for the use of fish in evaluating the biological
integrity of surface waters, and it provides biomonitoring programs with fisheries methods for measuring the
status and trends of environmental pollution. Separate chapters in the document describe a variety of fish ,
collection methods, including the use of nets, electricity, chemicals, and hook and line; specimen handling;
specimen analysis; and methods to calculate the age of fish. The use of fish for rapid bioassessments of habitat
and water quality impacts to fish populations is also discussed. A section on special techniques discusses flesh
tainting methodology (used to relate flavor impairment to a particular waste source), fish kill investigations, and
Instream Flow Incremental Methodology (IFIM), which measures impacts to fish and other aquatic organisms
resulting from changes in instream flow. An extensive bibliography that is organized by topic is provided. It
includes a section on fish identification, with the references separated by geographic region of the United States
and by marine and freshwater species.
-------
37.
Bioaccumulation Monitoring Guidance: Selection of Target Species and Review of Available
Bioaccumulation Data. USEPA 1987.
REFERENCE: USEPA. 1987. Bioaccumulation Monitoring Guidance: Selection of Target Species and
Review of Available Bioaccumulation Data. Volume I. U.S. Environmental Protection Agency, Office of
Marine and Estuarine Protection, Washington, DC. EPA 430/9-86-005. March.
CONTENTS: Recommended target species; Additional sampling considerations; Historical data for target
species; Summary of recommendations.
MAIN FOCUS: Specifically addresses monitoring fish and macroinvertebrates for bioaccumulation of toxic
substances. A separate section discusses the use of historic data, and data summaries of metals and priority
, pollutant concentrations for target species are provided for reference. >
This guidance is intended for 301(h) programs, but the information presented is applicable to bioaccumulation
monitoring in general. Guidance for the selection of target species for bioaccumulation monitoring is its main
focus. A compilation, evaluation, and summarization of 1987 data on concentrations of priority pollutants in the
suggested target species is included. This information provides a set of data for comparative purposes, to aid the
user in interpreting data. The document explains the ranking procedure and criteria used to select the target
species. Selected target species are tabulated geographically. Species of fish are selected for the geographic areas
of Massachusetts to Virginia, and California and Washington, and macroinvertebrates are selected for
Massachusetts to Virginia; Alaska to California; Florida, the Virgin Islands, and Puerto Rico; and Hawaii.
Information on the types of tissue to analyze, the time of sampling, and the use of historical data is provided as
well.
38.
Biological Field and Laboratory Methods for Measuring the Quality of Surface Waters and Effluents
USEPA 1973.
REFERENCE: USEPA. 1973. Biological Field and Laboratory Methods for Measuring the Quality of
Surface Waters and Effluents. C.I. Weber, ed. U.S. Environmental Protection Agency, National
Environmental Research Center, Office of Research and Development, Cincinnati, Ohio. Program Element
1BA027. EPA-670/4-73-001. July.
CONTENTS: Biometrics; Plankton; Periphyton; Macrophyton; Macroinvertebrates; Fish; Bioassay; Appendix.
MAIN FOCUS: A section on biometrics provides a complete discussion of data analysis. Bioassays for
phytoplankton/algae, periphyton, macroinvertebrates, and fish are discussed.
This manual was published to provide pollution biologists with a methods reference guide for measuring the
effects of environmental contaminants on freshwater and marine organisms. Both field and laboratory methods
are discussed for fish, macroinvertebrates, plankton, periphyton, and macrophyton. A section on biometrics
provides a full discussion of sampling (simple random and stratified random) and statistical analysis methods (T-
test, chi square, F-test, analysis of variance, confidence intervals, and linear regression). Sections on different
types of organisms to be sampled (e.g., fish, periphyton) discuss sample collection and preservation, sample
preparation and analysis, sampling methods, and special techniques where appropriate. References are provided
for each section of the manual. Special sections on fathead minnow and brook trout chronic tests are included in a
-------
Appendix A
section on bioassay techniques. An appendix contains data recording sheets and a discussion of equipment and
supplies.
A.3,7 Program-specific Monitoring Guidances
39. Watershed Monitoring and Reporting for Section 319 National Monitoring Program Projects. USEPA
1991.
REFERENCE: USEPA. 1991. Watershed Monitoring and Reporting for Section 319 National Monitoring
Program Projects. U.S. Environmental Protection Agency, Office of Water, Washington, DC. August.
CONTENTS: Selection criteria for National Monitoring Program projects; NonPoint Source Management
System (NPSMS) software; Management file; Monitoring plan file; Annual report file.
MAIN FOCUS: Outlines the types of data that should be collected and documentation that should be kept for
watershed monitoring projects; specifically addresses the requirements of the National Monitoring Program
established pursuant to CWA Section 319.
Under section 319 of the Clean Water Act as amended in 1987, EPA is establishing a national program to
intensively monitor and evaluate a subset of watershed projects. A nationally consistent protocol is to be followed
by the projects, and EPA has developed and distributed a national framework for the National Monitoring
Program. This guidance provides monitoring and reporting guidelines for the program.
Nonpoint Source Management System (NPSMS) software has been developed and distributed to states that have
received grants under section 319 of the Clean Water Act. The software facilitates information tracking and
reporting under the National Monitoring Program. It is menu-driven, and this document discusses the proper
ontry of data into the software system and provides a step-by-step guide to it. Much of the information presented
' s therefore not of relevance to the reader interested hi designing a monitoring program, though there is limited
discussion of monitoring objectives, monitoring program design, and monitoring parameters.
40. NPDES Storm Water Sampling Guidance Document. USEPA 1992.
REFERENCE: USEPA. 1992. NPDES Storm Water Sampling Guidance Document. U.S. Environmental
Protection Agency, Office of Water. Washington, DC. EPA 833-B-92-001. July.
CONTENTS: Background for storm water sampling; Fundamentals of sampling; Analytical considerations;
Flexibility in sampling; Health and safety.
MAIN FOCUS: Contains a good discussion of grab and composite samples, sample collection methods, and
sample handling and documentation. Addresses many points specific to the NPDES program.
This guidance is intended for operators of facilities that discharge storm water containing industrial pollutants and
operators of large and medium-sized municipal separate storm sewer systems. Its purpose is to assist facility
operators and/or owners in planning for and fulfilling the NPDES storm water discharge sampling requirements
for NPDES permit applications. The information presented pertains to individual industrial storm water
-------
applications, group storm water applications, and municipal storm water permit applications. The guidance was
issued in support of EPA regulations and policy initiatives involving the development and implementation of a
national storm water program, and serves as Agency guidance.
The legal requirements of storm water sampling under the Clean Water Act are explained, and storm water
sampling methodologies, other measurements necessary for permit compliance (e.g., flow, rainfall), sample
documentation, and sample analysis requirements are discussed. Modifications to standard sampling procedures,
which are allowed under specific circumstances on a case-by-case basis, are explained. Acceptable techniques for
manual and automatic sample collection are described. Health and safety considerations are also discussed.
NPDES storm water permit requirements are the focus of this guidance, and much of the information provided in
it is not directly related to monitoring the water quality and habitat effects of NFS pollution. However, the
technical information that it contains on choosing sampling locations and sampling procedures should be useful to
those monitoring storm water as part of an NFS monitoring program.
41. Monitoring Guidance for the National Estuary Program. USEPA 1991.
REFERENCE: USEPA. 1991. Monitoring Guidance for the National Estuary Program. US Environmental
Protection Agency, Office of Water. EPA 503/8-91-002. August.
CONTENTS: Develop monitoring objectives and performance objectives; Establish testable hypotheses and
select statistical methods; Select analytical methods and alternative sampling designs; Evaluate monitoring
program performance; Implement monitoring study and data analysis; Communicate program results;
Appendices: Case studies; Methods.
MAIN FOCUS: Appendices contain case studies of the Puget Sound and Chesapeake Bay monitoring
programs, and detailed methods sections. The methods sections include water column physical and chemical
parameters, sediment, plankton, aquatic vegetation, benthos, fish, bioaccumulation. and bacteria and viruses.
Each method section discusses monitoring design, analytical methods, QA/QC, and statistics for the parameter.
The National Estuary Program (NEP) was created by the Water Quality Act of 1987 to promote long-term
planning and management in nationally significant estuaries threatened by pollution, development, or overuse.
Management conferences, with representatives from EPA, the affected state(s), local governments, the scientific
community, and citizens' groups, are established to develop Comprehensive Conservation and Management Plans
(CCMPs) for the estuaries. The first task of a management conference is to identify and characterize the
problems in the estuary. Then, based on the findings, a CCMP is developed to guide the implementation of
actions undertaken to overcome the identified problems and protect the estuarine environment. A requirement of
the enacting legislation is that the effectiveness of actions taken pursuant to CCMPs be monitored, and this
document provides guidance on the design, implementation, and evaluation of NEP monitoring programs.
NEP monitoring programs are designed to serve two goals—to measure the effectiveness of management actions
and programs implemented under CCMPs, and to provide essential information that can be used to redirect and
refocus the estuarine management efforts. Because the intended audience for this document is those involved in
estuary management efforts, including environmental managers, governmental agencies, and citizens, this
guidance discusses both background issues and technical aspects relevant to estuarine monitoring programs.
This guidance presents a systems design approach to designing a monitoring program, with discussions of each of
the steps involved in the approach: developing monitoring program objectives, designing a monitoring program
establishing hypotheses, selecting statistical methods and sampling designs, evaluating the monitoring program's'
A-29
-------
AppendixA
performance, and managing and analyzing data. An extensive methods section is organized according to the
parameter being monitored, e.g., water column chemistry, sediment grain size, aquatic vegetation, fish
community structure, viral pathogens. Numerous references to other texts providing in-depth discussions of each
step in monitoring program design are provided. Two case studies, the Puget Sound Ambient Monitoring
Program and the Chesapeake Bay Monitoring Program, are used to provide examples from existing estuarine
monitoring programs. In addition, the case studies also address options for funding monitoring programs, how to
incorporate existing monitoring studies Into a coordinated basin-wide monitoring effort, and methods for
determining the effectiveness and feasibility of monitoring efforts.
42. Water Quality Standards Handbook. USEPA 1983.
REFERENCE: USEPA. 1983. Water Quality Standards Handbook. U.S. Environmental Protection Agency,
Office of Water Regulations and Standards. Washington, DC. December. •
CONTENTS: Water quality standards review and revision process; General program guidance; Water body ,
survey and assessment guidance for conducting use attainability analyses; guidelines for deriving site-specific
water quality criteria. Appendices: Bioassay test methods; Determination of statistically significantly different'
LC50 values; Case studies. , , , , .
MAIN FOCUS: Provides a complete discussion of the state water quality standards development process and
the process for conducting waterbody surveys and. use attainability analyses. Excellent background material on
state water quality standards and stream classification systems.
This guidance is meant to assist states in implementing the 1983 water quality standards regulation (48 PR 51400,.
November 8). It is not a monitoring guidance per se, but rather a guidance for determining whether awaterbody
survey and/or use attainability analysis, as required by die Clean Water Act, meets specifications set forth by
EPA. Numerous case studies illustrate acceptable state approaches.
The handbook provides a general description of the standards setting process, information on program
administrative policies and procedures, and a description of analyses used to determine appropriate water uses and
criteria. States are to use the data and analyses set forth in this document, or similar data and analyses, to conduct
use attainability analyses or to establish water quality criteria. Certain regulatory requirements to which states
must adhere when they develop water quality criteria are discussed as well. EPA has determined that certain
types of scientific and technical data and analyses are necessary in order for the public and EPA to-conduct
informed reviews of proposed water quality standards. Data and analyses required for this purpose are noted.
Explanations of terms and concepts used in the regulatory language, e.g., mixing zone and antidegradation, are
provided.
While this document does not provide guidance on the development or implementation of a monitoring program, it
is a useful reference for those interested in the rationale behind and derivation of water quality standards.
Monitoring is an essential step in setting and modifying site-specific water quality standards. Therefore, this
document is valuable as a companion to documents that deal more directly with monitoring program design and
implementation.
-------
43. Ecological Assessments of Hazardous Waste Sites: A Field and Laboratory Reference Document
USEPA 1989.
REFERENCE: USEPA. 1989. Ecological Assessments of Hazardous Waste Sites: A Field and Laboratory
Reference Document. W. Warren-Hicks, B.R. Parkhurst, and S.S. Baker, Jr., eds. U.S. Environmental
Protection Agency, Office of Research and Development, Washington, DC. EPA/600/3-89/013. March.
CONTENTS: Ecological endpoints; Assessment strategies and approaches; Field sampling design; Quality
assurance and data quality objective; Toxicity tests; Biomarkers; Field assessments; Data interpretation.
MAIN FOCUS: A comprehensive discussion of all aspects of ecological risk assessment. A specific list of
references follows each subsection that discusses a technical aspect of risk assessment.
This is a comprehensive field and laboratory reference document for the design, implementation, and
interpretation of ecological risk assessments, and it specifically addresses assessments of hazardous waste sites.
Complete discussions of ecological endpoints, assessment methods, statistical considerations, toxicity testing, field
assessments, and data interpretation are provided. The section on toxicity testing is divided into subsections on
aquatic, terrestrial, and microbial tests; the section on field assessments is divided into subsections on aquatic,
vegetation, terrestrial vertebrates, and terrestrial invertebrates.
44. Environmental Monitoring and Assessment Program: Ecological Indicators. USEPA 1990.
REFERENCE: USEPA. 1990. Environmental Monitoring and Assessment Program: Ecological Indicators.
U.S. Environmental Protection Agency, Office of Research and Development, Washington DC EPA/600/3-
90/060. September.
CONTENTS: EMAP indicator concepts; Indicator strategy for near coastal waters; Indicator strategy for
inland surface waters; Indicator strategy for wetlands; Indicator strategy for forests; Indicator strategy for arid
lands; Indicator strategy for agroecosystems; Indicators relevant to multiple resource categories; Indicator
lospheric stressors; Conclusions and future directions; Appendices (indicator fact sheets).
luuuo, .Liiuivaiuj auaicgy j
strategy for atmospheric:
MAIN FOCUS: EMAP indicators are related to ecological and social (e.g., recreation) variables and to
monitoring objectives. The reasoning behind the choice of each indicator is fully explained.
The Environmental Monitoring and Assessment Program (EMAP) is a nationwide initiative to assess and
document the status and trends in the condition of the Nation's ecological resources. The program is organized
into seven resource groups that focus monitoring and assessment activities on defining the environmental condition
of agricultural lands, estuaries, forests, the Great Lakes, lakes and streams, rangelands, and heterogeneous
ecological resources (landscape ecology). Regional EMAP (REMAP) projects that use the same approach as the
nationwide EMAP program focus On issues of particular importance in the region or a state or states within the
region.
This report presents the approach proposed to describe ecological condition; defines a common strategy within the
program for selecting and prioritizing; and summarizes the indicators chosen for evaluation as core indicators for
major types of ecosystems. The discussions in this document of monitoring indicators for the major types of
ecosystems provide valuable information on types of indicators to choose for different monitoring goals.
-------
Appendix A
Indicators that were considered but not chosen for use in EMAP are also discussed, and this is valuable in that it
points out the purposes for which these monitoring, parameters would be inappropriate. Fact sheets for each of the
indicators chosen for EMAP are included as appendices. Each fact sheet discusses the particular indicator's ,
application, measurement,Variability, and primary problems, and provides pertinent references.
45. RCRA Ground-Water Monitoring Technical Enforcement Guidance Document. USEPA 1986.
REFERENCE: USEPA. 1986. RCRA Ground-Water Monitoring Technical Enforcement .Guidance
Document. U.S. Environmental Protection Agency, Office of Solid Waste and Emergency Response,
Washington, DC. "OSWER-9950.1. September.
CONTENTS: Characterization of site hydrogeology; Placement of detection monitoring wells; Monitoring well
design and construction; Sampling and analysis; Statistical analysis of detection monitoring data; Assessment
monitoring. :
MAIN FOCUS: Provides technical information on site characterization, well placement, and well.design and
construction. Sample collection, handling, and preservation techniques are thoroughly discussed.
This guidance describes hi detail what EPA deems to be the essential components of a ground water monitoring
system that meets the goals of the Resource Conservation and Recovery Act (RCRA). It is intended to be used by
enforcement officials, permit writers, field inspectors,' and attorneys at the federal and state levels to assist them
in making informed decisions regarding the adequacy of existing or proposed ground water monitoring systems.
The guidance contains technical information on site characterization, well design and construction, and assessment
of contamination of ground water. Hydrogeologic regimes vary widely from site to site, and this guidance does
not attempt to address all possible circumstances for the purposes of ground water monitoring programs. 'It does
provide a framework within which a decision-making process can be applied using site-specific considerations.
Ground water monitoring is a specific type of monitoring and may be beyond the scope of many monitoring •
programs. Although this guidance is specific to the RCRA program, the protocols presented are rigorous and
could be used to provide defensible data for any ground water monitoring program. ' *
46. Summary of U.S. EPA-Approved Methods, Standard Methods, and Other Guidance for 301 (h) Monitoring
Variables. USEPA 1985. ,
REFERENCE: USEPA. 1985. Summary of U.S. EPA-Approved Methods, Standard Methods, and Other •
Guidance for 301(h) Monitoring Variables. U.S. Environmental Protection Agency, Office of Water,
Washington, DC. EPA 503/4-90-002. September.
CONTENTS: Introduction; Table 1: U.S. EPA-Approved Methods and Guidance Documents for Measuring '
Biological, Sediment, and Water Quality Variables in 301(h) Monitoring Programs; Water Quality Variables;
Sediment Analyses; Biological Variables; .References.
MAIN FOCUS: A convenient listing of where to find information on analytical techniques for many
monitoring variables.
-------
This short (16-page) document tabulates the types of analytical methods available for the water quality, sediment,
and biological variables used in 301(h) monitoring programs. The methods are listed as EPA-approved, EPA-
suggested, standard, or additional, and the availability of guidance for each method is noted. Following the table
is a brief note specifying method numbers (e.g., USEPA method No. 150.1; additional procedure No. 413.2) for
each of the variables listed. A list of references at the end of the document indicates where to find information
about each of the monitoring variables and analytical methods.
47.
Statistical Analysis of Ground-Water Monitoring Data at RCRA Facilities - Interim Final Guidance
USEPA 1989. .
REFERENCE: USEPA. 1989. Statistical Analysis of Ground-Water Monitoring Data at RCRA Facilities -
Interim Final Guidance. U.S. Environmental Protection Agency, Office of Solid Waste, Waste Management - •
Division, Washington, DC. EPA/530-SW-89-026. NTIS PB89-151047. February. ' .
CONTENTS: Regulatory overview; Choosing a sampling interval; Choosing a statistical method; Background
well to compliance well comparisons; Comparisons with MCLs or ACLs; Control charts for intra-well
comparisons; Miscellaneous topics; Appendices.
MAIN FOCUS: Detailed discussion of all aspects of statistical analyses of ground-water monitoring data.
Flow charts are provided to assist the reader in choosing the proper statistical method and interpreting data:.
The hazardous waste regulations under the Resource Conservation and Recovery Act (RCRA) require owners and
operators of hazardous waste facilities to use design features and control measures that prevent the release-'of
hazardous waste into ground water. This document provides guidance to RCRA facility permit applicants.and
writers concerning the statistical analysis of ground water monitoring data at RCRA facilities. Sections of the
document provide an overview of regulations concerning the statistical analyses of ground water monitoring data,
hydrogeologic parameters to consider when choosing a sampling interval, guidance on choosing an appropriate
statistical method, statistical methods that may be used to evaluate ground water monitoring data, statistical
procedures that are appropriate for special circumstances, and special topics. Appendices cover general statistical
considerations, a glossary of statistical terms, statistical tables, and references. :
48. CWA Section 403: Procedural and Monitoring Guidance. USEPA 1994. :
REFERENCE: USEPA. 1994. CWA Section 403: Procedural and Monitoring Guidance. U.S.
Environmental Protection Agency, Office of Water, Oceans and Coastal Protection Division Washington DC
EPA 842-B-94-003. March. , ' ' '
CONTENTS: Section 403 procedure; Options for monitoring under the basis of "no irreparable harm";
Summary of monitoring methods: physical characteristics, water chemistry, sediment chemistry, sediment grain
size, benthic community structure, fish and shellfish pathology, fish populations', plankton, habitat identification
methods, bioaccumulation, pathogens, effluent characterization, mesocosms and microcosms. : •
MAIN FOCUS: Each monitoring parameter is dealt with separately, with a separate discussion of monitoring
design, analytical methods, QA/QC, statistical considerations, and use of data. An appendix contains an
extensive list of monitoring methods references. '. • •
-------
Appendix A
This document is designed to provide EPA Regions and NPDES-authorized states with a framework for the
decision-making process for section 403 (ocean dumping) evaluations and to provide guidance on the type and
level of monitoring that should be required as part of permit issuance under the "no irreparable harm" provisions
of section 403. Options for monitoring under the basis of no irreparable harm, including criteria for evaluating
perceived potential impact and establishing monitoring requirements to assess actual impacts, are discussed.
Summaries of monitoring methods for evaluating numerous parameters (see contents listing) are provided. Each
method section contains an explanation of the usefulness of the parameter of concern in a 403 monitoring program
and a discussion of analytical methods, the use of data generated, and considerations of monitoring design,
statistical design, and quality assurance/quality control.
49. Methods for Collecting Benthic Invertebrate Samples as Part of the National Water Quality Assessment
Program. Cuffney, Gurtz, and Meador 1993.
REFERENCE: Cuffney, T.F., M.E. Gurtz, and M.R. Meador. 1993. Methods for Collecting Benthic Invertebrate
Samples as part of the National Water Quality Assessment Program. USGS Open-File Report 93-406. U.S.
Geological Survey, Reston, Virginia.
CONTENT: National Water Quality Assessment Program sampling design; Sampling design for benthic
invertebrates; Methods for collecting benthic invertebrates; Maintenance of sampling equipment; Sample processing
and labeling; Field data sheets; Contract laboratories and the Biological Quality Assurance Unit; Adapting collection
methods for other National Water Quality Assessment Program objectives; Safety and health.
MAIN FOCUS: Evaluating benthic invertebrate communities as part of the ecological survey component of the U.S.
Geological Survey's National Water Quality Assessment Program.
This document presents a variety of sampling methods and procedures for collecting benthic invertebrates as part
Of the National Water Quality Assessment (NAWQA) Program. Numerous sample-collecting techniques,
equipment, and data forms are presented for use at basic fixed sampling sites. Each technique or method is
thoroughly explained with diagrams, flowcharts, or examples. These techniques and methods are easily adaptable
for use in other components of the NAWQA Program, or where needed in other programs of the USGS's Water
Resources Division. Field data sheets are provided.
The objectives of benthic invertebrate community characterizations are to develop for each site a list of taxa
within the associated stream reach and determine the structure of the benthic invertebrate communities within
selected habitats of that reach. This document presents the nationally consistent approach used by the USGS to
achieve these objectives. It provides guidance on site, reach, and habitat selection and methods and equipment for
qualitative multihabitat sampling and semi-quantitative single-habitat sampling. Appropriate quality assurance and
quality control guidelines are used to maximize the ability to analyze data within and among study units.
50. Methods for Collecting Algal Samples as Part of the National Water Quality Assessment Program.
Porter, Cuffney, Gurtz, and Meador 1993.
This document describes the sampling methods, procedures and equipment for collecting algal samples as part of
the National Water Quality Assessment (NAWQA) Program. The approach used in the sampling design for algal
communities provides a common spatial scale to assess biological communities and habitat characteristics. The
design also addresses seasonal and hydrologic conditions that affect algal communities. The NAWQA Program
provides an integrated assessment of water quality within selected environmental settings by collecting and
analyzing a combination of physical, chemical, and biological data. The algal component of the data is designed
-------
to characterize the species distribution and community structure of benthic algae and their relation to water
quality.
