United States
Environmental Protection
Agency
Office of Water
Regulations and Standards Division
WH-585
Washington, DC 20460
February 1988
Water
NWQEP 1987 Annual Report
Status of Agricultural
Nonpoint Source Projects
National Water Quality
Evaluation Project
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NWQEP 1987 Annual Report:
Status of Agricultural Nonpoint Source
Projects
BY
National Water Quality Evaluation Project
of the
Department of Biological and Agricultural Engineering
North Carolina Agricultural Extension Service
North Carolina State University
Raleigh, North Carolina 27695
In Cooperation With:
U.S. Department of Agriculture
U.S. Environmental Protection Agency
Or. Michael 0. Smolen - Principal Investigator
Dr. Frank J. Humenik - Project Director
Jean Spooner
David W. Miller
Principal Authors
Sarah L Brichford
Contributors
Alicia L Lanier
Richard P. Ma as
Lane Wyatt
Richard Magleby (USDA-ERS) Steve Piper (USDA-ERS)
C. Edwin Young (USDA-ERS)
U.S.EPA USDA Interagency Agreement:
RW-12932650
EPA Project Officer
James W. Meek
Nonpoint Sources Branch
Office of Water Regulations and Standards
Washington, DC
USDA and NCSU Cooperative Agreement:
88-EXCA-3-0853
USDA Project Officer
Fred N. Swader
Extension Service
Agricultural Programs
Washington, DC
February 1988
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DISCLAIMER
This publication was developed by the National Water Quality Evaluation Project, a
special project of the North Carolina Agricultural Extension Service, sponsored by the USD A
and the U.S. EPA under Interagency Agreement RW12932650 through the Cooperative
Agreement 88-EXCA-3-0853 between the Agricultural Extension Service, North Carolina
State University and the Extension Service, USD A. The contents and views expressed in this
document are those of the authors and do not necessarily reflect the policies or positions of
the North Carolina .Agricultural Extension Service, the USDA, the U.S. EPA, or other
organizations named in this report, nor does the mention of trade names for products or
software constitute their endorsement
ACKNOWLEDGMENTS
The authors would like to acknowledge the following persons for their help, on different
parts of this report: Drs. David A. Dickey and Larry A, Nelson (Dept. of Statistics, North
Carolina State University); Gary Ritter and Eric Flaig (South Florida Water Management
District); Lynn Hester (USDA-SEA-AR, Southeast Watershed Research Laboratory, Tifton,
Georgia); Tom Johengen (Dept. of Atmospheric and Oceanic Sciences, University of
Michigan); Monica Wnuk (Iowa Dept. of Natural Resources); and JoAnn Carter and Rosie
Coenen (Shelby County ASCS, Iowa).
The authors would also like to thank Naomi Muhammed-Feaste and Terri Hocutt for
clerical assistance and Sandy Sullivan for design assistance in producing this report.
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Executive Summary
FOREWORD
This report is one in a series of annual water quality reports published by the National
Water Quality Evaluation Project (NWQEP) in cooperation with the United States Depart-
ment of Agriculture and the United States Environmental Protection Agency. NWQEP brings
to light current findings and observations from agricultural nonpoint source (NPS) control
projects around the country. Particular attention is given to the Rural Clean Water Program
(RCWP) projects because RCWP is an experimental program to test voluntary agricultural
NPS pollution control. Through analysis of these projects, RCWP lessons can be transferred
to other NPS programs and projects now being developed under section 319 of the Water
Quality Act of 1987.
.
The introductory chapter of this report provides an overview of information gained from
RCWP in several areas of NPS control. This chapter is followed by brief profiles from the
NWQEP database of the 20 active RCWP projects and three other NPS projects. Each profile
covers the project's contribution^ to NPS control efforts, characteristics and results, and les-
sons learned about NPS control. The report also features in-depth analyses of three RCWP
projects focusing on the detection of water quality improvements in their designated impaired
water resource using water quality monitoring aijd land treatment data.
LESSONS FROM RCWP
Target water resources of high economic value for priority watershed treatment.
Water quality alone is often insufficient to measure economic damage or potential
economic benefits for selection of NPS control projects. Other important elements
are the type and extent of water use impairment, the number of people affected,
and the extent to which water quality may improve with NPS control.
Identify critical NPS areas within the watershed. Targeting project resources to criti-
cal areas is the most cost-effective approach and allows projectmanagers to set clear
goals for NPS treatment within the scope of the project.
Treat critical areas with appropriate, cost-effective BMPs. This implies that the
selected BMPs are widely acceptable to project participants and that participants
have a sufficient level of expertise to maintain them. A combination of structural
and management practices may be needed to meet water quality goals. Project ef-
fectiveness, long-term, may benefit from implementation of management practices
that farmers are likely to continue. Structural practices to reduce erosion and pol-
lutant delivery require higher-cost and commitment to maintenance.
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Emphasize nutrient management to protect both surface and ground water from over-
application of fertilizers and animal waste. A strong management program should
provide cost-sharing and/or technical assistance for soil and manure testing, includ-
ing soil sampling and testing for available nitrogen. Active I & E programs can ef-
fectively promote proper use of animal waste storage facilities for optimal timing
of manure nutrient utilization and proper calibration of manure and fertilizer
spreaders.
Promote farmer participation through: 1) communication of clearly stated and ac-
ceptable purposes and goals; 2) extensive, sustained one-on-one contact between
farmers and project personnel; and 3) appropriate incentives (e.g., cost share, tech-
nical assistance) to ensure successful levels of BMP implementation in critical NFS
areas. .
Employ a water quality monitoring design that is appropriate for the pollutants of
concern, sources of pollutants (e.g., animals, fertilizers, pesticides), type of impaired
waterresource (e.g., river, estuary, lake), size of the watershed, and distance of sour-
ces from impaired water resource. It is usually beneficial to monitor not only the
impaired water resource but also major inflow sources to the resource. Monitoring
inflow sources may reduce the time needed to document BMP effectiveness,
however, monitoring at the impaired water resource is necessary to document real
changes in its water quality. In addition, a knowledge, or estimation, of the water
and pollutant budgets for the impaired water resource can provide perspective on
where to monitor, how much water quality effect to expect from land treatment,
and what monitoring timeframe is reasonable.
Follow a rigorous, consistent monitoring protocol through the pre-, during-, and post-
BMP implementation phases of the project. It appears that at least two to four years
of pre-implementation monitoring and two to four years of post-implementation
monitoring are necessary to document trends in water quality using a grab sample
protocol.
Account for hydrologic and meteorologic variability as well as land use changes in the
analysis of water quality monitoring data.
FINDINGS FROM ANALYSIS OF SELECTED RCWP PROJECTS
Saline Valley. Michigan
Analysis of the Saline Valley project indicates that most of the phosphorus loading from
this project area to Lake Erie is from urban NPS and point sources rather than agricultural
NFS. Two important implications of this rinding are: 1) International Joint Commission phos-
phorus loading reduction goals (30%) for this watershed require point source phosphorus
removal and urban NPS control; 2) even in predominantly agricultural watersheds, small
domestic sewage water treatment plants, if present, are likely to be the main source of phos-
phorus.
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Executive Summary
Prairie Rose Lake. Iowa
The 4,568-acre Prairie Rose Lake watershed includes a 215-acre impoundment and 3,648
acres of cropland. Recreational use of the impoundment is impaired by excessive sedimenta-
tion, turbidity, and eutrophic conditions. Seventy-four percent of the critical area is considered
adequately treated, primarily by terraces.
Analysis of water quality data adjusting for precipitation and chlorophyll a shows that lake
water clarity was at its best during 1982 and 1983, shortly after the start of RCWP. In sub-
sequent years, however, water clarity has declined and remained at the pre-project level.
-f
Results of our analysis confirm some tradeoff of sediment turbidity for algal turbidity. A
precipitation index explained 6 percent of the variability in water clarity and adjustment for
chlorophyll a concentration explained an additional 26 percent. After correcting for both
precipitation and chlorophyll a, however, there is no significant improving water quality trend.
There is some indication of an association between water clarity and corn acres put into cover
crop through PIK and other ACR programs. .
Considering the variability observed to date, the lake monitoring scheme should be able
to document, as significant, a change in lake water clarity of as little as 20 percent with ten
years of consistent monitoring.
Taylor Creek - Nubbin Slough Basin. Florida
The Taylor Creek - Nubbin Slough Basin is located directly north of Lake Okeechobee.
The watershed covers 110,000 acres of which 63,109 acres have been identified as critical
agricultural sources of phosphorus to Lake Okeechobee. These sources are primarily im-
proved pastures and dairies. BMP emphasis is on stream protection, animal waste manage-
ment, and grazing land management.
The project has a valuable pre-BMP water quality data base for statistical comparison
with post-BMP data. In addition, most of the BMP implementation occurred'in 1985 and 1986,
allowing for 4 to 5 years post-BMP water quality monitoring. This project should, therefore,
be able to document land treatment effects on water quality.
We analyzed water quality monitoring data from in-stream sampling to determine the
magnitude of measured concentration change (minimum detectable change, MDC) in TP and
OP required to say with confidence that the change is real. High variability in this hydrologic
system contributes to a high MDC. The impact of adjustments for precipitation, seasonality,
upstream concentrations, and ground water levels on reducing the MDC were investigated.
The MDC for TP ranges from 10 to 59 percent with 9 years of monitoring and adjusting for
available covariates. MDC was found to be a function of watershed size, precipitation, ground
water levels, season, and upstream concentrations.
We found a significant decreasing trend forTP in three subbasins and at the outflow from
the project area. These trends appear to be related to land treatment under RCWP and to
dairy closures independent of RCWP.
in
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IV
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Table of Contents
Executive Summary i
Foreword '. i
Lessons From RCWP i
Findings From Analysis of RCWP Projects w. .. ii
Table of Contents ; ., ; ....v
List of Abbreviations viii
List of Tables '.:. x
List of Figures : xii
Chapter One: Overview
Focus on RCWP ; 1.1
Introduction, .... , 1.1
Land Treatment .... ;...... 1.1
Targeting Critical Areas ...; .....* 1.1
BMP Effectiveness ....-. 1.3
BMP Selection 1.4
BMP Impact on Quality of Subsurface Flow ; 1.5
Nutrient Management 1.6
Farmer Participation , ; 1.7
Water Quality Monitoring i.a
Documenting Real Water Quality Changes 1.8
Monitoring Timeframe '. 1.9
Monitoring Design and Sensitivity , 1.9
Economic Evaluation of RCWP 1.11
Cost-Effective BMPs , 1_..11
Incentives To Participate ...,1.12
Economic Benefits Versus Costs in Project Selection 1.13
Conclusion., 1,14
References '. 1.14
Chapter Two: Project Profiles
Profiles of Nonpoint Source Projects ; 2.1
Lake Thoiocco, Alabama - RCWP 1 2.3
Appoquinimink River, Delaware - RCWP 2.. :. 2.7
Rock Creek, Idaho - RCWP 3 '. 2.13
Highland Silver Lake, Illinois - RCWP 4 '._ '-. 2,21
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Prairie Rose Lake, Iowa - RCWP 5 2.29
Bayou Bonne Idee, Louisiana - RCWP 7 2.34
Double Pipe Creek, Maryland - RCWP 8 2.37
Saline Valley, Michigan - RCWP 9. 2.41
Reelfoot Lake, Tennessee - RCWP 10 -.2.45
Snake Creek, Utah - RCWP 11 2.49
St. Albans Bay, Vermont - RCWP 12 2.53
Lower Manitowoc River Watershed, Wisconsin - RCWP 13 2.59
Taylor Creek-Nubbin Slough Basin, Florida - RCWP 14 2.63
Westport River Watershed, Massachusetts - RCWP 15 2.69
Garvin Brook, Minnesota - RCWP 16 2.73
Long Pine Creek, Nebraska - RCWP 17 2.78
Tillamook Bay, Oregon - RCWP 18 .2.83
Conestoga Headwaters, Pennsylvania - RCWP 19 2.88
Oakwood Lakes - Poinsett, South Dakota - RCWP 20 2.93
Nansemond-Chuckatuck, Virginia - RCWP 21 2.98
Lake Le-Aqua-Na, Illinois 2.103
Blue Creek Watershed, Illinois : 2.107
LaPlatte River Watershed, Vermont 2.111
Chapter Three: Project Analysis
Saline Valley RCWP, Michigan 3.1
Agricultural Setting and Water Resource Problem 3.1
Water Quality Monitoring Design 3.1
Analysis of Water duality Data: Preliminary Results......... 3.1
Literature Cited :...3.6
Chapter Four.Project Analysis
Prairie Rose Lake RCWP, Iowa 4.1
Abstract 4.1
Introduction 4.2
Background 4.2
Project Perspectives 4.2
Land Treatment Strategy 4.3
Water Quality Monitoring Strategy 4.3
BMP Implementation Achievements 4.4
Water Quality Analysis 4.6
Objectives , 4.6
Methods :. 4.7
Inspection ofPre-RCWP Water Quality Data , 4.7
Inspection of Bathymetric Mapping Data 4.7
Preliminary Inspection of RCWP Water Quality Data. 4.7
Examination of Geometric Mean, Minimum, and Maximum for Each Site-Depth Over
Time 4.7
Comparison of Precipitation Data With Water Quality Measurements 4.8
VI
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Table of Contents
Development of a Precipitation Index ; .".... 4.8
Adjustment of Water Quality Measurements for Antecedent Precipitation: Analysis of
Covariance With the Precipitation Index as a Covariate 4.8 .
Adjustment of Water Clarity Measurements for Chi a and Precipitation 4.9
Association of Water Clarity With Land Treatment 4.9
Minimum Detectable Change 4.9
Results and Discussion 4.10
Inspection ofPre-RCWP Water Quality Data 4.10
Inspection of Bathymetric Mapping Data 4.11
Examination of Geometric Mean, Minimum, and Maximum for Each Site-Depth Over
Time 4.13,
Comparison of Precipitation With Water Quality Measurements 4.24
Adjustment of Water Quality Measurements for Antecedent Precipitation.: 4.26
Adjustment of Water Clarity Measurements for Chi a and Precipitation , 4.33
Association of Water Clarity With Land Treatment 4.37
Minimum Detectable Change 4.38
Literature Cited 4.42
Appendix 4.A
Development of a Precipitation Index , 4.43
Chapter Five: Project Analysis
Taylor Creek - Nubbin Slough RCWP, Florida ...... 5.1
Abstract ;.... 5.1
Introduction... -. -. ..5.1
.Background , 5.1
Project Perspectives 5.2
Land Treatment Strategy. 5.3
BMP Implementation Achievements ; I....5.4
Water Quality Monitoring Strategy 5.4
Methods 5.9
Results and Discussion.. 5.11
Conclusions 5.19
Literature Cited , ,5.19
VII
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List of Tables
Table Page
3.1 Comparison of Total Phosphorus Concentrations (mean TP not flow weighted)
at Upstream Tributaries (stations 3,4,5,6,7) with the Watershed Outlet (station 8) 3.2
4.1 BMP Installation in Prairie Rose Lake RCWP as of October 1986
(Progress Report, 1986) 4.5
4.2 Com Acreage Within the Prairie Rose Lake RCWP Set Aside Under Commodity
Programs (1981-1987) (Carter and Coenen, 1987) 4.5
4.3 Lake Surface Arithmetic Mean Values 1 4.11
4.4 Kolomogorov-D Tests for Normality of the Water Quality Measurements
for both the Original Scale and Log Transformed Values (pooled over sites) 4.13
4.5 Total Precipitation During RCWP Sampling Period (May - September) and
Yearly Mean Value of Precipitation Index from 1981 to 1986 4.24
4.6 Analyses of Coyariance Models That Examine the Adjustment of Water Quality
Measurements for Antecedent Precipitation...., 4.27
4.7 Evidence of Linear, Quadratic, and Cubic Trends Over Time in the Water Quality
Measurements 4.33
4.8 Analyses of Covariance Models that Examine the Adjustment of Water Quality
Measurements for Antecedent Precipitation and Chi a 4.35
4.9 Mean Square Error and R2 Values Obtained from Regression Models With and
Without Precipitation and Chi a Covariates.. 4.39
4.10 Minimum Detectable Change (MDC) Required between the Pre- and Post- Periods
to be Considered Statistically Significant. ..; 4.41
4.11 The Percent Change in Predicted Geometric Mean Values from 1981 to 1986 4.42
4.12 R2 Values from Covariate Analyses to Document the Effect of the Precipitation
Index Covariate (pooled over sites) 4.45
4.13 R2 Values for the 'Best' Models from Multivariate Covariate Analyses to Document
the Effect of Precipitation and DAY Terms as Statistical Model Covariates 4.46
5.1 BMP Implementation Under the RCWP by Subwatershed and Year for the
Taylor Creek - Nubbin Slough RCWP Project Area 5.5.
VIII
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List of Tables
Table Page
5.2 BMP Emphasis, Dairy Cow Numbers, and Land Use Changes by Subwatershed
and Year in the Taylor Creek - Nubbin Slough RCWP Project Area from
1978 to 1987 5.7
5.3 Minimum Detectable Change Required in the Initial Geometric Mean Concentration
of Total Phosphorus at Each Water Quality Monitoring Station over a 9 Year
Monitoring Scheme 5.16
5.4 The Percent Change in Predicted Geometric Mean Values from 1978 to 1986
Measured at the Water Quality Monitoring Stations After Adjustment for the
Appropriate Covariates 5.17
5.5 Linear Trends Over 9 Years for the TP Concentrations Measured at the Water
Quality Monitoring Stations 5.18
IX
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List of Figures
Figure Page
1.1 Locations of RCWP Projects 1.2
3.1 Saline Valley RCWP Project Area Map (after Johengen, 1987) 3.2
3.2 Discharge Normalized, Unit Areal Loadings for Suspended Solids
(Johengen, 1987) 3.4
3.3 Discharge Normalized, Unit Areal Loadings for Total Phosphorus
(Johengen, 1987) 3.4
3.4 Discharge Normalized, Unit Areal Loadings for Soluble Reactive Phosphorus
(Johengen, 1987) 3.5
3.5 Discharge Normalized, Unit Areal Loadings for Nitrate-Nitrogen
(Johengen, 1987) .- ...^...3.5
4.1 Prairie Rose Lake RCWP. Shelby County, Iowa (after Monitoring Report, 1986). 4.3
4.2 Prairie Rose Lake RCWP Monitoring Sites (after Monitoring Report, 1986), 4.4
4.3 Prairie Rose Lake Volume Estimated by Bathymetric Mapping 4.12
4.4 Geometric Means of Measured Water Quality Parameters for
Each Site-Depth-Year 4.14
4.5 Geometric Means and Ranges of Measured Water Quality Parameters for
Each Site-Depth-Year.. 4.18
4.6 Measured Precipitation, Secchi Depth, Surface Turbidity, Surface Chi a for
Sites 1, 2, and 3. Each sampling year is May 1 -September 30 ..4.25
4.7 Comparison of Adjusted Yearly Means for Common Precipitation Index to
Unadjusted Yearly Means 4.29
4.8 Relative Yearly Mean Values of Water Clarity and Precipitation Index 4.32
4.9 Comparison of Adjusted Yearly Means for Common Precipitation Index and
Chlorophyll a Values to Unadjusted Yearly Means (pooled over sites) 4.36
4.10 Estimated Sediment Delivery to Prairie Rose Lake from the RCWP Project Area 4.37
4.11 Corn Acres Set Aside Under Annual Federal Commodity
Programs for 1981 to 1986 4.37
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List of Figures
Figure Page
5.1 Taylor Creek-Nubbin Slough Basin. Water quality trend stations and
ground water wells are indicated. 5.3
5.2 Total Phosphorus and Orthophosphate Concentrations for
Station S191, the Outflow to Lake Okeechobee 5.12
5.3 Total Phosphorus and Orthophosphate Concentrations for the
Upstream/Downstream Otter Creek Water Quality Stations 5.13
5.4 Total Phosphorus for Station S191 and Downstream Otter Creek
Station 06 from 1978 to 1986. , 5.14
5.5 The Monthly Minimum Mean, and Maximum Water Table Depth for the
Jjjdson Well Water Gage. Total monthly precipitation at this site is also shown.
This is in close proximity to water quality monitoring stations 03, 06, and 23 5.15
XI
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List of Abbreviations
(Terms, Agencies, Programs)
ACP Agricultural Conservation Program
ACR Acres Conservation Reserve (Federal Commodity Program)
AGNPS Agricultural Nonpoint Source Pollution Model
ANSWERS Areal Nonpoint Source Watershed Environment Response Simulation
(Model)
ARS Agricultural Research Service, USDA
ASCS Agricultural Stabilization Conservation Service, USDA
A.U Animal Unit
BMP(s) Best Management Practice(s)
BOD Biological Oxygen Demand
CES Cooperative Extension Service
Chi a Chlorophyll a
CL .Chloride
CLP Clean Lakes Program, Section 314 of PL92-500
CM&E Comprehensive Monitoring and Evaluation
COD Chemical Oxygen Demand
CREAMS ChemicalRunoffandErosion from Agricultural Management Systems (Model)
DO Dissolved Oxygen
DP Dissolved Phosphorous
ERS Economic Research Service, USDA
FC Fecal Coliform
FS Fecal Streptococci
HUC Hydrologic Unit Code (and Cataloging Unit)
I&E Information and Education Programs
IN Inorganic Nitrogen
JTU Jackson Turbidity Unit
MLRA Major Land Resource Areas
MPN. Most Probable Number/100 ml
NWQEP National Water Quality Evaluation Project
NOs Nitrate Nitrogen
NHs Ammonia Nitrogen
NPS Nonpoint Source
NTU Nephelometric Turbidity Unit
OP Orthophosphate
PL-566 Watershed Protection and Flood Prevention Act (PL83-566)
PLUARG Pollution of the Great Lakes from Land Use Activities,
Reference Group
RCWP Rural Clean Water Program
SCS... Soil Conservation Service, USDA
XII
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List of Abbreviations
Section 108a Section 108a PL92-500; USEPA Pollution Control Demonstration in
the Great Lakes Basin
Section 208 Section 208 PL92-500; Planning for Wastewater Management
Section 319 Section 319 Water Quality Act of 1987
STORET EPA Storage and Retrieval Data Base for Water Quality
STP Sewage Treatment Plant
TC Total Coliform
TDS Total Dissolved Solids
TKN Total Kjeldahl Nitrogen
TN Total Nitrogen
TP Total Phosphorus
TSS Total Suspended Solids
TVS... Total Volatile Solids
USLE Universal Soil Loss Equation, Wischmeier & Smith, 1965.
USDA United States Department of Agriculture
USEPA United States Environmental Protection Agency
USGS United States Geologic Survey
VSS Volatile Suspended Solids
WATSTORE....USGS Water Data Storage System
RCWP PROJECT NAME ABBREVIATIONS
AL-RCWP Lake Tholocco, Alabama
DE-RCWP Appoquinimink, Delaware
FL-RCWP Taylor Creek - Nubbin Slough, Florida
IA-RCWP Prairie Rose Lake, Iowa
ID-RCWP ...Rock Creek, Idaho
IL-RCWP Highland Silver Lake, Illinois
TN-RCWP Reelfoot Lake, Tennessee/Kentucky
LA-RCWP Bayou Bonne Idee, Louisiana
MA-RCWP Westport River, Massachusetts
JylD-RCWP Double Pipe Creek, Maryland
MI-RCWP Saline Valley, Michigan
MN-RCWP Garvin Brook, Minnesota
NE-RCWP Long Pine Creek, Nebraska
OR-RCWP ..Tillamook Bay, Oregon
PA-RCWP Conestoga Headwaters, Pennsylvania
SD-RCWP Oakwood Lakes - Poinsett, South Dakota
UT-RCWP Snake Creek, Utah
VA-RCWP Nansemond - Chuckaruck, Virginia
VT-RCWP St. Albans Bay, Vermont
WI-RCWP.. Lower Manitowoc, Wisconsin
XIII
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Chapter One Overview
Focus on RCWP
INTRODUCTION
The Rural Clean Water Program (RCWP) is an experimental program to evaluate the
social/economic, and technical aspects of controlling agricultural nonpoint source (NFS) pol-
lution. Of the 21 original RCWP projects started in 1980 and 1981,20 remain active in 1987
providing valuable experience and information for other NPS projects and programs (Figure
1.1). Analysis of RCWP progress as of October 1986 gives insight into factors that promote or
inhibit land treatment programs, practices that improve water quality, and costs and benefits
attributable to BMPs and NPS pollution control.