REFERENCE: Porter, S.D., T.F. Cuffney, M.E. Gurtz, and M:R. Meador. 1993. Methods for Collecting Algal
Samples as part of the National Water Quality Assessment Program. USGS Open-File Report 93-409. U.S.
Geological Survey, Reston, Virginia. «
CONTENT: National Water Quality Assessment Program sampling design; Sampling design for algal communities;
Methods for collecting algal samples; Sample processing and labeling; Contract laboratories and the Biological Quality
Assurance Unit.
MAIN FOCUS: Describes methods, procedures, and equipment for collecting algal samples at basic fixed sites as
part of the U.S. Geological Survey's National Water Quality Assessment Program.
A variety of sample collection methods focus on qualitative multihabitat and quantitative targeted habitat
periphyton samples. Forms for recording sampling data are provided. This manual also discusses preservation
methods, preparation of subsamples, and labeling while processing algal samples.
51.
Methods for Characterizing Stream Habitat as Part of the National Water Quality Assessment Program.
Meador, Hupp, Cuffney, and Gurtz 1993.
REFERENCE: Meador, M.R., C.R. Hupp, T.F. Cuffney, and M.E. Gurtz. 1993. Methods for Characterizing
Stream Habitat as pan of the National Water Quality Assessment Program. USGS Open-File Report 93-408. U.S.
Geological Survey, Reston, Virginia. :
CONTENT: National Water Quality Assessment Program sampling design; Stream habitat sampling design; Methods
for characterizing stream habitat.
MAIN FOCUS: Provides detailed procedures for characterizing stream habitat as part of the U.S. Geological
Survey's National Water Quality Assessment Program.
This document provides detailed instructions for characterizing stream habitat as part of the National Water
Quality Assessment (NAWQA) Program. These procedures allow for appropriate habitat descriptions and
standardization of measurement techniques to create unbiased evaluations of habitat influences on water resource
conditions' at a variety of spatial levels. Using the methods presented, evaluation of stream habitat is based on a
spatially hierarchical framework that incorporates habitat data at basin, segment, reach, and microhabitat scales.
This framework provides a basis for national consistency in collection techniques while allowing flexibility in
habitat assessment within individual study units. Procedures are described for collecting habitat data at basin and
stream segment scales using geographic information system (GIS) databases, maps, and aerial photographs.
Detailed diagrams and characterization forms are provided.
-------
Appendix A
52. Guidelines for the Processing and Quality Assurance ofBenthic Invertebrate Samples Collected as Part of
the National Water Quality Assessment Program. Cuffney, Gurtz, and Meador 1993.
REFERENCE: Cuffney, T.F., M.E. Gurtz, and M.R. Meador. 1993. Guidelines for the Processing and Quality
Assurance ofBenthic Invertebrate Samples Collected as part of the National Water Quality Assessment Program.
USGS Open-File Report 93-407. U.S. Geological Survey, Reston, Virginia.
CONTENT: Sample processing strategy; Specifications for sample processing by contract laboratories; Biological
quality assurance unit.
MAIN FOCUS: Specifies the procedures and guidelines that contract laboratories should use to identify and quantify
invertebrates from the large-rare and main-body components of benthic invertebrate samples collected as part of the
U.S. Geological Survey's National Water Quality Assessment Program.
This report provides nationally consistent guidelines and criteria for the processing of benthic invertebrate samples
collected as part of the National Water Quality Assessment (NAWQA) Program. These guidelines include
procedures for the tracking and labeling of samples, the use of standard methods and equipment for removing
benthic invertebrates from the sample matrix, subsampling procedures, target levels of identification for major
groups of invertebrates, quantification procedures, quality assurance/quality control procedures, and standard
formats for reporting data. In addition, standards and procedures for the initial qualification and continual review
of contract laboratories are discussed. Diagrams, flow charts, example data sheets, and examples of situations are
used in the presentation of these guidelines.
53. Methods for Sampling Fish Communities as a Part of the National Water Quality Assessment Program.
Meador, Cuffney, and Gurtz. 1993.
REFERENCE: Meador, M.R., T.F. Cuffney, and M.E. Gurtz. 1993. Methods for Sampling Fish Communities as a
pan of the National Water Quality Assessment Program. USGS Open-File Report 93-104. U.S. Geological Survey,
Reston, Virginia.
CONTENT: National Water Quality Assessment sampling design; Fish community sampling design; Fish community
sampling considerations; Methods for sampling fish communities; Biological Quality Assurance Unit; Field data
sheets.
MAIN FOCUS: Provides detailed procedures for use by biologists to evaluate stream fish communities as part of the
U.S. Geological Survey's National Water Quality Assessment Program.
This manual provides complete instructions for procedures used to sample and evaluate stream fish communities
as part of the National Water Quality Assessment (NAWQA) Program. The methods allow standardization of
collection methods and descriptions of fish communities to facilitate unbiased evaluations of the relationships
between physical, chemical, and biological components of water quality conditions. The methods are established
standard procedures for characterizing fish communities in streams ranging from headwaters to large rivers. The
focus of the sampling procedures is electrofishing and seining techniques, but other methods are mentioned.
Taxonomic identification, physical measurements, examination of fish for external anomalies, and preservation of
specimens are covered in the discussion of sample processing. Forms for recording these data are provided.
-------
54. Guidelines for Studies of Contaminants in Biological Tissues for the National Water Quality Assessment
Program. Crawford and Luoma 1994.
REFERENCE: Crawford, J.K., andS.N. Luoma. 1994. Guidelines for Studies of Contaminants in Biological
Tissues for the National Water Quality Assessment Program. USGS Open-File Report 92-494. U.S. Geological
Survey, Reston, Virginia.
CONTENT: Tissue analysis in the National Water Quality Assessment Program. Tissue analysis activities of other
agencies—USFWS, NOAA, EPA National Study of Chemical Residues in Fish, EPA EMAP, Regional and state.
Approach of tissue analysis surveys—General approach; Objectives, priorities, and time line; Study strategies; Factors
affecting selection of target chemicals; Target compounds for contaminant occurrence; Targeted chemical analyses
following the contaminant-occurrence survey; Selection of taxa for analysis; Field procedures for collecting and
processing tissue samples; Field records; Laboratory procedures for analyzing tissue samples; Quality assurance and
quality control; Voucher collections and sample archival; Data management; Data interpretation.
MAIN FOCUS: Describes the concepts and field methods to be used by the U.S. Geological Survey's National Water
Quality Assessment Program for evaluating contaminants in tissues of organisms.
This document presents the rationale, objectives, approach, and procedures to be used in the National Water
Quality Assessment (NAWQA) Program for determining the occurrence, distribution, and trends in concentrations
of trace elements and synthetic organic compounds in tissues. Part 1 of this manual describes the rationale for
analyzing tissue contaminants and overviews the approach used. Part 2 explains the tissue-contaminant strategies
of other agencies and compares them to the strategy used in the NAWQA Program. Part 3 discusses the approach
for the use of tissue analysis as an aid to interpret water quality in NAWQA study units. Also, suggestions for
interpretation of data are offered to facilitate consistency between study units.
-------
Appendix A
K
=>
UI
O
§
(£.
HI
0
1
§
co
ID
in
5
CO
CM
-
O
v«
o>
CO
h-
s.
0
m
c
ro
OL
«
•
«
«
1
1
t
«
«
'
|| Watersheds
4-*
(0
O)
e
0-
•
•
•
•
•
•
•
•
•
•
•
|| Objectives |
•
•
•
•
•
•
•
•
•
•
•
•
C
.E?
•
•
•
•
•
•
•
|| Protocols 1
•
•
•
•
•
•
•
•
•
1 Parameters |
%
§
1
i
•
•
•
•
•
| Data Analysis |
•
•
•
•
•
•
•
|| Data Management |
•
•
•
•
•
•
•
•
•
o
a
•
| Stream Classification |
(Table continued on next page)
-------
UJ
CQ
5
3
UJ
O
UJ
a:
U.
%
HI
o
2
C3
CO
CO
N-
co
m
•*
CO
CM
"*"
1 SUBJECTS DISCUSSED
| Samples and Sampling;
•
II Types of Samples
•
|| Sampling Equipment
•
|| Sampling Techniques
e
II Sample Handling
II Sample Documentation
|| Sample Analysis
•
•
|| Sampling Locations
*
•
|| Sampling Frequency
Health and Safety
|| Monitoring Parameters:
•
•
•
|| Physical Constituents
•
•
Chemical Constituents
•
•
|| Nutrients
•
|| Sediment
•
|| Bacteria & Viruses j
Phytoplankton
•
|| Zooplankton |
•
•
Macroinvertebrates
•
sz
.
-------
Appendix A
$
•I
z
LU
IS
CC
1
O
C0
CO
u>
CO
CO
8
&
CO
9
CM
CO
CM
S
CO
a
a
8
X
Cvi
O
CM
O>
|| SUBJECTS DISCUSSED
I Guidance for Monitoring;
•
•
•
•
•
•
•
•
•
1 Streams & Rivers
•
•
•
•
II Lakes & Reservoirs
*
II Estuaries/Marine
•
•
•
•
II Ground Water
|| Storm Water
•
•
CD
CO
T3
C
CO
•
|| Land Management
•
CD
CO
o:
•
•
1
I
•
•
•
•
•
|| Watersheds
M
1
CO
in
e
I
•
•
•
•
•
•
|| Objectives
•
•
•
•
•
•
•
•
c
TO
'co
a
•
•
•
•
I Protocols
•
•
•
•
•
I Parameters
1
V)
1
•
•
•
|| Data Analysis
•
•
•
| Data Management
•
•
•
•
o
a
•
|| Stream Classification j
CD
D>
ro
CL
-------
OL
111
CQ
3
UJ
O
HI
{£
u.
Ml
o:
UJ
o
<
Q
e>
CO
CO
m
co
s
CO
CO
CO
CO
8
JK
I
i
•
•
•
|| Physical Constituents
•
•
|| Chemical Constituents
1 Nutrients
•
II Sediment |
| Bacteria & Viruses |
•
•
|| Phytoplankton
|| Zooplankton
•
•
| Macroinvertebrates |
•
•
•
•
•
.c
(/>
k.
•
|| Aquatic Vegetation
| Riparian Vegetation
•
•
•
|| Habitat Quality
•
•
r£
•
1 Channel Morphology |
•
•
| Bank Stability |
-------
Appendix A
&
m
2
Z
U)
i
UJ
a:
HI
i
§
§
s
8
T™
m
g
o
•*r
3
l*.
•
-------
m
z
o
fc
UJ
o
1
o
in
CM
to
8
"*
!J
"*
5
CO
9
"*
o
o>
CO
oo
CO
£
1 SUBJECTS DISCUSSED
j
B
C
"3
i
•a
I
"5
i
•
•
•
•
•
I Types of Samples
I Sampling Equipment
•
•
|| Sampling Techniques |
•
•
•
*
Sample Handling
•
•
*
|| Sample Documentation
•
•
•
•
Sample Analysis
•
•
•
•
|| Sampling Locations
•
•
•
•
Sampling Frequency
•
|| Health and Safety
CO
CL
D)
1
C
a
I Physical Constituents
•
|| Chemical Constituents
|| Nutrients
Sediment
|| Bacteria & Viruses
•
|| Phytoplanktori
•
CO
Q.
O
O
N
•
•
|| Macroinvertebrates
•
•
JC
in
il
Aquatic Vegetation
|| Riparian Vegetation
|| Habitat Quality
1
u-
|| Channel Morphology
| Bank Stability
-------
Appendix A
Q
i
CO
fe
1
T^
Water Quality Indicators Guide. USDA-NRCS 1997.
c\i
g
O
T-
1
1
j. Coffey, Spooner and '
The Nonpoint Source Manager's Guide to Water Quality and Land Treatment Monitorint
CO
0
en
2
£
irtment of Health and We
Coordinated Nonpoint Source Water Quality Monitoring Program for Idaho. Idaho Deps
rt
en
CO
o
i
S
inesota Pollution Control
C.
s
1
s
CD
•5;
i
i
c
CO
a
CD
i
'2
!
fe
i
ui
1
W
s
Quality Goals orStandai
i
i
'5
|
CO
.8
Q.
}
~n
1o
i£
•5
%
CD
c
'o
I
1
§
UJ
CD
OJ
s
CO
CO
1
£
'c
1
O
u.
i
1
}
i
1
'to
!-:
Volunteer Estuary Monitoring: A Methods Manual. USEPA 1993.
CO*
en
r-
LU
CO
S2
!
s
CO
<£
I
'£
-£
i
te
a
|
cri
c
•c
o
i
"5
a
1
1
i_
CD
B
C
3
£
•s
&
CD
la
c
o
to
CD
|
L.
CD
a
1
ri
o
in'
gj
"co
s
c
1
lental Task Force on Mo
c
0
B
co"
'to
CO
I
^
|
1
O
1
1
!
1
l
iS
^~
en
en
^j
"to
O
CD
1
c»
•c
'E
o
o
8
£
!s
c
tpendixes. Intergovernm
1
1
1
CO
T3
1
1
i
i
§
i
\
CO
cu
CM
ui
Guidance for State Water Monitoring and Wasteload Allocation Programs. USEPA 1 98
CO
3A1981.
Guidelines for Evaluation of Agricultural Nonpoint Source Water Quality Projects. USEI
•t
_J
• Streams of Wisconsin.
Using the Index ofBiotic Integrity (IBI) to Measure Environmental Quality in Warmwatel
en
en
•^
8
3>
•o
c
Evaluation Monitoring of Stream Habitat During Priority Watershed Projects. Simonson
0
CM
CD
05
en
i
CO
ion of Environmental Qu
.2
ct
m
1
o
1
s
8
1
CD
1
CO
-3;
t
CQ
S
i
CO
en
O
t—
CM
al Quality 1996.
"S
c
2
'>
iS
•s
o
'a
o
CO
jD
f
O
•S,
|
1
8
CD
S
C
CD
CD
CO
O)
o
T-
CM
CM
Handbook: Stream Sampling for Waste Load Allocation Applications. USEPA 1 986.
£3
CM
o>
Simonson and Lyons 1 £
Evaluation Monitoring of Stream Fish Communities During Priority Watershed Projects.
•tf
CM
-------
Appendix/V
I
•a
c
co
LU
OT
(U
to
c
-3;
I
S3 <•
2 LU
^ CO
£0 _;
^S CD
$ 3
c? 1
I I
I i
S ^
! I
1
I
i
LU
CO
i
§
o
1
1
-J
•8
o
CO
LU
CO
to
8.
O)
I
I
I .
•ss
0. en
f5<
•sS:
g LU
CD CO
CO 13
CM
CD
en
LU
CO
§
I
S
CO
LU
co
E
I
I
1 §
« -S
g> ^
I i
is
I
I
CD
I
c
CO
CO
en
LU
CO
I
I
CO
15
1
in
03
en
tu
co
§ -
§» &
•S LU
O CO
1 =>
I
CO
CD
"<5
.c
U.
i
oo
i
I
CD
o
•a
I
CO
<^J
-8 I
i i
-S
CD
Q
t
i
1
*4~.
O
•2
§
«B .§
S4^
-------
Appendix A
O)
CO O
Oi i-
to to
1
o
T3
•g
ro
o
i-T
O
•g
CD
o
S
g
g
CD
E
I
5> S
Gf
CO
I
'ro
.1
1
CD
O
I
CO
CO
CD
CO
1
S
i
5
CO
§
«
S i?
I
.c
V)
c
<0 -0
8 e
t
CD
CO
<§
W
1
-------
APPENDIXB. DATA SOURCES
The data sources and information on the sources provided in this appendix are current as of August
1996. Availability of the data should be investigated prior to development of a project data collection
plan.
1. STORAGE AND RETRIEVAL (STORET) SYSTEM USEPA/OWOW
The Storage and Retrieval (STORET) System is one of the oldest and largest water information systems
currently in use. STORET stores information on ambient, intensive-survey effluent, and biological
water quality monitoring information. Although most STORET information has been added since
1975, records go back to 1899. STORET has four information areas:
• Water Quality System: WQS, the main component of STORET, contains chemical and
physical information obtained during monitoring of waterways within and contiguous to the
United States. This includes information for estuaries, streams, lakes, rivers, ground water,
canals, and coastal and international waters.
• Biological System Field Survey File: BIOS contains information on the distribution,
abundance, and physical condition of aquatic organisms in waters within and contiguous to the
United States, as well as descriptions of their habitats. BIOS provides a central repository for
biological information and analytical tools for data analyses.
• Daily Flow File: DFF contains daily observations of stream flow and miscellaneous water
quality parameters collected at gaging stations belonging to the U.S. Geological Survey's
(USGS's) national network. The DPS contains essentially the same information as the USGS
Daily Values File; the DPS provides an alternative source for the information and simplifies
linkages to other, non-USGS water databases.
• Fish Kill File: The Fish Kill File tracks fish kills caused by pollution that have occurred
throughout the United States. The kills are a result of a variety of industrial, municipal,
agricultural, and transportation-related operations.
Currently about 800 organizations have submitted information to STORET. There are over 735,000
sampling stations in STORET and more than 180 million parametric observations covering some
12,000 water quality parameters.
Many organizations, including federal, state, interstate, and international agencies, submit information
to STORET. Users submit new information in the appropriate format daily. STORET data files are
updated weekly. Each organization is responsible for the information it submits to STORET; STORET
is a user-owned system. States submitting information follow quality assurance and control procedures.
All STORET data are checked for invalid data ranges or missing mandatory fields before they are
added to the system. Although STORET software edits incoming data for errors and inconsistencies,
the owners of the data have the primary responsibility for its content.
-------
Appendix B
Contact:
Office of Wetlands, Oceans, and Watersheds
USEPA Fairchild Building (4503F)
499 South Capitol Street, SW
Washington, DC 20024
Phone: 202-260-7166
2. WATER QUALITY ANALYSIS SYSTEM (WQAS)
USEPA/OWOW
The EPA Water Quality Analysis Branch has developed several databases and procedures that operate
under the umbrella of the Water Quality Analysis System (WQAS). WQAS databases are available on
die same mainframe as the STORET files, and many procedures intrinsic to one system access and
manipulate data from the other. WQAS includes many databases; the following are a sample of those
which would be most useful in a nonpoint source monitoring and evaluation study.
• Association of State and Interstate Water Pollution Control Administrators (ASIWPCA): Data
covering water quality impairments for 1972, 1982, and 1984.
• CITY: Data on 53,000 cities in the United States and its territories.
• GAGE: Data on 36,000 stream gaging locations across the United States.
• Reach File (RF): Data on stream reaches across the United States. Information on RF3
streams is limited; 100 percent of all RF1 and RF2 streams are included.
Contact:
Office of Wetlands, Oceans, and Watersheds
USEPA Fairchild Building (4503F)
499 South Capitol Street, SW
Washington, DC 20024
Phone: 202-260-7166
USDOI/USGS
3. NATIONAL WATER INFORMATION SYSTEM (NWIS)
USGS is in the process of designing and developing a new National Water Information System (NWIS).
The goal of the NWIS effort is to develop and implement a highly flexible hydrologic data management
and processing system—one that can be easily changed and expanded in a rapidly changing
technological environment. The NWIS will replace the following two systems:
• National Water Data Storage and Retrieval System (WATSTORE): WATSTORE consists of
several files in which data are grouped and stored by common characteristics and data
collection frequencies. The system also is designed to allow for the inclusion of additional data
files as needed. Currently, files are maintained for the storage of (1) surface water, quality-of-
water, and ground water data measured on a daily or continuous basis; (2) annual peak values
for stream flow stations: (3) chemical analyses for, surface and ground water sites; (4) water
data parameters measured more frequently than daily; (5) geologic and inventory data for
ground water sites; and (6) summary data on water use. In addition, an index file of sites for
Which data are stored hi the system is maintained.
-------
• National Water Data Exchange System (NAWDEX): NAWDEX is a national confederation of
water-oriented organizations working together to improve access to water data. Its primary
objective is to assist users of water data in the identification, location, and acquisition of needed
data. NAWDEX consists of member organizations from the water data community. The
members are linked so that their water data holdings can be readily exchanged for maximum
use. It encompasses four major areas of operation: (1) maintaining an internal data center,
including access to automated data processing facilities for maintenance and use of its
information files; (2) indexing water data held by participating organizations; (3) providing
facilities and personnel for responding to requests for water data; and (4) formulating
recommended water data handling and exchange standards.
NWIS will be a single integrated system that will have the functionality of current data systems as well
as expanded capability for processing and managing additional chemical constituent, sediment,
biological, and spatial data.
Contact:
National Water Information System
U.S. Geological Survey
National Center, MS 437
12201 Sunrise Valley Drive
Reston, VA 22092
Phone: 703-648-5659
4. NATIONAL STREAM QUALITY ACCOUNTING NETWORK (NASQAN)
USDOI/USGS
The National Stream Quality Accounting Network (NASQAN) program, started hi 1972, provides a
nationally uniform basis for assessing large-scale and long-term trends in the physical, chemical, and
biological characteristics of the Nation's surface waters. Water quality monitoring is carried out at
stations that are generally located on major rivers at the downstream end of the accounting unit.
NASQAN was redesigned in 1995 to focus on four of the largest river basins in the Nation—the
Mississippi, the Columbia, the Colorado, and the Rio Grande. Monitoring stations are located at the
confluence of major rivers within these basins and at the mouths of the rivers. Coastal stations are
operated at a few additional large rivers (the St. Lawrence, Susquehanna, Alabama, Tombigbee, and
Yukon) to more fully describe the export of chemicals from the continent to the ocean. NASQAN
complements the other national water quality monitoring and assessment programs operated by the
USGS by providing information on very large rivers.
NASQAN seeks to characterize the water quality of these large rivers by measuring concentration and
mass transport of a wide range of dissolved and suspended constituents including:
• Major ions (salts such as sodium, potassium, and sulfate)
• Nutrients (including various forms of nitrate and phosphate that contribute to eutrophication of
lakes and rivers)
• Dissolved and sediment-bound heavy metals (such as lead, cadmium, and arsenic)
-------
Appendix B
• Common pesticides that can dissolve in water (including herbicides such as atrazine)
• Inorganic and organic forms of carbon
This information will be used to describe the long-term trends and changes in the concentration and
transport of these constituents within the four river basins (USGS, 1996a).
Contact:
Office of Water Quality
U.S. Geological Survey
National Center, MS 412
12201 Sunrise Valley Drive
Reston, VA 22092
Phone: 703-648-6861
5. NATIONAL WATER-QUALITYASSESSMENT PROGRAM (NAWQA)
USGS/OWQ
The National Water-Quality Assessment Program (NAWQA) is designed to assess historical, current,
and future water quality conditions in representative river basins and aquifers nationwide. One of the
primary objectives of the program is to describe relations between natural factors, human activities, and
water quality conditions and to define those factors which most affect water quality in different parts of
the Nation. Information from the NAWQA should be useful to water resource managers, planners, and
policy makers, and for guiding research, monitoring, and regulatory activities in cost-effective ways.