Unlike most NPS control projects, RCWP projects include water quality monitoring to
evaluate and document their impact on the quality of their designated water resources. Each
project has produced a substantial body of information including detailed documentation of
the water resource use impairments, planning and development of the land treatment
program, description of the monitoring program, and documentation of the water quality
monitoring results. Profiles of each RCWP project and other NPS projects in the NWQEP
data base are presented in this NWQEP annual report. Together the RCWP projects encom-
pass the most comprehensive body of information yet available on NPS control.
LAND TREATMENT
Targeting Critical Areas
Targeting best managejnent practices (BMPs) to critical areas, areas where land treat-
ment is likely to provide the greatest improvement of the designated water resource, is an es-
sential component of any NPS control project. Targeting provides a framework for setting clear
achievable goals and focuses project resources on the most important sources of pollutants.
Analysis of RCWP projects indicates that thorough pre-project assessment to identify
critical areas accurately is very important. Although all RCWP projects were expected to iden-
tify their critical area at the start of the program in 1980, few guidelines were available at that
time. Individual projects have since developed practical guidelines for selecting critical areas.
These have been analyzed previously by NWQEP (Maas et. al., 1987).
1.1
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UGENO: PROJECT
Ct
GEN
Figure 1.1 Locations of RCWP Projects
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Overview: Focus on RCWP
Specific criteria and methods for selecting critical areas used by RCWPs reflect differen-
ces in water use impairments, pollution sources, and land treatment needs. Many projects
have refined their selection criteria, and some have designated new or additional critical areas
since the program began. Several projects changed critical area boundaries or definitions to
adjust for new project goals or spending available monies. For example, the Virginia RCWP
expanded its critical area in 1985 because the project felt it had done all it could within the
original critical area but had land treatment funds remaining. Three years after the program
began, the Minnesota RCWP essentially started a new project, redefining its critical area to
focus attention on ground water protection instead of sedimentation of a trout stream. Ground
water contamination is more clearly perceived as a problem by project area residents.
The importance of balancing administrative discipline with flexibility is an important les-
son to be learned from the RCWP experience. Critical area selection must be flexible enough
to allow for changes in water quality objectives and integration of new information that be-
comes available as the project develops. However, such flexibility should be exercised with
care because it can confound analysis and complicate progress reporting. It is even more dif-
ficult than usual to relate water quality changes to land treatment if the project's goals and
records of progress toward those goals are unclear because of major changes.
Carefully selected critical area selection criteria are important for overall project success,
however, the criteria must be applied with consistency to be fully effective. Detailed informa-
tion about how project managers have applied the selection criteria helps in managing the
project efficiently and evaluating progress toward implementation goals. Furthermore, if such
information is not available and water quality improvement is not observed, then selection
criteria may falsely be judged inappropriate. Analysis of selection criteria can be improved by
detailed information about application of the criteria. The best information of this kind is
gained from projects such as the Oregon RCWP that have been very specific in describing the
application of critical area selection criteria.
Finally, the targeting approach is not immune to the problems inherent in using a volun-
tary cost-sharing approach to gain farmer participation in NPS projects. Voluntary participa-
tion provides no means of ensuring that the targeted population will enter into BMP contracts
in a timely fashion. An insufficient level of BMP installation in the targeted critical areas would
defeat the purpose of targeting. Thus, overall effectiveness of targeting can be strengthened
by careful selection of appropriate BMPs and cost share levels, and an active I & E program
to promote interest in and identification with project goals.
BMP Effectiveness
An effective land treatment program is dependent on widespread adoption of the BMPs
by farmers within the targeted project area. Extent of BMP adoption may be as important as
the mechanical effectiveness of an individual BMP. Even the most effective practices will not
improve the quality of the impaired water resource unless there is sustained correct use and
maintenance of the practices and a sufficient level of adoption. Thus, project activities that in-
crease the receptivity of area farmers to BMPs (i.e., well developed I & E programs and per-
sonalized technical assistance) are needed to increase BMP adoption and promote overall
BMP effectiveness.
1.3
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Documenting the effectiveness of specific BMPs using water quality data is difficult in
large projects like the RCWPs. To date, the best demonstration of effectiveness comes from
projects where excess animal waste and animal access to watercourses were the primary causes
of water quality impairment. Fecal coliform concentrations in surface waters have been
reduced significantly in several projects where waste management and fencing BMPs were in-
stalled (Florida, Utah, Oregon, Vermont RCWPs). Significant reductions in phosphorus con-
centrations have been observed in Florida and Vermont RCWPs after treatment of dairy
wastes, indicating the importance of such sources and the potential water quality benefits avail-
able from treatment These results suggest that, where phosphorus or fecal coliform bacteria
is a problem, dairies should be targeted early.
Models are useful to assess, BMP effectiveness and may:provide the only way to estimate
reductions in pollutant loadings (e.g., tons of soil saved, kilograms of phosphorus prevented
from reaching the impaired water resource). Such estimates, however, may be difficult to trans-
late directly into improved water quality. Models only provide an estimate of BMP effective-
ness in the context of our current understanding; they cannot account for all factors and cannot
predict the future. Models being used in RCWP include USLE, AGNPS and CREAMS. All
must be calibrated for site-specific conditions, and their use should complement the analysis
of water quality monitoring data.
Overall, measuring BMP effectiveness has been hampered in RCWP by inconsistencies
in reporting implementation goals and accomplishments. Many projects do not distinguish
clearly between those BMPs under contract and those installed. Also, some projects do not
account for pre-RCWP or non-RCWP activities. This makes it difficult to tell whether ob-
served changes in water quality are attributable to RCWP. Reporting procedures have been
addressed previously (Dressing et. ah, 1984) and should be studied further.
For progress reporting, some projects have divided their watersheds into subbasins to im-
prove BMP accounting. This has two important advantages for project management: 1) BMP
accounting by subbasin matches the BMP implementation data with the corresponding water
quality monitoring stations; 2) subbasin accounting gives a more accurate and detailed spatial
representation of progress toward project goals. Subbasin accounting can be very helpful, espe-
cially for large projects with a complex mixture of physical settings and intensive land uses.
Subbasin reporting is being used effectively by the Idaho, Virginia, Vermont and Florida
RCWPs.
BMP Selection
Determining which BMPs are most appropriate for a particular objective depends on trie
water resource and its use impairments, the acceptability of the BMPs to farmers in the project
area, the economic condition of the farm community and the farmer's level of technical ex-
pertise. RCWP experience shows that involving the farmers in design and selection of BMPs
.improves the fanners' acceptance of BMPs. A combination of structural (e.g., animal waste
handling facilities, sediment basins) and management (e.g., conservation tillage, nutrient
management) practices may be needed to meet project objectives. The relative importance of
structural versus management practices varies from project to project, however, management
practices have been receiving increasing attention in recent years.
1.4
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Overview: Focus on RCWP
In the Idaho RCWP, sediment concentrations in irrigation canals have decreased sig-
nificantly since structural devices (e.g., sedimentation basins and irrigation management sys-
tems) were implemented. This result suggests that such practices are quick and effective, and
prevent sediment and phosphorus from reaching Rock Creek. However, structural devices
such as settling basins do not correct the problem of on-site erosion and can be costly to main-
tain. The Idaho project is now promoting conservation tillage as the preferred BMP because
it is perceived to be a cost-saving practice in which maintenance costs are offset by benefits to
the farmer.
The debate on whether or not conservation tillage can be as effective as retention devices
in preventing sediment from reaching Rock Creek is currently open. Field research in the
Rock Creek area indicates that no-till practices in a cycle of crop rotation can reduce erosion
rates by 80% or more and minimum tillage can reduce erosion by 60-85%, with no significant
change in crop yields (Carter, 1987). In an area like Idaho, however, where farmers employ 8
to 14 tillage operations each year, conservation tillage requires drastic changes in farming prac-
tices. Therefore, a great deal of I & E and technical assistance is needed. Economic analysis
shows conservation tillage to be a more cost-effective practice than sediment retention struc-
tures for the Rock Creek project (Magleby, 1987), but wide-scale acceptance has not yet been
achieved.
The timeframe in which the success of selected BMPs is judged depends greatly upon the
type of water resource in which a response to land treatment is being measured. For example,
although terracing was implemented extensively (~ 80% of eligible area) in the Iowa RCWP,
ho improving trend in the water quality of Prairie Rose Lake has been documented after six
years of monitoring. In comparison to the Idaho RCWP, the Iowa RCWP may not be showing
a water quality impact because the response time of the lake is longer than that of irrigation
canals or because natural variabilityin the lake system is greater than the water quality change
induced by BMPs.
BMP Impact on Quality of Subsurface Flow
Some BMPs, such as terracing and conservation tillage, may increase the rate and volume
of nutrients and pesticides infiltrating to ground water. This issue has largely been ignored by
RCWP. To document pollutant transport in subsurface flow, elaborate and expensive monitor-
ing is needed such as that in the South Dakota and Pennsylvania projects. Most RCWPs were
not designed with this in mind and do not have the needed monitoring budget:
In projects where nutrient contamination of ground water has been documented (South
Dakota, Minnesota, Nebraska, and Pennsylvania RCWPs), efforts have been made to evaluate
the extent of contamination. Abandoned wells and sinkholes were among the first areas desig-
nated critical when the Minnesota RCWP expanded its critical area to address ground water
contamination. The project has since learned from research in an area with similar karst topog-
raphy (Big Springs Basin, Clayton County, Iowa) that sinkholes and abandoned wells may be
relatively unimportant entry points to ground water compared to either infiltration base flow
or diffuse flow (Garvin Brook RCWP, 1986 Progress Report). Certain soils can retain large
amounts of nutrients that are leached at a fairly constant rate. This nutrient reservoir may keep
pollutant concentrations in ground water high for many years. It is an extremely complex un-
1.5
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dertaking to determine how large the reservoir is, at what rate it is leaching and where the
nutrients are going. The South Dakota RCWP has found that glacial till within the project area,
previously thought to be a barrier to leaching of nutrients, is nearly as effective a conduit for
contaminant infiltration as the more permeable outwash soils.
NUTRIENT MANAGEMENT
Awareness of the potential damage to surface and ground water from over-application of
nutrients focuses attention on nutrient management as an important BMP. Concern is par-
ticularly high where animal manure production exceeds crop or pasture nutrient needs, and
where soil and landscape features promote rapid flushing of applied nutrients into, the im-
paired water resource. These conditions predominate in several RCWP projects (Pennsyl-
vania, Florida, Vermont, Virginia, Minnesota, Nebraska, South Dakota, Utah and Oregon).
Some of the projects have strong nutrient management programs in place. In the Pen-
nsylvania RCWP, for example, Extension Service personnel are actively pursuing one-on-one
contacts with project area farmers to assist them with nutrient management decisions and to
prepare tailored nutrient management plans. This information and education effort has been
welcomed by farmers where formal contracts for animal waste management BMPs were pre-
viously rejected because of cultural differences. A similar Extension program is proposed for
the Virginia RCWP with the following objectives: 1) to match fertilizer application rates to
soil test results; 2) to improve timing of fertilizer application to maximize plant utilization; and
3) to improve analysis and accounting of nutrient sources such as manure arid legume crops.
i
We propose the following elements to develop a strong nutrient management program
for improving water quality. The recommendations are intended to utilize the most advanced
technology accessible and to provide a framework for reporting progress.
1. Survey watershed landowners for current practices, attitudes, knowledge, awareness and skills in
nutrient management.
Use this information to:
a) estimate the range of soil fertility and crop nutrient requirements;
b) identify the distribution of nutrient sources and utilization areas for potential nutrient redistribution
from areas of oversupply to areas of undersupply, and,
c) estimate how much improvement in nutrient management can be accomplished given current
' practices in the watershed.
2. Provide cost sharing or technical assistance for soil testing, including soil sampling and testing for
available nitrogen (field or laboratory test). Provide the same assistance for manure testing.
3. Estimate nutrient mass balance for each field, each farm, and for the project area.
Example for dairy or mixed livestock project area:
IN: feed, fertilizer
OUT: milk, meat, crops, excess manure
RECYCLE: manure, crop residue
Use this information to determine current nutrient inputs for the area generating pollutants, or for
each field in the case of a small project area. Compare with required nutrient needs of crops and
pasture based on soil test results, soil type and land form. Adjust nutrient applications accordingly.
4. Use worksheets and written plans to formalize records of nutrient input requirements and application
rates for individual farm operations and fields. Where possible, plans should identify the range of
susceptibility of ground and surface water to nutrient over-application based on soil type and land form.
1.6
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Overview: Focus on RCWP
5. Provide educational materials, field demonstrations and technical assistance on the calibration of
manure and fertilizer spreaders.
6. Establish demonstration plots to show:
a) the nutrient value of animal waste;
b) advanced fertilizer application techniques such as banding, split application, and injection or improved
incorporation;
c) use of legume cover; and
d) other practices of local applicability.
7. Promote the effective use of animal waste storage facilities for optimal timing of manure nutrient
utilization.
8. Promote plant tissue testing to determine proper nutrient requirements and to verify recommended
nutrient applications.
Changes in management practices often conflict with traditional farming methods. Using
commercial fertilizers for insurance with animal waste is a difficult practice to break. Ex-
perience from the Pennsylvania RCWP, however, shows that educating fanners about soil test-
ing, crop nutrient needs, proper application rates, and optimal timing can be accepted and may
significantly reduce excessive application of nutrients on cropland. Improved nutrient
management appeals to fanners as an efficient way to manage farm costs. In the SD-RCWP,
results of analysis using the AGNPS model suggest that better incorporation and split applica-
tion of commercial fertilizer would be very cost effective practices in reducing nutrient load-
ings to local lakes.
We feel that estimating nutrient budgets at the farm and project levels can help deter-
mine the magnitude of the problem and the best approach to its solution. Projects could use
an accounting-based system to identify with greater accuracy the amount of nutrients imported
(e.g., commercial fertilizer and feed), the amount of nutrients exported via the hydrologic sys-
tem (e.g., excess fertilizer and animal waste), and the amount of nutrients trapped in sinks
(repositories in the soil profile) within the project area. This type of information is essential
to develop a project-wide strategy for improving nutrient management.
FARMER PARTICIPATION
Experiences from RCWP point to the following recommendations to strengthen farmer
participation in voluntary NPS control projects. The NPS project should have:
clearly stated and acceptable purposes and goals.
extensive, sustained one-on-one contact between farmers and project personnel.
appropriate incentives (cost sharing, technical assistance) to allow targeting of practices to criti-
cal areas.
Several RCWP projects have shown that the voluntary approach to NPS control can be
effective even in large project areas or depressed economies, although others have found these
to be insurmountable obstacles. BMPs must be acceptable to farmers in the project area and
incentives such as cost share rates must be high enough and the technical assistance available
for implementing the practices.
1.7
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In some projects regulatory authority has supported the voluntary incentive- based BMP
implementation program, assuring participation sufficient to control NFS. The Florida and
Oregon RCWPs have regulatory powers that can be invoked, a "threat" that seems to be an
important incentive for farmers to participate in the RCWP.
WATER QUALITY MONITORING
Documenting Real Water Quality Changes
Visual, random observations of changes in the state of an impaired water resource do not
necessarily reflect statistically significant water quality changes. RCWP is a unique NFS con-
trol program in that all projects conduct water quality monitoring to document statistically
their impact. The investment in detailed water quality monitoring is intended to allow other
non-RCWP NFS projects to proceed with a land treatment program with little or no monitor-
ing.
RCWP has documented improving water quality trends in several projects to date: Rock
Creek (Idaho), Snake Creek (Utah), St. Albans Bay (Vermont), Tillamook Bay (Oregon), Ap-
poquinimink River (Delaware), Highland Silver Lake (Illinois), and Taylor Creek-Nubbin
Slough (Florida). These projects have maintained a consistent monitoring program that started
before the land treatment program and will extend beyond the time when land treatment is
essentially complete.
Both the Idaho and Utah "projects had two or more years of pre-BMP monitoring data
and were able to document water quality improvements with two years of post-BMP monitor-
ing. The Vermont project demonstrated the effect of manure management in a paired water-
shed experiment. The Oregon project documented water quality improvement after an
intensive implementation program to control dairy waste. Lastly, the Appoquinimink River
project in Delaware attributed a decrease in total phosphorus concentration to intensive BMP
implementation.
Several projects expect improvements in the quality of their impaired water resources but
have not yet been able to substantiate their perceived improvement with statistical analysis.
For instance, the Highland Silver Lake (Illinois) and Prairie Rose Lake (Iowa) RCWPs have
estimated-substantial reductions in erosion using models, however, neither project has yet con-
firmed improved water quality at the lake. (The Illinois RCWP has documented improving
trends at gage sites in the watershed.) More time may be needed before the lakes respond to
BMP implementation in the watersheds. Similar problems characterize the Lake Tholocco
(Alabama) and Reelfoot Lake (Kentucky - Tennessee) RCWPs, i.e., both are large watersheds
where improvements in the impaired water resource may be very slow. It appears that even
small lakes require a considerable time to respond to BMP implementation in the watershed.
1.8
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Overview: Focus on RCWP
Monitoring Timeframe
The monitoring period needed to document water quality change is generally longer than
the time in which the water resource responds to land treatment. This is mainly due to
hydrologic and meteorologic variability, and the need for pre- and post-implementation data
to confirm the change. Monitoring in the various water resources encompassed by RCWP
helps to quantify and predict the response of different water resources under different land
treatment schemes.
RCWP project results from Idaho, Utah, and Vermont indicate that water quality chan-
ges are quickest where flushing rates are high as in streams and irrigation canals, however,
hydrologic variability in such systems is also high. Streams and irrigation canals are generally
smaller and more uniform hydrologically than impoundments, bays, estuaries, and rivers. In
addition, BMP implementation is generally located closer to lower order streams and canals
because these run close to pollutant sources (i.e., through or near livestock pens, erosive fields,
dairy facilities) on individual farms where BMP contracts are installed. Monitoring, too, in
headwater streams may be less confounded by unknown factors than downstream.
For the most part, RCWP projects treating impairments caused by animal waste show
greater water quality improvement in a shorter time than those treating vast acreages of
cropland. This is likely due to the resemblance of animal operations to point sources of pollu-
tion their locations are easily targeted for BMPs and monitoring stations. Furthermore,
fencing animals out of streams and controlling barnyard runoff are highly effective practices
to reduce surface water phosphorus and nitrogen concentrations in a fairly short time (1-2
years in Vermont RCWP and 5 years in Florida RCWP). Control of sediment and associated
pollutants is more difficult because all, or at least a large percentage, of the critical area must
be treated before water quality changes can be expected. This suggests that more BMPs must
be contracted and implemented in projects where cropland treatment is the focus.
It is important to remember that RCWP has a 10-year monitoring timeframe which may
be inappropriate for documenting water quality changes in some projects. It is our strong feel-
ing that monitoring should not be discontinued prematurely, regardless of whether or not the
project expects to improve water quality within the experimental RCWP period. Gaps in the
monitoring sequence hamper data analysis. Without continuous monitoring, it is difficult to
tell if a water quality trend (above and beyond system variability) exists or if observations rep-
resent random unrelated events. If significant water quality improvement is not observed, one
must conclude there is no impact of BMPs, the monitoring timeframe was not long enough,
or the monitoring design not sensitive enough to detect the impact.
Monitoring Design and Sensitivity
Much of the good work that goes into an NPS project is wasted if the monitoring design
is not appropriate for the application. The crucial first step in developing a successful monitor-
ing program is to make sure that all water quality impairments are documented and all poten-
tial sources are clearly identified. Design of the monitoring program should consider the
pollutants of concern, sources of pollutants (e.g., animals, fertilizers, pesticides), type of im-
paired water resource (e.g., river, estuary, lake), size of the watershed, and distance of sour-
1.9
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ces from impaired water resource. It is usually beneficial to monitor not only the impaired
water resource but also major inflow sources to the resource. Monitoring inflow sources may
reduce the time needed to document BMP effectiveness, however, monitoring at the impaired
water resource is necessary to document real changes in its water quality.
Once the monitoring program is underway a rigorous, consistent monitoring protocol
should be followed through the pre-, during-, and post-implementation phases of the project.
Changes generally reduce confidence levels by confounding the analysis. Uniformity and con-
sistency in data collection, reporting and analysis are essential.
It appears that a minimum of two to four years of pre-implementation monitoring fol-
lowed by two to four years of post-implementation monitoring is necessary to document real
trends in water quality using a grab sample protocol. Other protocols (i.e., paired watershed
monitoring, automatic sampling) may increase the sensitivity of documenting real changes.
Minimum detectable change (MDC) is an important concept that can help projects deter-
mine how much change in water quality must be measured to be considered a documented
real change. MDC is the change in parameter concentration required for statistically sig-
nificant results. As developed by Spooner et al. (1985, 1986, 1987), at least a 20% to about
60% improvement in water quality of the impaired resource (above the water quality level
measured prior to land treatment) must be obtained to be considered significant. Thus, small
changes in water quality (less than 20% improvement) may be masked by natural variability
in the system.
Hydrologic and meteorologic system variability .exerts a strong influence on the water
quality data from any water resource. The more information one has to account for system
variability and the variability of pollutant sources in the analysis, the smaller is the change
needed for statistical significance. It is generally helpful to adjust for factors such as precipita-
tion and flow of surface and ground water. Collecting such coincident hydrologic,
meteorologic, and chemical data at each water quality sampling event helps to distinguish real
water quality changes from background noise by accounting for the effect of factors that mask
the real changes.
The inertia, or resistance to change, in a watershed-water resource system also makes it
difficult to detect water quality changes attributable to land treatment. Chemical buffering
capacity, dilution, and the interaction of organisms with their aquatic environment tend to
compensate and mask changes in water quality. Water quality improvement is often difficult
to detect because large nutrient reservoirs in the soil profile or in lake sediments keep the
supply of nutrients nearly constant and mask the effect of BMPs by continually replenishing
nutrients from the reservoir. Other factors contributing to system inertia and "steady-state"
conditions include large watershed area, slow flushing rates, past land uses which stockpiled
animal wastes or fertilizers, and artificial manipulations of flow such as the operation of dams.
These factors must be assessed on a site-specific basis to evaluate potentially the system's
response to land treatment.
1.10
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Overview: Focus on RCWP
We feel that an appropriate first step in watershed analysis is to develop water and pol-
lutant budgets for the impaired resource. This provides perspective on how much water quality
effect to expect from land treatment and what monitoring timeframe is reasonable. For in-
stance, in the Idaho RCWP, the project area contributes only about 25% of Rock Creek's total
flow; thus, a large reduction in NFS pollutants entering Rock Creek from the RCWP is likely
to yield only a small change in Rock Creek's water quality. This points up the fact that even if
the project were 100% effective in controlling NPS, the expected effect on Rock Creek's water
quality could be small.
Similarly, the Florida RCWP has documented that flow from the project area comprises
only 4% of the total inflow to Lake Okeechobee but 27% of the total phosphorus input to the
lake from all sources. Thus, 73% of the phosphorus, a very large amount, comes from areas
outside the project area. Furthermore, Lake Okeechobee contains many years' effluent from
the RCWP project area. The project has recognized from the start that many years of monitor-
ing would be required to document any change in the lake and chose to monitor the outlet
from the project area instead of the lake. As a result, the project has documented decreasing
phosphorus concentrations in project area outflow, but the relative impact of this decrease is
far overshadowed by the large percentage of phosphorus coming from sources outside the
project area.
ECONOMIC EVALUATION OF RCWP1
Economic evaluation of RCWP provides valuable insight into the economics of reducing
nonpoint pollution from agricultural sources. Economic aspects evaluated for future program
guidance include costreffectiveness of particular BMPs, incentives to participate, and benefits
versus costs of individual projects. For each project, the available information on BMP cost-
effectiveness and project benefits is included in the project profiles in this publication.
Cost-Effective BMPs
Modeling results from the Idaho, Illinois and Pennsylvania RCWP projects suggest that,
for a given government expenditure, greater reductions in sediment delivery to water bodies
maybe achieved by widespread implementation of conservation tillage instead of more limited
implementation of structural practices. This suggests that some water quality benefits are not
well served by traditional conservation programs that spend large amounts of money control-
ling erosion on few acres. Such programs are changing, promoting conservation tillage and
other conservation practices under long-term agreements and contracts.