When fully implemented in 1997, the NAWQA will provide consistent and comparable information on
water resources in 60 important river basins and aquifers across the Nation, which combined account
for 60 to 70 percent of the Nation's water use and population served by public water supplies and cover
about one-half of the land area of the Nation. Each of these 60 river basins and aquifers is a study unit
within NAWQA. One-third of the study units will be studied intensively at any given time, for a 3- to
5-year period, followed by a 5- to 6-year period of less intensive study and monitoring. Coinciding
with the study unit investigations are national synthesis assessments. Two to four national synthesis
topics will be studied at any given time. The first issues being studied—the occurrence of nutrients and
pesticides in rivers and ground water—were chosen because of widespread environmental and public
health concerns and because the information necessary for a national assessment of these topics is
incomplete. The next topic for national synthesis will be the occurrence and distribution of volatile
organic compounds (USGS, 1994).
At the study unit level, each investigation has a local liaison committee that consists of representatives
with water resources responsibilities or interests from federal, state, and local agencies, universities,
and the private sector. Each liaison committee is charged with the following:
• The exchange of information about water quality issues of regional and local interest.
• The identification of sources of data and information.
• Assistance in the design and scope of project products.
* The review of project planning documents and reports.
-------
Contact:
NAWQA Program
U.S. Geological Survey
National Center, MS 413
12201 Sunrise Valley Drive
Reston, VA 22092
Phone: 703-648-
6. NATIONAL HYDROLOGIC BENCHMARK NETWORK (HBN)
USGS/OWQ
USGS's National Hydrologic Benchmark Network (HBN) was established in 1964 as a unique network
of stream discharge and water quality monitoring stations in basins that have little human influence on
hydrologic characteristics. The program operated a fixed-station, fixed-interval network of 50 sites for
water quality and quantity data collection. Drainage areas of the HBN basins range from 2 to 2,000
square miles, with a median basin size of 57 square miles. Elevations of the watersheds range from
about 100 to 14,000 feet above sea level. Many of the stations are located in national parks, wilderness
areas, state parks, national forests, and specially protected areas set aside for,scientific investigations.
Throughout the history of the network, sampling frequency has been monthly, bimonthly, or quarterly.
In 1996, all stations are scheduled to be sampled twice a year.
Constituent coverage includes:
• Field parameters, including discharge
• Dissolved and total nutrients
• Dissolved inorganics, major ions, and trace elements
• Suspended sediment
• Bacteriology
Precipitation quantity has also been measured at many stations. Depth- and width-integrating sampling
techniques are used.
The principal objectives of the network are as follows:
• To document natural changes in hydrologic characteristics.
• To provide a better understanding of the hydrologic structure of natural basins. .
• To provide a comparative base for studying the effects of humans on the hydrologic
environment.
Data from the network are used to detect water, quality trends and to describe water quality conditions,
Results from the HBN have both national and regional application for identifying natural hydrologic
conditions. Each station represents an integration of upstream water quality conditions with generally
minimal influence from land-use activities.
All data collected for the HBN are stored in USGS's WATSTORE and NWIS databases (see data
source 3 in this appendix). On a monthly basis, the data are transferred from WATSTORE to
-------
Appendix B
STORET (see data source 1 in this appendix) and can be accessed through either system (USGS,
1996b).
5. NATIONAL SURFACE WATER SURVEY(NSWS)
USEPA/ERL
The National Surface Water Survey (NSWS) consists of two parts: the National Lake Survey and the
National Stream Survey. The purpose of the National Lake Survey is to quantify, with known
statistical confidence, the current status, extent, and chemical and biological characteristics of lakes in
regions of the United States that are potentially sensitive to acidic deposition.
The purpose of the National Stream Survey (NSS) is to determine the percentage, extent, and location
of streams in the United States that are presently acidic or have low acid-neutralizing capacity and
might therefore be susceptible to future acidification, as well as to identify streams that represent
important classes in each region for possible use in more intensive studies or long-term monitoring.
The NSS provides an overview of stream water chemistry in regions of the United States that are
expected, on the basis of previous alkalinity data, to contain predominantly waters with a low acid-
neutralizing capacity.
Variables monitored include acid neutralizing capacity, aluminum, base cations, conductance, major
ions, metals, nitrate, organics, pH, and sulfate. A randomly selected subset of lakes was sampled using
appropriate methods. The sample results were then weighted to estimate the chemical compositions of
lake populations with known confidence. Uncertainties related to time of sampling, spatial variability,
and population definition are included in specific research projects to improve confidence in estimates.
The NSS employed a randomized, systematic sample of regional stream populations and used rigorous
quality assurance protocols for field sampling and laboratory chemical analysis.
Contact:
Environmental Research Laboratory
U.S. Environmental Protection Agency
200 SW 35th Street
Corvallis, OR 97333
Phone: 503-754-4423
6. NATIONAL COASTAL POLLUTANT DISCHARGE INVENTORY (NCPDI)
USDOC/NOAA
The National Coastal Pollutant Discharge Inventory (NCPDI) Program is a series of database
development and analytical activities within the National Oceanic and Atmospheric Administration's
(NOAA) Strategic Assessment Program for coastal and estuarine areas. The cornerstone of the
program is a comprehensive database and computational framework that has been developed over the
last 9 years. The database contains pollutant loading estimates for all major categories of point,
nonpoint, and riverine sources located in coastal counties of the 200-mile exclusive economic zone that
discharge to the estuarine, coastal, and oceanic waters of the conterminous United States (excluding the
Great Lakes). The pollutant discharge estimates in the NCPDI are made for each coastal component
for the following base years:
-------
• East coast - 1982
• West coast - 1984
• Gulf coast - 1987
The estimates can be considered to approximate pollutant discharge conditions for a 5-year period
around the base year. Estimates are made for nine major source categories and 17pollutants. Source
categories include:
• Point sources
• Urban nonpoint sources
• Nonurban nonpoint sources
• Irrigation return flow
• Oil and gas operations
• Marine transportation operations
• Accidental spills
• Dredging operations
Pollutant estimates can be aggregated by county, USGS hydrologic cataloging unit, or estuarine
watershed. Pollutant parameters include:
• Flow (wastewater flow or surface runoff)
• Biochemical oxygen demand
• Particulate matter
• Nutrients (total nitrogen and phosphorus)
• Metals (arsenic, cadmium, chromium, copper, iron, lead, mercury, and zinc)
• Petroleum hydrocarbons (oil and grease)
• Pesticides (35 compounds)
• Pathogens (fecal coliform bacteria)
• Wastewater treatment sludges
Estimates are based on a combination of computed methodologies and actual monitored observations.
Estimates are seasonal (winter, spring, summer, fall) for a base year. Updated discharge estimates for
1987 for the coastal areas of the Gulf of Mexico and for 1989 for the east coast are being prepared.
Contact:
Pollutant Sources Characterization Branch
National Oceanic and Atmospheric Administration
6001 Executive Boulevard, Room 220
Rockville, MD 20852
Phone: 301-443-0454
7. OCEAN DATA EVALUATION SYSTEM (ODES)
USEPA/OWOW
The Ocean Data Evaluation System (ODES) is a menu-driven system for storing and analyzing water
quality and biological data from marine, estuarine, and freshwater environments. The system supports
-------
Appendix B
federal, state, and local decision makers associated with marine monitoring programs. The system was
designed to support managers and analysts in meeting regulatory objectives through the evaluation of
marine monitoring information.
ODES contains over 2.5 million records of data from the National Estuary Program, the Great Lakes
National Program Office, the Ocean Disposal Program, the 301(h) Sewage Discharge Program, the
National Pollutant Discharge Elimination System (NPDES) Program, and the 403(c) Program.
Contact;
Office of Wetlands, Oceans, and Watersheds 5
USEPA Fairchild Building (4503F)
499 South Capitol Street, SW
Washington, DC 20024
Phone: 202-260-7028
8. WATERBODY SYSTEM (WBS) USEPA/OWQW
The Waterbody System (WBS) is an automated database of state water quality assessment information.
WBS facilitates collection, storage, retrieval, and analysis of water quality assessment information
collected by the states to meet EPA's congressional reporting requirements under section 305(b) of the
Clean Water Act.
The WBS contains information that helps program managers report accurately and quickly on the water
quality status of a particular waterbody. It might also be used to target resource expenditures and to set
surface water program priorities. Under the Clean Water Act, states submit information to EPA on
several types of surface waters affected by point or nonpoint source pollution, lakes monitored under
the Clean Lakes Program, and surface waters requiring the assigning of total maximum daily load
limits to restore or maintain their water quality.
WBS serves as an inventory of each state's navigable waters that have been assessed for water quality
and is used as the basis for the 305(b) report to Congress every 2 years. States assemble available
monitoring information and make judgments on water quality before summary information can be
entered into the system. WBS stores the components and the results of the assessment. The WBS is
not designed to store, manipulate, or analyze raw monitoring data.
Participation in the WBS is voluntary. The database is currently used by approximately 40 states,
territories, and river basin commissions. The database consists of assessments rather than monitoring
data and includes many optional fields. The consistency of WBS information within a state is quite
good. Those wishing to aggregate to a regional or national level should discuss data characteristics
with the WBS coordinator.
Contact:
Office of Wetlands, Oceans, and Watersheds
USEPA Fairchild Building (4503F)
499 South Capitol Street, SW
-------
Washington, DC 20024.
Phone: 202-260-3667
9. PESTICIDE INFORMATION NETWORK (PIN)
USEPA/OPP
The Pesticide Information Network (PIN), maintained by EPA's Office of Pesticide Programs, enables
pesticide monitoring data generated by a variety of sources to be routinely identified, obtained, and
used. PIN also provides federal, state, and local agencies with a means for sharing information and
expertise on pesticides. In addition, information in PIN is used to enhance the accuracy of pesticide
risk assessments and risk/benefit regulatory decisions regarding exposure and effects of pesticides
under the Federal Insecticide, Fungicide, and Rodenticide Act (FIFRA). PIN is composed of three
files:
• The Pesticide Monitoring Inventory (PMI) is a nationwide compilation of synopses of pesticide
monitoring projects conducted by federal, state, and local governments as well as private
groups. PMI includes the location of the monitoring project, the pesticides involved, an
abstract of the project, and the name and address of a contact person. PMI does not contain
hard data or results; these can be obtained from individual contact persons for each project.
• The Restricted Use Products (RUP) File is a regulatory file that serves as an information
resource for states. Information provided includes pesticide active ingredients, dates of
restriction, reasons for restriction, and all products that contain the restricted active ingredients.
• The Coordination File is a cross-referencing chemical index of all synonyms for the active
ingredients listed in the PMI and RUP files.
Contact:
Office of Pesticide Programs
USEPA Waterside Mall (H7507C)
401 M Street, SW
Washington, DC 20460
Phone: 202-557-5455
10. ENVIRONMENTAL CONTAMINANT DATA MANAGEMENT SYSTEM (ECDMS)
USDOI/USFWS
The Environmental Contaminant Data Management System (ECDMS) is the cataloging, sample
management, and data storage system for residue data from field studies conducted by the U.S. Fish
and Wildlife Service (USFWS). Data are from sample matrices consisting of animal and plant tissues,
sediments, soils, and water. The system contains data on pesticides, elements, PCBs, and other
compounds.
The National Contaminant Biomonitoring Program (NCBP) is maintained by USFWS to document
temporal and geographic trends in concentrations of persistent environmental contaminants that might
threaten fish and wildlife. NCBP data are maintained by the ECDMS. The NCBP is the USFWS
segment of the National Pesticide Monitoring Program, a multiagency monitoring effort by the member
-------
Appendix B
agencies of the Federal Committee on Pest Control. Since 1965, USFWS has periodically determined
concentrations of potential toxic elements and selected organochlorine chemicals in fish and wildlife
collected from a nationwide network of stations. The NCBP is being phased out with the
implementation of the broader Biomonitoring of Environmental Status and Trends (BEST) Program.
In addition to organochlorine chemical residues, freshwater fish, starlings, and waterfowl samples are
analyzed for arsenic, cadmium, copper, lead, mercury, selenium, and zinc. Composite samples of
whole freshwater fish are collected in replicate from 112 stations in major rivers throughout the United
States and in the Great Lakes. Starlings are collected in replicate from 139 terrestrial sites in the
conterminous 48 states. Wings of mallards and black ducks shot by hunters in the continental United
States are collected to assess body burden of organochlorine compounds in migratory birds. This
monitoring program has continued at 2- to 4-year intervals since 1965.
Contact:
Division of Environmental Contaminants
U.S. Fish and Wildlife Service
4401 North Fairfax Drive
Suite 330
Arlington, VA 22203
Phone: 703-358-2148
11. NATIONAL SHELLFISH REGISTER (NSR)
USDOC/NOAA
Classified shellfishing waters are monitored as an indicator of bacterial water quality nationwide.
Waters are classified for the commercial harvest of oysters, clams, and mussels based on the presence
of actual or potential pollution sources and coliform bacteria levels in surface waters. Each shellfish-
producing state classifies its waters hi accordance with guidelines established by the national Shellfish
Sanitation Program. Approximately 2,000 classified shellfishing areas are defined by:
• Name
• Location (nautical chart number, estuary, state, region)
• Classification (approved, prohibited, conditionally approved, restricted)
• Size
• Pollution sources (identified for all nonapproved areas)
Trends in classification by region from 1966 to 1990 and by selected estuaries in the Northeast,
Southeast, Gulf of Mexico, and Pacific from 1971 to 1990 are available. Areas that were reclassified
because of improved or diminished water quality are distinguished from those which were reclassified
as a result of improved monitoring. Data also are collected on administration of state programs,
including:
• Identification of state agencies responsible for monitoring waters, assigning classification,
analyzing water samples, etc.
• Number of personnel
• Budgets
-------
• Number of sampling stations
• Frequency of sampling
• Other factors that might influence classification
Data are collected by questionnaire, followed by interviews. Classifications are noted on 265 nautical
charts. Data were compiled in 1966, 1971, 1974, 1980, 1985, 1990, and 1995.
, Contact:
N/ORCA
National Oceanic and Atmospheric Administration
6001 Executive Boulevard
Rockville, MD 20852
Phone: 301-443-8843
12. NATIONAL STATUS AND TRENDS DATABASE (NST)
USDOC/NOAA
Beginning in 1984, NOAA undertook the task of providing information on the status and trends of
environmental quality in estuarine and coastal areas. The program defines the geographic distribution
of contaminant concentrations in tissues of marine organisms and in sediments. Status and trend data
are available from the Mussel Watch and Benthic Surveillance for 4 major elements, 12 trace elements,
DDT and its metabolites, selected chlorinated pesticides, selected PCB congeners, approximately 22
polyaromatic hydrocarbons, and ancillary sediment and tissue parameters.
Samples have been collected since 1984 at about 50 Benthic Surveillance sites and since 1986 at about
150 Mussel Watch sites. Sediment samples are collected at all sites. At Benthic Surveillance sites,
benthic fish.are collected and their livers excised and stored for subsequent chemical analysis. At
Mussel Watch sites, bivalve mollusks are collected for analysis. Data are collected annually.
Contact:
Ocean Assessments Division
National Oceanic and Atmospheric Administration
6001 Executive Boulevard
Rockville, MD 20852
Phone: 301-443-8655
13. NATIONAL CLIMATE DATA CENTER (NCDC)
USDOC/NOAA
The National Climatic Data Center (NCDC) collects, processes, and archives meteorological and
climatological data from a global network of stations. Records begin in the mid-19th century and
continue to the present. Climatic variables (e.g., temperature, precipitation, solar radiation, storms,
wind, and floods) are summarized for both short-term and long-term periods of record. Data are
available in published form, on microfiche, or on magnetic tape. Derived values relating to growing
season and heating and cooling degree days are also produced. Special statistical summaries of actual
and derived values of meteorological elements over the world's oceans, as well as summaries used in
the study of air pollution, are available.
-------
Appendix B
For about four decades, NCDC has been receiving climatic data from across the United States and
around the globe. Principal sources in the United States are the National Weather Service (NWS), the
Federal Aviation Administration, die U.S. Air Force, the U.S. Navy, and the U.S. Coast Guard. The
NWS's Cooperative Station Network is composed mainly of 10,000 volunteer observers and has been
recording daily records since the 1800s. As aircraft began to fill the skies, information on the upper
atmosphere was needed. Balloon-borne instruments radioed data; radars began to probe the clouds;
rockets reached the fringes of the atmosphere; weather satellites, both geo-stationafy and polar
orbiting, now continuously watch and record the weather. Technical advancements led NCDC to
archive some of its data on CD-ROMs so that users could look at a large amount of climatic data at one
time. NCDC plans to archive new data sets using the latest technical advances available. Observations
are taken at varying intervals, from every 15 minutes to once per month. Collections are daily or
monthly depending on type and source of information.
Contact:
Climate Services Division
NOAA/NESDIS E/CC3
Federal Building
Asheville, NC 28801-2696
Phone: 704-259-0682
14. SYNOPTIC RAINFALL DATA ANALYSIS PROGRAM (SYNOP)
USEPA
EPA maintains a Synoptic Rainfall Data Analysis Program (SYNOP) as a tool for summarizing and
statistically characterizing rainfall records. SYNOP summarizes hourly rainfall data by storm events,
calculating for each event the volume (inches), duration (hours), average intensity (inches per hour),
maximum intensity (inches per hour), time since the previous storm (hours), and antecedent rainfall
(inches); the hours of missing data; and the hours that the meter did not read. SYNOP then uses these
storm event statistics to determine monthly and annual means and coefficients of variation for the
various storm parameters. =
Contact:
SYNOP
USEPA Waterside Mall (H7507C)
401 M Street, SW
Washington, DC 20460
Phone: 202-382-7112
15. ACID DEPOSITION SYSTEM (ADS)
USDOI/USGS
The National Acid Deposition Program/National Trends Network (NADP/NTN) was the first, and
continues to be the only, U.S. network to monitor precipitation chemistry on a national scale. The
current network consists of 196 sites in the conterminous United States, Hawaii, Puerto Rico, and
American Samoa. Sites are located in predominantly rural areas to avoid the localized influences of
large point sources and major urban centers. Nearly 14 years of continuous data are available from the
-------
sites with the greatest longevity; many of these sites are associated with the State Agricultural
Experiment Stations.
The primary objective of the NADP/NTN is the determination of geographical patterns of temporal
trends in chemical deposition. The program provides scientists, managers, and policy makers with
weekly precipitation chemistry data and information on geographical patterns and temporal trends in
concentrations and deposition of hydrogen, sulfate, nitrate, ammonium, calcium, magnesium, sodium,
potassium and chloride, and ortho-phosphate ions in precipitation. Final quality-assured data are
available to a multitude of data users upon request, within 6 months of sample collection. Principal
constituents monitored in precipitation and analyzed for trends are:
• pH
• Specific conductance
• Hydrogen ions
• Sulfate and nitrate ions
• Ammonium and calcium ions
• Chloride, magnesium, sodium, and potassium ions
The NADP/NTN monitoring program has developed criteria and protocols that ensure uniformity in
siting, sampling methods, analytical techniques, data handling, and overall network operations.
Precipitation is collected by wet/dry precipitation collectors and rain gages. Analytical methods for the
chemical variables measured are:
pH
Field pH
Laboratory conductivity
Electronic detection of hydrogen (also reported as pH)
Automated calorimetric detection of ammonium
Atomic absorption spectrophotometric detection of calcium, magnesium, sodium, and
potassium
Ion chromatographic detection of sulfate, nitrate, and chloride
Samples are collected weekly. Data from some sites are available from 1979. The data are maintained
on the Acid Deposition System (ADS).
Contact:
U.S. Geological Survey
National Center, MS 416
12201 Sunrise Valley Drive
Reston, VA 22092
Phone: 703-648-6875
-------
Appendix's
16. MAJOR LAND USES IN THE UNITED STATES (MLU)
USDA/ERS
For more than 50 years, Economic Research Services (ERS) and its predecessor agencies have
estimated acreage and maintained an inventory of the major uses of land in the United States at
intervals coinciding with the Census of Agriculture. Estimates are made for major land use classes:
• Cropland
• Grassland pasture and range
• Forest land
• Special use
• Unclassified use
Each major class is further classified by specified uses, and some are classified by ownership. Land
uses are also designated as agricultural and nonagricultural. Agricultural land uses include: .
• Cropland (cropland harvested, cropland failure, cultivate summer fallow, and idle cropland)
• Grazing lands (cropland pasture and permanent pasture and range)
• Grazed forest land
• Miscellaneous agricultural uses (farmsteads, farm roads, and farmlands)
Special uses include:
• Forest land not grazed
• Intensive uses (highways and roads, railroads, and airports)
• Extensive uses (national parks, state parks, wilderness areas, federal wildlife areas, state
wildlife areas, national defense areas, and federal industrial facilities)
Other unclassified land uses include urban, special uses not inventoried, and other miscellaneous areas,
such as marshes, open swamps, bare rock areas, deserts, and tundra. Data are analyzed for trends.
Data from the Bureau of the Census, agencies of the Department of Agriculture, public land
management and conservation organizations, and other sources are assembled, analyzed, and
synthesized to estimate state, regional, and national land use acreage. The major uses of land are
inventoried every 5 years, coinciding with years in which the Census of Agriculture is completed. The
inventories generally have been comparable in format and coverage since 1945. The series on
"cropland used for crops" dates back to 1909. ,
Contact:
Economic Research Service
U.S. Department of Agriculture
1301 New York Avenue, NW, Room 408
Washington, DC 20005-4788
Phone: 202-219-0424
-------
17. AGRICULTURAL CENSUS (AGCENSUS)
USDOC/BOC
The Agricultural Census (AgCensus) database includes about 750 variables reported at the county level
for 1978, 1982, and 1987. The census is scheduled for years ending in 2 and 7. AgCensus variables
include simple statistics such as the number of farms, total acreage of farms, acreage of various major
crops, and total number of various livestock. It also includes such data as the number and acreage of
irrigated farms, the number and acreage of farms irrigated by various sources, the number and acreage
of farms by farm size, commercial fertilizer and other agricultural chemical expenditures, the number
of farms and livestock by herd size, the number and acreage of vegetable farms, and a subset of these
statistics for farms with sales greater than $10,000.
AgCensus does not include pesticide application rates or even the acreage to which specific pesticides
are applied. Fertilizer and manure application rates and acreage are also not reported. Irrigation rates
and farm management practices are likewise not included in the database.
Contact:
Agriculture Division
Bureau of the Census
U.S. Department of Commerce
Washington, DC 20233
Phone: 301-763-1113
18.
FOREST SERVICE RANGE MANAGEMENT INFORMATION SYSTEM (FSRAMIS)
USDA/USFS
The Forest Service Range Management Information System (FSRAMIS) collects and analyzes data on
grazing in national forests and grasslands. FSRAMIS provides grazing use statistical data. Data on the
number of grazing animals (cattle, horses and burros, sheep and goats), animal unit month, and number
of permittees are reported at the national level and for each type of Forest Service land, region, and
state. Other variables measured include:
• Allotment condition
• Improvement inventory and activity
• Grazing capacity
• Actual use
• Authorized use -.. ,
• Unauthorized use ; "
Data are analyzed for trends in ecological potential. Data on grazing on the National Forest System
lands are extracted from the grazing permits. Data on free-roaming horse and burro populations are
estimated by census. Data are collected on cycles ranging from annual to once every 3 to 5 years.
Contact:
Range Management Staff
U.S. Forest Service
Department of Agriculture
-------
Appendix B
P.O. Box 96090
Washington, DC 20090-6090
Phone: 202-205-1460
19. FOREST INVENTORY AND ANALYSIS PROGRAM (FIA)
USDA/USFS
The Forest Inventory and Analysis (FIA) program is responsible for making and keeping current a
comprehensive inventory and analysis of the renewable forest and rangeland resources of the United
States. Initial inventory efforts began in the West in 1930; by the 1960s, inventories had been ,
completed for all of the 48 conterminous states and many of the important forested states had been
reinventoried. The inventory data and analysis provide trend information on the extent, condition,
ownership, and composition of the Nation's forests as well as information about wildlife habitat, forage
production, and other resource characteristics needed for resource planning. At least 43 kinds of
resource data are collected for sample plots during the inventory, including:
• Land use ,
• Land ownership
• Forest type
• Stand age
* Stand size and volume classes
• Harvest history
• Soils data ,
• Tree data (species, diameter at breast height, height, cull, etc.)