Practices that are cost-effective in one area may be ineffective in others. For example,
long-term animal waste storage systems in the Vermont RCWP help control nutrient runoff
by permitting more timely manure application that meets crop needs and avoids periods of
high runoff from rainfall. In contrast, the same practice in the Pennsylvania project results in
higher nutrient delivery to ground and surface waters. This is because the high animal popula-
tion in the Pennsylvania project produces a very large quantity of manure that far exceeds crop
1 Economic analysis is based on contributions from the USDA Economic Research Service.
1.11
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nutrient needs when the manure is spread on project area cropland. Long-term manure storage
preserves nutrients that would have been lost by volatilization and overloads spreading areas
in a few, infrequent pulses. Also, because the practice increases efficiency of operations, the
fanner might be inclined to increase animal numbers and further worsen the excess nutrient
problem. Reducing animal numbers, installing short-term storage systems that increase
volatilization of nitrogen, and transporting manure out of the watershed appear to be the
preferred approaches.
In the Idaho project, the use of conservation tillage and irrigation water management ap-
pears to be more cost-effective for reducing sediment yield than the expensive structural ir-
rigation improvements (concrete ditches, gated pipe, etc.), sediment basins, and temporary
retention devices previously promoted. Also, the management practices require no periodic
cleaning or other out-of-pocket expenses and are, therefore, likely to be continued after the
end of the contract period. Conservation management is preferred, too, because it provides
long-term crop yield benefits by leaving soil in place on the field, rather than just keeping it
out of the stream.
1 Modeling results from the SD-RCWP indicate that split application and injection of fer-
tilizer would be very cost effective BMPs in reducing nutrient loadings to local lakes.
Incentives To Participate
Practices that reduce pollutant levels have to be adopted by high proportions of the
farmers in critical areas if lasting impacts on water quality are to be achieved. In most RCWP
projects government cost-sharing up to 75 percent of BMP installation cost and free technical
assistance have been sufficient, incentives to achieve farmer participation in the voluntary
program. ^
But will farmers continue the practices without cost share incentives after the contracts
expire? Economic analysis indicates that those practices that will continue include improved
management practices such as conservation tillage, pesticide and fertilizer management, and
irrigation water management, all of which tend to reduce production costs. Conservation til-
lage may also increase short- and long-term yields when the farmer becomes experienced in
its application, providing a further incentive to continue the practice. Fertilizer management
involving split application and injection may save sufficient nutrients to permit lower applica-
tion rates and fertilizer purchases to offset slightly increased_cpsts of application. BMPs that
will likely be discontinued include temporary structural practices such as sediment retention
basins and devices that require non-cost-shared expenditures for maintenance. Some terraces
may also cease to be maintained. However, with implementation of the conservation com-
pliance provisions of the Food Security Act of 1985, farmers may be less likely to abandon soil
and water conservation practices already in place.
In the Pennsylvania project, a program of technical assistance and an I & E program
without contracts has been developed to promote water quality practices. I & E efforts point
out to project area farmers that over-application of nutrients relative to crop needs can con-
taminate the farmers' own sources of groundwater. Technical assistance stresses improving
fertilizer and manure management by soil testing to match nutrient application with crop needs
12
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Overview: Focus on RCWP
and by replacing commercial fertilizer with animal waste. Although these efforts have im-
proved participation in the RCWP, it is still much lower than needed to generate significant
water quality improvements.
Economic Benefits Versus Costs in Project Selection
Water quality change alone is insufficient to measure economic damage or potential
economic benefits for selection of NPS control projects. Other important elements are the
type and extent of water use impairment, the number of people affected, and the extent to
which water quality may improve with NPS control. Even though a project improves water
quality, representing success from a physical impacts point of view, if use of the water changes
little and few people benefit, the project may not be successful from an economic and social
perspective.
The Vermont project has most of the elements needed for a cost-effective NPS project.
Nutrient pollution from animal waste and sewage treatment has impaired swimming, fishing,
and other recreation on St. Albans Bay and lowered the value of recreational property. Sub-
stantial improvement in water quality from point and nonpoint source pollution control is ex-
pected to restore beneficial uses for many people. Even though the project is costly, benefits
substantially exceed the costs largely because the impaired water uses are extensive and of
high value to the public.
Like the Vermont project, the South Dakota project also addresses nutrient pollution in
heavily used recreational lakes with high public value. Land treatment costs are low because
inexpensive management BMPs are used. Economic benefits from water quality improve-
ments could easily exceed project costs, but the project has not demonstrated a sufficiently
comprehensive effort to achieve significant water quality improvement.
In the Illinois project, there is little economic damage attributable to the high turbidity
and sediment from agricultural runoff. Because Highland Silver Lake is a domestic water
supply where recreational use is restricted, improved water quality would benefit few. Protect-
ing storage capacity also has little economic value because the sedimentation rate is low rela-
tive to capacity and has little effect on water treatment costs. Improved water quality would
thus produce only modest economic benefits that would not offset project costs.
The Idaho RCWP is reducing sediment in irrigation return flows to improve fishing in
Rock Creek and reduce sediment loadings to the Snake River. Economic benefits from recrea-
tional fishing in Rock Creek are modest because few people benefit directly. Reduced sedi-
ment loading to the Snake River produces few economic benefits because the Snake River is
a large resource in which other sources and channel erosion overshadow the impact of reduced
sediment loadings in Rock Creek on storage and power generation downstream from the
project area. Water quality economic benefits would have exceeded government costs,
however, if low cost management BMPs (e.g., conservation tillage) had been promoted instead
of expensive irrigation structures.
1.13
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The economic evaluation of RCWP outlined above focuses on economic benefits that are
readily quantified, such as recreation, drinking water, and commercial fishing, largely because
these data are available from existing sources that pre-date RCWP. Two additional types of
benefits, ecological and experimental, are also very important in RCWP even though they
resist quantification. Experimental benefits, in particular, may not be recognized until the les-
sons for RCWP are applied to NPS management programs throughout the nation.
CONCLUSION
After several years of recognizing NPS control as central to achieving the goals of the
1972 Clean Water Act, there is finally a public policy to implement NPS control nationwide.
With the Water Quality Act of 1987 comes new responsibility for action at the state level. States
are now in the process of developing management plans under section 319 to address their
particular NPS problems. RCWP experiences and results should figure prominently in these
plans.
It has been recognized in previous programs, such as the Model Implementation Program,
that documenting water quality improvements on a watershed basis may take a long time be-
cause of large variations in hydrologic and meteorologic conditions from year to year. While
many of the RCWP projects have yet to document water quality changes, and some may never
do this, the findings and experiences to date of RCWP are important guidelines for NPS
management. From an overall perspective, the RCWP experiment is providing many pieces
of information that will help to address broad questions about NPS control with well docu-
mented evidence. This valuable information is available now to meet the challenge of NPS
control under Section 319. "However, the RCWP experiment_is only half over and, thus, not
all the results are in. We have much to learn yet from RCWP.
REFERENCES
Carter, p.L., Personal communication, October 6,1987.
Dressing, S.A., R.P. Maas, M.D. Smolen, and FJ. Humenik, 1984. Proceedings of the Rural Clean Water
Program C, M, & E Workshop. April 2-5,1984, Raleigh, NC. North Carolina Agricultural Extension Ser-
vice, Raleigh, NC.
Garvia Brook RCWP, 1986 Annual Progress Report, p. 12. '
Maas, R.P.. M.D. Smolen. C.A. Jamieson. and A.C- Weinbere. 1987. Settin5 prioriries: The Key ro Nnpnninr
Source Control. USEPA Office of Water, Regulations and Standards Division, Washington, DC.
Magleby, R.S., Personal communication, September 3,1987.
Spooner, J., R.P. Maas, M.D. Smolen, and C.A. Jamieson, 1987. Increasing The Sensitivity of Nonpoint
Source Control Monitoring Programs. In: Proceedings of the Symposium on Monitoring, Modeling, and
Mediating Water Quality. American Water Resources Association, pp. 243-257.
Spooner, J., C.A. Jamieson, R.P. Maas, and M.D. Smolen. 1987. Determining Statistically Significant Chan-
ges in Water Pollutant Concentrations. J. Lake and Reservoir Management, 3:195-202.
1.14
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Chapter Two Project Profiles
Profiles of Nonpoint Source Projects
This chapter contains profiles of all active RCWP projects (20) and three other NFS con-
trol projects. The profiles provide brief sketches of each project in outline form, including
major contributions to NPS control, specific results and lessons learned. A listing of project
documents and personnel contacts also are included.
Information on cost of BMPs, changes in water resource, use, and potential economic
benefits was contributed by Richard Magleby, C. Edwin Young, and Steven Piper of the USDA
Economic Research Service.
projects Page
LakeTholocco, Alabama 23
Appoquinimink, Delaware 2.7
Rock Creek, Idaho 2.13
Highland Silver Lake, Illinois 2~21
Prairie Rose Lake, Iowa 239
Bayou Bonne Idee, Louisiana 234
Double Pipe Creek, Maryland 237
Saline Valley, Michigan 2.41
Reelfoot Lake, Tennesse-Kentucky 2.45
Snake Creek, Utah 2.49
St. Albans Bay, Vermont 2.53
Lower Manitowoc River, Wisconsin 2.59
Taylor CreekNubbin Slough, Florida 2.63
Westport River, Massachusetts 2.69
Garvin Brook, Minnesota 2.73
Long Pine Creek, Nebraska 2.78
Tillamook Bay, Oregon 2.83
Conestoga Headwaters, Pennsylvania 2.88
Oakwood LakesPoinsett, South Dakota 2.93
Nansemond-Chuckatuck, Virginia 2.98
Lake Le-Aqua-Na, Illinois 2.103
Blue Creek, Illinois 2.107
LaPlatte River, Vermont 2.111
2.1
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2.2
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LAKE THOLOCCO - RCWP 1
Dale and Coffee Counties, Alabama
MLRA: P-133A
H.U.C. 031402-01
I. Project's Major Contributions Toward Understanding the Effectiveness of NFS
Control Efforts
Voluntary farmer participation is possible even in an economically depressed agricultural area if practices
are acceptable and there is enthusiastic one-on-one contact by local agricultural agency representatives.
Preliminary water quality results indicate that fecal coliform concentrations can be reduced significantly by
treating a few key animal operations.
II. Project's Characteristics and Results
1. Project Type: RCWP
2. Timeframe: 1980-1991
3. Total Project Budget (excludes water quality monitoring funds and farmers' contributions): $1,831,048
4. Cost Share Budget:
a. Funds Allocated: $1,409,448
b. Total Fanners' Contributions: $276,132 as of Sept. 30,1986
5. Water Quality Monitoring Budget: $163,187
6. Watershed Area: 51,400 acres
7. Project Area: 51,400 acres
8. Critical Area: 9,270 acres
9. Project Land Use: (equivalent to watershed land use)
I and Use °>e project area
. cropland
pasture/range
woodland
urban/toads
other
15
7
55
4
19
10. Animal Operations in Project Area:
a. Dairy: none reported
b. Beef: none reported
c. Swine: 20 farms with average of 200 pigs/hogs (800 a. u.)
d. Poultry: none reported
11. Water Resource Type: streams, Lake Tholocco (impoundment)
12. Water Uses and Impairments:
Lake Tholocco was built primarily for swimming and water-skiing, and it's designated use is swimming,
fishing and wildlife. Watershed streams have a fish and wildlife classification. The lake is used for
2.3
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recreation by over 100,000 people each year. Boating and fishing account for about 20,000 user-days per
year.
a. The lake was closed to body contact recreation for 85 days during 1979 due to high bacteria levels.
b. The lake has not been closed to contact recreation since then.
c. Capacity of the lake is impaired by sediment, affecting boating and water-skiing.
13. Water Quality at Start of Project:
Fecal coliform densities often exceeded 200/lOOml in Lake Tholocco and 5000/lOOml in tributaries.
14. Meteorologic and Hydrologic Factors:
a. Mean Annual Precipitation: 55 inches
b. USLE 'R' Factor 400
- c. Geologic Factors: The project area is located in the Lower Coastal Plain. Soils range from loamy
sands to fine sandy loams. Topography is rolling to steep.
15. Water Quality Monitoring Program:
a. Timeframe: 1980-1990
b. Sampling Scheme:
1. Location and number of monitoring stations: 7 lake stations and 9 tributary stations
2. Sampling Frequency: biweekly summer, monthly other times t
3. Sample Type: grab
c. Pollutants Analyzed: suspended solids, turbidity, fecal coliform, total coliform, nitrate
d. Flow Measurements: began in 1983
16. Critical Areas:
a. Criteria: erosion rate, distance to watercourse, present cropping practices, present manure
management practices
b. Application of Criteria: The project has generally adhered to its critical area criteria in committing
cost share funds. ' ~ .
17. Best Management Practices:
a. General Scheme: Treat nearly all cropland; fix gullies; treat swine operations near streams
b. Quantified Implementation Goals: 6,953 acres, 8 swine operations
c. Quantified Contracting/Implementation Achievements:
location
project area
critical area
critical area
farms
project area
farms
d-CostofBMPs:
BMg
1 perm. veg.. cover
2 animal waste mgmt.
4 terraces
5 diversions
6 grazing land prot.
7 waterways
8 cropland prot.
9 conservation tillaee
10 stream prot.
11 perm. veg. cover
(continued on next page)
% under contract
14.5
80
82.6
18.6
implemented
11.6
64
66
15
Share Cgl
69/ac.
675 ea.
44/ac.
20/ac.
1,630 ea.
215/ac.
56/ac.
33/ac.
22/ac.
80/ac.
* v* j**«*
rvve. IVV-»T
Share ffl
128/ac.
12,800 ea.
176/ac.
79/ac.
4,900 ea.
870/ac.
84/ac.
97/ac-
90/ac.
450/ac.
Total Cost CS1
197/ac.
13,475 ea.
220/ac.
99/ac.
6,530 ea.
1,085/ac.
140/ac.
112/ac.
530/ac.
2.4
-------
Lake Tholocco RCWP, Alabama
12 sediment retention,
erosion control struc. 440 ea. 3,960 ea. 4,400 ea.
14 tree planting 11/ac. 33/ac. 44/ac.
16 pesticide mgmt. 2/ac. 6/ac. 8/ac.
e. Effectiveness of BMPs: Sediment control BMPs have reduced soil loss by 23 tons/acre on the 7,430
acres treated.
18. Water Quality Changes: Maximum fecal coliform concentration observed in Lake Tholocco has not
exceeded 200/lOOml since 1982.
19. Changes in Water Resource Use:
There is no documented change in water resource use since RCWP began. There have been no lake
' closures since 1980 due to high bacteria levels and lake use has remained steady at approximately
100,000 user-days per year. Reduced sedimentation is thought to have protected boating and fishing
areas from degradation. ;
20. Incentives:
a. Cost Share Rates: 75% for most practices
b. $ Limitations: $50,000
c. Assistance Programs: none
21. Potential Economic Benefits:
a. Cm-farm: not evaluated
b. Off-farm:
1) Recreation: $65,000 - $195,000 per year
2) Water Supply: 0 - $5,000 per year
3) Commercial Fishing: 0
4) Wildlife Habitat: unknown .
. 5) Aesthetics: unknown but positive
6) Downstream Impacts: 0
III. Lessons Learned
This is an extremely depressed farming area with an average net farm income of only $6,400 in 1974. The suc-
cess of the project in obtaining farmer participation shows that aggressive marketing by the local agricultural
agency personnel combined with water quality plans that integrate on-farm concerns can work even under
very economically depressed conditions.
IV. Project Documents
1. "Lake Tholocco Rural Clean Water Project. Application Dale and Coffee Counties, Alabama". July 15,
1979. Alabama Rural Clean Water Coordinating Committee.
2. "Water Quality Monitoring Report Lake Tholocco RCWP Project, Fiscal Year 1981". November 1981.
Alabama Water Improvement Commission.
3. "1982 Annual Progress Report, Lake Tholocco". 1982. Rural Clean Water Program.
4. "Water Quality Monitoring Report Lake Tholocco RCWP Project, Fiscal Year 1982". November 1982.
Alabama Department of Environmental Management.
5. "1983 Annual Progress Report, Lake Tholocco". 1983. Rural Clean Water Program.
6. "Water Quality Monitoring Report Lake Tholocco RCWP Project, Fiscal Year 1984". October 1984.
Alabama Department of Environmental Management.
7.1984 Annual Progress Report, Lake Tholocco". 1984. Rural Clean Water Program.
2.5
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8. "1985 Annual Progress Report, Lake Tholocco". 1985. Rural Clean Water Program.
9. "1986 Annual Progress Report, Lake Tholocco". 1986. Rural Clean Water Program.
V. NWQEP Project Contact
Water Quality Monitoring _
Mr. Victor Payne
USDA - Soil Conservation Service
P.O. Box 311
Auburn, AL 36830
teL (205) 821-8070
Land Treatment/Technical Assistance
Mr. Bennie Moore
USDA - SCS
984C E. Andrews Ave.
Ozark,AL 36360
teL (205) 774-4749
2.6
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APPOQU1NIMINK RIVER - RCWP 2
New Castle County, Delaware
MLRA: 149A
H.U.C. 020402-05
I. Project's Major Contributions Toward Understanding the Effectiveness of NPS
Control Efforts
This project shows a declining trend in P concentration, attributable to BMPs. This appears to be due to a
high level of BMP implementation early in the project timeframe and a consistent water quality monitoring
effort at a stream station within the project area. The project has also shown that farmers are willing to make
adjustments in their practices to help improve water quality.
II. Project's Characteristics and Results
1. Project Type: RCWP
2. Timeframe: 1980-1991
3. Total Project Budget: $977,027 (ref. 7)
4. Cost Share Budget:
a. Funds Allocated: $866,045
b. Farmer* Contributions: $426,109 as of 1991 (estimated, ref. 7)
5. Water Quality Monitoring Budget: $114,000
6. Watershed Area: 30,762 acres
7. Project Area: 30,762 acres
8. Critical Area: 13,000 acres
9. Project Land Use:
Usq % Project Area
cropland 63.7
pasture/range 4.1
woodland/wetlands ' 27.8
urban/roads . 4.4
There are about 160 farms in the project area, mostly grain and vegetable producers. Eighty-five percent
of these farms are located in the critical area.
10. Animal Operations in Project Area:
a. Dairy: 6 farms with average of 105 cows (635 a. u.)
b. Beef: 2 farms with average of 147 cattle (249 a. u.)
< c. Swine: 1 farm with unknown number of animals
d. Poultry: 1 with 70,000 layers (350 a. u.)
Most dairy, beef and hog operations are along or near streams. None had animal waste treatment
facilities before the RCWP.
2.7
-------
11. Water Resource Type: lakes, streams, Appoquinimink River
12. Water Uses and Impairments:
The lakes and streams of the Appoquinimink River watershed are used for recreation by approximately
.5 million people who live within 20 miles of the watershed. Water uses include passive recreation
(sightseeing and birdwatching) and active recreation (fishing, hunting and boating). Contact recreational
uses such as swimming have been constrained by degraded water quality at Silver Lake in recent years.
Appoquinimink River water quality is fair. All lakes have eutrophic conditions with dense aquatic
vegetation and algal growth due to excessive nutrient concentrations.
13. Water Quality at Start of Project (ref. 4)
The Appoquinimink River had high nutrient levels. All three impoundments were eutrophic.
Water Quality Characterization for the Appoquinimink River (1977)
Wiggins Mill Silver Lake Noxontown
RL446 Pond Pond
Pollutant . mg/1 . .
Total IN . 1.1 4.6 1.0
TKN 2J 5.4 ZO
TP 0.4 0.2 0.2
Oil a 27.0 38.0
Fecal coliform standards (200/lOOml) were typically violated throughout the watershed during ambient
conditions, even though point sources do not indicate violations of fecal coliform standards.
14. Meteorologic and Hydrogeologic Factors:
a. Mean Annual Precipitation: 45 inches
b. USLE'R'Factor: 200
c. Geologic Factors: The watershed is underlain by deep sediments covering the bedrock. The surface
formation consists largely of medium to coarse sands and gravels. This formation is an important water
supply presently used as a potable water source for public and private supplies. The predominant soil
type is deep, well-drained and medium to coarse textured. Slopes are nearly level in the uplands and
steep near the stream channels.
15. Water Quality Monitoring Program:
a. Timeframe: Monitoring began in 1980 at Wiggins Mill, in 1983 at Silver Lake and Noxontown Pond.
Groundwater monitoring began in 1984. Monitoring at the river and pond stations ended in 1986.
Groundwater monitoring ended in July 1987.
b. Sampling Scheme:
1. Location and Number of Monitoring Stations:
a) Wiggins Mill Pond - one station to monitor a 2,200-acre subwatershed of the project areas
b) Noxontown Pond, Silver Lake and Shallcross Lake - 3 stations for each waterbody (2 within the lake
and 1 at the outlet)
c) Groundwater - 2 row crop sites, 2 potato field sites
2. Sampling Frequency:
a) incathiy for baseline data development of ail physicai/chemicai parameters and generally bimonthly
for biological indicators
b) three storm event samples collected seasonally .
c) periodic water quality surveys taken at Silver and Shallcross Lakes
3. Sample Type: grab
c. Pollutants Analyzed: filtered and unfiltered N and P series, chl a, suspended and dissolved solids,
COD, DO, FC, FS, BOD.
d. Flow measurements: taken with each sample at Wiggins Mill
e. Other: temperature, alkalinity, acidity. pH also measured
2.8
-------
Appoquinimink River RCWP, Delaware
16. Critical Areas:
a. Criteria: - soil erosion exceeds T value
gully erosion (including ephemeral) is present
concentration of animal wastes are 1,500 feet or less from a stream
need for better farm management with respect to application of fertilizer, pesticides and animal wastes
b. Application of Criteria: Critical area designation for individual contracts was determined by soil
conservationists using the above criteria on a field by field basis.
17. Best Management Practices:
a. General Scheme: Primary BMPs are conservation tillage, fertilizer management and pesticide
management. There is some implementation of animal waste management systems, primarily in the areas
of manure - holding structures and calibration of manure application equipment.
b. Quantified Implementation Goals: The project goal was to treat 9,750 acres of the 13,000 critical area.
c. Quantified Contracting/Implementation Achievements: as of Sept.; 30,1985. (ref. 13)
Ijacation % under conn-act % implemented
project area 37.0 37.0
project area
cropland 85.0 NA
critical area 87.4 87.4
project area
farms 4&0 48
d. Cost of BMPs:
Ave. Farmer
BMP Share (r>
1 perm. veg. cover 85/ac.
2 animal waste mgmt. 10,110 ea. . .
'5 diversions " I/ft .-
7 waterways 1,350/ac
-8 cropland protection 14/ac. - '
9 conservation tillage . 13/ac,
11 perm. veg. on eric, acres 330/ac.
12 sediment retention,
erosion control structures 2,000 ea. *
e. Effectiveness of BMPs:
1. Cost shared BMP installation for FY1986 saved 7,233 tons of soil on 977 acres (7 tons of soil per acre).
2. Improved fertilizer and pesticide management (BMPs 15 &. 16) has reduced the rate of P application
on cropland to one-half the amount needed if P were broadcast applied. Split N application for corn has
minimized the opportunity for large amounts of N to wash away soon after application.
3. Installation of manure holding structures allows the farmer to store animal waste for timely
application to meet crop needs.
4. Meetings and printed fact sheets on how to calibrate fertilizer and pesticide application equipment
are expected to improve the calculation of correct amounts and rates for application.
5. Changing tillage practices and implementation of BMPs which disturb less acreage has resulted in a
decrease of more than 60 percent in the concentrations of suspended solids and total P reaching the
stream. The BMPs credited with this effect include the following practices: permanent vegetative cover,
waterway, cropland protection system, conservation tillage system, permanent vegetative cover for
critical area, and erosion/water control structure.
18. Water Quality Changes:
Wiggins Mill data for 1986 show a dramatic, steady decline of 90% in sediment concentrations since
1980. Total phosphorus concentrations have declined by 65-70% since 1980.
NOs-N concentrations at Wiggins Mill have declined slightly the last two years. Chlorophyll a
concentrations have increased sharply the last two years in Wiggins Mill and have increased through the
sampling history for all three ponds.
2.9
-------
19. Changes in Water Resource Use:
There are no documented changes in water use. However, swimming is not currently allowed in Silver
Lake due to high bacteria, and algae in Noxontown Pond impairs boating. Assuming the area is used for
recreation primarily by local residents and they would recreate at the state average, an additional 18,000
swimming user-days and 42,000 boating user-days could be possible if water quality improves in the
future.
20. Incentives:
a. Cost Share Rates: up to 75%
b. Limitation: $50,000
c. Assistance Programs: fertilizer and pesticide management programs conducted by the Extension
Service.
21. Potential Economic Benefits:
a. On-farm: not evaluated
b. Off-farm:
1) Recreation: $15,000 - $180,000 per year.
2) Water Supply: 0
3) Commercial Fishing: 0
4) Wildlife Habitat: unknown
5) Aesthetics: unknown but positive
6) Downstream Impacts: unknown
III. Lessons Learned
The project reports that implementation of BMPs existed prior to RCWP but no records are available that
track those accomplishments, thus the pre-project level of implementation is difficult to define. The project
feels that this factor combined with the lack of baseline data in the sampling program may preclude
demonstrating water quality improvements as a direct result of BMP implementation under RCWP. The sam-
pling program's ability to detect subtle changes in water quality may have been hampered by the timing of
RCWP efforts in relation to what had already been accomplished. .
/'
IV. Project Documents:
1. U.S. EPA National Eutrophication Survey Working Paper Series: Report on Silver Lake, New Castle
County, Delaware. Working Paper No. 240. June 1975.
2. State of Delaware. Water Quality Standards for Streams. Department of Natural Resources and Environ-
mental Control. Amended March 25,1979.
3. Regional Nutrient Technical Advisory Committee. Recommendations for Reducing Losses of Applied
Nutrients in Region III of the EPA. 12/31/79.
4. New Castle Conservation District and the Water Resources Agency for New Castle County. Agricultural
Nonpoint Source Control Program for the Appoquinimink River Basin. Rural Clean Water Program
P-£~Q<-£| Revis1"* Jul" 1979
5. Water Resources Agency for New Castle County. Rural Clean Water Program Monitoring and Evalua-
tion (DRAFT Plan). April 16,1980.
6. RCWP Appoquinimink Project, New Castle County, Delaware. Monitoring and Evaluation Report. 1981.
7. RCWP Appoquinimink Project. New Castle County Delaware. Plan of Work Update for 1982.
8. Appoquinimink Rural Clean Water Program. Annual Progress Report. 1982. '.
9. RCWP Appoquinimink Project. New Castle County, Delaware. Annual Report. 1983.
"2.10
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Appoquinimink River RCWP, Delaware
10. Appoquinimink Project. New Castle County, Delaware. RCWP Progress Summary for Fiscal Year 1983.
Plan of Work: Update for 1984.
11. RCWP Progress Summary for Fiscal Year 1984.
12. Ratter, W.F. and R.W. Lake. 1984 Summary of Water Quality Monitoring in the Appoquinimink Water-
shed Appendix D to RCWP Progress Report.
13. RCWP Progress Summary for Fiscal Year 1985.
14. RCWP Progress Summary for Fiscal Year 1986.
V. NWQEP Project Contacts
Water Quality Monitoring
Bruce Kraeuter
Water Resources Agency
2701 Capitol Trail
County Engineering Building
Newark, Delaware 19711
tel. (302) 731-7670
and
BillRitter
College of Agricultural Science
Department of Agricultural Engineering
TownsendHall
Newark, Delaware 19717-1303 "
tel. (302) 451-2468
Land Treatment/Technical Assistance
Jack Lakatosh
USDA - SCS
6 Peoples Plaza
Newark, Delaware 19702
tel. (302) 834-3560
2.11
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2.12
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ROCK CREEK - RCWP 3
Twin Falls County, Idaho
MLRA: B-ll
H.U.C.: 170402-12
I. Project's Major Contributions Toward Understanding the Effectiveness of NPS
Control Efforts
Information on the effectiveness of BMPs in an irrigated system has been gained from this project. After five
years of water quality monitoring, significant sediment concentration reductions have been found in at least
five subbasins. Additional documentation of the relationship between land treatment and water quality is ex-
pected. Detailed analysis of this project is available in the NWQEPCM&E Report, 1985.
II. Project's Characteristics and Results
1. Project Type: RCWP, Comprehensive Monitoring and Evaluation Project
2. Timeframe: 1981-1991 for water quality monitoring; BMP implementation will continue until 1996.
3. Total Project Budget: (excludes water quality monitoring and farmers' contributions): $3,422,719
4. Cost Share Budget:
a. Funds Allocated: 51,954,591
b. Total Farmers' Contributions: $2,083,246 (estimate as of 1996)
5. Water Quality Monitoring Budget: $1,133,000 ,
6. Watershed Area: 198,400 acres
7. Project Area: 45,238 acres
8. Critical Area: 28,159 acres (includes 46 critical animal operations)
9. Project Land Use: (ref. 35 and 16)
% project % watershed
Use area area
cropland (irrigated) 74.5 26
(includes alfalfa)
pasture/range NA. 55
woodland NA. 13
urban/roads NA. 6
10. Animal Operations in Project Area: (personal communication, Bill Clark, Idaho DOE, Aug. 1986)
a. 34 dairy farms with average of 200 cows (6,800 a.u.)
b. 21 beef cattle farms with average of 300 cattle (5,355 a.u.)
c. 1 mink farm with average of 20,000 mink (~" 200 a.u.) - not thought to be a critical farm
11. Water Resource Type: Irrigation canals and Rock Creek (approx. 20 miles) flowing into the Snake
River
12. Water Uses and Impairments:
Rock Creek provides diverse habitat for wildlife and is a popular stream for swimming, tubing and
fishing. Water-skiing and swimming are major recreational activities in.the Snake River, 10-15 miles
2.13
-------
downstream from the confluence with Rock Creek. Rock Creek receives irrigation return flow from the
RCWP project area.
The primary use impairments are to fishing and contact recreation in Rock Creek, and to irrigation
ditches, canals and drains which become clogged with sediment. Fishing use of Rock Creek in 1981 was
about 500 fishing days compared to an estimated 8,000 if it were a quality trout fishery.
High sediment loads in Rock Creek may have created additional equipment and maintenance costs for
filtering sediment and removing gravel at the hydroelectric plant near the confluence of Rock Creek with
Snake River (personal communication, Bill Clark, Idaho DOE, 10/13/87). These costs are not formally
documented and are subject to debate. The muddy color of Rock Creek is an aesthetic impairment
which also effects the Snake River. The primary pollutants are sediment, phosphorus, nitrogen and
bacteria coming mostly from irrigation return flow and feedlot runoff. Sediment loads entering the
Snake River from Rock Creek do not appear to be significantly impairing downstream reservoir capacity
and causing increased cost of power generation. The nearest power plant which relies on reservoir
capacity is 120 miles downstream. - '
13. Water Quality at Start of Project:
1980 flow-weighted mean concentrations at the mouth of Rock Creek: (Monitoring site S-l)
Pollutant dnncentration
TSS 158.0 mg/1 (irrigation season only)
TP , 0.123 mg/1 (irrigation season only)
TN 33 mgfl (water year)
FC 1182.0 mpn (geometric mean)
14. Meteorologic and Hydrogeologic Factors:
a. Mean Annual Precipitation: 8-5 inches
b. USLE 'R' Factor ~ 20 ' -
c Geologic Factors: The watershed is underlain by limestone, quartzite, shale, sandstone, granite and
metamorphosed sediments. This formation yields large supplies of groundwater to the northeast. Soils in
the project area are highly erosive. Subsoils range from silty to loamy. Surface soils are generally
medium textured. Slopes range from nearly level to very steep on hill and mountain sides.
15. Water Quality Monitoring Program:
a. Timeframe: 1981 - 1991 -
b. Sampling Scheme:
1. Location and Number of Monitoring Stations: Monitoring stations have been established on Rock
Creek since 1980, and at 6 of the 10 project subbasins since 1981. The subbasin stations are located on
irrigation ditches. Some of the subbasin stations have been positioned in pairs at inlets from supply
canals and at upstream and downstream points to Rock Creek. There are 19 monitoring stations on
irrigation ditches and 6 stations on Rock Creek. There was a monitoring station on the Twin Falls Main
Canal from 1980 -1983.
2. Sampling Frequency: Biweekly to weekly at the Rock Creek and and subbasin stations during the
irrigation period. Monthly monitoring is performed during the non-irrigation season.
3. Sample Type: grab samples
"c. Pollutants Analyzed: TP, OP, TSS. FC, TKN, inorganic-N for the Rock Creek and the subbasin
samples. Additional parameters are analyzed on Rock Creek including macroinvertebrates, fish
population analysis, pesticides, and metals.
d. Flow Measurements: instantaneous flow taken with each grab sample
16. Critical Areas:
a. Criteria: All the irrigated cropland and animal production facilities are considered critical. The 10
subbasins within the project area were prioritized by project personnel. In addition. NWQEP examined
the relative upstream- downstream water quality in subbasins 1,2,4,5, and 7. Subbasins 2 and 7 and the
su'obasia drained by sampling stations 4-4 and 4-3 have additional potential for sediment: reduction.
These subbasins and subbasin 5 also have potential for improvement in FC, phosphorus, and nitrogen
levels.
2.14
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Rock Creek RCWP, Idaho
b. Application of Criteria: The implementation of BMPs has not followed the order of subbasin priority
because of economic conditions and the desire to issue contracts in the order that applications were
received.
17. Best Management Practices: -
a. General Scheme: Focus during 1981-1984 was on sediment retention structures and irrigation
management systems with some permanent vegetative cover on critical areas (RCWP BMPs 12,13, and
11). Several other practices were approved, but few were implemented (i.e., RCWP BMPs 2,9,15, and
16). For the duration of the project, 1985-1991, emphasis has shifted to conservation tillage (BMP 9) and
animal waste management (BMP 2).
b. Quantified Implementation Goals: The project goal is to install BMPs on 75 percent of the critical
erosion acres within 10 years. The deadline for contracts was September 30,1986. However,
amendments to existing contracts will add conservation tillage beyond that date. It appears the
implementation may fall short of the stated goal, especially in animal waste management.
c. Quantified Contracting/Implementation Achievements: as of 9/30/86 (Ref. 38)
Location % under contract % implemented
project area 47 NA
critical area 75 5
critical area farms . 67 NA
project area farms 67 NA
l ' '
d. Cost of BMPs: Costs of implementing principal BMPs were estimated in terms of the total change in
variable and fixed costs per acre. Least costly were conservation tillage and irrigation water management
(IWM), which actually reduced total costs:
BMP S/Acre change in cost/vr. _ .
9 Conservation tillage . S33 cost savings '
13 IWM 54 cost savings -
11 Filter strips $2 cost savings .
12 Sediment retention $9-15 added cost . .
13 Irrigation structures : $20-48 added cost ' -
*
e. Effectiveness of BMPs: With 75% of the critical area under treatment, expected decreases in pollutant
loads to Rock Creek from subbasins are estimated at 70 percent sediment, 70 percent TP and 65 percent
toxics (mostly pesticides) (ref. 35, p.2). These estimated reductions appear to be feasible based on water
quality data analysis already conducted (NWQEP, 1985).
Sediment reduction coefficients for the sediment retention BMPs have been developed by the
USDA-ARS at Kimberly, ID. Mini-Basins, I-slots, sediment basins, and buried pipe runoff were
effective with coefficients between 75 and 92 percent. Vegetative filter strips have a coefficient of 50
percent, irrigation improvements 5 to 40 percent, and conservation tillage 60 percent.
Management practices are by far the most cost-effective for reducing sediment loss on a per acre basis:
SChangc in cost/acre per one %
BMP reduction in sediment/acre
9 Conservation tillage $0.55 reduced cost
13 IWM 0.11 reduced cost
11 Filter strips 0.04 added cost
12 Sediment retention 0.09-0.17 added cost
13 Irrigation structures 0.48-5.20 added cost
18. Water Quality Changes:
Suspended sediment has decreased significantly in five of six subbasins studied. Severe streambank
erosion on the upper reaches of Rock Creek may be masking some of the effect on Rock Creek. Fish
sampling shows an increase in native trout populations in Rock Creek since 1981.
Based on a model of the watershed, full implementation of the project as contracted would reduce
sediment loadings to Rock Creek by 20 to 31 percent compared with pre-project conditions.
Modification of contracts to implement 10,000 acres of conservation tillage is expected to reduce
sediment loadings by 52 to 63 percent (ref. ERS, 1987).
2.15
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19. Changes in Water Resource Use:
A 52-63 percent reduction in sediment loadings should help restore Rock Creek as a quality trout
fishery, increasing fishing days per year from 500 prior to the project up to possibly 8,000. Other
recreational uses of the Creek and the Snake River would be enhanced, but not so directly or
dramatically as the fishery.
20. Incentives:
x Cost Share Rates: 50 or 75 % depending on the practice
b. S Limitations: $50,000 maximum on sediment retention and agricultural waste control systems, less for
other BMPs
c. Assistance Programs: The University of Idaho has demonstration and research plots for conservation
tillage. Researchers at the USDA-ARS station at Kimberly, Idaho have conducted extensive research on
conservation tillage as a management practice for southern Idaho. There is a need for better technical
assistance for animal waste management. A full-time SCS position for I & E activities was created in
1986 and will continue until the end of the project. The project publishes a newsletter, creates media
contacts, and promotes publicity.
d. Other Incentives or Regulations: The General Permit for Confined Animal Feeding Operations in
Idaho (EPA Region X) was passed into law in June 1987. Since the deadline for BMP contracts was
September 1986, the new law will not have the significant incentive to implement animal waste
management that was hoped. Fines for violating the permitting system may, however, speed
implementation of animal waste management. Existing contracts can still be modified, to include BMP2.
21. Economic Benefits:
a. On-farm: Farmers are gaining soil productivity (long term yield) maintenance benefits from
conservation Ullage and irrigation practices which keep soil in place in the fields. Conservation tillage
also reduces short term costs. Farmers also get depreciation deductions on income tax for the structural
measures installed. Modification of contracts to add additional conservation tillage (CT) could
substantially increase on-farm benefits over 50 years:
Project as Project with 10,000
contracted 9/86 - acres of CT
Benefits: fin million S present valued
Cost share payments received \2' 13
Short & long term yield benefits 1.0 1.9 -
Tillage cost reduction 03 12
Tax savings on BMPs 0.9-1.0 0.9-1.0
Gross benefits 3.4-3.5 53-5.4
Less Cost of Benefits 2.8-3.1 2S-T1
Net on-farm benefits 0.6-0.4 2J-2J
b. Off-farm: Estimated benefits over 50 years are:
Project as Project with 10,000
contracted 9/86 acres of CT
Benefits: fin million S present valued
Improved water recreation - 0.3-0.5 0.8-1.0
Commercial fishing N/A N/A .
Improved hunting (habitat
benefit of CT) negligible 0.2
Reduced ditch cleaning costs 0.1 03
Aesthetic benefits not measured not measured
Reduced power generation costs negligible neyliyihle
Total Off-Farm 0.4-0.6 1 3-1 J
2.16
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Rock Creek RCWP, Idaho
c. Benefits versus Costs: (over 50 years)
Project as Project with 10,000
contracted 9/86 acres of CT
Benefits: fin million S present valued
On-farm benefits total 0.6-0.4 ZS-2J
Off-farm benefits total 0-4-0.6 1.3-U
Total benefits 1.0 3.3
Casts
Government Costs 1-9 2.1
Total benefits minus cost . -0.9 1.7
III. Lessons Learned:
Based on results from this project, irrigation canals appear to respond faster to land treatment than do
streams and non-irrigated, humid areas. This is probably due to a relatively low variability in the hydrologic
factors associated with the irrigated system, and to greater control of the water resource. Further com-
parisons with other projects will help to test this hypothesis. Although analyses showed less variability ex-
isted in the water quality and flow data of this project compared to projects in humid regions, a 40-60 per-
cent decrease .in mean concentrations over a period of 4 to 5 years is still necessary to have a statistically sig-
nificant change in the water quality of irrigation canals. Data variability is likely to be greater in the Rock
Creek and Snake River systems which are more strongly influenced by meteorologic factors. Adjusting for
sources of variability (i.e., upstream concentration) has allowed more efficient monitoring to document the
water quality changes. Water quality monitoring was used successfully to quantify sediment loads to the im-
paired resource from subbasins and to indicate the subbasins that could most benefit from BMPs.
Results from the nearby LQ Drain project show that significant reductions in sediment loads may be lost if
sediment retention devices are not properly maintained. It is possible that a similar situation could develop in
The Rock Creek RCWP.. Conservation tillage techniques to reduce in-Held erosion are receiving increased
emphasis as an effective, low-cost alternative to structural practices for improving water quality; however, the
CT adoption rate, is still very low after three years of cost share availability* Many fanners reject CT because
-it is a non-traditional farming method. Custom operators who farm rented land do not have an economic in-
centive to practice CT. Most of the crops grown in the project area are dry beans (garden and commercial
seed varieties) and sugar beets. Contractors for dry beans know that conventional tillage methods yield good
bean crops and they are prone to contract with farmers who practice conventional methods. While there are
several surface applied herbicides registered for use on soybeans, there are no such products registered for
dry beans. This is a deterrent to adopting CT. (personal communication with Dr. David Carter, USDA-ARS,
Kimberly, Idaho, Dct 6,1987).
Off-farm economic benefits from water quality improvement in Rock Creek are limited because no large
scale recreational or municipal uses are impaired. Even though off-farm benefits may be small, additional im-
plementation of conservation tillage could result in total benefits of the project exceeding costs, and would
certainly have done so if the practice could have been implemented earlier in place of the less cost-effective
irrigation structures. ' _
IV. Project .Documents:
1. Clark, W.H. May 1960. Report on Pollution in Rock Creek: Cassia and Twin Falls Counties, Idaho 1959.
Idaho Department of Health, Engineering and Sanitation Section. 30 p.
2. Clark, W.H. February 1973. Report on Effects of Waste Discharges on Water Quality of the Snake River
and Rock Creek Twin Falls Area, Idaho. USEPA, Office of Enforcement, National Field Investigations
Center, Denver Colorado. 25 p.
3. Itami, B., W. Johnson, J. Miller, G. Hage, J. Sering, J. Atkins, J. Bede, T. Iverson, J J. Kuska, W.H.
Snyder, R.Weils. May 1974. Rock Creek Recreational Resource Inventory and Analysis. 3 p.
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4. Clark, W.H. 1975. Water Quality Status Report Rock Creek, Twin Falls County, Idaho 1970-1974.
Division of Environment, Idaho Dept. of Health and Welfare, Boise, Idaho. 69 p.
5. Bauer, S.B. April 1979. Water Quality Status Report: Upper Rock Creek (Twin Falls and Cassia Coun-
ties). Department of Health and Welfare, Division of Environment, Boise, Idaho. 9 p.
6. Idaho Soil Conservation Commission. April 1979. Idaho Agricultural Pollution Abatement Plan. 79 p.
7. Application for Rural Clean Water Program Funds: Rock Creek, Twin Falls County, Idaho. July 1979.
Submitted by John V. Evans, Governor of Idaho. Prepared by Idaho Department of Health and Welfare,
Division of Environment 53 p.
8. Plan of Work Rock Creek Rural Clean Water Project, Twin Falls County, Idaho. July 1980.
9. Idaho Dept. of Health and Welfare. Idaho Water Quality Status Report 1980. April 1981. Division of En-
vironment (DOE), Bureau of Water Quality. 40 p.
10. Rural Clean Water Project Monitoring Plan, Rock Creek, Twin Falls County, Idaho. December 1980.
Soil Conservation Service, Economic Statistical Service, Idaho Dept. of Health and Welfare: DOE, and
Science Education Administration. 30 p.
11. Annual Report: Rock Creek RCWP Intensive Monitoring. 1981.
12. Intensive Monitoring Work Plan: Rock Creek Rural Clean Water Project. July, 1981.
13. Brockway, C.E., FJ. Watts, and C.W. Robison. November 1981. Annual Report: Development of a
Sediment Generation and Routing Model for Irrigation Return Flow, Rock Creek Intensive Monitoring
Program. University of Idaho: Dept. of Agricultural Engineering and Dept. of Civil Engineering, and
Idaho Water and Energy Resources Research Institute, Kimberly Idaho. 10 p.
14. Socioeconomic Monitoring and Evaluation Progress Report for FY1981, Rock Creek RCWP Project -
Idaho.January 1982.
15. Rock Creek Rural Clean Water Project Annual Progress Report. October 1,1982.12 p.
16. Description of Project Area. 1982.11 p.
17. Executive Report - Annual Report 1982: Comprehensive Monitoring and Evaluation of Rock Creek
RCWP. November 1982.5 p.
18. Martin, D.M. and S. Bauer. September 1982. Water Quality Monitoring Assessment of the Rural Clean
Water Program: First Year Baseline Report, Rock Creek, Water Year 1981. Idaho Dept. of Health and
Welfare, DOE, Boise Idaho. 51 p.
19. Carter, D.L. and R.D. Berg. 1982. Rock Creek Intensive Monitoring Project: ARS Activities Report for
1982. _
20. Brockway, C.E., FJ. Watts, C.E. Robison, R.P. Sterling. November, 1982. Annual Report: Development
of a Sediment Generation and Routing Model for Irrigation Return Flow. University of Idaho: Dept. of
Agricultural Engineering and Dept. of Civil Engineering, and Idaho Water and Energy Resources Re-
search Institute, Kimberly Idaho. 54 p.
21. Everts, C. November 1982. Rock Creek Rural Clean Water Project Report on Information and Educa-
tion Activities. University of Idaho. 6 p..
22. Walker, D J., J. Hamilton, and P. Patterson. September, 1982. Annual Report Fiscal Year 1982:
Economic Evaluation of the Rock Creek Idaho RCWP.
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Rock Creek RCWP, Idaho
23. Rock Creek Rural Clean Water Project Annual Progress Report: Executive Summary. October 1,1983.
USDA and SCS. Boise Idaho. 21 p.
24. Martin, D.M. 1983. Rock Creek Rural Clean Water Program Comprehensive Monitoring and Evalua-
tion Annual Report. (Attachment I of 1983 Annual Progress Report). Idaho DepL of Health and Wel-
fare, DOE, Boise, Idaho 83720.85 p.
25. Carter, DX. 1983. Rock Creek Rural Clean Water Project Intensive Monitoring Project Report of ARS
Activities for 1983. Attachment H of the 1983 Annual Progress Report. 4 p.
26. Brockway, CE., FJ. Watts, CW. Robison, RJ*. Sterling, VJL. Watkins. October 1983. Development of a
Sediment Generation and Routing Model For Irrigation Return Flow. Attachment III of the 1983 An-
nual Progress Report. University of Idaho: DepL of Agricultural Engineering and Dept. of Civil En-
gineering and Idaho Water and Energy Resources Research Institute, Kimberly Idaho. 44 p.
27. Brockway, C.E., FJ. Watts, C.W. Robison, R.P. Sterling, V.L. Watkins. October 1983. Development of a
Sediment Generation and Routing Model For Irrigation Return Flow. Attachment IV of the 1983 An-
nual Progress Report. Appendix I to attachment III. LQ Drain, An Experiment in Irrigation Return
Flow Water Quality Improvement. Attachment IV of the 1983 Annual Progress Report. University of
Idaho: Dept. of Agricultural Engineering and Dept. of Civil Engineering, and Idaho Water and Energy
Resources Research Institute, Kimberly Idaho, 69 p.
28. Gum, R.L. October 1983. Annual Report: Socioeconomic Evaluation.of Rock Creek RCWP. Attach-
ment V of the 1983 Annual Progress Report. Economic Research Service. 5 p.
29. Hamilton, J., P. Patterson, D J. Walker. September 1983. Economic Evaluation of the Rock Creek
Idaho RCWP project. Attachment VI of the 1983 Annual Progress Report. Dept. of Agricultural
Economics, University of Idaho. 46 p.
30. Martin, D.M. 1983. Rock Creek Rural Clean Water Program - Idaho. ASAE paper No. 83-2449. !-
31. Gum R.L. October 1982. Annual Report: Socioeconomic Evaluation of Rock Creek RCWP. Economic
Research Service. '"".'- .. '
32. Rock Creek Rural Clean Water Program Annual Progress Report: Executive Summary. 1984.33 p.
33. Martin, D.M. 1984. Rock Creek Rural Clean Water Program Comprehensive Monitoring and Evalua-
tion Annual Report. Idaho Dept. of Health and Welfare, DOE, Boise, Idaho 83720.151 p.
34. Rock Creek Rural Clean Water Program Annual Progress Report: Executive Summary. 1985.32 p.
35. Clark, W.H. 1985. Rock Creek Rural Clean Water Program Comprehensive Monitoring and Evaluation
Annual Report. Idaho Dept. of Health and Welfare, DOE, Boise, Idaho 83720.153 p.