• Other vegetation data
• Non-timber data
These data are used to make estimates of forest land areas, species composition, timber volume, and
net annual timber growth, removals, and mortality by forest type, state, region, ownership, softwood
and hardwood sawtimber species, productivity class, diameter class, and other classifications. The
volume of roundwood products harvested by material species group, region, and product is estimated.
Estimates also are made of areas harvested or otherwise disturbed, regenerated to forest, or cleared for
other uses. Additional estimates of recreation use, wildlife values, site productivity, physiographic
characteristics, and other items are made. The data are maintained in three databases:
• National Resources Planning Act (RPA) Timber Database
• Eastwide Forest Inventory Database
• Forest Inventory and Analysis Database
Data are gathered using a two-phase sampling design, with the first phase involving ground
measurements at sample plots, each covering an acre. Depending on the extent to which sensing is
used, ground sample intensity ranges from one plot per 3,000 acres to one plot per 10,000 acres.
Statewide timber inventory information has been collected continuously for about 50 years. In most
regions of the United States, the third inventory cycle has been completed and some areas have been
inventoried as many as five times. Each year, some 50 million acres are inventoried in the
-------
conterminous United States. Currently this rate of coverage translates into an inventory cycle of 12
years for the Nation.
Contact:
U.S. Department of Agriculture
Forest Service
P.O. Box 96090
Washington, DC 20090-6090
Phone: 202-205-1343
20. NATIONAL LAND USE AND LAND COVER MAPS
USDOI/USGS
As part of its National Mapping Program, the USGS produces and distributes land use and land cover
maps and digitized data. Land use refers to'human activities that are directly related to the land. Land
cover describes the vegetation, water, natural surface, and artificial constructions at the land surface.
Associated maps display information on political units, hydrologic units, census county subdivision,
and, in some cases, federal land ownership. Land use and land cover areas are classified into nine
major classes:
• Built-up land
• Agricultural land
• Rangeland
• Forest land
• Water areas
• Wetlands
• Barren land
• Tundra
• Perennial snow or ice
Each major class is subdivided into several minor classes, for 37 minor classes total. For example,
forest lands are further classified as deciduous, evergreen, or mixed forest land, and water is further
classified as streams and canals, lakes, reservoirs, or bays and estuaries. Remote sensing methods are
used, including satellite imagery, high-altitude imagery, medium-altitude remote sensing, and low-
altitude imagery. Data were collected in the late 1970s and early 1980s.
Contact:
Office of Geographic and Cartographic Research
U.S. Geological Survey
National Center, MS 590
12201 Sunrise Valley Drive
Reston, VA 22092
Phone: 703-648-5741
-------
Appendix B
21. POPULATION CENSUS DATA (CENDATA)
USDOC/BOC
The decennial census provides a comprehensive set of population statistics for the United States. Basic
demographic characteristics are collected on a 100-percent basis. Social and economic characteristics
are collected from a large sample of all households and persons in group quarters. The decennial
census provides demographic (e.g., age, race, sex, relationship), social (e.g., education, migration,
ancestry, language), and economic (e.g., occupation, industry, income, place of work) characteristics
of the population of the United States, Puerto Rico, the Virgin Islands, Guam, American Samoa, the
Norther Marianas, and Palau. Trend data are available from previous decennial censuses. Basic
demographic data are collected from 100 percent of the population. Social and economic
characteristics are collected from a large sample, approximately one in six in 1980 and 1990.
Contact:
Population Division
Bureau of the Census
U.S. Department of Commerce
Washington, DC 20233
Phone: 301-763-7890
22. NATIONAL RESOURCES INVENTORY (NRI)
USDA/NRCS
For 50 years, the Natural Resources Conservation Service (NRCS), formerly the Soil Conservation
Service (SCS), has been conducting periodic inventories of the Nation's soil, water, and related
resources. The National Resources Inventory (NRI), which is an extension and modification of earlier
inventories, provides data on the status, condition, and trends of these resources on.nonfederal land in
the United States. The many types of data collected by the NRI process are organized into eight
general categories:
• Soil characteristics and interpretations (including agricultural land capability)
• Land cover
• Land use (including irrigated and nonirrigated cropland, grazed and ungrazed forest land,
prime farmland, etc.)
• Erosion (such as sheet and rill, wind, and ephemeral gullies)
• Land treatment (such as irrigation, tillage, and windbreaks)
• Conservation treatment needs
• Vegetative conditions (such as wetlands, rangeland condition and species, and pasture
management)
• Potential for conversion to cropland
The NRI is a multiresource inventory based on soils and related resource data collected at scientifically
selected random sample sites. The NRI sample design was developed by the Iowa State University
Statistical Laboratory at Ames. It uses census area and point methods for data collection. Data
collection involves both field investigation and remote sensing. Data are collected on a 5-year cycle.
Recent surveys were conducted in 1,977, 1982, and 1987.
-------
The 1987 NRI data were collected from nearly 300,000 sample sites from all counties of the United
States, except those in Alaska, and in Puerto Rico and the Virgin Islands. Most of these samples were
part of the 1982 NRI, which had nearly 1 million sample sites. The 1987 NRI data have a high degree
of reliability at the state level, and the 1982 NRI date provide a high degree of reliability at the
multicounty level. Data estimates can be made by Major Land Resource Areas (MLRA), NRCS
Administrative Areas, Water Resources Council Aggregated Subareas, and other multicounty
geographic subdivisions.
Contact:
Resources Inventory and Geographic Information Systems Division
U.S. Department of Agriculture
Natural Resources Conservation Service
P.O. Box 2890
South Agricultural Building, Room 6175
Washington, DC 20013
Phone: 202-720-4530
23. ECOREGION MAPS
USEPA/ERL
Ecoregions are defined by EPA to be regions of relative homogeneity in ecological systems or in
relationships between organisms and their environments. Ecoregions of the United States have been
mapped to help water resource managers understand better the regional patterns of ecosystem quality
and the relative importance of factors that might be determining this quality. Specifically, the
ecoregion framework can establish a logical basis for characterizing ranges of ecosystem conditions or
quality that are realistically attainable. A national ecoregion map has been prepared at a scale of
1:7,500,000 and regional maps are prepared at a scale of 1:2,500,000. The maps are available in an
ARCINFO format for use by individual users.
Contact:
U.S. Environmental Protection Agency
Environmental Research Laboratory
200 SW 35th Street
Corvallis, OR 97333
Phone: 503-757-4601
24. NATIONAL WETLANDS INVENTORY (NWI)
USDOI/USFWS
In 1975, the U.S. Fish and Wildlife Service (USFWS) established the National Wetlands Inventory
(NWI) to develop technically sound and comprehensive information on the characteristics and extent of
wetland resources in the United States. Status and trends information is available for selected wetland
types including estuarine wetlands, palustrine wetlands, lacustrine wetlands, and deep water habitats in
the lower 48 states. In addition, statistical data are available for coastal waters and bay bottoms,
coastal marshlands and mangroves, recent changes in inland vegetated wetlands, recent changes in
lacustrine deepwater habitats, estimates of current annual wetland losses, estimates of wetland losses by
fly ways, states with significant changes in wetland resources, indicators of development pressures on
-------
Appendix B
wetland resources, and causes of wetland losses. The Emergency Wetlands Resources Act of 1986
requires that updates of the wetland status and trends be produced on 10-year cycle with reports due in
1990, 2000, 2010, etc. Data are collected continuously. The 1990 update provides trend data oh
wetlands losses and gains between the 1970s and the 1980s.
The wetland mapping phase of the project has produced map coverage for approximately 70 percent of
the lower 48 states, 22 percent of Alaska, and all of Hawaii, Puerto Rico, and Guam. Wetland status
and trends information is designed to provide statistical estimates on a national basis (lower 48 states).
In addition, regional intensification studies are available for the Chesapeake Bay Region and the
Central Valley of California. Other statewide status information is available for the states of Florida,
Delaware, New Jersey, Illinois, Washington, Maryland, and Connecticut. Status reports covering the
coastal wetlands of Alaska and the Prairie Pothole Region (North Dakota, South Dakota, Minnesota)
are also available.
Contact:
National Wetlands Inventory
U.S. Fish and Wildlife Service
Suite 101 Monroe Building
9720 Executive Center Drive
St. Petersburg, FL 3702-2440
Phone: 813-893-3624
-------
DATABASE
STORET
NAWDEX
WBS
NASQAN
NWIS
PIS
SUEDATABASES/
DATATYPES
• Water Quality File
• Biological System Field Survey File (BIOS)
• Daily Flow File. (DFF)
• Fish Kill File (FK)
• Water Data Sources Directory: Information
on organizations that collect water and water-
related data
• Master Water Data Index: Information on
sites at which surface water, ground water,
or water quality data are collected
• Surface waters
• Overall water quality, suitability for
designated uses, causes and sources of water
quality problems
• Concentration, mass transport
• Major ions, nutrients, heavy metals,
pesticides, inorganic and organic carbon
• Spatial, chemistry, flow, biological
• Surface water and ground water
• PMI (Pesticide Monitoring Inventory):
synopses of pesticide monitoring projects;
location, pesticides monitored, abstract,
contact
• RUP (Restricted Use Products file): Pesticide
active ingredients, restriction dates and
reasons, names of all products with restricted
active ingredients
• Chemical Index: Cross-references for
synonyms for active ingredients listed in PMI
and RUP
AVAILABILITY
• USEPA
mainframe
• USGS
NAWDEX
USGS NAWDEX
USEPA
mainframe
USGS NAWDEX
USGS
USEPA
'NOTES,
• A modernized
STORET, with greater
computing and data-
handling capabilities,
will be on-line in 1997.
• An interagency
program to facilitate
the exchange of water
data and promote the
improvement of water
data handling
• Provides access to
WATSTORE and
STORET
• Management tool to
track state assessments
of ambient water
quality for surface
waters
• Source of 305(b) report
information
• Focuses on large river
systems: Mississippi,
Columbia, Colorado,
Rio Grande, St.
Lawrence,
Susquehanna,
Alabama, Tombigbee,
Yukon
• Replacing
WATSTORE and
NAWDEX
• Designed to be a
flexible hydrologic data
management and
processing system
• Contains up-to-date
pesticide information
-------
Appendix B
DATABASE
ODES
WQAS
HBN
NAWQA
NSWS
NCPDI
ECDMS
SUBDATABASES/
DATATYPES
• Ocean outfall data (301(h), 403(c))
• NPDES discharge permit data
• Ocean dumping data
• National Estuary Program data
• GLNPOdata
• ASIWPCA: water quality impairments
• CITY: data on U.S. cities
• GAGE: stream gaging data
• REACH FILE: stream reach data
• Stream discharge
• Nutrients
• Inorganics, major ions, trace elements
• Suspended sediment
• Bacteriology
• Water quality
• Sediment
• NSS (National Stream Survey): stream
acidity, stream chemistry, acid neutralizing
capacity aluminum, base cations,
conductance, major ions, metals, nitrate,
organics, pH, sulfate
• NLS (National Lake Survey): lake chemistry
• Pollutant loading estimates for all major
categories of point, nonpoint, and riverine
sources in coastal counties.
• Flow, particulates, BOD, nutrients, metals,
petroleum hydrocarbons, pesticides,
pathogens
• Residue data from USFWS field studies
• Animal and plant tissues, sediments, soils,
water
• Pesticides, elements, PCBs, other compounds
* NCBP (National Contaminant Biomonitoring
Program): USFWS segment of National
Pesticide Monitoring Program; temporal and
geographic trends in concentrations of
persistent environmental contaminants
AVAILABILITY
USEPA
USEPA
mainframe
• USGS
WATSTORE
• USEPA
STORET
USGS
USEPA/ERL,
Corvallis, OR
NOAA
USFWS
NOTES
• For storing and
analyzing marine
environmental data
• Contains statistical,
graphical, and
modeling tools
• 50 monitoring sites in
basins with little
human influence on
hydrologic
characteristics
• Designed to assess
historical, current, and
future water quality
conditions in
representative river
basins and aquifers
• Covers 60 river basins
and aquifers that
account for 60%-70%
of U.S. water use and
1/2 of U.S. land area
• NSS's purpose is to
determine percentage,
extent, and location of
streams with low acid-
neutralizing capacity
• Baseline years:
1982 - east coast;
1984 - west coast;
19.87 -Gulf coast
• NCBP being phased
out with
implementation of
BEST Program
-------
DATABASE
NSR
NST
NCDC
SYNOP
ADS
MLU
National Land
Use and Land
Cover Maps
CENDATA
SUBDATABASES/
DATATYPES
• Classified shellfishing waters; name,
location, NSSP classification, size, pollution
sources
• State program administration information
• Estuarine and coastal waters
• Geographic distribution of contaminant
concentrations in tissues of organisms and
sediments
• 4 major elements, 12 trace elements, DDT
and metabolites, chlorinated pesticides, PCB
congeners, 22 polyaromatic hydrocarbons,
sediments, tissues
• Temperature, precipitation, solar radiation,
storms, floods, wind
• Derived values for growing season, heating
and cooling degree days
• Statistical summaries of meteorological
elements over the oceans
• Air pollution summaries
• Hourly rainfall data summarized by storm
event for volume, duration, average intensity,
maximum intensity, time since previous
storm, antecedent rainfall, hours of missing
data, hours not read
• Monthly and annual means, coefficients of
variation for storm parameters
• Specific conductance, pH, hydrogen ions,
sulfate and nitrate, ammonium, calcium
chloride, magnesium, sodium, potassium
• Cropland, grassland pasture and range, forest
land, special uses, unclassified uses
• Subclassified by specific use, ownership
• Human land use activities
• Land cover by vegetation, water, natural
surface, artificial construction
• Political unit, hydrologic unit, census county
subdivision, federal land ownership
• Population, demographic, social, economic
AVAILABILITY
NOAA
NOAA
NOAA
USEPA
uses
USDA
USGS
USDOC, Bureau
of the Census
NOTES
• Trends in
classifications are
available
• Data from Benthic
Surveillance sites and
Mussel Watch sites
• Available as published,
microfiche, or
magnetic tape
• Tool for summarizing
and statistically
characterizing rainfall
records
• Only national network
to monitor precipitation
chemistry
• Monitoring sites are
predominantly rural
• Special uses: intensive
(roads, airports,
railroads), extensive
(parks etc., defense,
federal industrial)
• Data for maps were
collected from the mid-
1970s to early 1980s
• Updated every
10 years
-------
Appendix B
DATABASE
AgCensus
FSRAMIS
FIA
NRI
Ecorcgion Maps
SUB1>ATABASES/
DATA TYPES
• Total acreage of farms, acreage of major
crops, total number of livestock, number and
acreage of irrigated farms, number and
acreage of farms irrigated by various
sources, number and acreage of farms by
size, commercial fertilizer and other
agricultural chemical expenditures, number
of farms and livestock by herd size, number
and acreage of vegetable farms, statistics for
farms with sales > $10,000.
• Grazing activity on national forests and
grasslands; cattle, horses, burros, sheep,
goats
• Number of grazing animals, animal unit
month, number of permittees
• National level, region, state, type of forest
service land
• Allotment condition, improvement inventory
and activity, grazing capacity, actual use,
authorized use, unauthorized use
• RPA Timber Database
• Eastwide Forest Inventory Database
• Forest Inventory and Analysis Database
• Land use, land ownership, forest type, stand
age, stand size and volume class, harvest
history, soils, trees, other vegetation, non-
timber
• Forest land area; species composition; timber
volume; net annual timber growth, removals,
mortality.; area harvested; area regenerated;
area converted to non-forest use
• Recreation use, wildlife value, site
productivity, physiographic characteristics
• Soil characteristics, land cover, land use,
erosion, land treatment, conservation
treatment needs, vegetative condition,
potential for conversion to cropland
• EcoregionsoftheU.S.
• National (1:7,500,000), regional
(1:2,500,000)
AVAILABILITY
USDOC, Bureau
of the Census
USDA, USFS
USDA, USFS
USDA, NRCS
USEPA/ERL,
Corvallis, OR
NOTES '
• County-level data
• Data are analyzed for
trends in ecological
potential
• Comprehensive
inventory and analysis
of forest land and
rangeland
• Information on non-
federal lands
• Data collected at
5-year intervals
• Intended for water
resource management
• Available in
ARCINFO format
-------
AppencEfT
DATABASE
NWI
SUBDATABASES/
.,<. -DATATYPES
• Estuarine, palustrine, lacustrine, deepwater
wetlands
• Coastal waters, bay bottoms, coastal
marshlands, mangroves
• Changes in inland vegetated wetlands,
changes in lacustrine deepwater habitats
• Current annual wetland loss, wetland loss by
flyway, states with significant changes in
wetland resources, causes of wetland loss
AVAILABILITY
USDOI, USFWS
NOTES'
• Updated every
10 years
• Regional intensive data
available for
Chesapeake Bay,
Central Valley (CA)
• Statewide information
for PL, DE, NJ, IL,
WA, MD, CT
• Status reports for
Coastal AK, Prairie
Pothole Region (ND,
SD, MM)
-------
-------
APPENDIX C. EXAMPLE MONITORING PROGRAMS
This appendix contains three examples of nonpoint source monitoring programs that are considered by
many nonpoint source experts to be good programs for the objectives they address. These examples
should not be copied indiscriminately for use in every other similar watershed, but should only be used
as references for designing good monitoring programs for similar situations.
C.1 ST. ALBANS BAY, VERMONT RURAL CLEAN WATER PROGRAM
The St. Albans Bay, Vermont, monitoring and evaluation program was funded at $1.6 million over 11
years (1980-1990), a price well beyond reach for many watersheds of similar size (33,344 acres)
(Smolen et al., 1986). The program had three objectives (Vermont RCWP Coordinating Committee,
1986):
• Document changes in the water quality of specific tributaries within the watershed resulting from
implementation of manure management practices.
• Measure changes in suspended sediment and nutrients entering St. Albans Bay resulting from,
implementation of water quality management programs within the watershed.
• Evaluate trends in the water quality of St. Albans Bay and the surface waters within the St. Albans
Bay watershed during the period of the St. Albans Bay RCWP Watershed Project.
To achieve these objectives, the monitoring strategy for the St. Albans Bay Watershed included long-
term water quality monitoring, related long-term monitoring, and short-term intensive studies. The
monitoring sites for all sampling are shown in Figure C-l and listed in Table C-l. .
C.1.1 Long-term Water Quality Monitoring
The long-term monitoring in St. Albans Bay included four monitoring levels. Level 1 Bay Sampling
was designed to determine long-term water quality trends in St. Albans Bay over the life of the project
(four monitoring stations in the bay). Level 2 Tributary Sampling was designed to determine the long-
term water quality trends for the major tributaries including the Bay and the St. Albans City wastewater
treatment plant (six monitoring stations located throughout the watershed). Level 3 monitoring was
designed to evaluate the effect of best manure management practices on the quality of surface runoff
from individual fields (two monitoring stations located in the headwaters of the Jewett Brook
sub watershed), while Level 4 was designed to supplement the Level 2 monitoring by sampling
additional tributaries to St. Albans Bay and to isolate subunits within the Level 2 subwatersheds (four
monitoring stations) (Vermont RCWP Coordinating Committee, 1984). Table C-2 lists the variables
monitored for each level of the long-term monitoring.
C.1.2 Related Long-Term Monitoring
Precipitation and other climatological data were collected in the St. Albans Bay Watershed at four
locations. Biological monitoring was conducted in St. Albans Bay and in bay tributaries. Bay
biological monitoring corresponded to the long-term Level 1 stations. Tributary biological monitoring
-------
Appendix C
N
Figure C-1. St. Albans Bay Watershed, Franklin County, Vermont, sampling locations. (Source: USEPA,
1982c) . ,
was conducted at five locations. A cooperative program was developed to collect land use and
agricultural activity information from each farm in the watershed. Both baseline information
and daily field log data were collected and entered into a geographic information system.
Table C-3 lists the parameters monitored for each of the related long-term monitoring efforts.
C.1.3 Short-term Intensive Studies
The short-term intensive studies included three components: wetland influences, bay and wetland
sediment, and bay circulation. Stevens Brook wetland was sampled to determine the effects of the
-------
Table C-1. St. Albans Bay watershed, Franklin County, Vermont, sampling station summary.
Type of Monitoring
Level 1 : Long-term Bay
and Biological
Level 2: Long-term Tributary
Level 3: Manure Management Evaluation
Level 4: Additional Tributary
Meteorological
Biological
Station Description
Station 11: Outer Bay
Station 12: Inner Bay
Station 13: Beach
Station 14: Off Bridge (new)
Station 21 : Jewett Brook
Station 22: Stevens Brook
Station 23: Rugg Brook
Station 24: Mill River
Station 25: St. Albans Wastewater Treatment Plant
Station 26: Stevens Wetland
Station 31 : Larose ditch - below site
Station 32: Larose ditch - above site
Station 41 : Jewett Brook
Station 42: Stevens Brook
Station 43: Guayland Brook
Station 44: Mill River
P-1 : St. Albans Radio Station
P-2: Dunsmore Farm
P-3: Franklin Ford Tractor
P-4: LaRose Farm
Station 21 : Jewett Brook
Station 22: Rugg Brook
Station 23: Mill River
Station 22A: Stevens Brook above STP outfall
Station 22B: Stevens Brook below STP outfall
Wetland on water entering St. Albans Bay from point and nonpoint sources.. Fifteen sampling stations
were located within the wetland along the brook channels. Station 26 served as a site for continuous
monitoring of the wetland outlet. Fifteen sampling stations were located within the bay and contiguous
wetland to determine the chemical and physical properties of the sediments. Six of these stations were
used for within-year temporal studies. Sediment phosphorus release studies were also conducted at
these sites. Wind, water current, and concentration data were collected in St. Albans Bay to determine
the effect of bay circulation on water quality. A model was developed to predict phosphorus
concentrations in the bay under different loading rates and meteorological conditions.
Table C-4 lists the parameters monitored for each of the short-term studies.
C.1.4 Sampling and Analytic Techniques
Table C-5 summarizes the frequency and type of sample collected for long-term water quality
monitoring, although not all stations were sampled throughout the 10-year study. The methods used to
determine each water quality parameter are given in Table C-6.
-------
Appendix C
Table C-2. Long-term variables monitored for each level in the St. Albans Bay watershed.
Variable
Turbidity
Total Suspended Solids
Volatile Suspended Solids
Total Phosphorus
Ortho-Phosphorus
Ammonia-Nitrogen
Total Kjeldahl Nitrogen
Nitrite + Nitrate as Nitrogen
Chlorophyll a
Fecal Coliform
Fecal Streptococcus
Temperature
Dissolved Oxygen
PH
Conductivity
Secchi Disc
Flow
Long-term Monitoring Level
1
•
•
•
•
•
*
«
•
•
•
•
•
a
a
•
o
2
•
•
•
•
•
• •
•
•
•
•
•
•
•
•
3
•
•
a
•
o
o
•
4
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
-------
Table C-3. Monitored variables for related long-term monitoring in the St. Albans Bay watershed.