36. Kasal, J. and R. Magleby. 1985. Economic Evaluation Progress Report for FY85, Rock Creek, Idaho
RCWP Project. Economic Research Service, USDA. 29 p.
37. Bauer, S.B. 1985. Pilot Study of Quality Assurance Sample Procedures for Division of Environment
Water Quality Surveys. Idaho Dept. of Health and Welfare, DOE, Boise, Idaho. 16 p.
38. USDA, M J. Neubeiser, W.H. Clark, D.L. Carter, R. Magleby, and ASCS, 1987. Rock Creek Rural
Clean Water Program 1986 Annual Progress Report. 31pp.
39. Clark, W.H. 1986. Rock Creek Rural Clean Water Program: Comprehensive Water Quality Monitoring
Report, 1981-1986. Idaho Dept. of Health and Welfare, Division of Environment, Boise, Idaho. 147pp.
40. Kasal, J., R. Magleby, D. Walker, and R. Gum, 1987. Economic Evaluation of the Rock Creek, Idaho,
Rural Clean Water Project. Economic Research Service, USDA.
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41. Gum, R. and Garifo. Recreation Impacts of Improved Water Quality In Rock Creek. Unpublished back-
ground paper. Economic Research Service/RTD, USDA. 1984.5p.
42. Kelly, S. and R. Gum. Income Distribution and the Rural Clean Water Project. Unpublished back-
ground paper. Economic Research Service/RTD, USDA. 1984.9p.
43. LaPlant, DJD., Martin, L. Wear, and R. Gum. Wildlife Habitat Impacts. Unpublished background
paper. Economic Research Service/RTD, USDA. 1984.21p.
44. Walker, D. P. Paterson, J. Hamilton. Costs and Benefits to Improving Irrigation Return Flow Water
Quality in Rock Creek, Idaho, Rural Clean Water Project Research Bulletin no. 139. Agricultural Re-
search Station, University of Idaho. 1986.30 p.
V. NWQEP Project Contacts
Water Quality Monitoring
William (BUI) H. Clark
Senior Water Quality Analyst
Project Officer, Rock Creek RCWP
Idaho Division of Environment
Dept. of Health and Welfare
450 West State Street
Boise, Idaho 83720
let (208) 334-5860
Information and Education
Gayle Stover
Information and Education Specialist,
Rock Creek RCWP
Soil Conservation Service -
634 Addison Ave. W.
Twin Falls, ID 83301
tel. (208) 733-5380
Land Treatment/Technical Assistance
RickYankey
Rock Creek RCWP
Soil Conservation Service
634 Addison Ave. W.
Twin Falls, ID 83301
tel. (208) 733-5380
Economic Evaluation
Richard Magleby
Economic Research Service/RTD
U.S. Dept. of Agriculture
1301 New York Ave., NW, Rm, 532
Washington, DC 20005-4788
teL (202)786-1435
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HIGHLAND SILVER LAKE - RCWP 4
Madison County, Illinois
MLRA: M-114
H.U.C. 071402-04
I. Project's Major Contributions Toward Understanding the Effectiveness of NPS
Control Efforts
It is unlikely that the water quality impairment of Highland Silver Lake will be reversed by RCWP. Field
study aspects of the project may help to determine if BMPs can reduce the erosion of fine sediment particles
from natric soils. (For more information see the RCWP Status Report on the CM&E Projects, 1985, pp. 65-
78.) . .
II. Project's Characteristics and Results
1. Project Type: RCWP, Comprehensive Monitoring & Evaluation Project
2. Timeframe: 1980-1990
3. Total Project Budget (excludes water quality monitoring funds and farmers' contributions): $2,078,406
4. Cost Share Budget:
a. Funds Allocated: $1,402372
b. Total Farmers' Contributions: $466,990 (estimated)
*
5. Water Quality Monitoring Budget: 51,655,757
6.,Watershed Area: 30,946 acres (ref. 26, p.6)
' 7. Project Area: 30,348 acres (ref, 26, p.6)
8. Critical Area: 6,525 acres
9. Project Land Use: (ref. 7, p.4)
Use . % project area
cropland 32
pasture/range S
woodland . 4
other 9
10. Animal Operations in Project Area: (ref.26, p. 17)
a. Dairy: 760 a.u.
b. Beef: 944 a.u.
c. Swine: 1178 a.u.
d. Poultry: not reported
.11. Water Resource Type: streams and a 600-acre impoundment, Highland Silver Lake
12. Water Uses and Impairments: (ref. 1)
Highland Silver Lake is a public water supply for about 8,500 residents in the county. Several industrial
firms located in the City of Highland also use the lake for water supply. Non-contact recreational use of
the lake includes boating, fishing, and waterfowl hunting. In 1979, the lake supported an estimated
2.21
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42,600 angler-days.
Use of the lake is impaired by sediments, nutrients and toxics. High turbidity levels are caused by
suspension and resuspension of fine natric soil particles. Lake volume is being lost to sedimentation.
Excessive nutrient concentrations contribute to eutrophic conditions. Agricultural chemicals in surface
runoff entering the lake are a public health concern.
13. Water Quality at Start of Project: (ref. 9, p. m-43)
Average water quality from Site 1, nearest the water intake at the base of the lake (May 1981 - April
1983).
Parameter Mgan £[
TSS 27.8 mg/l 18
Turbidity 57.4 mg/l 17
Secchi 11.4 inches 17
TP 0.18 mg/l 18
TN 2.0mg/l 18
Chi a 6J6ug/l 17
14. Meteorologic and Hydrogeologic Factors:
a. Mean Annual Precipitation: 405 inches
b. USLE 'R' Factor ~ 200
c. Geologic Factors: Soils in the project area are almost entirely glacial in origin. Topography ranges
from nearly level to very gently sloping.
15. Water Quality Monitoring Program:
a. Timeframe:
1) lake - May 1981 to 1990
. 2) streams - Jan. 1982 to Oct. 1984
3) field sites - spring 1982 to Oct. 1984
All monitoring discontinued Oct. 1984, except at lake sites.
b. Sampling Scheme: (location, frequency, sample type) ~
9 lake sites (5 main lake & 4 bay sites) sampled monthly
1 lake outflow site sampled daily (MWF) with automatic sampler
3 stream sites sampled daily (MWF) with automatic samplers
8 field sites sampled during events with automatic samplers
c. Pollutants Analyzed:
Daily (MWF) - TSS.TVS, Turb., Temp., DO, pH & Conductivity sampled at tributary & spillway sites
Semimonthly - TSS,TVS,Turb., TP, DP, TKN, NOa, NO2, Temp., DO, pH, Conductivity sampled at
tributary & spillway
Monthly - ICAP metals sampled at tributary & spillway / TSS, TVS, Turb., TP, TKN, DP, NOs, NO2,
NHs, Temp., DO, pH, Conductivity, Total alkalinity, Chi a, ICAP sampled at lake sites
Events - TSS, TVS, Turbidity, TKN, TP sampled at tributary & field sites
d. Flow measurements:
1) spillway - daily
2) streams - continuous
3) field sites - event
e. Other
1) precipitation at 3 sites within watershed
2) 3 stream sites biologically sampled twice a year
3) 1 channel and streambed survey
4) 1 lake sedimentation survey
16. Critical Areas:
a. Criteria: (1) crop and pasture lands composed of natric soils with fine particle size and high erodibility
and slopes greater than 2%. (2) crop and pasture lands of non-natric soils with slopes greater than 5%
with high erodibility and proximity to water course. Feedlots are also prioritized according to the
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Highland Silver Lake RCWP, Illinois
number of animal units and distance to stream.
b. Application of Criteria: The criteria are followed carefully in selection of areas under contract.
17. Best Management Practices:
a. General Scheme: Project uses practices that increase ground cover, decrease the velocity of surface
runoff, and improve the management of livestock waste (i.e., RCWP BMPs 1,2,4,5,7,8,9,10,11,12,
14, and 15).
b. Quantified Implementation Goals: The project has established a goal of implementing 75% of its
critical areas, which is equal to 4,894 acres. Implementation goals for each BMP have also been
established.
c. Quantified Contracting/Implementation Achievements: (as of 1985) (ref.22, p.20)
Location % under contract % implemented
project area 18 " 11
critical area. 82 52
critical area farms 89 57 :
project area farms 53 34
d. Cost of BMPs:
Ave. Annual Gov't
Expected Cost/Acre
Average Treated or
BMP Life fyenrO Benefited (f\
1 Permanent Veg. Cover 5 24
4 Terraces 10 24
5 Diversions .10 22
7 Grassed Waterways 10 5-6
9 Conservation Tillage 4-20 . 22-4
11 Critical Area Cover 5 8-9 . - . .
12 Sediment Retention System 10 24
15 Fertilizer Management 3-10 4-1
- (Based on CRES data and $17.60 per hour for technical assistance) " '
e. Effectiveness of BMPsj^BMPs have reduced erosion by approximately 42,000 tons per year (USLE)
which corresponds with about 19,700 tons of sediment delivered to the lake.
d. Cost-effectiveness of BMPs: Estimates of project-wide cost-effectiveness based on the AGNPS and
LP models for three categories of BMPs are:
cost of cost of cost of cost of
sediment control P control 1% reduction 1% reduction
BMP (delivered to lake) (in lake) (sed. in lake) (N & P in lake)
S/ton S/lb. Sl.QQQ Sl.QQO
Conservation Tillage 14-33 7-17 3-7 4-9
Structural Practices
(4,5,7,12) 266 172 59 108
Animal Waste
Systems NA 43 - 70
18. Water Quality Changes:
Gage sites: Multiple linear regression analysis of normalized loading data indicates statistically
significant reductions in TSS and TP over time.
Lake sites: No statistically significant improving trends have been documented.
The CREAMS and AGNPS models have been used to identify expected changes. According to the
AGNPS model, full implementation of RCWP contracts and the increasing adoption of conservation
tillage should reduce sediment yield 33 percent and N and P yield 18 percent by 1991 compared to
pre-project conditions. However, the net effectiveness of the RCWP project, taking out the conservation
tillage trend, gives only about 12 percent reduction in loading of the three pollutants to the lake.
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19. Changes in Water Resource Use:
Changes In use of Highland Silver Lake will likely be negligible. A survey of anglers conducted in 1982
indicated that most would increase trips to the lake by about 12 per year if water appearance (clarity)
improved to the point of two-foot visibility. Such an improvement is unlikely as projected by models.
20. Incentives:
a. Cost Share Rates: 75%
bT$ Limitations: $50,000 per landowner
- c. Assistance Programs: none reported other than the usual I&E and SCS technical assistance.
21. Economic Benefits:
a. On-farm:
~ Discounted Value
Over50Years
(@ 7-7/8%/Year)
Benefits million* S
Coct share payment 1.2
Tillage cost savings 1.1
Productivity benefits neiHigihle
Gross benefits 2-3
Costs
Installation of BMPs 1.6
Maintenance of BMPs JL2
Total costs L&
Net benefit before taxes O..S
Productivity benefits over 50 years were analyzed using the SOILEC modeL The model indicated that
benefits from BMP implementation are offset by a lack of productivity benefits, because most soils in the
project area are deep.
b. Off-farm: -
« Discounted Value
; fOver 50 Years
Benefits ((S> 7-7/8% /Year)
Boating Negligible
Fishing 524,000
Swimming Not Applicable
Property values Negligible
Water treatment cost reduction $225,000
Reservoir capacity Negligible
Total $249,000
Fifty-six percent of the surveyed anglers indicted willingness to pay an additional fee to improve water
.clarity in the lake and that they would increase their visits per year by over one-half. Boating benefits
apart from fishing are negligible because of limitations on boat and motor size. The lakes's capacity is
large relative to future water supply needs and sedimentation rate is low. Therefore, reducing the
sedimentation rate has negligible benefits.
Several other possible benefits such as increased picnicking and aesthetics, improved upland game
habitat and reduced maintenance of roadways were not estimated in this analysis due to lack of reliable
-4~»~
uaio.
III. Lessons Learned
The Highland Silver Lake project had much advance planning. Critical areas were defined and a sound
monitoring program was developed. However. BMP implementation levels were low and natric soils that
produce turbidity even when erosion rate is low were persistent problems. Therefore, the BMPs selected
may not be able to alleviate the lake's water quality problem. Most of the water quality monitoring has been
discontinued; this will diminish the project's ability to document potential water quality changes within the
2.24
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Highland Silver Lake RCWP, Illinois
watershed. A modeling approach was demonstrated. Although it was too late to be used for selecting critical
areas and BMPs in this project, it should be evaluated and considered in the initial phases of other programs.
Conservation tillage and fertilizer management were shown to be the least costly BMPs to implement, assum-
ing the practices will be continued well beyond the contract period. Grassed waterways were also shown to
have low average annual costs per acre benefited compared with other structural measures and permanent
cover.
Conservation tillage was the most cost-effective method of reducing the delivery of pollutants to the lake.
Grass waterways and impoundments and animal waste management systems further reduced the generation
of pollutants. However, these practices have a very high cost for their expected pollution reduction.
The modeling and economic evaluation show that the cost effectiveness of the project in achieving water
quality could have been improved by promoting more extensive adoption of conservation tillage (reduced til-
lage or no-till) and certain crop rotations (e.g. soybean-wheat/double crop soybean) on all cropland in the
'- watershed rather than using more costly structural measures to reduce erosion to tolerance levels on fewer
acres.
The SOILEC model indicated that no significant long-term on-farm benefits are likely from BMPs primarily
because of the deep soil over most of the project area.
IV. Project Documents
1. Madison County Soil and Water Conservation District, 1979. Highland Silver Lake: Application for Rural
Clean Water Program. Madison County, Illinois.
2. Madison County Local Coordinating Committee, 1980. Plan of Work: Highland Silver Lake RCWP.
Madison County, Illinois.
3. Illinois State Coordinating Committee, 1981. Comprehensive Monitoring and Evaluation Program for the
Highland Silver Lake Watershed RCWP, Springfield, IL, 40 pp.
4. Illinois State Coordinating Committee, 1981. RCWP Comprehensive Monitoring and Evaluation Report
. on Highland Silver Lake Watershed. Springfield, IL, 63pp.
5. Makowski, P. and M.T. Lee, 1982. Highland Silver Land Silver Lake Reservoir Yield Analysis. State
Water Survey Division, Champaign, IL, 5 pp. /'
6. Davenport, T.E., 1982. Soil Erosion and Sediment Delivery in the Highland Silver Lake Watershed.
Preliminary Analysis. Illinois EPA, Springfield, IL, 35 pp.
7. Illinois State Coordinating Committee, 1982. Highland Silver Lake RCWP CM&E: Annual Report Fiscal
Year 1982, Springfield, IL.
8. Davenport, T.E. and M. H. Kelly, 1982. Water Resource Data and Preliminary Trend Analysis for the
Highland Silver Lake Monitoring and Evaluation Project: Phase I. Illinois EPA, Springfield, IL, 121 pp.
9. Illinois State Coordinating Committee, 1983. Highland Silver Lake RCWP CM&E: Annual Report Fiscal
Year 1983. Springfield, IL,
10. Davenport, T.E. and Kelly, M.H., 1983. Water Resource Data and Preliminary Trend Analysis for the
Highland Silver Lake Monitoring and Evaluation Project: Phase II. Illinois EPA, Springfield, IL, 145 pp.
11. Eleveld, B., 1983. Baseline On-Site/On-Farm Conditions for the Highland Silver Lake Watershed,
Madison and Bond Counties, Illinois (Revised) Agricultural Economics Department, University of Il-
linois; Champaign-Urbana, IL.
12, Eleveld, B., 1983. Farm Enterprise Budgets for Cropping Activities in the Highland Silver Lake Rural
Clean Water Program. Agricultural Economics Department, University of Illinois; Champaign-Urbana,
IL.
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13. Eleveld, B., 1983. Sail Productivity-Soil Erosion Relationships for Selected Soils Affected by the High-
land Silver Lake Rural Clean Water Program. Agricultural Economics Department, University of U-
lino'is; Champaign-Urbana, IL.
14. Eleveld, B., 1983. A Summary of Highland Silver Lake Rural Clean Water Program Cooperators' Con-
servation Farm Plans, Agricultural Economics Department, University of Illinois; Champaign-Urbana,
IL. .
9
15. Southwestern Illinois Metropolitan and Regional Planning Commission, 1983. Highland Silver Lake
Comprehensive Monitoring and Evaluation Project: Assessment of Off-Site Socio-Economic Impacts.
SIMAPC; Collinsville, IL.
16. Thomerson, J.E. and S.B. Reid, 1984. An Evaluation of the Fisheries of Highland Silver Lake, Madison
County, Illinois, Southern Illinois University, Edwardsville, IL.
17. Davenport, T.E., 1984. A Review of the Sediment Delivery Ratio Techniques Component of the High-
land Silver Lake Watershed Project. Illinois EPA, Springfield, IL, 27 pp.
18. Davenport, T.E., 1984. Field Modeling in the Highland Silver Lake Watershed: Interim Report. Illinois
EPA, Springfield, IL, 41 pp.
19. Illinois State Coordinating Committee, 1984. Highland Silver Lake Watershed RCWP: Summary Report
Fiscal Year 1984. Springfield, IL, 127 pp.
20. Davenport, T.E. and Kelly, M.H., 1984. Water Resource Data and Preliminary Trend Analysis for the
Highland Silver Lake Monitoring and Evaluation Project: Phase HI. Illinois EPA, Springfield, IL, 216 pp.
21. Makowski, P.B. and M.T. Lee, 1985. Hydrologic Investigation of the Highland Silver Lake Watershed:
1984 Progress Report. State Water Survey Division, Champaign, IL, 68 pp.
22. Illinois State Coordinating Committee, 1985. Highland Silver Lake Watershed RCWP: Summary Report
Fiscal Year 1985. Springfield, IL, 96 pp.
t
r".
23. Kelly, MJH. and T.E. Davenport, 1986. Water Resource Data and Trend Analysis for the Highland Sil-
ver Lake Monitoring and Evaluation Project: Phase IV. Illinois EPA, Springfield, IL, 198 pp.
24. Makowski, P.B., M.T. Lee, and M. Grinter," 1986. Hydrologic Investigation of the Highland Silver Lake
Watershed: 1985 Progress Report. State Water Survey Division, Champaign, IL, 98 pp.
25. Makowski, P.B., M. Grinter, and M.T. Lee, 1986. Stream Geometry and Streambed Material Charac-
teristics of the Streams Within the Highland Silver Lake Watershed. State Water Survey Division, Cham-
paign, IL, 66 pp.
26. Illinois State Coordinating Committee, 1987. Highland Silver Lake Rural Clean Water Project: Summary
Report, Fiscal Year 1986. Springfield, IL, 104 pp.
27. Carvey, D. G. 1982. Highland Silver Lake Angler Opinion Survey: Preliminary Results, Economic Re-
search Service, U.S. Department of Agriculture, East Lansing, Michigan.
28. Eleveld, B. and V. Starr. 1983. Evaluating the Effectiveness of RCWP Cost Share Payments in Illinois
Through Representative Farm Analysis. Department of Agricultural Economics, University of Illinois:
Champaign-Urbana, Illinois.
29. Southwestern Illinois Metropolitan and Regional. Planning Commission, (1981-1985). Highland Silver
30. Whice, D., B. Eieveid, and J. Braden. 1985. On-Farm Economic Impacts of Proposed Erosion Control
Policies. Agricultural Economics Department, University of Illinois; Champaign-Urbana, Illinois.
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Highland Silver Lake RCWP, Illinois
V. NWQEP Project Contacts
Water Quality Monitoring
Robert L. Hite
Environmental Protection Specialist
Div. Water Pollution Control
Planning Section^IEPA
2209 W. Main Street
Marion, IL 62959
teL (618) 997-4371
Economic Evaluation =
Parveen Setia or Richard Magleby
Economic Research Service/RTD
U.S. Dept. of Agriculture
1301 New York Ave., NW, Rm. 532
Washington, DC 20005-4788
teL (202)786-1435
Land Treatment / Technical Assistance
Sandy Andres
S.W. Illinois Metro-area Planning Commission
203 West Main Street
Coinnsville, IL 62234
teL (618) 344-4250
2.27
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2.23
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PRAIRIE ROSE LAKE - RCWP 5
Shelby County, Iowa
MLRA M-107
H.U.C. 102400-020
I. Project's Major Contribution Toward Understanding the Effectiveness of NFS
Control Efforts
The project shows that a very high rate of implementation is possible in a voluntary NFS control project. Fac-
tors that may contribute to the high rate of participation include: water quality objectives that are visible, sub-
stantial amounts of money available for cost sharing (approx, $163 per acre), preferred BMPs (terracing in
this case), a technical assistance program, active publicity programs, and services to assist farmers in fertility
management and integrated pest management
The institutional relationships in this project could provide a model for other NPS projects. In addition, com-
pletion of the implementation and monitoring programs will provide a definitive test of the effectiveness of
terracing as a BMP for protection of water quality in a small midwestern lake. (See chapter in this report for
in-depth analysis of this project.)
II. Project's Characteristics and Results
1. Project Type: RCWP
2. Timeframe: 1980 - 1991-
3. Total Project Budget: $596,072
4. Cost Share Budget:
a. Funds Allocated: $446,200 ' -
b. Total Farmers' Contribution: $148,748 (estimated to 1991)
5. Water Quality Monitoring Budget: not reported
6. Watershed Area: 4,568 acres (Ref. 9)
7. Project Area: 4,568 acres (includes 433 acre park and 215 acre lake)
8. Critical Area: 3,920 acres (entire project area excluding park and lake)
9. Project Land Use:
% Project Area % Watershed Area
Cropland 92.1 79.1
Pasture/range 3.7 32
Woodland & parkland 0.1 14.2
Farmsteads/roads 4.0 3.5
10. Animal Operations: 8 (type unknown)
1 1 . Water Resource Type: lake
12. Water Uses and Impairments:
Prairie Rose Lake is a 215- acre man-made lake located in one of the largest parks in west-central Iowa.
The lake is used for swimming, boating, and fishing by about one-quarter million .park visitors each year.
2.29
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The lake is unpaired by sediment, turbidity and agricultural chemicals. Between 19/71 and 1980,19% of
the lake volume was lost to sediment. The lake is eutrophic.
13. Water Quality at Start of Project: Upper Mixed Zone and Bottom Sites 1981 (n = 10)
(Annual means were calculated from STORET values for this project Observations reported with less
than detection limit values were set to one half the detection limit)
Parameter
Turbiditv(NTU)
Secchi depth (in)
TP(mg/I-P)'
OP(mgfl-P)-
Chla(ug/1)
TP & OP n - 5
Sitel
21.0/31.0
16.0/-
0.12/0.15
0.04/0.05
33.7/33.0
(upper/bottom)
Site 2
11.0/103.0
21.0/-
0.08/0.18
0.02/0.05
21.8/24.2
Site 3
9.0/84.1
23.0/-
0.08/0.16
0.02/0.06
17.4 / 24.1
14. Meteorologic and Hydrogeologic Factors:
a. Mean Annual Precipitation: 29.15 inches
b. USLE 'R' Factor 150-175
c. Geologic Factors: Upland soils are generally well-drained, silty clay loams that developed in loess.
Soils in the drainageways are alluvial. Slopes in the watershed range from 0-18%.
15. Water Quality Monitoring Program:
a. Timeframe: 1981 to completion of the project
b. Sampling Scheme:
1. Location and number of monitoring stations: 3 lakes stations sampled at surface and bottom
2. Sampling frequency: bi-weekly sampling summers only
3. Sample type: grab
c. Species Analyzed: NCb + NO2, NK» and free NHs, Dissolved P, TP, Sediment, DO, Chi a, FC, Secchi,
Turbidity .
16. Critical Areas: All croplands are critical acres.
17. Best Management Practices:
a. General Scheme: Most of the land treatment effort focused on controlling soil loss through practices
such as terracing. Conservation tillage is encouraged, and there are I&E programs to introduce fertilizer
management and integrated pest management
b. Quantified Implementation Goals: includes non-RCWP implementation
BMP Amount
1 Perm. veg. cover 111 ac.
2 Animal waste mgmc 6 units
4 Terracing 75 miles
5 Diversions 2,000 ft.
7 Waterway System 20 ac.
BMP Amount
9 Conservation Tillage 2,100 ac.
11 Perm. Veg. on Crit. Acres 10 ac.
12 Sediment Control Struc. 6 units
15 Nutrient Management 3,170 ac.
16 Pesticide Management 3,170 ac.
c. Quantified Contracting/Implementation Achievements:
Location
project area
critical area
critical area farms
project area farms
under contract
83
83
72.0
72.0
% implemented
74.0
74.0
N.A
N_A
in
-------
Prairie Rose Lake RCWP, Iowa
d. Cost of BMPs: (from RCWP Table 4, Ref. 14)
Ave. Fanner Awe. RCWP
BMT> Share (5\ Share (3\ Total Cost (5\
1 perm. veg. cover 7.50/ac. 2230/ac. 30/ac.