Type of Monitoring
Variables
Meteorological
Continuous precipitation and streamflow
Continuous temperature (Level 3 only)
Wind speed and direction (Bay only)
Biological
Fish species and abundance, benthic invertebrates, periphyton,
macroinvertebrates
Land Use
Land use - field by field, activity dates
Livestock - type, number, housing, dates
Manure management - type, capacity, fields, dates
Fertilizer - type, amount, field, dates
Pesticide/agrichemicals - type, amount, field, dates
Milkhouse - type, disposal
Barnyard - size, paving, use schedule
Drainage - type, fields
Soils, topography, streams, farm and watershed boundaries
Table C-4. Monitored variables for the short-term intensive studies in the St. Albans Bay watershed.
Type of Monitoring
Wetland Influences
Bay and Wetland
Sediments
Bay Circulation
Temperature
Dissolved Oxygen
Discharge
Chloride
Total Suspended Solids
Volatile Suspended Solids
Redox potential
Porosity
Grain size distribution
% organic matter
Iron
Total phosphorus
Chloride
Variables
Total Phosphorus
Ortho-phosphorus
Total Kjeldahl Nitrogen
Ammon ia-N itrogen
Nitrate + Nitrite as Nitrogen
PH
Total phosphorus
NH4CI phosphorus
NH4Ac phosphorus
NaOH phosphorus
HCI phosphorus
Water velocity and direction
Wind speed and direction
-------
Appendix C
Table C-5. Water quality sampling schedule for the St. Albans'Bay watershed.
Level
1
2
3
4
Variables
All
Fecal coliform
Fecal streptococcus
Temperature
Turbidity
PH
Dissolved oxygen
Solids
Nitrogen
Phosphorus
All
All
Sample
Type
Grab
Grab
In situ
Composite
Composite
Grab
Season
October-April
May-July
August-September
All
All
All
All
May-February
March-April
Sample
Frequency
Monthly
Biweekly
Weekly
Weekly
Biweekly
2-48 hour
and 1-72 hour
Weekly
4 hour
20 days (avg.)
Weekly
-------
Table C-6. Methods of water quality analysis used in the St. Albans Bay RCWP.
Variable
PH
Dissolved Oxygen
Conductance/Temperature.
Turbidity :
Total Suspended Solids3
Volatile Suspended Solids
Total Phosphorus
Orthophosphate3
Nitrate + Njtrite-N9
Ammonia-N
Total Kjeldahl. Nitrogen
Chemical Oxygen Demand
Fecal Coliform
Fecal Streptococci
Storage
in situ .
in situ
. in situ
cool, 4°C
cool, 4°C
cool, 4°C
cool, 4°C
acid (pH<:2)
cool, 4°C
cool, 4°C
acid (pH<;2)
cool, 4°C
.acid (pH^2)
cool, 4°C
acid (pH^2)
co6l,4°C
acid (pl-k2)
cool, 4°C
cool, 4°C
Preservation/Method of
Analysis
Orion 399A meter
YSI 51 B meter
YSI 33 S-C-T meter
Hach Ratio Turbidimeter
(NTU)
Gravimetric filtration
Filter ignition at 550 °C
Persulfate digestion
Ascorbic acid
Cd-Cu reduction
sulfanifamide
Boric acid distillation
Macro-digestion with
sulfuric acid
Potassium dichromate
oxidation
Membrane filtration
Membrane filtration
Reference
Method 1 50. 1b
Method 360.1b
Method 120.1b
Method 1 80. 1b
Method 160.2b
Method 160.4b
Method 365.2b
Method 365.2b
Method 353.3b
Method 350.2b
Bremner1965
Oceanography
International Corp.
Method 909AC
Method 909CC
a Filtration through glass fiber filter.
"USEPA, 1974.
"American Public Health Administration, 1975.
-------
Appendix C
C.2' BELLEVUE, WASHINGTON NATIONWIDE URBAN RUNOFF PROGRAM
The Nationwide Urban Runoff Program (NURP) was designed such that each project met minimum
monitoring requirements to support national analyses. Thus, the monitoring programs were very
similar for all projects in NURP. The Bellevue, Washington, monitoring program is one example.
Bellevue (25 square miles, 1982 population about 75,000) is located in the Puget Sound lowlands on
the west side of the Cascade Mountains and immediately east of Lake Washington (Figure C-2)
(USEPA, 1982c). Land use is primarily residential and mean annual precipitation, mostly rain, is
about 42 inches. Bellevue is hilly, with moderate slopes predominating. Drainage is carried by a
system of separate storm sewers (i.e., separate from sanitary wastes), open channels, and streams into
Lake Washington through Mercer Slough (Figure C-3) (USEPA, 1982c). Some drainage flows east
into Lake Sammamish through Phantom Lake and another stream.
The project objectives were (USEPA, 1982c):
• To apply uniformly, in selected drainage basins, a variety of management practices that are
available to and achievable by local units of government.
• To improve standard practices and operations by varying the frequency and manner of application,
developing management programming methods, and altering monitoring and inspection practices
for greater responsiveness to water quality needs.
• To test, analyze, and document the impact of local management practices on storm water quality,
isolating causal factors and their impacts on water quality and evaluating and developing functional
relationships between the quantity and quality of runoff and the hydrologic and cultural
characteristics of the basins involved.
• To develop, test, and document methods of source control of common urban storm water pollutants.
• To document temporal changes in storm runoff and constituent concentrations within several
drainage basins of differing land use.
• To develop and document means of incorporating best management practices into the institutional
and operational framework of local government agencies.
• To expand the toxic metals, sediment, herbicides and pesticides, and other databases for various
land use categories, contributing to the data base of storm water quality modeling efforts
nationally.
• To develop methods for estimating storm and annual loads of water quality constituents from
itnsampled watersheds in each urban-study area.
* To evaluate methods of transferring the data to ungaged watersheds in other regions.
C.2.7 Monitoring Approach
Monitoring activities took place hi three catchment areas (Figure C-3) (USEPA, 1982c): (1) Surrey
Downs, (2) Lake Hills, and (3) 148th Avenue. Three groups participated in the monitoring efforts: the
U.S. Geological Survey (USGS), the City of Bellevue, and the municipality of Metropolitan Seattle ;
(Metro). Two of the three study catchments, Surrey Downs and Lake Hills, were single-family
residential areas of similar size. These two basins were used to investigate the effectiveness of street
sweeping for reducing the amount of pollutants in storm runoff. The third catchment, 148th Avenue,
-------
Sammamlsh
Washington
TACOMA
Figure C-2. State locus of Bellevue, Washington, NURP. (Source: USEPA, 1982c)
contained a divided 4-lane arterial street. The data from this site were used to investigate the effects of
detention basins on the runoff quality. The monitoring activities also allowed investigators to:
• Define pollutant hydrographs for each of the three catchments during approximately 12 storms per
year.
• Determine the effectiveness of street cleaning equipment for various levels of effort under the actual
conditions encountered.
• Describe the quantities and characteristics of sewerage system particulates hi the study area.
• Obtain a continuous mass balance relationship between total runoff yields and all the sources of
urban runoff pollution.
• Analyze samples for the 129 EPA toxic or "priority" pollutants.
-------
Appendix C
REDMOND
LEGEND
A Rain gauge
A. Storm sewer discharge,
water quality sampling station,
and rain gauge
• Study basin
Figure C-3. Bellevue, Washington, NURP stream systems and sampling sites. (Source: USEPA, 1982c)
-------
AppengbfCL
The following, taken from NURP project summaries (USEPA, 1982c), describes in some detail the
sampling approaches and equipment used to meet the monitoring objectives.
The area comprising the Surrey Downs catchment consisted of single family homes and the Bellevue
Senior High School. Slopes in the basin were generally moderate, with the exception of the steep
slopes on the west side. Surrey Downs was relatively isolated from neighboring communities by the
lack of easy vehicular access and convenient "short cuts" through this residential neighborhood.
The Lake Hills catchment contained single family residences and the St. Louise Parish Church and
School. Although there were relatively isolated residential areas within the catchment, two through-
streets, which carry more traffic than a typical residential street, cross the area.
The 148th Avenue catchment contained 4,960 feet of 148th Avenue, a four-lane, divided arterial street,
and some adjacent land with sidewalks, apartments, parking lots, office buildings, and grassy swales
that were used as detention basins. A little over one-fourth of the catchment area was taken up by the
148th Avenue street surface.
USGS sample collection and management procedures were essentially the same at all three sites. A
digital paper punch recorder recorded: (1) clock time, (2) a number code which indicated if a sample
was taken by the automatic sampler, (3) accumulated precipitation in up to three rain gages, and (4) up
to two stages for computing discharge. Data were recorded at 5-minute intervals whenever the gage .
exceeded a [preset] threshold or whenever there was measurable precipitation. Precipitation was
measured with tipping-bucket rain gages. Three gages were operated for the Surrey Downs catchment
: and two each were operated for the Lake Hills and 148th Avenue catchments. Rainfall and dry
deposition quality samples were collected at one location in each catchment. Discrete runoff samples
were taken during storms for defining the temporal variation of water quality during storm
hydrographs. Samples were taken at a preset time interval (5 to 50 minutes) once the stage exceeded a
preset threshold.
The Manning composite sampler [used by Bellevue to collect composite flow and proportional
stormwater runoff samples] was triggered at pre-determined increments of flow (300 and 500 cubic
feet, the former to obtain more subsamples when small events were expected). The Manning flowmeters
used an ultrasonic transducer to sense relative stage. Stage was converted to discharge by a
programmed microprocessor in theflowmeter and presented on a circular flow chart as a percentage of
maximum rated flow. The microprocessor was programmed from a stage/discharge rating developed by
the USGS. Storm samples were removed from the samplers as soon as possible after storms, typically
within t\vo or three hours. Samples were kept on ice until pH, conductivity and turbidity were
measured in-house. Subsamples were preserved and sent to a contract lab in Seattle for the remaining
chemical analysis.
To obtain street surface paniculate samples, the City of Bellevue used the following procedures.
Because the street surfaces were more likely to be dry during daylight hours (necessary for good sample
collection), collection did not begin before sunrise nor continue after sunset, unless additional
personnel were available for traffic control. Subsamples were collected in a narrow strip about six
inches wide from one side of the street to the other (curb-to-curb). In heavily traveled streets where
-------
Appendix C
traffic was a problem, some subsamples consisted of two separate half-street strips (curb-to-crown). A
pick-up truck was used to carry the equipment components, consisting of a generator, tools, fire
extinguisher, vacuum hose and wand, and two wet-dry vacuum units during sample collection. The
truck had warning lights, including a roof-top flasher unit.
To carry out the catch basin sampling tasks, all catch basins in each study area were surveyed for
location, length, size and slope of pipes, and depth of catchment. Another survey was done to record
the dimensions of each catch basin. Sediment volume was then calculated from a measurement of
sediment depth.
Some experimental design work was done in 1979 and early 1980 to determine the concentrations of
some pollutant constituents. Grab samples of supernatant and sediment were taken from selected catch
basins in each study area and submitted to a contract lab for chemical analysis. During 1980, two
complete catch basin inventories were made; recording sediment depth, and thus mass loading in the
system. Monthly inventories were scheduled for 1981. After December, 1980, spot checks of fifteen to
t\venty-five selected catch basins in each study area were made after each significant storm event. This
information, along with storm and street loading data allowed characterization of flushing and
deposition within the sewerage system.
For the toxicant inventory portion of the study, stormwater runoff samples were collected as flow
proportioned composites using a Manning S300T automated sampler (all teflon and glass contact
surfaces) activated by ultrasonic flowmeters, except for the volatile samples which were collected as
grabs early in the storm events. Samplers and containers were cleaned between events according to
USEPA protocols using "Micro" brand soap and nitric acid; the hydrochloric acid and methylene
chloride rinses were not used. Deionized distilled water blanks were taken through each sampler before
use and have proven to be completely clean of organic and metal contaminants. Street surface dust
samples were collected as described above using a stainless steel vacuum and PVC flexible hose. No
special cleaning protocol was applied to the vacuum- Some sample contamination could have occurred
from the PVC hose, but no functional alternatives [have] been found for collecting the dust samples.
Interstitial water samples from the stream-bed in Kelsey Creek were collected through aluminum
sfandpipes set in the stream gravel, using a Manning S3000T sampler to draw the water up from the
perforated base of the standpipe. This sampling was in conjunction with the "Ecological Impacts of
Stormwater Runoff in Urban Streams" project of the University of Washington.
C.2.2 Equipment
For the City's street sampling task various vacuum and hose lengths were tested. Relative airflows and
suction pressures in the hose were monitored for different test set-ups. Both one- and two-vacuum
configurations and 1.5 inch hoses in lengths varying from 10 to 35 feet were tested, along with a Vacu-
Max unit. The standard "reference" system was two vacuums and a 35-foot hose. The best suction and
higher air velocities were observed with two vacuums and short hose lengths (10 feet), but the short
hose length would require that the vacuums be dismounted from the truck at each subsampling location.
Tlie longer hose, with the two vacuums, was judged adequate, and resulted in great cost and time
savings.
-------
Two industrial vacuum cleaners (2-hp) with one secondary filter and a primary dacron filter bag were
used. The vacuum units were heavy duty and made of stainless steel to reduce contamination of the
samples. The two 2-hp vacuums were used together by using a wye connector at the end of the hose.
This combination extended the useful length of the 1.5 inch hose to 35 feet and increased the suction.
-------
Appendix C
C.3 CONESTOGA HEADWATERS, PENNSYLVANIA RURAL CLEAN WATER PROGRAM
The following are the key elements of the ground water monitoring program of the Pennsylvania Rural
Clean Water Program (Pennsylvania RCWP Coordinating Committee, 1984):
There were four components to the monitoring strategy and which included three scales or levels of
monitoring [Figure C-4]. The first component, the regional network, consisted of general monitoring on
a regional scale and included the entire 188 square-mile area. The second component involved more
detailed monitoring in a small watershed area of about 5.8 square miles (Little Conestoga Creek
\'\
LEGEND
I small watershed monitoring site
field monitoring sites
. project boundary
town
Figure C-4. Location of Conestoga River headwaters area and four monitoring components of the
Pennsylvania RCWP. (Source: Pennsylvania RCWP Coordinating Committee, 1984)
-------
basin). The third and fourth components consisted of intensive monitoring on afield scale. Both field
sites -were on farms.
All four components were designed to permit the comparison of water quality before and after the
implementation of BMPs. As such, they included pre-BMP and post-BMP monitoring periods.
Although the basic strategy was the same, each component addressed a slightly different problem
[Table C-7]. All four components addressed the problem of nutrients in ground water and surface
water; three addressed the sediment problem in surface water; and two addressed the pesticide problem
in ground and surface waters. Since the single most important source of water quality problems
appeared to be the excess of nutrients, nutrient management BMPs were emphasized in most of the
monitoring components.
C.3.1 Regional Network:
The monitoring schedule for the regional network consisted of three one-year monitoring periods: (1) a
pre-BMP period; (2) an early post-BMP period; and (3) later post-BMP period [Table C-8]. The
quality of ground and surface waters, as well as precipitation, were monitored.
The 43 ground water sites included 42 private domestic, and farm wells and one spring. Of the 43
ground water sites, 33 were located in carbonate rocks [Figure C-5]. This difference in geology
(carbonate vs. noncarbonate areas) played an important role in the quality of water. The areas having
the highest levels of nutrients, sediment, pesticides, and bacteria in water consistently coincided with
areas underlain by carbonate rocks. This was probably due to the greater number of farms in the
carbonate areas and the greater permeability of the carbonate rocks. Because of the greater problems
Table C-7. Specific problems to be addressed for each monitoring component of the Pennsylvania
:
REGIONAL NETWORK (188 mi2)
Problem: What is the composite effect of all implemented BMPs on sediment, nutrients,
and pesticides in streams and nutrients and pesticides in ground water?
SMALL WATERSHED SITE-LITTLE CONESTOGA CREEK (5.8 mi2)
Problem: What is the effect of a typical combination of BMPs (with emphasis on nutrient
management) on sediment and nutrients in the creek and nutrients in around
water?
FILED SITE 1 - DAIRY FARM (21.7 acres)
Problem: What is the effect of manure storage and terracing on sediment, nutrients, and
pesticides in runoff and nutrients and pesticides in groundwater?
FIELD SITE 2- HOG/STEER FARM (55 acres)
Problem: What is the effect of nutrient management on nutrients in runoff and ground
water?
-------
Appendix C
in the carbonate areas, the regional net\vork, as well as the other three monitoring components, were
concentrated in the carbonate areas.
C.3.2 Small Watershed Site:
In addition to ground water, surface water, and precipitation data, data on nutrients in soils and
manure were collected [Table C-9]. More detailed land-use data were also collected. One gage, four
additional base-flow sites, and all the ground water sites were located in the eastern end of the basin
where BMP implementation was projected to be greatest [Figure C-6].
C.3.3 Field Site 1
In addition to the same data that was collected at the small watershed site (with the exception of base-
flow data), data were obtained from lysimeters [Table C-10]. The locations of the data collection
facilities at one field site are shown in [Figure C-7].
C.3.4 Field Site 2
The planned schedule, approach, and data collection were similar to those for field site 1 [Table C-11].
The only significant difference was that data collection concentrated on nutrients at field site 2 since
nutrient management BMPs were planned for this component. The locations of the data collection
Table C-8. Monitoring plan for regional network of the Pennsylvania RCWP.
SCHEDULE
1982-1983 Pre-BMP
1985-1986 Early Post-BMP
1988-1989 Later Post-BMP
APPROACH
Compare concentrations and/or loads of sediment, nutrients, and pesticides before and
after BMP implementation.
WATER QUALITY DATA COLLECTION
2 Stream Gages - sediment, nutrients, and pesticides for
major storms
4 Base-flow Sites - monthly sediment, nutrients, and
pesticides
43 Ground Water Sites - nutrients and pesticides in
spring; summer, and fall
3 Precipitation Gages
LAND USE DATA
Aerial photograph analysis for distribution of major land uses for each of the 3 time periods.
Types and locations of BMPs implemented.
(Source; Pennsylvania RCWP Coordinating Committee, 1984.)
-------
facilities [at this field site] are shown in [Figure C-8]. Field site 2 was in a carbonate area and was
monitored for two years forpre-BMP monitoring.
/1
LEGEND
A. stream guage
O base flow site
* ground water site
NC Non-carbonate
C Carbonate
project boundary
town
SCALE
Figure C-5 Location of monitoring facilities for the regional network of the Pennsylvania RCWP. (Source:
Pennsylvania RCWP Coordinating Committee, 1984)
-------
Appendix C
Table C-9. Monitoring plan for small watershed site of the Pennsylvania RCWP.
SCHEDULE
1982-1985 Pre-BMP
1985-1986 BMP
1986-1988 Post-BMP
APPROACH
Compare concentrations and loads of sediment and nutrients before and after BMP
implementation
WATER-QUALITY DATA COLLECTION
2 Stream Gages - sediment and nutrients for major storms
7 Base-flow Sites - sediment and nutrients every 3 weeks
5-10 Ground Water Sites - nutrients 4 times per year
Soil - spring, fall
Manure - spring
1 Precipitation Gage
LAND-USE DATA
Monthly report from each farmer, including information on:
Plowing Planting
Harvesting Field conditions
Liming Fertilizing
Manure spreading Pesticide application
(Source: Pennsylvania RCWP Coordinating Committee, 1984)
-------
Itltot)
nutrient management subbasin
(1.«equaremllea)
control aubbasln
(1.43 aquare miles)
Figure C-6. Location of monitoring facilities for the small watershed site of the Pennsylvania RCWP.
(Source: Pennsylvania RCWP Coordinating Committee, 1984)
N
0 10^200
SCALE IN FEET
LEGEND
Field Monitoring Site 1
• monitoring wall
O characterization well
• tysimefer
A precipitation gauge
• runoff gauge
• tpring
Hold boundary
I
I
O \
:o
Figure C-7. Location of monitoring facilities for field site 1 of the Pennsylvania RCWP. (Source:
Pennsylvania RCWP Coordinating Committee, 1984)
-------
Appendix C
Table C-10. Monitoring plan for field site 1 of the Pennsylvania RCWP.
SCHEDULE
1982-1984 Pre-BMP
1984-1985 BMP
1985-1987 Post-BMP
APPROACH
Compare concentrations and loads of sediment, nutrients, and pesticides before and after
BMP implementation.
WATER-QUALITY DATA COLLECTION
1 Runoff Gage - sediment and nutrients for major storms
(pesticides for selected storms)
7 Groundwater Sites - nutrients monthly and 4 storms per
year (pesticides less frequently)
5-10 Lysimeters - nutrients 4 storms per year (pesticides
for selected storms)
Soil - spring, fall
Manure - spring
1 Precipitation Gage
LAND-USE DATA
Biweekly reports from farmer, including information on:
Plowing Planting
Harvesting Field conditions
Liming Fertilizing
Manure spreading Pesticide application
(Source; Pennsylvania RCWP Coordinating Committee, 1984)
-------
LN1674
0 100 200
SCALE IN FEET
LEGEND
Field Monitoring Site 2
• sampling well or spring
o characterization well
A runoff gauge
> terrace (flow direction indicated)
• terrace drain pipe
B6*"""it* grassed waterway
—too— contour line ,
I I farm structures
field boundary
Figure C-8. Field site 2, Lancaster County, Pennsylvania, Pennsylvania RCWP. (Source:
Pennsylvania RCWP Coordinating Committee, 1984)
-------
Appendix C
Table C-11. Monitoring plan for field site 2 of the Pennsylvania RCWP.
SCHEDULE
1984-1986
1984
1986-1988
Pre-BMP
BMP
Post-BMP
APPROACH
Compare concentrations and loads of nutrients before and after BMP implementation.