2 animal waste mgrat 1,000 ea. 3,000 ea. 4,000 ea.
4 terraces 0.15/ft 0.75/ft. 0.90/fL
5 diversions 0.33/ft. 0.67/ft. 17ft
7 waterways 845 ea. 2430 ea. 3,375 ea.
9 conservation tillage 5/ac. 15/ac. 20/ac.
11 perm. veg. on crit. ac. 9/ac. 21/ac. 30/ac.
12 sediment retention A
erosion control struc. 2400 ea. 7,500 ea. 10,000 ea.
e. Effectiveness of BMPS: Soil loss has decreased from 80,800 tons/year (1980) to 36,900 tons/year
(1985). Data from three bathymetric surveys indicate a reduction in sedimentation rate. Confirmation of
this trend, however, depends on completing the fourth bathymetric survey planned at the conclusion of
the RCWP project.
18. Water Quality Changes:
There has been no documented decrease in turbidity since the RCWP began. The project's water quality
monitoring data indicate high variability with no consistent trend in surface turbidity and water clarity.
Chlorophyll a concentration may explain a large portion of this variability, and improved clarity may be
masked by increasing algal growth.
19. Changes in Water Resource Use:
Total recreational use of the lake increased from 1981 to 1985 before declining in 1986 to the lowest level
since 1981. Fishing use decreased from 1981 to 1983, following a total fishery renovation, but increased
from 1983 to 1985. Use of the swimming beach also increased annually from 1981 to 1985. The project
notes that increased swimming use may have been a reflection of improved public perception of lake
aesthetics. Construction on the park access road in the latter part of 1985 may have depressed the annual
increase of park visitors and contributed to decreased user totals in 1986. The sudden decline in lake use
in 1986 may be attributable to the institution of a state park user fee, predominantly wet weather, and
additional roadway construction.
20. Incentives:
a. Cost Share: Rates are generally 75%, except for nutrient management and pesticide management,
which are handled under the I&E program and are not cost shared.
b. S Limitation: $50,000 per farm
c. Assistance Programs: Extensive I&E program handles all the nutrient and pesticide management in
the project (program conducted by the Extension Service).
21. Potential Economic Benefits:
a. On-farm: not evaluated
b. Off-farm:
1) Recreation: $30,000 - $85,000 per year
2) Water supply: 0 - $45,000 per year
3) Commercial fishing: 0
4) Wildlife habitat: unknown
5) Aesthetics: .unknown but positive
6) Downstream impacts: 0 .
III. Lessons Learned
A high rate of BMP implementation is possible when water quality objectives are clear and where the prac-
tices are considered desirable by the landowners. In this case, the farmers recognize the need for terracing to
prevent soil erosion, and they believe this will improve the quality of the recreational lake. Assistance in the
2.31
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form of cost sharing, soil testing, and pest scouting provided enough incentive to promote this project.
Recreational use of the lake has increased during the project period. This may be at least partially at-
tributable to the attention it has .received for the RCWP project. Some water quality improvement has ap-
parently been perceived by lake users, although water quality data do not yet confirm this.
Reduction of the sedimentation problem by extensive adoption of conservation practices (primarily terrac-
ing) may have improved water clarity, but this appears to have allowed algal density to increase. Evidence to
date suggest that BMPs have not reversed eutrophication.
The project has met its implementation goals, and the monitoring program has been consistent throughout
the project period. Water quality effects attributable to erosion control should be documented by the end of
the implementation period in 1991.
Positive net economic benefits are possible when treating sediment which adversely affects recreation.
IV. Project Documents
1. Prairie Rose Lake RCWP Application. July 1979.
2. Prairie Rose Lake RCWP Supplement to Application. Monitoring and Evaluation Plan. August 1979.
3. EPA Comments on Work Plan. June 2,1980.
4. Experimental RCWP Plan of Work, Prairie Rose Lake Watershed. June 1980.
5. Prairie Rose Lake. Plan of Work-Amendment 2. September 5,1980.
6. Prairie Rose Lake Monitoring RCWP Project-Year 1 (1981). March 23,1982.3,-4,-5, and SCS Report of
Project Accomplishments.
7. Corrections and Additions to the Report Entitled "Prairie Rose Lake Monitoring RCWP-Project-Year 1
(1981), March 23,1982".
8. Prairie Rose Lake Monitoring RCWP Project-Year 2 (1982). October 19,1982.
9:1982 Annual Report. November 30,1982.
10.1983 Annual Report. November 30,1983.
11.1984 Annual Report. November 30,1984 (Includes Lake Monitoring Report).
12.1985 Annual Report. November 30,1985.
13. Prairie Rose Lake Monitoring RCWP Project-Year 5 (1985). April 9,1986.
14.1986 Annual Report. November 30,1986.
15. Prairie Rose Lake Monitoring RCWP Project - Year 6 (1986).
V. NWQEP PROJECT CONTACTS
Water Quality Monitoring Land Treatment/Technical Assistance
Monica Wnuk Merle Lawyer
Iowa Dept. of Natural Resources SCS
900 E. Grand Ave. 1112 Morningview Dr.
Des Moines, LA 50319 RR# 4
tel. (515) 281-8879 Harlan, IA 51537
tel. (712) 755-2417
2.32
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Prairie Rose Lake RCWP, Iowa
Information and Education
DuaneR. Feltz
Shelby County Extension Service
1105 8th Street
Harlan,IA 51537
teL (712) 755-3104
2.33
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BAYOU BONNE IDEE - RCWP 7
Morehouse Parish, Louisiana
MLRA: O-134
H.U.C. 080500-01
I. Project's Major Contributions Toward Understanding the Effectiveness of NPS
Control Efforts
The project's primary contribution will be to document the rate of organo-chlorine residue dissipation from
an agricultural watershed. ,
II. Project's Characteristics and Results
1. Project Type: RCWP
2. Timeframe: 1980-1991
3. Total Project Budget (excludes water quality monitoring funds and farmers' contributions): $5,159,000
4. Cost Share Budget:
a. Funds Allocated: $3,930,000
b. Total Farmers' Contributions: $1,956,000 estimated as of 1990
5. Water Quality Monitoring Budget: $722,000
6. Watershed Area: 66,000 acres
7. Project Area: 66,000 acres
8. Critical Area: 44,880 acres
9. Project Land Use:
Use <%> project area
cropland . 74.4
pasture/range 4.0
woodland 113
urban/roads 10.3
10. Animal Operations in Project Area: not applicable
11. Water Resource Type: Bayou Bonne Idee
12. Water Uses and Impairments:
Bayou Bonne Idee is used mainly for water sports and fishing. It is popular for recreation that
contributes significantly to the local economy. Approximately 10,000 people use the Bayou for fishing
and water sports each year. Use of project area water resources has declined due to poor water quality
attributed to pesticides in runoff from surrounding cropland.
13. Water Quality at Start of Project: organo-chlorine pesticide concentrations of about 0.5 ppm in fish
2.34
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Bayou Bonne Idee RCWP, Louisiana
14. Meteorologic and Hydrogeologic Factors:
a. Mean Annual Precipitation: 48 inches
b. USLE 'R' Factor 350
c. Geologic Factors: The project area is in the Arkansas River Alluvial Plain within the Southern
Mississippi Valley Alluvium Major Land Resource Area. Topography is nearly level to gently sloping.
15. Water Quality Monitoring Program:
a. Timeframe: 1980 - 1990
b. Sampling Scheme
L Location and Number of Monitoring Stations: 5 bayou stations
2. Sampling Frequency: monthly - water; bi-annual-fish tissue
3. Sample Type: grab
c. Pollutants Analyzed: 27 pesticides plus 26 conventional parameters
d. Flow Measurements: Instantaneous flow measurements are taken with each grab sample.
16. Critical Areas:
a. Criteria: Three-quarter mile proximity to Bayou Bonne Idee, all cotton land.
b. Application of Criteria: No information available on how strictly criteria have been applied.
17. Best Management Practices:
a. General Scheme: Treatment emphasizes furrow irrigation improvements and field borders.
b. Quantified Implementation Goals: Goal is to treat 2/3 of cropland acreage.
c. Quantified Contracting/Implementation Achievements:
Location
project area
critical area
critical area farms
project area farms
d. Cost of BMPs:
°fn under contract
41
60
70.6
56.4
implemented
30{approx.)
42
50
NA
BMP
1 Fencing
4 Terraces
7 Grassed Waterways
8 Green Manure Crop
9 Land Smoothing
9 Crop Residue Use
9 Conservation Tillage
11 Critical Area Veg.
Ave. RCWP
Share rSI
0.26/ft.
1.08/ft
960/ac.
31/ac.
350/ac.
3/ac.
33/ac.
44/ac.
Ave. RCWP
BMP . Share CS1
11 Field Border 17/ac.
11 Filter Strip 55/ac.
12 Grade Stabilization Struc 440 ea.
12 Heavy Use Struc. 1,430 ea.
13 Irrig, Land Leveling 170/ac.
13 Irrig. Water Conveyance 2.50/ft.
15 Pert. Management 2-50/ft.
16 Pest Management IJO/ac.
e. Effectiveness of BMPs: not available
18. Water Quality Changes:
Turbidity levels in Bonne Idee were higher in 1984-1985 than in 1980-83, but this may be due to
manipulation of Bayou water levels. Toxaphene concentrations in fish tissue have dropped dramatically
since 1980. This appears to be due to the use of synthetic pyrethroids to replace toxaphene on cotton.
The estimated half-life of toxaphene in fish tissue is about one year. No water quality report was
included in the project's 1986 Annual Progress Report.
19. Changes in Water Resource Use:
Currently, an estimated 10,000 recreational fisherman use the project area water resources each year.
2.35
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20. Incentives:
a. Cost Share Rates: 75% for soil conservation practices, 50% for irrigation improvements and 90% for
fanners located adjacent to the Bayou Bonne Idee
b. $ Limitations: $50,000 maximum
c. Assistance Programs: none
21. Potential Economic Benefits:
a. On-farm: not evaluated
b. Off-farm:
1) Recreation: 0 - $40,000 per year.
2) Water supply: 0
3) Commercial fishing: 0
4) Wildlife habitat: unknown ' .
5) Aesthetics: unknown but positive
6) Downstream impacts: 0
. * -
III. Lessons Learned
1. The original 220,000 acre project area was much too large to achieve adequate BMP coverage.
2. High participation levels can be achieved at fairly low cost share rates (50%) for practices which are per-
ceived to have significant productivity benefits.
3. Practices that have primarily off-site benefits can be tacked onto contracts that include practices with high
on-site benefits such as irrigation improvements.
4. Treating a large project area will not result in high off-farm benefits unless impaired water uses are sub-
stantial. -
IV. Project Documents
1. Bayou Bonne Idee RCWP Annual Progress Report, 1982.
2. Bayou Bonne Idee RCWP Annual Progress Report, 1983.
3. Bayou Bonne Idee RCWP Annual Progress Report, 1984.
4. Bayou Bonne Idee RCWP Annual Progress Report, 1985.
5. Bayou Bonne Idee RCWP Annual Progress Report, 1986.
V. NWQEP Project Contacts
Water Quality Monitoring
Kent Milton
USDA-SCS
3737 Government Street
Alexandria, LA 71302
tel. (318) 473-7808
and
Lewis Johnson
Louisiana Dept. of Env.Quality
Baton Rouge, LA
teL (318) 342-6363
Land Treatment/Technical Assistance
Bennett C. Landreneau
USDA-SCS
3737 Government Street
Alexandria, LA 71302
te!. (318) 473-7759
and
Harry Hawthorne
(same address as above)
2.36
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DOUBLE PIPE CREEK - RCWP 8
Carroll County, Maryland
MLRA: S-148
H.U.C. 020700-09
I. Project's Major Contributions Toward Understanding the Effectiveness of NPS
Control Efforts
This project's water quality monitoring has contributed little information to date. The critical area is very
small and clearly designated, allowing efficient I & E and technical assistance efforts. Also, there has been a
significant shift in BMP emphasis to conservation tillage, without RCWP funding, in the project area.
II. Project's Characteristics and Results
1. Project Type: RCWP
2.Timeframe:1980-1991
3. Total Project Budget (excludes water quality monitoring funds and farmers' contributions): $5,118,051
4. Cost Share Budget:
a. Funds Allocated-$3,730,800 .
b. Total Farmers' Contributions: $1,463,200 estimated as of 1990
5. Water Quality Monitoring Budget: not available .
6. Watershed Area: 110,000 acres
7. Project Area: 110,000 acres
8. Critical Area: 18,180 acres
9. Project Land Use:
Use % pmiect area
cropland 65
pasture/range 12
woodland IS
urban/roads 8
10. Animal Operations in Project Area:
a. Dairy: 19,774 a.u.
b. Beef: 6,958 a.u.
c. Swine: 6,222 a.u.
d. Poultry: 5,000 a.u.
e. Horses: 7,747 a.u.
11. Water Resource Type: streams
12. Water Uses and Impairments:
Project area streams and ponds provide public water supply for the city of Westminster and surrounding
areas, approximately 18,000 people and several businesses. Secondary uses of water resources are
. contact recreation and fishing.
2.37
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Water quality impairments are caused by suspended sediment and bacteria. There is also concern about
nutrient export to the Chesapeake Bay.
13.Water Quality at Start of Project:
Maximum FC bacteria concentrations were 40,000/100 ml Turbidity after runoff events was often
greater than 100 ntu.
14. Meteorologie and Hydrogeologic Factors:
a. Mean Annual Precipitation: 45 inches
b. USLE'R'Factor 200
c. Geologic Factors: The project area lies within the north central Piedmont Region and is characterized
by gently rolling to steep uplands with streams of average to steep gradient feeding into the bottomlands.
Predominant soils are moderately credible. Ground water within the project area occurs primarily in
fractures and bedding-plane partings of rocks. It may also occur in solutional cavities in limestone and
marble.
15. Water Quality Monitoring Program:
a. Timeframe: 1980 -1990
b. Sampling Scheme:
1. Location and Number of Monitoring Stations: four on-farm sites; one station at downstream terminus
of project area.
2. Sampling Frequency: nine stonns/yr.
3. Sample Type: automatic
c. Pollutants Analyzed: suspended sediment, fecal coliform, NHs, NOa, TKN, P-total, P-ortho
d. Flow Measurements: continuous -
16. Critical Areas:
a. Criteria: distance from major streams, size of farm operation, present conservation status
b. Application of Criteria: no evidence that criteria have been rigorously applied
17. Best Management Practices:
a^ General Scheme: Treat cropland with conservation tillage and install grassed waterways; build waste
storage structures for critical animal operations and spread manure based on soil tests.
b. Quantified Implementation Goals: 13,635 acres (12% of project area)
c. Quantified Contracting/Implementation Achievements:
IjQcation
project area
critical area
critical area farms
% under contract
19.6
159.0*
60
^implemented
-10
-70
-35
'figure represents 100% of designated critical areas plus other contracted acres
d. Cost of BMPs: (from RCWP Table 4, Ref. 8)
BMP
i perm. veg. cover
2 animal waste mgmt.
3 stripcropping
5 diversions
6 grazing land prot.
7 waterways
8 cropland prot.
9 conservation tillage
(continued on next page)
Ave. Farmer
Share ($\
48/ac.
6^00 ea.
5/ac.
0.55/ft.
625-5,850 ea.
ISO/ft.
lZ50/ac.
18/ac.
Ave. RCWP
Share f <>
72/ac.
19^00 ea.
15/ac.
1.70/ft.
1,875-5350 ea.
4.50/ft.
12.50/ac.
0/ac.
Total Cost (S\
120/ac.
26,000 ea.
20/ac.
2^5/fL
2400-11,700 ea.
6/ft.
25/ac.
18/ac.
2.38
-------
Double Pipe Creek RCWP, Maryland
Ave. Farmer Ave. RCWP
BMP Share (S\ Share (ft Total Cost (ft
10 stream prot. 860/ea. 2,600 ea. 3,460 ea.
11 perm. veg. on crit. ac 165/ac. 160/ac. 325/ac.
12 sediment retention,
erosion control strut 875 ea. 2,625 ea. 3,500 ea.
15 fertilizer mgmt. (US/ac. 0.75/ac. 1/ac.
16 pesticide mgmt. 1.50/ac. 4.50/ac. 6/ac.
e. Effectiveness of BMPs: 18,427 tons soil saved per year / 3,267,357 cu.ft. of animal waste stored per year
18. Water Quality Changes:
No water quality changes have been documented to date. Three farm sites that had intensive pre-BMP
monitoring were discontinued because the farm operator withdrew his support.
19. Changes in Water Resource Use:
There are no documented changes in water resource use. There is very little recreational use and the
cost of water treatment for the City of Westminster has not changed since RCWP began.
20. Incentives:
a. Cost Share Rates: 75% for most practices
b. $ Limitations: $50,000
c. Assistance Programs: Several landowners have been assisted through ACP.
21. Potential Economic Benefits:
a. On-farm: not evaluated
b. Off-farm:
1. Recreation: 0
2. Water Supply (cost saved in treatment): 0
3. Commercial Fishing: 0
4. Wildlife Habitat: unknown -
5. Aesthetics: unknown
6. Downstream Impacts: unknown but positive. AS part of a larger effort to improve water quality in the
Chesapeake Bay the project could generate off-site benefits.
HI. Lessons Learned
Project may be a good test of whether an observable pollutant reduction can be achieved by treating
specified critical areas that comprise only about 20% of the watershed.
Project personnel consciously directed recruitment efforts to the large producers. The level of treatment indi-
cates that this was an effective strategy.
Several years and much money were spent monitoring three specific 17-175 acre farm sites. It now appears
that all three of these farms will not implement BMPs. This illustrates the importance of developing a bind-
ing contract with landowners whose participation is essential to the project even if it means providing crop in-
surance or inconvenience payments to the landowner.
IV. Project Documents
1. Rural Clean Water Project: Double Pipe Creek Water Quality Plan of Work 1980 -1995,1980.
2. Double Pipe Creek Project: Carroll County Maryland, Annual Progress Report, 1983.
3. Non-Point Source Water Quality Assessment Of Monocaey River Basin With Special Attention to the
Double Pipe Creek Watershed. Versar Inc., 1983.
. 4. Rural Clean Water Project: Double Pipe Creek Water Quality Plan of Work 1980 -1995 (Revised), 1983.
2.39
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5. Rural Clean Water Project: Double Pipe Creek Project, 1984 Progress Report, 1984.
6. Rural Clean Water Project: Double Pipe Creek Project, 1985 Progress Report, 1985.
7. Results of the Nonpoint Source Water Quality Program Conducted in the Monocacy River Basin With
Special Attention to the Double Pipe Creek Watershed. Versar Inc., February 1986.
8. Rural Clean Water Program: Double Pipe Creek Project, 1986 Plan of Work and Progress Report, 1986.
V. NWQEP Project Contact
Water Quality Monitoring
Karl Weaver
Water Management Administration
Maryland Department of the Environment
P.O. Box 13387
Baltimore, MD 21203
tel. (301) 225-6285
2.40
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SALINE VALLEY - RCWP 9
Washtenaw County, Michigan
MLRA:M-lllandL-99
H.U.C. 041000-01
I. Project's Major Contributions Toward Understanding the Effectiveness of NPS
Control Efforts
Although no major contributions have yet been made, we believe that within the next 1-2 years this project
may be one of the first to document basin level (1000-8000 acres) phosphorus reductions from cropland treat-
ment (See chapter on this project for in-depth analysis.)
II. Project's Characteristics and Results
1. Project Type: RCWP
. 2.Timeframe:1980-1990
.3. Tot^l Project Budget (excludes water quality monitoring funds and farmers' contributions): $ 2,545,870
a. Funds Allocated: $ 1,883,106
b. Total Fanners' Contributions: $629,386 estimated as if 1990
5. Water Quality Monitoring Budget: $200,000 estimated
6. Watershed Area: 77,000 acres
7. Project Area: 77,000 acres -
t "
8. Critical Area: 32,000 acres (approx.)
9. Project Land Use:
Use % project area
cropland . 66.4
pasture/range 10.4
woodland 20.8
urban/roads 2
10. Animal Operations in Project Area:
a. Dairy: 4,193 a. u.
b. Beef: 816 a. u.
c. Swine: 172 a. u.
e. Horses: 141 a. u.
11. Water Resource Type: streams and river draining to Lake Erie
12. Water Uses and Impairments:
Water resources in the project area are used for recreation and public water supply. Water quality
impairments are caused by high nutrient concentrations and sedimentation.
13. Water Quality at Start of Project:
Eutrophic streams. Orthq-P concentrations about 0.1 mg/1. Highest per acre P loading to Lake Erie of
any watershed in the area.
2.41
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14. Meteorologic and Hydrogeologic Factors:
a. Mean Annual Precipitation: 32 inches
b. USLE 'R' Factor 125
c. Geologic Factors: Project area soils vary from clay loam to organic deposits to sand. Glacial moraines
run through the center of the project area. Steep slopes occur on about 20% of the farmland.
15. Water Quality Monitoring Program:
a. Timeframe: 1980 - 1990
b. Sampling Scheme:
1. Location and Number of Monitoring Stations: 9 tributary and river stations
2. Sampling Frequency: weekly
3. Sample Type: grab
c. Pollutants Analyzed: TSS, Ortho-P, Total P, NCb, NHs, turbidity
d. Flow Measurements: weekly
e. Other: biomonitoring using diatoms
*. - *
16. Critical Areas:
a. Criteria: All cropland and animal operations within 1/4 mile of perennial watercourses.
b. Application of Criteria: Strict adherence to criteria.
17. Best Management Practices:
a. General Scheme: nutrient loading reduction from animal waste manage, conservation tillage, and
fertilizer management
b. Quantified Implementation Goals: 31,824 acres, 27 animal operations
c. Quantified Contracting/Implementation Achievements:
% under contract "fa implemented
project area 18.6 ~ IS
critical area 45.1 ~40
critical area farms ' . 3Z8 "30
project area farms 323 ~30
d. Cost of BMPs: not available by BMP
18. Water Quality Changes:
No water quality changes have yet been documented; however, seasonal trends have been clearly
established.
19. Changes in Water Resource Use:
There has been no documented change in recreational use and there is no documented water supply
impairment. Recreational use of the project area continues to be low.
20. Incentives:
a. Cost Share Rates: 75% for most practices
b. $ Limitations: $50,000 maximum
c. Other Incentives or Regulations: conservation tillage demonstration fields
21. Potential Economic Benefits:
a. On-farm: not evaluated
b. Off-farm:
1) Recreation: 0
2) Water Supply: 0
3) Commercial Fishing: 0
4) Wildlife Habitat: unknown
5) Aesthetics: unknown but positive
6) Downstream Impacts: unknown but positive
* 2.42
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Saline Valley RCWP, Michigan
HI. Lessons Learned
The original 200,000 acre project area was too large to achieve adequate BMP coverage with the available
cost share funding and technical assistance personnel.
BMP effects can only be observed in the project if monitoring focuses on smaller subbasins with a high level
of BMP implementation.
0
IV. Project Documents .
L Saline Valley Rural Clean Water Project, Michigan. Revised Plan of Work, July 1983.
2. Saline Valley Rural Clean Water Project, Michigan. Annual Progress Report, 1984.
3. Saline Valley Rural Clean Water Project, Michigan. Annual Progress Report, 1985.
4. Holland, R. E., A.M. Beeton and D. Conley. Saline Valley Rural Clean Water Project Interim Report on
Monitoring. Great Lakes and Marine Waters Center. October 1985.
5. Saline Valley Rural Clean Water Project, Michigan. Annual Progress Report, 1986.
6. Johengen, T. H., Documenting the Effectiveness of Best Management Practices to Reduce Agricultural
Nonpoint Source Pollution. University of Michigan, Department of Atmospheric and Oceanic Sciences.
Ann Arbor, MI. 1987.
V. NWQEP Project Contacts
Water Quality Monitoring
Mr. Thomas Johengen
Dept. of Atmospheric and Oceanic Sciences
University of Michigan
Ann Arbor, MI 48109
Land Treatment/Technical Assistance
Robert Payne
ASCS
1405 S. Harrison Rd.
Room 1116
7.' Fort Lansing, MI 48823
teL (517) 337-6671
and
Gary Rinkenberger
Soil Conservation Service
6101 Jackson Rd.
Ann Arbor, MI 48103
tel. (313) 761-6722
2.43
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2.44
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REELFOOT LAKE - RCWP10
Obion and Lake Counties, Tennessee
and Fulton County, Kentucky
MLRA: 0-1-31 and P-134
H.U.C. 080102-02
I. Project's Major Contributions Toward Understanding the Effectiveness of NPS
Control Efforts
The project, is an example of interagency and interstate cooperation in a NPS project. However, with the im-
plementation of the PL-566 project in the RCWP project area, it is not possible to monitor the effectiveness
of BMP land treatment for erosion control.
II. Project's Characteristics and Results
1. Project Type: RCWP
2. Timeframe: 1980-1990
3. Total Project Budget (excludes water quality monitoring funds and farmers' contributions): $ 4,198,026
4. Cost Share Budget:
a. Funds Allocated: 53,727,784
b. Total Fanners' Contributions: $1,175,926 estimated
5. Water Quality Monitoring Budget: $20,000
6. Watershed Area: 153,600 acres
7. Project Area: 153,600 acres
8. Critical Area: 52,072 acres
9. Project Land Use (equivalent to watershed land use): (ref. 2, p. 7)
use % project area
cropland 41
pasture/range 19
(grassland)
woodland 20.
urban/roads 1
water and wetlands 12
state park and
wildlife refuges 7
10. Animal Operations in Project Area: not applicable
11. Water Resource Type: streams with a receiving lake, Reelfoot Lake
12. Water Use Impairment:
Reelfoot Lake is located in a popular state park in Tennessee used primarily for fishing, boating, and
waterfowl hunting. The park had over 850,000 visitors during fiscal year 1974 ("ref. 2). Other water uses
within the project area are irrigation.and livestock watering.
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Impairments of Reelfoot Lake are: decreased lake volume, decreased fishery and wildlife habitat, and
impaired recreational use caused mainly by sediment and high nutrient concentrations. The lake has a
severe eutrophication problem. Pesticides are reported to be a cause of impairment, but data do not
support this claim. .
13. Water Quality at Start of Project: (ref. 7, pp39-42A)
Concentrations (mg/1) at Lake Sites (1977-1982)
Station 1 Station 2 Station 4
Parameter fopen water) (near outflow! (near creek confluence1)
x n x n x n
Suspended solids 33-8 27-7 26-7
Phosphates1 0.16-8 0.20-8 0.12-8
TKN 1.53-5 102-6 0.97-6
NO-3&NO-2 0.05-8 0.09-8 0.04-8
Species not noted.
14. Meteorologic and Hydrogeologic Factors:
a. Mean Annual Precipitation: ~ 48 inches
b. USLE'R* Factor ~ 260
c. Geologic Factors: The project area lies within the Mississippi embayment section of the Gulf Coastal
Plain. Uplands and bottomlands are divided by a distinct bluff running north-south through the area.
Substrate consists primarily of compact silt and clay mixtures. Bottomlands are covered by deep alluvial
deposits of silt, clay, sand and gravel. Uplands are covered by fluvial gravels topped with silty loess. .
Predominant soils are moderately well-drained to somewhat poorly drained loams. All soils in the area
are highly susceptible to gully and sheet erosion. Topography is nearly level on uplands to steeply sloped
along bluffs adjacent to the lake.
15. Water Quality Monitoring Program: . -
a. Timeframe: 1981 to 1995 .
b. Sampling Scheme: -
1. Lake Monitoring: Six stations are sampled twice a year (May and October). Four stations are near
confluences of tributaries, one is near the outflow and the other in open water.
2, Tributary Monitoring: Three stations representing the three main tributaries in the watershed are grab
sampled monthly (9 to 12 times per year). In addition, the major outflow of the lake is monitored for
flow only.
3. Sample Type: Five ungaged stream sites are grab sampled three times per year.
c. Pollutants Analyzed:
1. Lake Monitoring~BOD, DO, Secchi disk, Chi a, pH, temperature, suspended solids, dissolved solids,
settleable solids, total solids, NO2 and NOs, phosphates, algal growth, potential pesticides (only once
per year), including fish tissue and those adsorbed to sediment.
2. Tributary Monitoring: suspended solid settlement-monthly; pesticides (water)quarterly, pesticides
(bed materials)twice per year
3. Ungaged Stream Sites: Suspended sediment
d. Flow Measurements:
1. Tributary monitoring: Continuous flow measurements
2. Ungaged stream sites: Instantaneous flow measurements at time of sampling
16. Critical Areas:
a. Criteria: Currently, 83% of the cropland is designated as critical and is prioritized in three
classifications based on cropping intensity, erosion rate, and proximity to the lake and streams.
b. Application of Criteria: Contracts are being obtained for critical areas but reports do not indicate if
the prioritization of critical areas is being followed.
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Reelfoot Lake RCWP, Tennessee
17. Best Management Practices:
a. General Scheme: Land treatment emphasized by this project includes erosion controls (e.g.
conservation tillage), stream protection, fertilizer and pesticide management. These include RCWP
BMPs 1-16, excluding 13.
b. Quantified Implementation Goals:
L Treat 80% of critical area (41,658 acres)
2. Reduce sediment delivered to the lake by 75%, which is equivalent to sediment reduction of 638,019
tons/year.
c. Quantified Contracting/Implementation Achievements: as of September, 1986 (ref. 11, p. 15)
location % under contract % implemented
project area 18 12
critical area 53 33
critical area
farms 61 NA
project area * '
farms NA NA
d. cost of BMPs: Not available by BMP.
e. Effectiveness of BMPs: None have been reported to date.
18. Water Quality Changes:
None have been reported to date. With the ACP and extensive PL-566 project with the RCWP pf eject
area, the monitoring program will not document the water quality impacts of RCWP alone.
19. Changes in Water Resource Use:
There are no documented changes in water use at Reelfoot Lake since RCWP began. However, if the
installed BMPs reduce sediment, then the loss of lake capacity and severity of recreational impairments
may be reduced.
20. Incentives: . ' .
a. Cost Share Rates: 75%
b. $ Limitations: $50,000 per landowner
c. Assistance Programs: TN will conditionally pay the other 25% cost share to establish alfalfa on
designated steep, erodible lands within the project area.
d. Other Incentives or Regulations: The Conservation Reserve Program provides additional incentives to
farmers to convert highly erodible lands to more permanent vegetation.
21. Potential Economic Benefits:
a. On-farm: not evaluated
b. Off-farm:
1) Recreation: 0 - $30,000 per year
2) Water Supply: 0 - $2,000 per year
3) Commercial fishing: 0
4) Wildlife Habitat: unknown
5) Aesthetics: unknown but positive
6) Downstream Impacts: 0
III. Lessons Learned
Interstate cooperation is an essential element for the success of this project. Not only is the apparent
cooperation between the two states good, but the cooperative efforts of several programs (local, state, and
federal) also appear worthy of examination as a model of how multiple agencies can coordinate to address a
common water quality goal.
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IV. Project Documents
1. Tennessee Department of Public Health, Division of Water Quality Control, 1978. Reelfoot Lake Pes-
ticide Survey, Lake and Obion Counties.
2. Application for RCWP Grant, Reelfoot Lake Drainage Area, 1979.57pp.
3. USDA-Soil Conservation Service, 1979. Land Treatment Plan for Erosion Control and Water Quality Im-
provement, Reelfoot Lake Drainage Area. 34pp.
4. Reelfoot Lake RCWP Project Plan of Work, 1980.
5. Tennessee Department of Public Health, Division of Water Quality Control, .1981. Monitoring and
Evaluation Plan Reelfoot-Indian Creek Watershed RCWP. 21pp.
6. Smith, W.L. and TJX Pitts, 1982. Reelfoot Lake: Summary Report. University of Tennessee, Martin, TN.
128pp.
7. Local Coordinating Committee Reelfoot Lake RCWP, 1982. Reelfoot Lake RCWP Annual Progress
Report. 151pp.
8. Local Coordinating Committee Reelfoot Lake RCWP, 1983. Reelfoot Lake RCWP Annual Progress
Report.
9. Local Coordinating Committee Reelfoot Lake RCWP, 1984. Reelfoot Lake RCWP Annual Progress
Report.
10. Local Coordinating Committee Reelfoot Lake RCWP, 1985. Reelfoot Lake RCWP Annual Progress
Report. .. . . .
11. Local Coordinating Committee Reelfoot Lake RCWP, 1986. Reelfoot Lake RCWP Annual Progress
Report.
V. NWQEP Project Contact
Water Quality Monitoring & Land Treatment
Louis Godbey
Soil Conservation Service
U.S. Court House
Room 675
801 Broadway Street
Nashville, TN 37203
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SNAKE CREEK - RCWP 11
Wasatch County, Utah
MLRA:E-47
H.U.C. 160202-03
I. Project's Major Contributions Toward Understanding the Effectiveness of NFS
Control Efforts
This project adds information on the effectiveness of BMPs in arid, irrigated areas and the effectiveness of
animal waste management systems. The project accomplished nearly complete BMP implementation over a
small area. Significant reductions in phosphorus concentration (40-65%), nitrogen concentration (45- 60%),
and fecal coliform bacterial densities (50-90%) followed animal waste BMP implementation. These results
were documented with five years of water quality data (two years pre-implementation, one year during im-
plementation, and two years post-implementation), a much shorter period than generally required to docu-
ment effectiveness for projects in humid, non-irrigated regions.
II. Project's Characteristics and Results
1. Project Type: RCWP
2. Timeframe: 1980-1990
3. Total Project Budget: (excludes water quality monitoring funds and farmers' contributions) $242,400
4. Cost Share Budget: (1983 Progress Report, RCWP 5)
a. Funds Allocated: $161,000
b. Total Farmers' Contributions: $64,850
5. Water Quality Monitoring Budget: $191,230
6. Watershed Area: 523,403 acres (Deer Creek Reservoir Watershed)
7. Project Area: 700 acres
8. Critical Area: 489 acres
9. Project Land Use and Watershed Land Use: (ref. 4, p. 16-17, and ref.6, p.5)
% project % watershed
use area area
cropland 90 6
(mostly alfalfa)
pasture/range 4 37
woodland 0 0
urban/roads 6 3
multiple use 0 54
10. Animal Operations in Project Area:
a. Dairy: 4 farms with a total of 650 a.u.in 1981 and 790 a.u. in 1983
b. Beef: 4 farms with a total of 100 a,u,
c. Horse: 2 farms with a total of 35 a.u!
11. Water Resource Type: The water resources are irrigation canals draining into Snake Creek which flows
into the Provo River slightly upstream from the river's discharge into Deer Creek Reservoir.
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12. Water Uses and Impairments:
Water is stored in Deer Creek Reservoir, located just outside of the project area, primarily for
municipal, industrial and irrigation use in neighboring valleys. Recreational use of the reservoir is also
important (351,571 visitors during 1978 - ref. 1). About 500,000 people in the Salt Lake Valley received
potable water from the reservoir when the project began in 1980.
The reservoir has a eutrophication problem which impairs its use for water supply and recreation. High
concentrations of fecal coliform bacteria and phosphorus occur frequently in Snake Creek; however,
Snake Creek is a relatively minor source of the total pollutants entering Deer Creek reservoir (ref. 10).
13. Water Quality at Start of Project; Nov. 1979 to Dec 1981 (ref.4)
Station 14 (Snake Creek near base Station 6 (ditch downstream from
of project area) dairy farm)
TP(mg/l) 0.02 0.71 0.14 33 0.04 0.56 0.19 31
TKN(mgA) 0.10 3.90 0.851 33 0.10 4.60 1.08 31
FC(#/100ml)* 30.00 7500 889 13 19.00 12£00 1762 10
Feb. 1981-Dec. 1981 only
14. Meteorologic and Hydrogeologic Factors:
a. Mean Annual Precipitation: 16.4 inches
b. USLE'R'Factor ~ 30
c. Geologic Factors: The project area is in a valley which has a floor underlain by beds of unconsolidated
material from 40 to over 1,000 feet deep. Soils range from well drained deep soils formed in alluvium and
residium from sedimentary rocks on foothills and alluvial fans to moderately well drained and poorly
drained deep soils formed in mixed alluvium on flood plains, low stream terraces and valley bottoms.
Surface drainage patterns indicate that all surface water entering the valley runs in a direct manner
toward the reservoir adjacent to the project area. -
15. Water Quality Monitoring Program: .
a. Timeframe: Nov. 1979 -1990
b. Sampling Scheme:
1. Location and Number of Monitoring Stations: Initially, the project monitored water quality at 20
stations along Snake Creek, Provo River and several irrigation ditches. As of 1986, monitoring has been
reduced to seven stations.
2, Sampling Frequency: monthly, with weekly samples taken during spring runoff.
3. Sample Type : grab
c. Pollutants Analyzed: TP, OP, TKN, NOs, NOa, NHs, BOD, TSS, TDS, conductivity, temperature,
andpH
d. Flow Measurements: instantaneous at time of sampling
16. Critical Areas:
a. Criteria: Since this is a small project area, all major animal operations were considered critical.
b. Application of Criteria: adequate
17. Best Management Practices:
a. General Scheme: Project proposed to install animal waste management systems (BMP 2) on all farms
in the project area.
b. Quantified Implementation Goals: Contracts were planned for all four dairies and two of the beef
operations in the project area; the other two beef operations had agreed to use conservation methods
without the aid of the RCWP project. The two horse operations were not considered critical and were
act included in the contracting plans.
c. Quantified Contracting/Implementation Achievements: as of December, 1986 (ref. 8)
6 contracts have been completed.
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Snake Creek RCWP, Utah
d. Cost of BMPs: Cost shares not available by BMP.
e. Effectiveness of BMPs: Examination of recent data indicates continued water quality improvement.
18. Water Quality Changes:
Significant water quality improvements attributable to BMP implementation have been reported (ref. 9,
p. 101). On the main reach of Snake Creek, analysis showed 43 to 90% reduction in TP, OP, TKN and
FC concentrations. Recent data (1985 and 1986) from stations 10 and 14 indicate continued water
quality improvement. Analysis of Huffaker Ditch (ref. 9, p. 101) shows a 48 to 66% reduction in TP, OP,
TKN, and FC concentrations attributable to BMP implementation. No significant water quality impact
on Deer Creek Reservoir is expected from this project, however, because the project area constitutes
less than 1% of the reservoir drainage. (For further discussion see appendix to NWQEP Annual Report,
1985.)
19. Changes in Water Resource Use:
Actual visitation appears to have increased as a result of opening the park for year-round use. The
reservoir is still used as a primary water supply for several nearby towns and is considered to be of good
quality.
20. Incentives:
a. Cost Share Rates: 75%
b. $ Limitations: $50,000 per landowner
c. Assistance Programs: none reported
21. Potential Economic Benefits:
a. On-farm: not evaluated
b. Off-farm:
1) Recreation: 0
2) Water Supply: $4,000 per year.
3) Commercial-Fishing: 0
4) Wildlife Habitat: unknown
5) Aesthetics: unknown-
6) Downstream impacts: 0
III. Lesson Learned
This project not only was successful in reducing nutrient and bacterial concentrations, but also was ex-
emplary for its region. Other dairies in the Heber Valley area now are considering installing similar practices
after seeing the success of the Snake Creek RCWP. However, treating only a small project like this RCWP is
unlikely to benefit a large reservoir downstream unless other projects are also initiated.
The small area of this project made it ideal for nearly complete implementation and ease of tracking. Water
quality data analyses by NWQEP identified two critical areas: one small reach of the Snake Creek and Huf-
faker Ditch. These analyses also indicated that it may not have been necessary to install practices outside of
these two critical areas.
IV. Project Documents
1. Mountain Land Association of Governments, 1979. Application for Rural Clean Water Program Funds,
Snake Creek, Wasatch County, Utah. 34 pp.
2. Snake Creek Experimental Rural Clean Water Program, 1980. Plan of Work. 25pp.
3. Mouniainlauu Association of Governments, 1980. Snake Creek RCWP Monitoring Study Progress
Report. Provo, Utah. 53pp.
4. Snake Creek Local Coordinating Committee, 1982. Annual Progress Report on the Snake Creek Rural
Clean Water Program. Wasatch County, Utah.
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5. Snake Creek Local Coordinating Committee, 1983. Annual Progress Report on the Snake Creek Rural
Clean Water Program. Wasatch County, Utah.
6. Snake Creek Local Coordinating Committee, 1984. Annual Progress Report on the Snake Creek Rural
Clean Water Program. Wasatch County, Utah.
7. Snake Creek Local Coordinating Committee, 1985. Annual Progress Report on the Snake Creek Rural
Clean Water Program. Wasatch County, Utah.
8. Snake Creek Local Coordinating Committee, 1986. Annual Progress Report on the Snake Creek Rural
Clean Water Program. Wasatch County, Utah.
9. NWQEP1986 Annual Report: Status of Agricultural NPS Projects, National Water Quality Evaluation
Project. Biological and Agricultural Engineering Department, North Carolina State University.
10. So'wby and Berg Consultants. Deer Creek Reservoir and Proposed Jordanelle Reservoir Water Quality
Management Plan. Prepared for Wasatch and Summit Counties, Provo, Utah. (1984)
V. NWQEP Project Contacts
Water Quality Monitoring Land Treatment
Ray Loveless Jack Young
Utah Mountain Land Association of Governments USDA - SCS
2545 N. Canyon Rd. P.O. Box 87
Provo, UT 84604 Heber City, UT 84032
tcL (801) 377-2262 teL (801) 654-0242
Land Treatment/Technical Assistance
Mr. Tracy Hicken, Chairman
Local Coordinating Committee
Snake Creek RCWP
Wasatch County ASCS Office
P.O. Box 6
Heber City, Utah 84032
and
Bryant Brady
USDA-SCS
Heber City, UT 84032
teL (801) 377-5580
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ST. ALBANS BAY- RCWP 12
Franklin County, Vermont
MLRA: R-142
H.U.C 020100-05,07
I. Project's Major Contributions Toward Understanding the Effectiveness of NFS
Control Efforts:
This project has made substantial contributions in the following areas:
a) Water Quality Monitoring: The project has provided information on the level of monitoring needed to
detect changes in watershed nutrient loadings and concentrations and design of watershed monitoring
programs. .
b) Land Use Monitoring: The project is using CIS to examine what level of land use tracking is needed to tie
water quality changes to land use activities.
c) Effectiveness of BMPs: Modeling and monitoring by the project provide additional information about the
nutrient reductions from various animal waste management practices.
Contributions in each of these areas are discussed in detail in the 1985 NWQEP RCWP-CM&E Report.
II. Project's Characteristics and Results
1. Project Type: RCWP
2. Timeframe: 1980-1991
3. Total Project Budget: (excludes water quality monitoring funds and farmers' contributions) $2,388,977
4. Cost Share Budget:
a. Funds Allocated: $1,682,144
b. Total Fanners' Contributions: $560,715 as of 1990
5. Water Quality Monitoring Budget: $1,576,000
6. Watershed Area: 33344 acres
7. Project Area: 33344 acres
8. Critical Area: 15,257 acres
9. Project Land Use:
Use % pmjett area
cropland 105
pasture/range 51.4
woodland 213
urban/roads 10.7
other ' 6.0
10. Animal Operations in Project Area:
a. Dairy: 9310 a.u.
b. Beef: none reported
c. Swine: none reported
11 .Water Resource Type: streams, St. Albans Bay
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12.Water Uses and Impairments:
St Albans Bay has been used heavily for recreation in the past From 1960 to 1978, annual day use of St.
Albans State Park declined from 27,456 to 3,458 users (ref. 1). Worsening eutrophic conditions in the
bay were the cause of this decline. Boating, swimming and aesthetic enjoyment of the Bay are impaired
by excessive macrophytes and algal growth.
13. Water Quality at Start of Project
St Albans Bay frequently had eutrophic conditions in summer.
14. Meteorologic and Hydrogeologic Factors:
a. Mean Annual Precipitation: 33 inches
b.USLETT Factor 100
c. Geologic Factors: Topography ranges from steep slopes in the eastern region of the project area to
fairly level terrain in the western region near Lake Champlain. Soils of the eastern region are largely
glacial tills. .
15. Water Quality Monitoring Program:
a. Timeframe: 1980 -1990
b. Sampling Scheme:
1. Location and Number of Monitoring Stations: 4 bay stations; 5 tributary stations.
2. Sampling Frequency: bi-weekly-bay, tributaries- storm and ambient
3. Sample Type (e.g. grab, automatic): bay-grab; tributaries-automatic
c. Pollutants Analyzed: TSS, VSS, TP, OP, Turbidity, FC, NO* NH3, TKN
d. Flow Measurements: continuous
e. Other biological monitoring
16. Critical Areas: :
a. Criteria: Amount of manure, distance from watercourse, present manure management practices,
manure spreading rates.
b. Application of criteria: The project appears to have applied criteria rigorously to cost share
applications.
17. Best Management Practices:
a. General Scheme: Install waste storage systems, control barnyard runoff, spread manure at proper rates
b. Quantified Implementation goals: treat 11,443 acres and 64 dairies
c. Quantified Contracting/Implementation Achievements:
"fa implemented
-27
~46
59
39
d. Cost of TBMPs: Installation costs of the two major types of manure storage systems (180 day storage)
for a 48 cow herd are:
Total Per cow
System costfS^ cost (SI
Earthen-pit 15,230 263
Above-ground 43,844 756
Of these costs, RCWP pays about 75%.
f. Effectiveness of BMPs: Barnyard cleanup is estimated to be about 85% effective in reducing
phosphorus runoff. .
15. VY*iief Guaiiiy Changes:
Bay stations: Trend analysis indicates that turbidity, TP, TKN, and chl a. concentrations have decreased
in certain parts of the Bay since 1983. . '
2.54
I -ocation °i
project area
critical area
critical area farms
project area farms
'o under contract
40
74
74
63
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St. Albans Bay RCWP, Vermont
There is some indication of a phosphorus concentration reduction in Jewett Brook. A field-level paired
watershed experiment showed that significant reductions in phosphorus and nitrogen export from the
Jewett Brook subwatershed can be accomplished by eliminating the practice of spreading manure in the
winter.
19. Changes in Water Resource Use:
Recreational use of the bay could more than double if significant improvements in water quality are
perceived. Use of shoreline properties will also increase as water quality improves.
20. Incentives:
a. Cost Share Rates: 75% for animal waste management
b. $ Limitations: 50,000 maximum
c. Assistance Programs: ACP funds are also being used
21. Economic Benefits:
a. On-farm: Farmer's net income is likely to improve with installation of manure management systems
when cost-shared 75% by RCWP. For the typical 48 cow herd and 180 day storage the increase in
pre-tax income ranges from $900 for an above-ground system to $2,000 for an earthen-pit system. In
total, fanners' net income over 50 years is projected to be $800,000 higher (discounted to present value)
as a result of RCWP. This benefit comes primarily from labor, fertilizer and tax savings which exceed a
farmer's share of costs.
b. Off-farm: Improving water quality in St. Albans Bay to that found in Lake Champlain would produce
the following benefits (total over 50 years, discounted):
Benefit . S Million
Recreation enhancement
(swimming and boating) . 5.2 . '
Property value increase
around bay - U 4
: Reduced bay weed treatment minor - -
Total* - 6J
Part of these benefits would be due to improvements in municipal wastewater treatment.
III. Lessons Learned
Agricultural nonpoint source control projects can be designed so that benefits associated with water quality
improvement exceed the costs of the project, even when the cost of treatment is relatively high.
1. Even in expensive dairy waste management projects, a high level of fanner participation can be obtained if
there is:
a) 75% cost share rates;
b) a full-time coordinator who promotes participation;
c) a high level of community and landowners' awareness of the water quality problems; and
d) substantial on-farm labor and fertilizer savings.
2. In project area with a history of over-application of nutrients, simply reducing nutrient application rate to
meet crop uptake demand may not be sufficient to achieve nutrient loading reductions in the near term be-
cause of the large nutrient reservoir in the soil.
IV. Project Documents
1. An Application for Assistance for a Rural Clean Water Program - St. Albans Bay, Lake Canni Water-
sheds, Vermont Agency of Environmental Conservation.