WATER-QUALITY DATA COLLECTION
1 Runoff Gage - nutrients for major storms
5-10 Groundwater Sites - nutrients monthly and 4 storms per year
5-10 Lysimeters - nutrients 4 storms per year
Soil - spring, fall
Manure - spring
1 Precipitation Gage .
LAND-USE DATA
Biweekly report from farmer, including information' on:
Plowing Planting
Harvesting Field conditions
Liming Fertilizing
Manure spreading
(Source: Pennsylvania RCWP Coordinating Committee, 1984)
-------
APPENDIX D
Statistical Tables
Dl Cumulative areas under the Normal distribution (values ofp corresponding to
D2
D3
D4
D5
D6
D7
Percentiles of the ta df distribution (values of t such that lOO(l-a) % of the
distribution is less than t)
Upper and lower percentiles of the Chi-square distribution
Coefficients at for the Shapiro-Wilk test for normality
Quantiles of the Shapiro-Wilk test for normality (values of -W such that •10Qp.%
of the distribution of W is less than
Upper percentiles of the F distribution for/? = 0.995, p = 0.99, p = 0.975,
p = 0.95, p = 0.90
Noncentral t distribution. Values of the noncentrality parameter for a one-sided
test with a = 0.050, a = 0.025, a = 0.010
D8 Binomial distribution with y successes out of n trials
D9 Population correlation coefficients
D10 Quantiles of the Spearman test statistic
Dll Sample sizes for one-sided nonparametric tolerance limits
-------
-------
Appengix;Dw
Table Dl. Cumulative areas under the Normal distribution (values of p corresponding
toZp)
zp
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
1.1
1.2
1.3
1.4
1.5
1.6
1.7
1.8
1.9
2.0
2.1
2.2
2.3
2.4
2.5
2.6
2.7
2.8
2.9
3.0
3.1
3.2
3.3
3.4
0.00
0.5000
0.5398
0.5793
0.6179
0.6554
0.6915
0.7257
0.7580
0.7881
0.8159
0.8413
0.8643
0.8849
0.9032
0.9192
0.9332
0.9452
0.9554
0.9641
0.9713
0.9772
0.9821
0.9861
0.9893
0.9918
0.9938
0.9953
0.9965
0.9974
0.9981
0.9987
0.9990
0.9993
0.9995
0.9997
^
0.01
0.5040
0.5438
0.5832
0.6217
0.6591
0.6950
0.7291
0.7611
0.7910
0.8186
0.8438
0.8665
0.8869
0.9049
0.9207
0.9345
0.9463
0.9564
0.9649
0.9719
0.9778
0.9826
0.9864
0.9896
0.9920
0.9940
0.9955
0.9966
0.9975
0.9982
0.9987
0.9991
0.9993
0.9995
0.9997
A
0.02
0.5080
0.5478
0.5871
0.6255
0.6628
0.6985
0.7324
0.7642
0.7939
0.8212
0.8461
0.8686
0.8888
0.9066
0.9222
0.9357
0.9474
0.9573
0.9656
0.9726
0.9783
0.9830
0.9868
0.9898
0.9922
0.9941
0.9956
0.9967
0.9976
0.9982
0.9987
0.9991
0.9994
0.9995
0.9997
1
0.03
0.5120
0.5517
0.5910
0.6293
0.6664
0.7019
0.7357
0.7673
0.7967
0.8238
0.8485
0.8708
0.8907
0.9082
0.9236
0.9370
0.9484
0.9582
0.9664
0.9732
0.9788
0.9834
0.9871
0.9901
0.9925
0.9943
0.9957
0.9968
0.9977
0.9983
0.9988
0.9991
0.9994
0.9996
0.9997
k
^^^~
ZP
0.04
0.5160
0.5557
0.5948
0.6331
0.6700
0.7054
0.7389
0.7704
0.7995
0.8264
0.8508
0.8729
0.8925
0.9099
0.9251
0.9382
0.9495
0.9591
0.9671
0.9738
0.9793
0.9838
0.9875
0.9904
0.9927
0.9945
0.9959
0.9969
0.9977
0.9984
0.9988
0.9992
0.9994
0.9996
0.9997
/Area
0.05
0.5199
0.5596
0.5987
0.6368
0.6736
0.7088
0.7422
0.7734
0.8023
0.8289
0.8531
0.8749
0.8944
0.9115
0.9265
0.9394
0.9505
0.9599
0.9678
0.9744
0.9798
0.9842
0.9878
0.9906
0.9929
0.9946
0.9960
0.9970
0.9978
0.9984
0.9989
0.9992
0.9994
0.9996
0.9997
= a
0.06
0.5239
0.5636
0.6026
0.6406
0.6772
0.7123
0.7454
0.7764
0.8051
0.8315
0.8554
0.8770
0.8962
0.9131
0.9279
0.9406
0.9515
0.9608
0.9686
0.9750
0.9803
0.9846
0.9881
0.9909
0.9931
0.9948
0.9961
0.9971
0.9979
0.9985
0.9989
0.9992
0.9994
0.9996
0.9997
0.07
0.5279
0.5675
0.6064
0.6443
0.6808
0.7157
0.7486
0.7794
0.8078
0.8340
0.8577
0.8790
0.8980
0.9147
0.9292
0.9418
0.9525
0.9616
0.9693
0.9756
0.9808
0.9850
0.9884
0.9911
0.9932
0.9949
0.9962
0.9972
0.9979
0.9985
0.9989
0.9992
0.9995
0.9996
0.9997
0.08
0.5319
0.5714
0.6103
0.6480
0.6844
0.7190
0.7517
0.7823
0.8106
0.8365
0.8599
0.8810
0.8997
0.9162
0.9306
0.9429
0.9535
0.9625
0.9699
0.9761
0.9812
0.9854
0.9887
0.9913
0.9934
0.9951
0.9963
0.9973
0.9980
0.9986
0.9990
0.9993
0.9995
0.9996
0.9997
0.09
0.5359
0.5753
0.6141
0.6517
0.6879
0.7224
0.7549
0.7852
0.8133
0.8389
0.8621
0.8830
0.9015
0.9177
0.9319
0.9441
0.9545
0.9633
0.9706
0.9767
0.9817
0.9857
0.9890
0.9916
0.9936
0.9952
0.9964
0.9974
0.9981
0.9986
0.9990
0.9993
0.9995
0.9997
0.9998
-------
Appendix D
Table D2. Percentiles of the ta^f distribution (values of t such that lOO(l-cc) % of ,the
distribution is less than t)
df
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
35
40
50
60
80
100
150
200
Inf.
a = 0.40
0.3249
0.2887
0.2767
0.2707
0,2672
0.2648
0.2632
0.2619
0.2610
0.2602
0.2596
0.2590
0.2586
0.2582
0.2579
0.2576
0.2573
0.2571
0.2569
0.2567
0.2566
0.2564
0.2563
0.2562
0.2561
0.2560
0.2559
0.2558
0.2557
0.2556
0.2553
0.2550
0.2547
0.2545
0.2542
0.2540
0.2538
0.2537
0.2533
_X
a = 0.30
0.7265
0.6172
0.5844
0.5686
0.5594
0.5534
0.5491
0.5459
0.5435
0.5415
0.5399
0.5386
0.5375
0.5366
0.5357
0.5350
0.5344
0.5338
0.5333
0.5329
0.5325
0.5321
0.5317
0.5314
0.5312
0.5309
0.5306
0.5304
0.5302
0.5300
0.5292
0.5286
0.5278
0.5272
0.5265
0.5261
0.5255
0.5252
0.5244
/~
a = 0.20
1.3764
1.0607
0.9785
0.9410
0.9195
0.9057
0.8960
0.8889
0.8834
0.8791
0.8755
0.8726
0.8702
0.8681
0.8662
0.8647
0.8633
0.8620
0.8610
0.8600
0.8591
0.8583
0.8575
0.8569
0.8562
0.8557
0.8551
0.8546
0.8542
0.8538
0.8520
0.8507
0.8489
0.8477
0.8461
0.8452
0.8440
0.8434
0.8416
\
I
t
a = 0.10
3.0777
1.8856
1.6377
. 1.5332
1.4759
1.4398
1.4149
1.3968
i.3830
1.3722
1.3634
1.3562
1.3502
1.3450
1.3406
1.3368
1.3334
1.3304
1.3277
1.3253
1.3232
1.3212
1.3195
1.3178
1.3163
1.3150
1.3137
1.3125
1.3114
1.3104
1.3062
1.3031
1.2987
1.2958
1.2922
1.2901
1.2872
1.2858
1.2816
Areas
a = 0.05
6.3137
2.9200
2.3534
2.1318
2.0150
1.9432
1.8946
1.8595
1.8331
1.8125
1.7959
1.7823
1.7709
1.7613'
1.7531
1.7459
1.7396
1.7341
1.7291
1.7247
1.7207
1.7171
1.7139
1.7109
1.7081
1.7056
1.7033
1.7011
1.6991
. 1.6973
1.6896
1.6839
1.6759
1.6706
1.6641
1.6602
1.6551
1.6525
1.6449
"
a = 0.025
12.7062
4.3027
3.1824
2.7765
2.5706
2.4469
2.3646
2.3060
2.2622
2.2281
2.2010
2.1788
2.1604
2.1448
2.1315
2.1199
2.1098
2.1009
2.0930
2.0860
2.0796
2.0739
2.0687
2.0639
2.0595
2.0555
2.0518
2.0484
2.0452
2.0423
2.0301
, 2.0211
2.0086
2.0003
1.9901
1.9840
1.9759
1.9719
1.9600
a = 0.010
31.8210
6.9645
4.5407
3.7469
3.3649
3.1427
2.9979
, 2.8965
2.8214
2.7638
2.7181
2.6810
2:6503
2.6245
2.6025
2.5835
2.5669
2.5524
2.5395
2.5280
2.5176
. 2.5083
2.4999
2.4922
2.4851
2.4786
2.4727
2.4671
2.4620
2.4573
2.4377
2.4233
2.4033
2.3901
2.3739
2.3642
2.3515
2.3451
2.3264
a = 0.005
63.6559
9.9250
5.8408
4.6041
4.0321
3.7074
3.4995
3.3554
3.2498
3.1693
3.1058
3.0545
3.0123
2.9768
2.9467
2.9208
2.8982
2.8784
2.8609
2.8453
2.8314
2.8188
2.8073
2.7970
2.7874
2.7787
2.7707
2.7633
2.7564
2.7500
2.7238
2.7045
2.6778
2.6603
2.6387
2.6259
2.6090
2.6006
2.5758
-------
Table D3. Upper and lower percentiles of the Chi-square distribution
off
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
35
40
50
60
70
80
90
100
200
0.001
0.002
0.024
0.091
0.210
0.381
0.599
0.857
1.152
1.479
1.834
2.214
2.617
3.041
3.483
3.942
4.416
4,905
5.407
5.921
6.447
6.983
7.529
8.085
8.649
9.222
9.803
10.391
10.986
11-588
14.688
17.917
24.674
31.738
39.036
46.520
54.156
61.918
143.84
(
0.005
0.010
0.072
0.207
0.412
0.676
0.989
1.344
1.735
2.156
2.603
3.074
3.565
4.075
4.601
5.142
5.697
6.265
6.844
7.434
8.034
8.643
9.260
9.886
10.520
11.160
11.808
12.461
13.121
13.787
17.192
20.707
27.991
35.534
43.275
51.172
59.196
67.328
152.24
\
0.010
0.020
0.115
0.297
0.554
0.872
1.239
1.647
2.088
2.558
3.053
3.571
4.107
4.660
5.229
5.812
6.408
7.015
7.633
8.260
8.897
9.542
10.196
10.856
11.524
12.198
12.878
13.565
14.256
14.953
18.509
22.164
29.707
37.485
45.442
53.540
61.754
70.065
156.43
>
T^
X2
0.025
0.001
0.051
0.216
0.484
0.831
1.237
1.690
2.180
2.700
3.247
3.816
4.404
5.009
5.629
6.262
6.908
7.564
8.231
8.907
9.591
10.283
10.982
11.689
12.401
13.120
13.844
14.573
15.308
16.047
16.791
20.569
24.433
32.357
40.482
48.758
57.153
65.647
74.222
162.73
Area
*
^ta__
0.050
0.004
0.103
0.352
0.711
1.145
1.635
2.167
2.733
3.325
3.940
4.575
5.226
5.892
6.571
7.261
7.962
8.672
9.390
10.117
10.851
11.591
12.338
13.091
13.848
14.611
15.379
16.151
16.928
17.708
18.493
22.465
26.509
34.764
43.188
51.739
60.391
69.126
77.929
168.28
= 1-p
P
0.100
0.016
0.211
0.584
1.064
1.610
2.204
2.833
3.490
4.168
4.865
5.578
6.304
7.041
7.790
8.547
9.312
10.085
10.865
11.651
12.443
13.240
14.041
14.848
15.659
16.473
17.292
18.114
18.939
19.768
20.599
24.797
29.051
37.689
46.459
55.329
64.278
73.291
82.358
174,84
0.900
2.706
4.605
6.251
7.779
9.236
10.645
12.017
13.362
14.684
15.987
17.275
18.549
19.812
21.064
22.307
23.542
24.769
25.989
27.204
28.412
29.615
30.813
32.007
33.196
34.382
35.563
36.741
37.916
39.087
40.256
46.059
51.805
63.167
74.397
85.527
96.578
107.57
118.50
226.02
0.950
3.841
5.991
7.815
9.488
11.070
12.592
14.067
15.507
16.919
18.307
19.675
21.026
22.362
23.685
24.996
26.296
27.587
28.869
30.144
31.410
32.671
33.924
35.172
36.415
37.652
38.885
40.113
41.337
42.557
43.773
49.802
55.758
67.505
79.082
90.531
101.88
113.15
124.34
233.99
0.975
5.024
7.378
9:348
11.143
12.832
14.449
16.013
17.535
19.023
20.483
21.920
23.337
24.736
26.119
27.488
28.845
30.191
31.526
32.852
34.170
35.479
36.781
38.076
39.364
40.646
41.923
43.195
44.461
45.722
46.979
53.203
59.342
71.420
83.298
95.023
106.63
118.14
129.56
241.06
0.990
6.635
9.210
11.345
13.277
15.086
16.812
18.475
20.090
21.666
23.209
24.725
26.217
27.688
29.141
30.578
32.000
33.409
34.805
36.191
37.566
38.932
40.289
41.638
42.980
44.314
45.642
46.963
48.278
49.588
50.892
57.342
63.691
76.154
88.379
100.43
112.33
124.12
135.81
249.45
0.995
7.879
10.597
12.838
14.860
16.750
18.548
20.278
21.955
23.589
25.188
26.757
28.300
29.819
31.319
32.801
34.267
35.718
37.156
38.582
39.997
41.401
42.796
44.181
45.558
46.928
48.290
49.645
50.994
52.335
53.672
60.275
66.766
79.490
91.952
104.21
116.32
128.30
140.17
255.26
0.999
10.827
13.815
16.266
18.466
20.515
22.457
24.321
26.124
27.877
29.588
31.264
32.909
34.527
36.124
37.698
39.252
40.791
42.312
43.819
45.314
46.796
48.268
49.728
51.179
52.619
54.051
55.475
56.892
58.301
59.702
66.619
73.403
86.660
99.608
112.32
124.84
137.21
149.45
267.54
-------
Appendix D
Table D4. Coefficients a,- for the Shapiro-Wilk test for normality.
(Source: Shapiro and Wilk, 1965)
10
t
2
3
*
5
0.7071
»
-
.
-
0.7071
0.0000
.
-
0.6872
0.1677
_
-
0.66*6
0.2413
0.0000
•
-
0.6431
0.2806
0.0875
•
-
0.6233
0.3031
0.1*01
0.0000
-
0.6052
0.316*
0.17*3
0.0561
-
0.5888
0.32**
0.1976
0.09*7
0.0000
0.5739
0.3291
0.21*1
0.122*
0.0399
11
12
15
16
17
18
19
21
22
23
2*
25
26
27
28
29
20
1
2
3
*
S
6
7
a
9
10
0.5601
0.3315
0.2260
0.1*29
0.0695
0.0000
_
.
.
-
0.5*75
0.3325
0.23*7
0.1S86
0.0922
0.0303
_
.
.
-
0.5359
0.3325
0.2*12
0.1707
0.1099
0.0539
0.0000
.
.
-
0.5251
0.3318
0.2*60
0.1802
0.12*0
0.0727
0.02*0
.
.
-
0.5150
0.3306
0.2*95
0.1878
0.1353
0.0880
0.0*33
0.0000
.
-
0.5056
0.3290
0.2521
0.1939
O'.1**7
0.1005
0.0593
0.0196
.
-
0.4968
0.3273
0.25*0
0.1988
0.152*
0.1109
0.0725
0.0359
0.0000
-
0.0886
0.3253
0.2553
0.2027
0.1587
0.1197
0.0837
0.0*96
0.0163
-
0.4808
0.3232
0.2561
0.2059
0.16*1
0.1271
0.0932
0.0612
0.0303
0.0000
0.4734
0.3211
0.2565
0.2085
0.1686
0.133*
0.1013
0.0711
0.0*22
0.01*0
30
1
2
3
*
S
6
7
a
9
10
11
12
13
1*
15
0.*6*3
0.3185
0.257S
0.2119
0.1736
0.1399
0.1092
0.080*
0.0530
0.0263
0.0000
»
.
•
-
0.4590
0.3156
0.2571
0.2131
0.176*
0.1*43
0.1150
0.0878
0.0618
0.0368
0.0122
.
»
•
-
0.4542
0.3126
0.2563
0.2139
0.1787
0.1*80
0.1201
0.091)1
0.0696
0.0*59
0.0228
0.0000
.
-
-
0.**93
0.3098
0.255*
0.21*5
0.1807
0.1512
0.12*5
0.0997
0.076*
0.0539
0.0321
0.0107
-
•
-
O.**50
0.3069
0.25*3
0.21*8
0.1822
0.1539
0.1283
0.10*6
0.0823
0.0610
0.0*03
0.0200
0.0000
•
-
O.**07
0.3043
0.2533
0.2151
0.1836
0.1563
0.1316
0.1089
0.0876
0.0672
0.0*76
0.028*
0.009*
-
-
0.4366
0.3018
0.2522
0.2152
0.1848
0.1584
0.13*6
0.1128
0.0923
0.0728
0.05*0
0.0358
0.0178
0.0000
-
0.4328
0.2992
0.2510
0.2151
0.1857
0.'6C1
0.1372
0.1162
0.0965
0.0778
0.0598
0.0*2*
0.0253
0.008*
-
O.*291
0,2968
0.2*99
0.2150
0.1864
0.1616
0.1395
0.1192
0.1002
0.0822
0.0650
0.0*83
0.0320
0.0159
0.0000
0.*25*
0.29*4
0.2*87
0.21*8
0.1870
0.1630
0.1*15
0.1219
0.1036
0.0862
0.0697
0.0537
0.0381
0.0227
0.0076
-------
Table D4. (continued)
V
1
2
3
4
5
6
7
8 •
9
10
11
12
13
14
15
16
17
18
19
20
31
0.4220
0.2921
0.2475
0.2145
0.1874
0.1641
0.1433
0.1243
0.1066
0.0899
0.0739
0.0585
0.0435
0.0289
0.0144
0.0000
.
-
_
-
32
0.4188
0.2898
0.2462
0.2141
0.1878
0.1651
0.1449
0.1265 •
0.1093
0.0931
0.0777
0.0629
0.0485
0.0344
0.0206
0.0068
.
-
-
-
33
0.4156
0.2876
0.2451
0.2137
0.1880
0.1660
0.1463
0.1284
0.1116
0.0961
0.0812
0.0669
0.0530
0.0395
O.C262
0.0131
0.0000
-
-
-
34
0.4127
0.2854
0.2439
0.2132
0.1882
0.1667
0.1475
0.1301
0.1140
0.0988
0.0844
0.0706
0.0572
0.0441
0.0314
0.0187
0.0062
-
~
-
35
C.4096
C.2834
C.2427
0.2127
0.1883
0.1673
0.1487
C.1317
0.1160
0.1013
0.0873
0.0739
0.0610
. 0.0484
C.0361
0.0239
0.0119
0.0000
-
-
36
0.4068
0.2813
0.2415
0.2121
0.1883
0.1678
0.1496
C.1331
0.1179
0.1036
0.0900
0.0770
0.0645
0.0523
0.0404
0.0287
0.0172
0.0057
-
-
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
37
.4040
.2794
.2403
.2116
.1863
.1683
.1505
.1344
.1196
.1056
.0924
.0798
.0677
.0559
.0444
.0331
.0220
.0110
.0000
-
38
0.4015
0.2774
0.2391
0.211C
0.1881
0.1686
0.1513
0.1356
0.1211
0.1075
0.0947
0.0824
0.0706
0.0592
0.0481
0.0372
0.0264
0.0156
0.0052
-
3S
0.3989
0.2755
0.2380
0.2104
0.1880
0.1689
0.1520
0.1366
0.1225
0.109:
0.0967
0.0846
0.0733
o.oe::
0.0515
0.0409
0.0305
0.0203
0.0101
O.OOOC
40
0.3964
0.2737
0.2368
0.2098
0.1878
0.1691
0.1526
0.1376
0.1237
0.1108
0.0986
0.0870
0.0759
0.0651
0.0546
0.0444
0.0343
0.0244
0.0146
0.0049
\n 41
(V
42
43
49
50
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
0.3940
0.2719
0.2357
0.2091
0.1876
0.1693
0.1531
0.1384
0.1249
0.1123
0.1004
0.0891
0.0782
0.0677
0.0575
0.0476
0.0379
0.0283
0.0188
0.0094
0.0000
-
_
-
-
0.3917
0.2701
0.2345
0.2085
0.1874
0.1694
0.1535
0.1392
0.1259
0.1136
0.1020
0.0909
0.0804
0.0701
0.0602
0.0506
0.0411
0.0318
0.0227
0.0136
0.0045
-
-
-
-
0.3894
0.2684
0.2334
0.2C78
0.1871
C.1695
0.1539
0.1398
0.1269
0.1149
0.1035 .
0.0927
0.0624
0.0724
0.0628
0.0534
0.0442
0.0352
O.C263
0.0175.
0.0087
0.0000
-
-
-
0.3672
0.2667
0.2323
0.2C72
0.1868
0.1695
0.1542
0.1405
0.1278
0.1160
C.1049
0.0943
0.0842
0.0745
0.0651
0.0560
0.0471
0.0383
0.0296
0.0211
0.0126
0.0042
-
-
-
0.3850
0.2651
0.2313
0.206S
0.1865
C.1695
C.1545
0.1410
C.1286
0.1170
0.1062
0.0959
0.0860
C.0765
0.0673
0.0584
0.0497
0.0412
0.0328
O.G245
C.0163
C.0081
C.OOOO
-
-
C.3830
0.2635
0.2302
0.2058
0.1862
C.1695
0.1548
0.1415
0.1293
0.1180
0.1073
0.0972
C.0876
0.0783
0.0694
0.0607
0.0522
0.0439
0.0357
0.0277
C.0197
0.0118
0.0039
-
-
0.3806
0.2620
0.2291
0.2052
0.1859
0.1695
0.1550
0.1420
0.1300
0.1189
0.1085
0.0986
0.0892
0.0801
0.0713
0.0628
0.0546
0.0465
0.0385
0.0307
0.0229
0.0153
0.0076
0.0000
• -
0.3789
0.2604
0.2281
0.2045
0.1855
0.1693
0.1551
0.1423
0.1306
0.1197
0.1095
0.0998
0.0906
0.0817
0.0731
0.0648
0.0568
0.0489
0.0411
0.0335
0.0255
0.0185
0.0111
0.0037
-
0.377C
C.2589
0.2:71
0.2036
0.1851
0.169:
0.1553
0.1427
0.13li
0.1205
0.1105
0.1010
0.091S
0.063:
0.0748
C.0667
0.0586
o.osr
0.0«3t
0.0361
C.028E
0.0:15
0.01*2
0.0071
0.0000
0.3751
0.2574
0.2260
0.2032
0.1647
0.1691
C.1554
0.1430
0.1317
0.1212
0.1113
0.1020
0.0932
0.0846
0.0764
0.0685
0.0608
0.0532
0.0459
0.0386
0.0314
0.0244
O.C174
0.0104
0.0035
-------
Appendix D
Table D5. Quantiles of the Shapiro-Wilk test for normality (values of W
such that 100/7% of the distribution of Wis less than Wp). (Source: Shapiro
and Wilk. 1965)
n
3
*
S
6
7
a
9
10
11
12
13
1*
15
16
17
18
19
20
21
22
23
2*
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
-------
Table D6. Upper percentiles of the F distribution for p =0.995
fri
1
2
3
4
5
6
7
8
9
10
12
15
20
24
30
40
50
60
70
80
100
120
,„
1
2
3
4
5
6
7
8
9
10
12
15
20
24
30
40
50
60
70
80
100
120
fn
1
1.6E+4
198.5
55.55
31.33
22.78
18.63
16.24
14.69
13.61
12.83
11.75
10.80
9.944
9.551
9.180
8.828
8.626
8.495
8.403
8.335
8.241
8.179
15
2.5E+4
199.4
43.08
20.44
13.15
9.814
7.968
6.814
6.032
5.471
4.721
4.070
3.502
3.246
3.006
2.781
2.653
2.570
2.513
2.470
2.411
2.373
fn and
2
2.0E+4
199.0
49.80
26.28
18.31
14.54
12.40
11.04
10.11
9.427
8.510
7.701
6.987
6.661
6.355
6.066
5.902
5.795
5.720
5.665
5.589
5.539
20
2.5E+4
199.4
42.78
20.17
12.90
9.589
7.754
6.608
5.832
5.274
4.530
3.883
3.318
3.062
2.823
2.598
2.470
2.387
2.329
2.286
2.227
2.188
3
2.2E+4
199.2
47.47
24.26
16.53
12.92
10.88
9.597
8.717
8.081
7.226
6.476
5.818
5.519
5.239
4.976
4.826
4.729
4.661
4.611
4.542
4.497
24
2.5E+4
199.4
42.62
20.03
12.78
9.474
7.645
6.503
5.729
5.173
4.431
3.786
3.222
2.967
2.727
2.502
2.373
2.290
2.231
2.188
2.128
2.089
4
2.3E+4
199.2
46.20
23.15
15.56
12.03
10.05
8.805
7.956
7.343
6.521
5.803
5.174
4.890
4.623
4.374
4.232
4.140
4.076
4.028
3.963
3.921
30
2.5E+4
199.5
42.47
19.89
12.66
9.358
7.534
6.396
5.625
5.071
4.331
3.687
3.123
2.868
2.628
2.401
2.272
2.187
2.128
2.084
2.024
1.984
5
2.3E+4
199.3
45.39
22.46
14.94
11.46
9.522
8.302
7.471
6.872
6.071
5.372
4.762
4.486
4.228
3.986
3.849
3.760
3.698
3.652
3.589
3.548
40
2.5E+4
199.5
42.31
19.75
12.53
9.241
7.422
6.288
5.519
4.966
4.228
3.585
3.022
2.765
2.524
2.296
2.164
2.079
2.019
1.974
1.912
1.871
6
2.3E+4
199.3'
44.84
21.98
14.51
11.07
9.155
7.952
7.134
6.545
5.757
5.071
4.472
4.202
3.949
3.713
3.579
3.492
3.431
3.387
3.325
3.285
50
2.5E+4
199.5
42.21
19.67
12.45
9.170
7.354
6.222
5.454
4.902
4.165
.'• 3.523
2.959
2.702
2.459
2.230
2.097
2.010
1.949
1.903
1.840
1.798
7
2.4E+4
199.4
44.43
21.62
14.20
10.79
8.885
7.694
6.885
6.303
5.524
4.847
4.257
3.991
3.742
3.509
3.376
3.291
3.232
3.188
3.127
3.087
60
2.5E+4
199.5
42.15
19.61
12.40
9.122
7.309
6.177
5.410
4.859
4.123
3.480
2.916
2.658
2.415
2.184
2.050
1.962
1.900
1.854
1.790
1.747
8
2.4E+4
199.4
44.13
21.35
13.96
10.57
8.678
7.496
6.693
6.116
5.345
4.674
4.090
3.826
3.580
3.350
3.219
3.134
3.076
3.032
2.972
2.933
70
2.5E+4
199.5
42.10
19.57
12.37
9.088
7.276
6.145
5.379
4.828
4.092
3.450
2.885
2.627
2.383
2.150
2.015
1.927
1.864
1.817
1.752
1.709
9
2.4E+4
199.4
43.88
21.14
13.77
10.39
8.514
7.339
6.541
5.968
5.202
4.536
3.956
3.695
3.451
3.222
3.092
3.008
2.950
2.907
2.847
2.808
80
2.5E+4
199.5
42.07
19.54
12.34
' 9.062
7.251
6.121
5.356
4.805
4.069
3.427
2.861
2.603
2.358
2.125
1.989
1.900
1.837
1.789
1.723
1.679
a =
10
2.4E+4
199.4
43.68
20.97
13.62
10.25
8.380
7.211
6.417
5.847
5.085
• 4.424
3.847
3.587
3.344
3.117
2.988
2.904
2.846
2.803
2.744
2.705
100
2.5E+4
199.5
42.02
19.50
12.30
9.026
7.217
6.087
5.322
4.772
4.037
3.394
2.828
2.569
2.323
2.088
1.951
1.861
1.797
1.748
1.681
1.636
0.005
12
2.4E+4
199.4
43.39
20.70
13.38
10.03
8.176
7.015
6.227
5.661
4.906
4.250
3.678
3.420
3.179
2.953
2.825
2.742
2.684
2.641
2.583
2.544
120
2.5E+4
199.5
41.99
19.47
12.27
9.001
7.193
6.065
5.300
4.750
4.015
3.372
2.806
2.546
2.300
2.064
1.925
1.834
1.769
1.720
1.652
1.606
ffj equal the degrees of freedom in the numerator and denominator
-------
Appendix D
Table D6. Upper percentiles of the F distribution/? =0.99 (continued)
fd
1
2
3
4
5
6
7
8
9
10
12
15
20
24
30
40
50
60
70
80
100
120
fd
1
2
3
4
5
6
7
8
9
10
12
15
20
24
30
40
50
60
70
80
100
120
fn
1
4.1 E+3
98.50
34.12
21.20
16.26
13.75
12.25
11.26
10.56
10.04
9.330
8.683
8.096
7.823
7.562
7.314
7.171
7.077
7.011
6.963
6.895
6.851
fn
15
6.2E+3
99.43
26,87
14.20
9.722
7.559
6.314
5.515
4.962
4.558
4.010
3.522
3.088
2.889
2.700
2.522
2.419
2.352
2.306
2.271
2.223
2.191
fn and fd
2
5.0E+3
99.00
30.82
18.00
13.27
10.92
9.547
8.649
8.022
7.559
6.927
6.359
5.849
5.614
5.390
5.178
5.057
4.977
4.922
4.881
4.824
4.787
20
6.2E+3
99.45
26.69
14.02
9.553
7.396
6.155
5.359
4.808
4.405
3.858
3.372
2.938
2.738
2.549
2.369
2.265
2,198
2.150
2.115
2.067
2.035
3
5.4E+3
99.16
29.46
16.69
12.06
9.780
8.451
7.591
6.992
6.552
5.953
5.417
4.938
4.718
4.510
4.313
4.199
4.126
4.074
4.036
3.984
3.949
24
6.2E+3
99.46
26.60
13.93
9.466
7.313
6.074
5.279
4.729
4.327
3.780
3.294
2.859
2.659
2.469
2.288
2.183
2.115
2.067
2.032
1.983
1.950
4
5.6E+3
99.25
28.71
15.98
11.39
9.148
7.847
7.006
6.422
5.994
5.412
4.893
4.431
4.218
4.018
3.828
3.720
3.649
3.600
3.563
3.513
3.480
30
6.3E+3
99.47
26.50
13.84
9.379
7.229
5.992
5.198
4.649
4.247
3.701
3.214
2.778
2.577
2.386
2.203
2.098
2.028
1.980
1.944
1.893
1.860
equal the degrees of freedom
5
5.8E+3
99.30
28.24
15.52
10.97
8.746
7.460
6.632
6.057
5.636
5.064
4.556
4.103
3.895
3.699
3.514
3.408
3.339
3.291
3.255
3.206
3.174
40
6.3E+3
99.48
26.41
13.75
9.291
7.143
5.908
5.116
4.567
4.165
3.619
3.132
2.695
2.492
2.299
2.114
2.007
1.936
1.886
1.849
1.797
1.763
6
5.9E+3
99.33
27.91
15.21
10.67
8.466
7.191
6.371
5.802
5.386
4.821
4.318
3.871
3.667
3.473
3.291
3.186
3.119
3.071
3.036
2.988
2.956
50
6.3E+3
99.48
26.35
13.69
9.238
7.091
5.858
5.065
4.517
4.115
3.569
3.081
2.643
2.440
2.245
2.058
1.949
1.877
1.826
1.788
1.735
1.700
7
5.9E+3
99.36
27.67
14.98
10.46
8.260
6.993
6.178
5.613
5.200
4.640
4 .1 42
3.699
3.496
3.305
3.124
3.020
2.953
2.906
2.871
2.823
2.792
60
6.3E+3
99.48
26.32
13.65
9.202
7.057
5.824
5.032
4.483
4.082
3.535
3.047
2.608
2.403
2.208
2.019
1.909
1.836
1.785
1.746
1.692
1.656
8
6.0E+3
99.38
27.49
14.80
10.29
8.102
6.840
6.029
5.467
5.057
4.499
4.004
3.564
3.363
3.173
2.993
2.890
2.823
2.777
2.742
2.694
2.663
70
6.3E+3
99.48
26.29
13.63
9.176
7.032
5.799
5.007
4.459
4.058
3.511
3.022
2.582
2.377
2.181
1.991
1.880
1.806
1.754
1.714
1.659
1.623
9
6.0E+3
99.39
27.34
14.66
10.16
7.976
6.719
5.911
5.351
4.942
4.388
3.895
3.457
3.256
3.067
2.888
2.785
2.718
2.672
2.637
2.590
2.559
80
6.3E+3
99.48
26.27
13.61
9.157
7.013
5.781
4.989
4.441
4.039
3.493
3.004
2.563
2.357
2.160
1.969
1.857
1.783.
1.730
1.690
1.634
1.597
cc =
10
6.1 E+3
99.40
27.23
14.55
10.05
7.874
6.620
5.814
5.257
4.849
4.296
3.805
3.368
3.168
2.979
2.801
2.698
2.632
2.585
2.551
2.503
2.472
100
6.3E+3
99.49
26.24
13.58
9.130
6.987
5.755
4.963
4.415
4.014
3.467
2.977
2.535
2.329
2.131
1.938
1.825
1.749
1.695
1.655
1.598
1.559
0.01
12
6.1E+3
99.42
27.05
14.37
9.89
7.718
6.469
5.667
5.111
4.706
4.155
3.666
3.231
3.032
2.843
2.665
2.563
2.496
2.450
2.415
2.368
2.336
120
6.3E+3
99.49
26.22
13.56
9.112
6.969
5.737
4.946
4.398
3.996
3.449
2.959
2.517
2.310
2.111
1.917
1.803
1.726
1.672
1.630
1.572
1.533
in the numerator and denominator
-------
Table D6. Upper percentiles of the F distribution for p= 0.975 (continued)
fd
1
2
3
4
5
6
7
8
9
10
12
15
20
24
30
40
50
60
70
80
100
120
fd
1
2
3
4
5
6
7
8
9
10
12
15
20
24
30
40
50
60
70
80
100
120
fn
1
647.8
38.51
17.44
12.22
10.01
8.813
8.073
7.571
7.209
6.937
6.554
6.200
5.871
5.717
5.568
5.424
5.340
5.286
5.247
5.218
5.179
5.152
15
984.9
39.43
14.25
8.657
6.428
5.269
4.568
4.101
3.769
3.522
3.177
2.862
2.573
2.437
2.307
2.182
2.109
2.061
2.028
2.003
1.968
1.945
fn and
2
799.5
39.00
16.04
10.65
8.434
7.260
6.542
6.059
5.715
5.456
5.096
4.765
4.461
4.319
4.182
4.051
3.975
3.925
3.890
3.864
3.828
3.805
20
993.1
39.45
14.17
8.560
6.329
5.168
4.467
3.999
3.667
3.419
3.073
2.756
2.464
2.327
2.195
2.068
1.993
1.944
1.910
1.884
1.849
1.825
3
864.2
39.17
15.44
9.979
7.764
6.599
5.890
5.416
5.078
4.826
4.474
4.153
3.859
3.721
3.589
3.463
3.390
3.343
3.309
3.284
3.250
3.227
24
997.3
39.46
14.12
8.511
6.278
5.117
4.415
3.947
3.614
3.365
3.019
2.701
2.408
2.269
2.136
2.007
1.931
1.882
1.847
1.820
1.784
1.760
4
899.6
39.25
15.10
9.604
7.388
6.227
5.523
5.053
4.718
4.468
4.121
3.804
3.515
3.379
3.250
3.126
3.054
3.008
2.975
2.950
2.917
2.894
30
1001
39.46
14.08
8.461
6.227
5.065
4.362
3.894
3.560
3.311
2.963
2.644
2.349
2.209
2.074
1.943
1.866
1.815
1.779
1.752
1.715
1.690
fd equal the degrees of freedom
5
921.8
39.30
14.88
9.364
7.146
5.988
5.285
4.817
4.484
4.236
3.891
3.576
3.289
3.155
3.026
2.904
2.833
2.786
2.754
2.730
2.696
2.674
40
1006
39.47
14.04
8.411
6.175
5.012
4.309
3.840
3.505
3.255
2.906
2.585
2.287
2.146
2.009
1.875
1.796
1.744
1.707
1.679
1.640
1.614
6
937.1
39.33
14.73
9.197
6.978
5.820
5.119
4.652
4.320
4.072
3.728
3.415
3.128
2.995
2.867
2.744
2.674
2.627
2.595
2.571
2.537
2.515
50
1008
39.48
14.01
8.381
6.144
4.980
4.276
3.807
3.472
3.221
2.871
2.549
2.249
2.107
1.968
1.832
1.752
1.699
1.660
1.632
1.592
1.565
in the numerator and
7
948.2
39.36
14.62
9.074
6.853
5.695
4.995
4.529
4.197
3.950
3.607
3.293
3.007
2.874
2.746
2.624
2.553
2.507
2.474
2.450
2.417
2.395
60
1010
39.48
13.99
8.360
6.123
4.959
4.254
3.784
3.449
3.198
2.848
2.524
2.223
2.080
1.940
1.803
1.721
1.667
1.628
1.599
1.558
1.530
8
956.6
39.37
14.54
8.980
6.757
5.600
4.899
4.433
4.102
3.855
3.512
3.199
2.913
2.779
2.651
2.529
2.458
2.412
2.379
2.355
2.321
2.299
70
1011
39.48
13.98
8.346
6.107
4.943
4.239
3.768
3.433
3.182
2.831
2.506
2.205
2.060
1.920
1.781
1.698
1.643
1.604
1.574
1.532
1.504
9
963.3
39.39
14.47
8.905
6.681
5.523
4.823
4.357
4.026
3.779
3.436
3.123
2.837
2.703
2.575
2.452
2.381
2.334
2.302
2.277
2.244
2.222
80
1012
39.49
13.97
8.335
6.096
4.932
4.227
3.756
3.421
3.169
2.818
2.493
2.190
2.045
1.904
1.764
1.681
1.625
1.585
1.555
1.512
1.483
a =
10
968.6
39.40
14.42
8.844
6.619
5.461
4.761
4.295
3.964
3.717
3.374
3.060
2.774
2.640
2.511
2.388
2.317
2.270
2.237
2.213
2.179
2.157
100
1013
39.49
13.96
8.319
6.080
4.915
4.210
3.739
3.403
3.152
2.800
2.474
2.170
2.024
1.882
1.741
1.656
1.599
1.558
1.527
1.483
1.454
0.025
12
976.7
39.41
14.34
8.751
6.525
5.366
4.666
4.200
3.868
3.621
3.277
2.963
2.676
2.541
2.412
2.288
2.216
2.169
2.136
2.111
2.077
2.055
120
1014
39.49
13.95
8.309
6.069
4.904
4.199
3.728
3.392
3.140
2.787
2.461
2.156
2.010
1.866
1.724
1.639
1..581
1.539
1.508
1.463
1.433
denominator
-------
Appendix D
Table D6. Upper percentiles of the F distribution for p =0.95 (continued)
fd
2
3
4
5
6
7
8
9
10
12
15
20
24
30
40
50
60
70
80
100
120
fd
1
2
3
4
5
6
7
8
9
10
12
15
20
24
30
40
50
60
70
80
100
120
fn
1
161.4
18.51
10.13
7.709
6.608
5.987
5.591
5.318
5.117
4.965
4.747
4.543
4.351
4.260
4.171
4.085
4.034
4.001
3.978
3.960
3.936
3.920
15
245.9
19.43
8.703
5.858
4.619
3.938
3.511
3.218
3.006
2.845
2.617
2.403
2.203
2.108
2.015
1.924
1.871
1.836
1.812
1.793
1.768
1.750
2
199.5
19.00
9.552
6.944
5.786
5.143
4.737
4.459
4.256
4.103
3.885
3.682
3.493
3.403
3.316
3.232
3.183
3.150
3.128
3.111
3.087
3.072
20
248.0
19.45
8.660
5.803
4.558
3.874
3.445
3.150
2.936
2.774
2.544
2.328
2.124
2.027
1.932
1.839
1.784
1.748
1.722
1.703
1.676
1.659
3
215.7
19.16
9.277
6.591
5.409
4.757
4.347
4.066
3.863
3.708
3.490
3.287
3.098
3.009
2.922
2.839
2.790
2.758
2.736
2.719
2.696
2.680
24
249.1
19.45
8.638
5.774
4.527
3.841
3.410
3.115
2.900
2.737
2.505
2.288
2.082
1.984
1.887
1.793
1.737
1.700
1.674
1.654
1.627
1.608
4
224.6
19.25
9.117
6.388
5.192
4.534
4.120
3.838
3.633
3.478
3.259
3.056
2.866
2.776
2.690
2.606
2.557
2.525
2.503
2.486
2.463
2.447
30
250.1
19.46
8.617
5.746
4.496
3.808
3.376
3.079
2.864
2.700
2.466
2.247
2.039
1.939
1.841
1.744
1.687
1.649
1.622
1.602
1.573
1.554
fn and ffj equal the degrees of freedom
5
230.2
19.30
9.013
6.256
5.050
4.387
3.972
3.688
3.482
3.326
3.106
2.901
2.711
2.621
2.534
2.449
2.400
2.368
2.346
2.329
2.305
2.290
40
251.1
19.47
8.594
5.717
4.464
3.774
3.340
3.043
2.826
2.661
2.426
2.204
1.994
1.892
1.792
1.693
1.634
1.594
1.566
1.545
1.515
1.495
6
234.0
19.33
8.941
6.163
4.950
4.284
3.866
3.581
3.374
3.217
2.996
2.790
2.599
2.508
2.421
2.336
2.286
2.254
2.231
2.214
2.191
2.175
50
251.8
19.48
8.581
5.699
4.444
3.754
3.319
3.020
2.803
2.637
2.401
2.178
1.966
1.863
1.761
1.660
1.599
1.559
1.530
1.508
1.477
1.457
7
236.8
19.35
8.887
6.094
4.876
4.207
3.787
3.500
3.293
3.135
2.913
2.707
2.514
2.423
2.334
2.249
2.199
2.167
2.143
2.126
2.103
2.087
60
252.2
19.48
8.572
5.688
4.431
3.740
3.304
3.005
2.787
2.621
2.384
2.160
1.946
1.842
1.740
1.637
1.576
1.534
1.505
1.482
1.450
1.429
8
238.9
19.37
8.845
6.041
4.818
4.147
3.726
3.438
3.230
3.072
2.849
2.641
2.447
2.355
2.266
2.180
2.130
2.097
2.074
2.056
2.032
2.016
70
252.5
19.48
8.566
5.679
4.422
3.730
3.294
2.994
2.776
2.609
2.372
2.147
1.932
1.828 .
1.724
1.621
1.558
1.516
1.486
1.463
1.430
1.408
9
240.5
19.38
8.812
5.999
4.772
4.099
3.677
3.388
3.179
3.020
2.796
2.588
2.393
2.300
2.211
2.124
2.073
2.040
2.017
1.999
1.975
1.959
80
252.7
19.48
8.561
5.673
4.415
3.722
3.286
2.986
2.768
2.601
2.363
2.137
1.922
1.816
1.712
1.608
1.544
1.502
1.471
1.448
1.415
1.392
a =
10
241.9
19.40
8.785
5.964
4.735
4.060
3.637
3.347
3.137
2.978
2.753
2.544
2.348
2.255
2.165
2.077
2.026
1.993
1.969
1.951
1.927
1.910
100
253.0
19.49
8.554
5.664
4.405
3.712
3.275
2.975
2.756
2.588
2.350
2.123
1.907
1.800
1.695
1.589
1.525
1.481
1.450
1.426
1.392
1.369
0.05
12
243.9
19.41
8.745
5.912
4.678
4.000
3.575
3.284
3.073
2.913
2.687
2.475
2.278
2.183
2.092
2.003
1.952
1.917
1.893
1.875
1.850
1.834
120
253.3
19.49
8.549
5.658
, 4.398
3.705
3.267
2.967
.: 2.748
2.580
2.341,
2.114
1.896
1.790
1.683
1.577
1.511
1.467
1.435
1.411
1.376
1.352
in the numerator and denominator
-------
AppendixJIL
Table D6. Upper percentiles of the F distribution for/? =0.90 (continued)
f
f(\
1
2
3
4
5
6
7
8
9
10
12
15
20
24
30
40
50
60
70
80
100
120
ffl
1
2
3
4
5
6
7
8
9
10
12
15
20
24
30
40
50
60
70
80
100
120
ii
1
39.86
8.526
5.538
4.545
4.060
3.776
3.589
3.458
3.360
3.285
3.177
3.073
2.975
2.927
2.881
2.835
2.809
2.791
2.779
2.769
2.756
2.748
15
61.22
9.425
5.200
3.870
3.238
2.871
2.632
2.464
2.340
2.244
2.105
1.972
1.845
1.783
1.722
1.662
1.627
1.603
1.587
1.574
1.557
1.545
fn and
2
49.50
9.000
5.462
4.325 '
3.780
3.463
3.257
3.113
3.006
2.924
2.807
2.695
2.589
2.538
2.489
2.440
2.412
2.393
2.380
2.370
2.356
2.347
20
61.74
9.441
5.184
3.844
3.207
2.836
2.595
2.425
2.298
2.201
2.060
1.924
1.794
1.730
1.667
1.605
1.568
1.543
1.526
1.513
. 1.494
1.482
3
53.59
9.162
5.391
4.191
3.619
3.289
3.074
2.924
2.813
2.728
2.606
2.490
2.380
2.327
2.276
2.226
2.197
2.177
2.164
2.154
2.139
2.130
24
62.00
9.450
5.176
3.831
3.191
2.818
2.575
2.404
2.277
2.178
2.036
1.899
1.767
1.702
1.638
1.574
1.536
1.511
1.493
1.479
1.460
1.447
4
55.83
9.243
5.343
4.107
3.520
3.181
2.961
2.806
2.693
2.605
2.480
2.361
2.249
2.195
2.142
2.091
2.061
2.041
2.027
2.016
2.002
1.992
30
62.26
9.458
5.168 :
3.817
3.174
2.800
2.555
2.383
2.255
2.155
2.011
1.873
1.738
1.672
1.606
1.541
1.502
1.476
1.457
1.443
1.423
1.409
fd equaj the degrees of freedom
5
57.24
9.293
5.309
4.051
3.453
3.108
2.883
2.726
2.611
2.522
2.394
2.273
2.158
2.103
2.049
1.997
1.966
1.946
1.931
1.921
1.906
1.896
40
62.53
9.466
5.160
3.804
3.157
2.781
2.535
2.361
2.232
2.132
1.986
1.845
1.708
1.641
1.573
1.506
1.465
1.437
1.418
1.403
1.382
1.368
6
58.20
9.326
5.285
4.010
3.405
3.055
2,827
2.668
2.551
2.461
2.331
2.208
2.091
2.035
1.980
1.927
1.895
1.875
1.860
1.849
1.834
1.824
50
62.69
9.471
5.155
3.795
3.147
2.770
2.523
2.348
2.218
2.117
1.970
1.828
1.690
1.621
1.552
1.483
1.441
1.413
1.392
1.377
1.355
1.340
7
58.91
9.349
5.266
3.979
3.368
3.014
2.785
2.624
2.505
2.414
2.283
2.158
2.040
1.983
1.927
1.873,
1.840
1.819
1.804
1.793
1.778
1.767
60
62.79
9.475
5.151
3.790
3.140
2.762
2.514
2.339
2.208
2.107
1.960
1.817
1.677
1.607
1.538
1.467
1.424
1.395
1.374
1.358
1.336
1.320
8
59.44
9.367
5.252
3.955
3.339
2.983
2.752
2.589
2.469
2.377
2.245
2.119
1.999
1.941
1.884
1.829
1.796
1.775
1.760
1.748
1.732
1.722
70
62.87
9.477
5.149
3.786
3.135
2.756
2.508
2.333
' 2.202
2.100
1.952
1.808
1.667
1.597
1.527
1.455
1.412.
1.382
1.361
1.344
1.321
1.305
9
59.86
9.381
5.240
3.936
3.316
2.958
2.725
2.561
2.440
2.347
2.214
2.086
1.965
1.906
1.849
1.793
1.760
1.738
1.723
1.711
1.695
1.684
80
62.93
9.479
5.147
3.782
3.132
2.752
2.504
2.328
2.196
2.095
1.946
1.802
'1.660
1.590
1.519
1.447'
1.402
1.372
1.350
1.334
1.310
1.294
-------
Appendix D
Table D7. Noncentral t distribution. Values of the noncentrality
parameter for a one-sided test with a = 0.050. (Source- Owen
1965)
— type 2 error
.01
.05
.10
.20 .30 .40 .50 .60 .70 .80
.90
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
40
60
100
00
16.47
6.88
5.47
4.95
4.70
4.55
4.45
4.38
4.32
4.28
4.25
4.22
4.20
4.18
4.17
4.16
4.14
4.13
4.12
4.12
4.11
4.10
4.10
4.09
4.09
4.08
4.08
4.07
4.07
4.07
4.04
4.02
4.00
3.97
12.53
5.52
4.46
4.07
3.87
3.75
3.67
3.62
3.58
3.54
3.52
3.50
3.48
3.46
3.45
3.44
3.43
3.42
3.41
3.41
3.40
3.40
3.39
3.39
3.38
3.38
3.38
3.37
3.37
3.37
3.35
3.33
3.31
3.29
10.51
4.81
3.93
3.60
3.43
3.33
3.26
3.21
3.18
3.15
3.13
3.11
3.09
3.08
3.07
3.06
3.05
3.04
3.04
3.03
3.03
3.02
3.02
3.01
3.01
3.01
3.00
3.00
3.00
3.00
2.98
2.96
2.95
Z93
8.19
3.98
3.30
3.04
2.90
2.82
2.77
2.73
2.70
2.67
2.65
2.64
2.63
2.62
2.61
2.60
2.59
2.59
2.58
2.58
2.57
2.57
2.56
2.56
2.56
2.55
2.55
2.55
2.55
2.54
2.53
2.52
2.50
2.49
6.63
3.40
2.85
2.64
2.53
2.46
2.41
2.38
2.35
2.33
2.31
2.30
2.29
2.28
2.27
2.27
2.26
2.26
2.25
2.25
2.24
2.24
2.24
2.23
2.23
2.23
2.23
2.22
2.22
2.22
2.21
2.19
2.18
2.17
5.38
2.92
2.48
2.30
2.21
2.15
2.11
2.08
2.06
2.04
2.02
2.01
2.00
2.00
1.99
1.98
1.98
1.97
1.97
1.97
1.96
1.96
1.96
1.95
1.95
1.95
1.95
1.95
1.94
1.94
1.93
1.92
1.91
1.90
4.31
2.49
2.13
1.99
1.91
1.86
1.82
1.80
1.78
1.77
1.75
1.74
1.74
1.73
1.72
1.72
1.71
1.71
1.71
1.70
1.70
1.70
1.69
1.69
1.69
1.69
1.69
1.69
1.68
1.68
1.67
1.66
1.66
1.64
3.35
2.07
1.79
1.67
1.61
1.57
1.54
1.52
1.51
1.49
1.48
1.47
1.47
1.46
1.46
1.45
1.45
1.45
1.44
1.44
1.44
1.44
1.43
1.43
1.43
1.43
1.43
1.43
1.42
1.42
1.42
1.41
1.40
1.39
2.46'
1.63
1.43
1.34
1.29
1.26
1.24
1.22
.21
.20
.19
.19
.18
.18
.17
1.17
1.17
1.16
1.16
1.16
1.16
1.16
1.15
1.15
1.15
1.15
.15
.15
.15
.15
.14
.13
.13
.12
1.60
1.15
1.02
.96
.92
.90
.89
.88
.87
.86
.86
.85
.85
.84
.84
.84
.84
.83
.83
.83
.83
.83
.83
.83
.83
.82
.82
.82
.82
.82
.82
.81
.81
.80
.64
.50
.46
.43
.42
.41
.40
.40
.39
.39
.39
.38
.38
.38
.38
.38
.38
.38
.38
.38
.38
.37'
.37
.37
.37
.3?
.37
.37
.37
.37
;37
.37
.37
.36
Pr [noncentrml t > t^ \ 6 - Oj -
n/
-------
Table D7. (continued) Values of the noncentrality parameter for a
one-sided test with a = 0.025. (Source: Owen, 1965)
/? - type
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
40
60
100
00
.01
32.83
9.67
6.88
5.94
5.49
5.22
5.06
4.94
4.85
4,78
4.73
4.69
4.65
4.62
4.60
4.58
4.56
4.54
4.52
4.51
4.50
4.49
4.48
4.47
4.46
4.46
4.45
4.44
4.44
4.43
4.39
4.36
4.33
4.29
.05
24.98
T.77
5.65
4.93
4.57
4.37
4.23
4.14
4.07
4.01
3.97
3.93
3.91
3.88
3.86
3.84
3.83
3.82
3.80
3.79
3.78
3.77
3.77
3.76
3.75
3.75
3.74
3.73
3.73
3.73
3.69
3.66
3.64
3.60
.10
20.96
6.80
5.01
4.40
4.09
3.91
3.80
3.71
3.65
3.60
3.57
3.54
3.51
3.49
3.47
3.46
3.44
3.43
3.42
3.41
3.40
3.39
3.39
3.38
3.37
3.37
3.36
3.36
3.35
3.35
3.32
3.29
3.27
3.24
.20
16.33
5.65
4.26
3.76
3.51
3.37
3.27
3.20
3.15
3.11
3.08
3.05
3.03
3.01
3.00
2.98
2.97
2.96
2.95
2.95
2.93
2.93
2.93
2.92
2.92
. 2.92
2.91
2.90
2.90
2.90
2.87
2.82
2.83
2.80
.30
13.21
4.86
3.72
3.31
3.10
2.98
2.89
2.83
2.79
2.75
2.73
2.70
2.69
2.67
2.66
2.65
2.64
2.63
2.61
2.61
2.60
2.60
2.59
2.59
2.58
2.58
2.58
2.57
2.57
2.57
2.55
2.53
2.51
2.48
2 error
.40
10.73
4.21
3.28
2.93
2.75
2.64
2.57
2.52
2.48
2.45
2.43
2.41
2.39
2.38
2.37
2.36
2.35
2.34
2.33
2.33
2.32
2.32
2.31
2-31
2.30
2.30
2.30
2.29
2.29
2.29
2.27
2.23
2.23
2.21
.50
8.60
3.63
2.87
2.58
2.43
2.34
2.27
2.23
2.20
2.17
2.15
2.13
2.12
2.11
2.09
2.09
2.08
2.07
2.06
2.06
2.05
2.05
2.05
2.04
2.04
2.04
2.03
2.03
2.03
2.02
2.01
1.99
1.98
1.96
.60
6.68
3.07
2.47
2.23
2.11
2.03
1.98
1.94
1.91
1.89
1.87
1.85
1.84
1.83
1.82
1.81
1.81
1.80
1.80
1.79
1.79
1.78
1.78
1.78
1.77
1.77
1.77
1.77
1.77
1.76
1.75
1.73
1.73
1.71
.70
4.91
2.50
2.05
1.86
1.76
1.70
1.66
1.63
1.60
1.59
1.57
1.56
1.55
1.54
1.53
1.53
1.52
1.52
1.51
1.51
1.50
1.50
1.50
1.50
1.49
1.49
1.49
1.49
1.48
1.48
1.47
1.46
1.45
1.44
.30
3.22
1.88
1.57
1.44
1.37
1.32
1.29
1.27
1.25
1.23
1.22
1.21
1.21
1.20
1.19
1.19
1.18
1.18
1.17
1-17
1.17
1.17
1.17
1.16
1.16
1.16
1.16
1.16
1.16
1.16
1.15
1.14
1.12
1.12
.90
1.58
1.09
.94
.87
.82
.80
.78
.77
.76
.75
.74
.74
.73
.73
.72
.72
.72
.72
.71
.71
.71
.71
.71
.71
.71
.70
.70
.70
.70
.70
.69
.69
.68
.68
Pr [noncentni t > f1-€ | 6 «• OH — M»X ^
1 -
-------
Appendix D
Table D7. (continued) Values of the noncentrality parameter for a
one-sided test with a = 0.010. (Source: Owen, 1965)
/
I
2
3
4
5
6
7
8
9
10
11
12
13
14 .
15
16
17 i
18
19
20 }
i
21 '
22 '
23 .
24 :
25
j
26 !
27 !
28 <
29 i
30 I
i
40 |
60 !
100 I
« i
.01
82.00
15.22
9.34
7.52
6.68
6.21
5.91
5.71
5.56
5.45
5.36
5.29
5.23
5.18
5.14
5.11
5.08
5.05
5.03
5.01
4.99
4.97
4.96
4.94
4.93
4.92
4.91
4.90
4.89
4.88
4.82
4.76
4.72
4.65
0 - type 2 error
.05
62.40
12.26
7.71
6.28
5.62
5.25
5.01
4.85
4.72
4.63
4.56
4.50
4.46
4.42
4.38
4.35
4.33
4.31
4.29
4.27
4.25
4.24
4.23
4.22
4.20
4.19
4.19
4.18
4.17
4.16
4.11
4.06
4.03
3.97
.10
52.37
10.74
6.86
5.64
5.07
4.74
4.53
4.39
4.28
4.20
4.14
4.09
4.04
4.01
3.98
3.95
3.93
3.91
3.89
3.88
3.86
3.85
3.84
3.83'
3.82
3.81
3.80
3.79
3.79
3.78
3.74
3.69
3.66
3.61
.20
40.80
8.96
5.87
4.88
4.40
4.13
3.96
3.84
3.75
3.68
3.63
3.58
3.55
3.51
3.49
3.47
3.45
3.43
3.42
3.40
3.39
3.38
3.37
3.36
3.35
3.34
3.34
3.33
3.32
3.32
3.28
3.24
3.21
3.17
.30
33.00
7.73
5.17
4.34
3.93
3.70
3.55
3.44
3.37
3.31
3.26
3.22
3.19
3.16
3.14
3.12
3.10
3.09
3.07
3.06
3.05
3.04
3.03
3.02
3.02
3.01
3.00
3.00
2.99
2.99
2.95
2.92
Z89
2.85
.40
26.79
6.73
4.59
3.88
3.54
3.33
3.20
3.11
3.04
2.99
2.94
2.91
2.88
2.86
2.84
2.82
2.80
2.79
2.78
2.77
2.76
2.75
2.74
2.73
2.73
2.72
2.72
2.71
2.71
2.70
2.67
2.64
2.62
2.58
.50
21.47
5.83
4.07
3.47
3.17
2.99
2.88
2.80
2.74
2.69
2.65
2.62
2.60
2.57
2.56
2.54
2.53
2.52
2.50
2.50
2.49
2.48
2.47
2.47
2.46
2.45
2.45
2.44
2.44
2.44
2.41
2.38
2.36
2.33
.60
16.69
4.98
3.56
3.06
2.81
2.66
2.56
2.49
2.43
2.39
2.36
2.33
2.31
2.29
2.28
2.26
2.25
2.24
2.23
2.22
2.22
2.21
2.20
2.20
2.19
2.19
2.18
2.18
2.17
2.17
2.15
2.12
2.10
2.07
.70
12.27
4.12
3.03
2.63
2.42
2.30
2.22
2.16
2.11
2.08
2.05
2.03
2.01
1.99
1.98
1.97
1.96
1.95
1.94
1.93
1.92
1.92
1.91
1.91
1.90
1.90
1.90
1.89
1.89
1.89
1.86
1.84
1.83
1.80
.80
8.07
3.20
2.44
2.14
1.98
1.83
1.82
1.77
1.74
1.71
1.69
1.67
1.65
1.64
1.63
1.62
1.61
1.60
1.60
1.59
1.59
1.58
1.58
1.57
1.57
1.57
1.56
1.56
1.56
1.55
1.54
1.52
1.51
1.48
.90
4.00
2.08
1.66
1.48
1.38
1.32
1.27
1.24
1.22
1.20
1.18
1.17
1.16
1.15
1.14
1.14
1.13
1.13
1.12
1.12
1;11
1.11
1.11
1.11
1.10
1.10
1.10
1.10
1.10
1.09
1.08
1.07
1.06
1.04
Pr [noncemral / >
- ^ V«/
-------
ApperSiixIP.!
Table D8. Binomial distribution with y successes out of n trials.
n
1
2
3
4
5
6
7
8
y
0
1
0
1
2
0
1
2
3
0
1
2
3
4
0
1
2
3
4
5
0
1
2
3
4
5
6
0
1
2
3
4
5
6
7
0
1
2
3
4
5
6
7
8
p(Y
-------
Appendix D
Table D8. Binomial distribution withy successes out of n trials (continued).
n
1
2
3
4
5
6
7
8
y
u
0
2
0
•
'4
3
0
'
t
;
i
0
'
4
3
^
*
0
1
2
3
4
c
6
0
1
2
3
4
5
6
7
0
1
2
3
4
5
6
7
8
p(V
-------
Table D8. Binomial distribution with y successes out of n trials (continued).
n
9
10
11
12
y
0
1
2
3
4
5
6
7
8
9
0
1
2
3
4
5
6
7
8
9
10
0
1
2
3
4
5
6
7
8
9
10
11
0
1
2
3
4
5
6
7
8
9
10
11
12
p(Y
-------
Appendix D
Table D8. Binomial distribution withj> successes out of n trials (continued).
n
9
10
11
12
Y
0
•
4
f.
t
i
(.
7
8
g
0
1
2
c
A
e
6
7
8
c
10
0
1
2
3
4
5
6
7
8
g
10
11
0
1
2
3
4
5
6
7
8
9
10
11
12
P(Y
-------
Table D8. Binomial distribution with y successes out of n trials (continued).
n
13
14
15
V
0
1
2
3
4
5
6
7
8
g
10
11
12
13
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
p(Y
-------
Appendix D
Table D8. Binomial distribution withj successes out of n trials (continued).
n
13
14
15
Y
o
'
t
;
i
i
e
i
8
<
10
11
12
13
0
1
2
;
4
c
6
7
8
9
10
11
12
13
14
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
p(V
-------
Table D8. Binomial distribution withy successes out of n trials (continued).
n
16
17
18
y
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
P(Y
-------
Appendix D
Table D8. Binomial distribution with j successes out of n trials (continued).
n
16
17
18
y
0
1
2
j
4
K
6
7
8
9
10
11
12
13
14
15
16
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
p(Y
-------
Appenajx p
Table D8. Binomial distribution withj successes out of n trials (continued).
n y
19 0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20 0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
p(Y0.9996
0.9999
1.0000
1.0000
1.0000
1.0000
1.0000
1.0000
1.0000
1.0000
1.0000
1.0000
1.0000
1.0000
0.15
0.0456
0.1985
0.4413
0.6841
0.8556
0.9463
0.9837
0.9959
0.9992
0.9999
1.0000
1.0000
1.0000
1.0000
1.0000
1.0000
1.0000
1.0000
1.0000
1.0000
0.0388
0.1756
0.4049
0.6477
0.8298
0.9327
0.9781
0.9941
0.9987
0.9998
1.0000
1.0000
1.0000
1.0000
1.0000
1.0000
1 .0000
1.0000
1.0000
1.0000
1.0000
0.20
0.0144
0.0829
0.2369
0.4551
0.6733
0.8369
0.9324
0.9767
0.9933
0.9984
0.9997
1.0000
1.0000
1.0000
1.0000
1.0000
1.0000
1.0000
1.0000
1.0000
0.0115
0.0692
0.2061
0.4114
0.6296
0.8042
0.9133
0.9679
0.9900
0.9974
0.9994
0.9999
1.0000
1.0000
1.0000
1.0000
1.0000
1.0000
1.0000
1.0000
1.0000
0.25
0.0042
0.0310
0.1113
0.2631
0.4654
0.6678
0.8251
0.9225
0.9713
0.9911
0.9977
0.9995
0.9999
1.0000
1.0000
1.0000
1.0000
1.0000
1.0000
1.0000
0.0032
0.0243
0.0913
0.2252
0.4148
0.6172
0.7858
0.8982
0.9591
0.9861
0.9961
0.9991
0.9998
1.0000
1.0000
1.0000
1.0000
1.0000
1.0000
1.0000
1.0000
0.30
0.001 1
0.0104
0.0462
0.1332
0.2822
0.4739
0.6655
0.8180
0.9161
0.9674
0.9895
0.9972
0.9994
0.9999
1.0000
1.0000
1.0000
1.0000
1.0000
1.0000
0.0008
0.0076
0.0355
0.1071
0.2375
0.4164
0.6080
0.7723
0.8867
0.9520
0.9829
0.9949
0.9987
0.9997
1.0000
1.0000
1.0000
1.0000
1.0000
1.0000
1.0000
0.35
0.0003
0.0031
0.0170
0.0591
0.1500
0.2968
0.4812
0.6656
0.8145
0.9125
0.9653
0.9886
0.9969
0.9993
0.9999
1.0000
1.0000
1.0000
1.0000
1.0000
0.0002
0.0021
0.0121
0.0444
0.1182
0.2454
0.4166
0.6010
0.7624
0.8782
0.9468
0.9804
0.9940
0.9985
0.9997
1.0000
1.0000
1.0000
1.0000
1.0000
1.0000
0.40
0.0001
0.0008
0.0055
0.0230
0.0696
0.1629
0.3081
0.4878
0.6675
0.8139
0.9115
0.9648
0.9884
0.9969
0.9994
0.9999
1.0000
1.0000
1.0000
1.0000
0.0000
0.0005
0.0036
0.0160
0.0510
0.1256
0.2500
0.4159
0.5956
0.7553
0.8725
0.9435
0.9790
0.9935
0.9984
0.9997
1.0000
1.0000
1.0000
1.0000
1.0000
0.45
0.0000
0.0002
0.0015
0.0077
0.0280
0.0777
0.1727
0.3169
0.4940
0.6710
0.8159
0.9129
0.9658
0.9891
0.9972
0.9995
0.9999
1.0000
1.0000
1.0000
0.0000
0.0001
0.0009
0.0049
0.0189
0.0553
0.1299
0.2520
0.4143
0.5914
0.7507
0.8692
0.9420
0.9786
0.9936
0.9985
0.9997
1.0000
1.0000
1.0000
1.0000
0.50
0.0000
0.0000
0.0004
0.0022
0.0096
0.0318
0.0835
0.1796
0.3238
0.5000
0.6762
0.8204
0.9165
0.9682
0.9904
0.9978
0.9996
1.0000
1.0000
1.0000
0.0000
0.0000
0.0002
0.0013
0.0059
0.0207
0.0577
0.1316
0.2517
0.4119
0.5881
0.7483
0.8684
0.9423
0.9793
0.9941
0.9987
0.9998
1.0000
1.0000
1.0000
-------
Appendix D
Table D8. Binomial distribution with j> successes out of n trials (continued).
n y
19 0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20 0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
p(Y
-------
Table D9. Population correlation coefficients. (Source: Remington and
Schork, 1970)
CHARTS FOR COMPUTING CONFIDENCE LIMITS FOR THE
POPULATION CORRELATION COEFFICIENT p
CONFIDENCE LEVEL 95%
Scale of r (sample correlation coefficient)
-------
Appendix D
Table D9. (continued)
CHARTS FOR COMPUTING CONFIDENCE LIMITS FOR THE
POPULATION CORRELATION COEFFICIENT p
CONFIDENCE LEVEL 99%
Scale off (sample correlation coefficient)
-------
Table DIG. Quantiles of the Spearman test statistic. (Source:
Conover, 1980)
n
4
5
6
7
8
9
10
11
12 '•'(.
13"
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
p = .900
.8000
.7000
.6000
.5357
.5000
.4667
.4424
.4182
.3986
.3791
.3626
.3500
.3382
.3260
.3148
.3070
.2977
.2909
.2829
.2767
.2704
.2646
.2588
.2540
.2490
.2443
.2400
.950
.8000
.8000
.7714
.6786
.6190
.5833
.5515
.5273
.4965
.4780
.4593
.4429
.4265
.4118
,3994
.3895
.3789
.3688
.3597
.3518
.3435
.3362
.3299
.3236
.3175
.3113
.3059
.975
.9000
.8286
.7450
..7143
.6833
.6364
.6091
.5804
.5549
.5341
.5179
.5000
.4853
.4716
.4579
.4451
.4351
.4241
.4150
.4061
.3977
.3894
.3822
.3749
.3685
.3620
.990
.9000
.8857
.8571
.8095
.7667
.7333
.7000
.6713
.6429
.6220
.6000
.5824
.5637
.5480
.5333
.5203
.5078
.4963
.4852
.4748
.4654
.4564
.4481
.4401
.4320
.4251
.995
.9429
.8929
.8571
.8167
.7818
.7455
.7273
.6978
.6747
.6536
,6324
.6152
.5975
.5825
.5684
.5545
.5426
.5306
.5200
.5100
.5002
.4915
.4828
.4744
.4665
.999
.9643
.9286
.9000
.8667
.8364
.8182,
.7912
.7670
.7464
.7265
.7083
.6904
.6737
.6586
.6455
.6318
.6186
.6070'
.5962
.5856
.5757
.5660
.5567
.5479
For n greater than 30 the approximate quantiles of p may be obtained from
where xp is the p quantile of a standard normal random variable obtained from Table 1.
a The entries in this table are selected quantiles wp of the Spearman rank correlation coefficiem
p when used as a test statistic. The lower quantiles may be obtained from the equation
The critical region corresponds to values of p smaller than (or greater than) but not including the
appropriate quantile. Note that the median of p is 0.
-------
Appendix D
Table Dll. Sample sizes for one-sided nonparametric tolerance
limits2 (Source: Practical Nonparametric Statistics, W.J. Conover, Copyright *
(1980 John Wiley & Sons, Inc.). Reprinted by permission of John Wiley & Sons, Inc.)
1 - 2 q = .500 .700 .750 .800 .850 .900 .950 .975 .980 .990
.500
.700
.750
.800
.850
.900
.950
.975
.980
.990
.995
.999
1
2
2
3
3
4
5
6
6
7
8
10
2
4
4
5
6
7
9
11
11
13
15
20
3
5
5
6
7
9
11
13
14
17
19
25
4
6
7
8
9
11
14
17
18
21
24
31
5
8
,9
10
12
15
19
23
25
29
33
43
7
12
14
16
19
22
29
36
38
44
51
66
14
24
28
32
37-
45
59
• 72
77
90
104
135
28
48
55
64
75
91
119
146
155
182
210
273
35
60
69
80
94
144
149
183
194
228
263
342
69
120
138
161
189
230
299
368
390
459
528
688
1 The quantity tabled is the sample size n such that q" :£<*. for use in finding the tolerance limit*
P( X'" s p of the population) a 1 - a
P(CI of the population sXfl") S: 1 - a
= U.S. GOVERNMEMT PRINTING OFFICE: 1 997-521-701/90285
------- |