2. Rural Clean Water Program - St. Albans Bay Project Plan of Work. 1980.
3. Technical Manual for the SNR Water Resource Research Center (WRRC) - Computerized Data
Management System (COMS)
2.55
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4. Comprehensive Monitoring and Evaluation Plan for the St. Albans Bay, Vermont Rural Clean Water
Program. February 1981. Vermont Rural Clean Water Coordinating Committee. .
5. St. Albans Bay Watershed RCWP Project Comprehensive Monitoring & Evaluation. June - November
1981. Progress Report
6. Comprehensive Monitoring and Evaluation - Progress Report for 1981 St. Albans Bay, Vermont, Rural
Clean Water Program. January 1982. Vermont Rural Clean Water Coordinating Committee.
7. Socioeconomic Evaluation St Albans Bay, Vermont - Annual Report. 1982. C. Edwin Young.
8. St. Albans Bay Rural Clean Water Program - Annual Report. November 1982. U.S. Department of
Agriculture, Vermont Water Resources Research Center.
9. St. Albans Bay Rural Clean Water Program - Annual Progress Report. 1983. U.S. Department of Agricul-
ture, Vermont Water Resources Research Center.
10. St. Albans Bay Rural Clean Water Program - Summary Report. 1984. U.S. Department of Agriculture,
Vermont Water Resources Research Center.
11. St. Albans Bay Watershed RCWP Project Comprehensive Monitoring and Evaluation - Progress
Report. November 1984.
12. St. Albans Bay Watershed RCWP Project Comprehensive Monitoring and Evaluation - Progress
Report. February 1985.
13. St. Albans Bay Rural Clean Water Program - Annual Progress Report. 1985. U.S. Department of
Agriculture, Vermont Water Resources Research Center.
14. St. Albans Bay Watershed RCWP Project Comprehensive Monitoring and Evaluation - Progress
Report. November 1985.
15-. St. Albans Bay Watershed RCWP Project Comprehensive Monitoring and Evaluation Progress
Report. May 1986.
16. St. Albans Bay Rural Clean Water Program. Annual Progress Report. 1986.
17. Ribaudo, Mark O., C. E. Young and D. J. Epp. Recreation Benefits from Improvements in Water
Quality at St. Albans Bay, Vermont. Staff Report no. AGES840127, Economic Research Services,
U.S.D.A., March 1984.
18. Young, C. Edwin. "Perceived Water Quality and the Value of Seasonal Homes." Water Resources Bul-
letin, 20:153, April 1984.
19. Young, D. Edwin and Frank A. Teti. The Influence of Water Quality on the Value of Recreational
Property Adjacent to St. Albans Bay, Vermont. Staff Report No. AGES831116, Economic Research Ser-
vice, U.S.D A~, January 1984.
20. Frevert, Kathleen and Bradley Crowder. Analysis of Agricultural Nonpoint Pollution Control Options in
the St. Albans Bay Watershed, Staff Report No. AGES870423. Economic Research Service, U.S.D.A.,
June 1987.
21. Ribaudo, Mark, C. Edwin Young, and James S. Shortle. Impacts of Water Quality Improvement on Site
Visitation: A Probabilistic Modeling Approach. Water Resources Bulletin, Vol. 22. No. 4. August 1986.
on. 559-563=
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St Albans Bay RCWP, Vermont
V. NWQEP Project Contacts
Water Quality Monitoring
Dr. Jack Clausen
University of Vermont
Aiken Center
Burlington, VT 05405
Telephone(802) 656-4057
Economic Evaluation
C Edwin Young
Economic Research Service/RTD
U-S.DepL of Agriculture .
1301 New York Ave. NW, Rm. 508
Washington, DC 20005-4788
teL (202) 786-1401
Land Treatment/Technical Assistance .
JeffMahood
USDA - Soil Conservation Service
69 Union Street
Winooski, VT 05404
Telephone (802) 655-9430
Information & Education
Bill Jokela
University,of Vermont
Coop. ExL Service
Aiken Center
Burlington, VT 05405
teL (802)656-4057
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2.58
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LOWER MANITOWOC RIVER WATERSHED - RCWP 13
Manitowoc, Brown, and Calumet Counties, Wisconsin
MLRA.-L-95A&B
H.U.C. 040301-01
I. Project's Major Contributions Toward Understanding the Effectiveness of NFS
Control Efforts
Little information on the water quality effectiveness of BMPs will be determined by this project Although im-
plemented practices may improve water quality, monitoring is not designed to detect it. .
II. Project's Characteristics and Results
1. Project Type: RCWP
2.. Timeframe:1980-1990
3. Total Project Budget (excludes water quality monitoring funds and farmers' contributions): $ 1,867,026
(ref.8, p. 17)
4. Cost Share Budget:
a. Funds Allocated: $ 1,575,807
b. Total Farmers' Contributions 1985: $1,027,200 estimation as of August 1985
5. Water Quality Monitoring Budget: $5,000
6. Watershed Area: 352,000 acres
7. Project Area: 102,000 acres (lower section of Manitowoc River Basin)
8. Critical Area: 23,598 acres
9. Project Land Use (ref. 4, p.8)
Use ' % project area
cropland 67
woodland 28
urban/roads 5
10. Animal Operations in Project Area: (ref.4 pp. 8,13, and 15)
Dairy farming is the primary agricultural activity in the project area. There are 333 livestock operations
with approximately 13,000 cows (13,000 a.u.) and an average of 39 cows per operation. There are 83
smaller herds with less than 20 milk cows and 250 larger herds with more than 20 milk cows.
11. Water Resource Type: A small lake, Manitowoc River, wetlands and streams, all draining to Lake
Michigan. '
12.Water Uses and Impairments:
The nearshore waters of Lake Michigan are used for recreation (swimming, fishing and boating),
shipping, and public water supply for the City of Manitowoc. These waters are impaired by algal growth
due to the excessive quantities of phosphorus and by high bacteria levels. The harbor capacity is-reduced
by sedimentation which necessitates dredging to maintain shipping channels.
2.59
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The river, streams and lakes within the project area are used primarily for fishing and other recreational
activities. The lake is eutrophic as a result of excess phosphorus which impairs the fishery. The fishery in
the river is also impaired by high phosphorus levels and high fecal coliform levels. Sedimentation of the
riverbed is also a problem. Project area water resources are used by about 40,000 people in and adjacent
to the watershed. This number does not include recreational visitors to the watershed.
13. Water Quality at Start of Project: (ref. 4, p5)
Phosphorus Loadings Measured at the Mouth of the Manitowoc River
Year Pounds of P Per Year
1973 211,000
1974 196,000
1975 106,000
1976 103,000
1977 39,000
1978 182,000
Mean 139,500
P loads ate from multiple point and nonpoint sources. The estimated P load from livestock waste and cropland erosion from
the project area is 55,080 pounds of P per year (ref.7 p.20).
14. Meteorologic and Hydrogeologic Factors:
a. Mean Annual Precipitation: ** 29 inches
b. USLE'R* Factor: ~ 100
c. Geologic Factors: Topography varies from rolling to moderately steep. Soils are generally
fine-textured with clay loams predominant. Precipitation does not readily infiltrate into these heavy soils
and runoff is high.
15. Water Quality Monitoring Program:
a. Timeframe:
- 1. Mouth of river. 1973-1990 and could continue -
2. Biological Monitoring: Mostly in the upper reaches and tributaries in the project area from 1979 to
1982. One site on a tributary will continue to be biologically monitored probably from 1985 to 1987.
b. Sampling Scheme:
L Location and Number of Monitoring Stations:
Two water quality stations at the base of the project area are located above and within the City of
Manitowoc. These stations are influenced, however, by the backwash of Lake Michigan, point sources,
urban NPS, the RCWP project area, and areas upstream from the project.
Thirteen sites were biologically monitored, 4 are located on the lower Manitowoc River and 9 are
located on tributaries to the river.
2. Sampling Frequency: (ref.4, p.22)
Mouth of river: In the zone of influence of urban sources and Lake Michigan) 1973-1979monthly and
high flow 1979-1982--biweekly, 1982-1990-monthly
Biological monitoring: Once in the fall and spring of 1979 and 1982. Fall and spring sampling of one site
will continue from 1985 to 1987. The continuing site, however, was not selected to show impact of the
project. . .
3. Sample Type:
Mouth of riven not reported, probably grab sample
Biological monitoring: sampling arthropods by grab samples with D-frame aquatic net
c. Pollutants Analyzed:
Mouth of river: suspended solids, VSS, TP, soluble P, dissolved silica, total lead, chloride, total zinc,
total solids, conductivity, copper, cadmium, and nickel
d. Flow Measurements: only at mouth of river with automatic, continuous equipment
e. Other: The model CREAMS is being used to estimate potential farm-scale benefits of various
practices. Comparable results of conventional vs. conservation practices have not yet been reported.
-------
Lower Manitowoc River RCWP, Wisconsin
16. Critical Areas:
a. Criteria: 1. all lands within 1/8 mile of water course
2. lands with slopes 6% or greater that are 1/4 mile from water course
3. livestock operations have been categorized as follows:
L need of barnyard runoff controls and manure storage104 large operations
iL need of manure storage88 large operations
iiL small water quality impact83 smaller operations
iiii. no impact on water quality58 large operations
b. Application of Criteria: procedures well established and consistent
17. Best Management Practices:
a. General Scheme: Land treatment practices that deal with animal waste management and erosion
control have been emphasized by the project. BMPs approved for the project include RCWP BMPs 1,2,
3,4,5,7,9,10,11, and 12.
b. Quantified Implementation Goals: The project's treatment goals are to treat 75% of the critical areas
(17,711 acres), including 122 dairies and 28 erosion sources other than livestock farms.
c. Quantified Contracting/Implementation Achievements as of September 1986 (ref. 8, p. 16)
IjK-ation <
project area
critical area
critical area farms
project area farms
founder contract
13
57
40
38
% implemented
7
31
NA
NA
(animal operations only)
d. Cost of BMPs:
BMP
1 Perm. veg. cover
2 Animal waste mgmt.
3 Stnpcropping
4 Terracing
5 Diversions
7 Waterways
9 Contour Farming
9 Conservation Till.
10 Stream Crossings
10 Fencing
11 Perm. veg. on crit. ac.
12 Sediment retention,
erosion, water control
Ave. Farmer
Share (T\
35/ac.
890-9,000 ea.
7/ac.
3/ft. .
0.47/ft
865/ac.
2.40/ac.
8.25/ac.
300 ea.
0.27/ft
46/ac.
128 ea.
Ave. RCWP
Share (D
35/ac,
2,075-11,860 ea.
16.40/ac.
7/ft.
1.10/ft.
2,015/ac.
5.60/ac.
19.30/ac,
700 ea.
0.62/ft
106/ac.
300 ea.
Total Cn*t '(S\
70/ac.
2,965-16,940 ea.
23.40/ac.
10/ft.
1.57/ft.
2,880/ac.
8/ac
27.55/ac.
1,000 ea.
0.89/ft.
152/ac.
428 ea.
e. Effectiveness of BMPs: riot reported
18. Water Quality Changes: not reported
19. Changes in Water Resource Use:
The city of Manitowoc continues to pump water from the harbor for domestic use. About 10 days per
year high bacteria levels due to heavy rains preclude use of the harbor as a water supply and secondary
rain collector wells are used as a water supply. The secondary wells need to be maintained as long as
periods of high bacteria levels occur. There is no information indicating any change in recreational use.
The average amount of material dredged from the harbor since RCWP began has been 25,000 cubic
yards per year, compared to 41,400 cubic yards per year prior to RCWP. However, there has been a
large amount of variation in dredging rates due to varying rainfall levels.
20. Incentives:
a. Cost Share Rates: BMPs 1 and 3 are cost shared at 50%; BMP 2, animal waste transfer components,
are cost shared at 40%; all other BMP 2 components have 70% rate; BMPs 4,5,7,9,10,11, and 12 have
2.61
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70% rate.
b. $ Limitations: $50,000 per landowner
c. Assistance Programs: Within the project area a state cost sharing program is being used in
conjunction with the RCWP project
d. Other Incentives or Regulations: none reported
2t. Potential Economic Benefits:
a. On-farnu not evaluated
b. Off-farm:
1) Recreation: 0
2) Water Supply: $40,000 per year.
3) Commercial Fishing: 0
4) Wildlife Habitat: unknown
5) Aesthetics: unknown
6) Downstream Impacts: unknown but positive
III. Lessons Learned
If a majority of the practices under contract are installed, there could be an improvement in water quality
from reducing agricultural NFS in the project area. The biological monitoring was performed prior to sub-
stantial BMP implementation and the two monitoring sites at the base of the watershed reflect the influence
of the total watershed including urban areas. Thus, the water quality monitoring design cannot adequately
document the sources of contamination (i.e., incoming waters from the upper portion of the watershed, back-
wash from Lake Michigan, point sources, and urban and agricultural NPS) nor the cause of any potential
water quality improvement. Thus, any water quality benefit from this project will not be documented.
IV. Project Documents .
1. Lower Manitowoc River Watershed Application for RCWP, 1979. Manitowoc, Brown, and Calumet
Counties, Wisconsin, 17pp.
2. The Lower Manitowoc River Priority Watershed Plan, 1979. Wisconsin. 50pp.
3. Lower Manitowoc River Watershed RCWP, (no date). 44 pp.
4.1982 Annual Report of the Lower Manitowoc River Watershed RCWP, 1982. Wisconsin. 68 pp.
5.1983 Annual Report of the Lower Manitowoc River Watershed RCWP, 1983. Wisconsin.
6.1984 Annual Report of the Lower Manitowoc River Watershed RCWP, 1984. Wisconsin.
7.1985 Annual Report of the Lower Manitowoc River Watershed RCWP, 1985. Wisconsin.
8.1986 Annual Report of the Lower Manitowoc River Watershed RCWP, 1986. Wisconsin.
V. NWQEP Project Contacts
Water Quality Monitoring
Jim Baumann
Dept. of Natural Resources
P.O. Box 7921
Madison, WI53707
tel. (608)266-9278
Land Treatment/Technical Assistance
Robert L. Wenzel, Chairman
Manitowoc County LCC
Route 2
Brillion,WI 54110
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TAYLOR CREEK-NUBBIN SLOUGH BASIN - RCWP 14
Okeechobee and Martin Counties, Florida
MLRA: U-156A
H.U.C. 030901-02
I. Project's Major Contributions Toward Understanding the Effectiveness of NFS
Control Efforts
The effectiveness of reducing phosphorus levels in Lake Okeechobee by preventing dairy cows from lounging
in streams should be documented by this project. The combined effectiveness of stream protection, gracing
land management, fertilizer management, and animal waste management on the subbasin and watershed
scales should be measured as well.
II. Project's Characteristics and Results
1. Project Type: RCWP
2. Timeframe: 1981-1991
3. Total Project Budget (excludes water quality monitoring funds and farmers' contributions): $1,534,202
4. Cost Share Budget:
a. Funds Allocated: $1,104,250
b. Total Fanners' Contributions: $272,157 as of 1985
5. Water Quality Monitoring Budget: $400,000
6. Watershed Area: 110,000 acres
7. Project Area: 110,000 acres
8. Critical Area: 63,109 acres
9. Project Land Use:
Use % Project Area
cropland 2
(mostly citrus groves)
pasture/range
a. daily 30
b. beef 45
woodland and
wet prairies 18
urban/roads 5
10. Animal Operations in Project Area:
a. Dairy: 24 farms with average of 1167 cows (28,000 a.u.)
b. Beef: 56 cattle farms with average of 446 cattle (12^00 a.u.)
. c. Swine: none
d. Poultry: none
11.Water Resource Type: streams, Lake Okeechobee
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12. Water Uses and Impairments: (ref. 1,7, 9)
Lake Okeechobee is the source of public drinking water for five towns around the lake. It is also the
secondary source of water supply for the lower east coast from West Palm Beach to Miami. A
commercial fishery worth $63 million annually is supported by the lake. The lake's sport fishing industry
is worth $22 million annually (ref. 9). In addition, a diverse wildlife habitat draws many tourists to the
lake area.
The Taylor Creek-Nubbin Slough Basin contributes a disproportionate amount of phosphorus to Lake
Okeechobee ( ~" 30% of P load in only ~ 4% of inflow to the lake). Use of lake waters is impaired by
eutrophic conditions.
13. Water Quality at Start of Project:
1980 mean annual concentrations at station S-191, the outlet of project area (ref.l, p.19).
Pollutant
TP 0.99
OP 0.88
TN 333
NO3NH3N02 1.01
14. Meteorologic and Hydrogeologic Factors:
a. Mean Annual Precipitation: 50.0 inches
b.USLE'R' Factor: -400
c. Geologic Factors: Topography is relatively flat. Soils are coarse textured, mostly poorly drained with
rapid permeability and medium drainage. An organic hard pan underlies much of the area with loam or
marl under the rest, all of a depth of less than 50 inches. The water table is very shallow. Seasonal
groundwater fluctuations are closely related to the seasonality of rainfall.
15. Water Quality Monitoring Program:
a. Timeframe: RCWP monitoring .is from 1981 to 1991; some stations have been monitored for water
quality since 1978. Discharge at 5 reaches has been monitored since early 1970's.
b. Sampling Scheme:
1. Location and Number of Monitoring Stations:
There are 23 instream grab stations within the project area. These do not include Lake Okeechobee,
which is monitored by other programs.
2. Sampling Frequency: biweekly
3. Sample Type: grab samples, with instantaneous flow measurements starting May, 1983 for those
stations that had not been monitoring discharge
c. Pollutants Analyzed: TP, OP, NOs, NOa, NH3, TKN, pH, conductivity, turbidity, and color
d. Flow Measurements: Five stations have had flow monitored since the early 1970's. The other stations
have had flow monitored since May, 1983.
e. Other: Precipitation and ground water levels have also been monitored within the project area.
16. Critical Areas:
a.-Criteria: (1) all dairy farms in the project area, (2) beef cattle farms that have been extensively
drained, and (3) areas within 1/4 mile of streams, ditches, and channels that hold water year-round
b. Application: Application of criteria is exceptionally strict, with graphic reports of the critical areas
and contracted areas of the project on a subbasin scale. There appears to be little contracting of
non-critical areas.
17. Best Management Practices:
a. General Scheme: The emphasis of BMP contracts is on grazing land management and protection,
animal waste management, and stream protection (i.e., RCWP BMPs 1, 2, 6, and 10). Other BMPs
include diversion systems and sediment retention (RCWP BMPs 5 and 12).
b. Quantified Implementation Goals: The project has achieved its two implementation goals: (1)
contracting 75% of the critical area and (2) contracting all 24 dairy farms in the project area.
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Taylor Creek/Nubbin Slough RCWP, Florida
c. Quantified Contracting/Implementation Achievements: as of September 30,1986. (ref.7, p.7-8)
I .ocation % under contract % imnlemented
project area 50
critical area 87
critical area farms 82
project area farms 83
69
78
not available
not available
dcostofBMPs:
Ave. Farmer Ave.RCWP State
BMP . Share ffi Share (f\ Share fD Total Cost CS1
2 animal watte mgmt 51.35/ac. 45.75/ac. 13.60/ac, 110.70/ac.
5 diversions &80/ac. 1.40/ac. 16.70/ac. 24.90/ac.
6 grazing land prot. 2.65/ac. 8JO/ac. 1.70/ac. 12^5/ac.
10 stream prot. 7.20/ac. 28-50/ac. 930/ac. 45.50/ac.
.12 sediment retention &
erosion control struc. 9.80/ac, 31/ac. 8.20/ac. 49/ac.
e. Effectiveness of BMPs: The project has & study to document the effectiveness of removing cows from
a stream. The results should be reported at a later date.
18. Water Quality Changes:
This project has had a high rate of BMP implementation with most of the implementation occurring in
1985 and 1986. This allows for a very nice pre-BMP water quality data base which can be quantified
more accurately in the next few years. There is strong evidence, however, that two dairy closures in the
Otter Creek subbasin (Sept. 1981 and 1985) had a possible impact on the phosphorus level in Otter
Creek (Ref. 8). Mosquito Creek also shows a significant decrease in total phosphorus (Ref. 8).This
subbasin has an intensive BMP implementation program. In contrast, in northwest Taylor Creek
subbasin (in the upper part of the project area), increased animal densities have had a negative effect on
water quality (Ref. 8). .
There has been an overall decrease in total P concentration at station S191 (the main discharge to Lake
Okeechobee from this watershed) (Ref. 8). It is postulated that this decrease is largely a function of the
dairy closures in Otter Creek and the high number of BMPs installed in the Mosquito Creek subbasin.
Fencing cows away from stream access, manure management, and fertilizer management are thought to
be significant contributors to the decreased total P concentrations.
19. Changes in Water Resource Use:
Lake Okeechobee continues to be used for commercial fishing and as a primary water supply for
approximately 27,000 people. Commercial fishing harvests have increased from 3.08 million pounds in
1981-1982 to 6.26 million pounds in 1984-1985. Water for domestic use continues to need treatment for
algae related problems. No recreational fishing use data is available to indicate user trends. However,
recreational fish harvests have increased from 660,300 fish in 1981-1982 to 1,248,100 fish in 1984-1985.
Most of the variation in recreational fishing appears to be the result of low water levels in the early
_1980's.
20. Incentives:
a. Cost Share Rates: 75% for structural BMPs
b. $ Limitations: $50,000 per landowner
c. Assistance Programs: include supplemental state funds for cost sharing BMPs in some parts of the
basin
d. Other Incentives or Regulations: The landowners have two incentives for implementing BMPs during
this project period: cost-sharing is available for structural BMPs and technical assistance is available for
all contracted BMPs. Another incentive for installing the non-structural BMPs is the threat of a permit
system that could require these BMPs at a later date when the technical assistance is not available.
2.65
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21. Potential Economic Benefits:
a. On-fanm: not evaluated
b. Off-farm:
1) Recreation: 0 - $1,800,000 per year.
2) Water Supply: $80,000 per year.
3) Commercial Fishing: $250,000 - $1,000,000 per year.
4) Wildlife Habitat: unknown
5) Aesthetics: unknown but positive
6) Downstream Impacts: 0
III. Lessons Learned
This project has used two tactics to attract fanner participation: the threat of regulation and the incentive of
higher cost share rates in some subbasins (with supplemental state funds). These methods appear to have
been successful in that the project has exceeded its contracting goal. This project could demonstrate how a
large project can be successful.
In a large project area with several impaired water uses the off-farm benefits are potentially very high. When
combined with low cost land treatment, positive out benefits from nonpoint source control are possible.
IV. Project Documents
1. Taylor Creek-Nubbin Slough RCWP No. 14, November, 1981. Project Plan of Work. Okeechobee County,
FL.
2. Taylor Creek-Nubbin Slough RCWP No. 14, November, 1982. Annual Progress Report. Okeechobee
County, FL.
3. Ritter, G J. and L.H. Allen, Jr., 1982. Taylor Creek Headwaters Project Phase I Report; Water Quality.
South Florida Water Management District, West Palm Beach, FL.
4. Taylor Creek-Nubbin Slough RCWP No. 14, November, 1983. Annual Progress Report. Okeechobee
County, FL. -
5. Taylor Creek-Nubbin Slough RCWP No. 14, November, 1984. Annual Progress Report. Okeechobee.
County, FL.
6. Taylor Creek-Nubbin Slough RCWP No. 14, November, 1985. Annual Progress Report. Okeechobee
County, FL. .
7. Taylor Creek-Nubbin Slough RCWP No. 14, November, 1986. Annual Progress Report. Okeechobee
County, FL.
8. Ritter, G J. and E.G. Flaig. 1987. Technical Memorandum: 1986 Annual Report, Rural Clean Water
Program. South Florida Water Management District, Department of Resource Planning - Water Quality
Division.
9. Bell, F.W. 1987. Economic Impact and Valuation of the Recreational and Commercial Fishing Industries
of Lake Okeechobee, Florida. Department of Economics, Florida State University, Tallahassee, Florida.
fifi
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Taylor Creek/Nubbin Slough RCWP, Florida
V. NWQEP Project Contacts
Water Quality Monitoring Land Treatment/Technical Assistance
Gary Ritter Lorin Boggs
South Florida Water Management District USDA - SCS
P.O. Box 938 611S.W.ParkSt.
Okeechobee, FL 34973 Okeechobee, FL 34972
teL (813) 763-3776 teL (813) 763-3617
and and
EricFlaig Jack Stanley
South Florida Water Management District USDA - ASCS
P.O. Box 24680 609 S. W. Park Street
3301 Gun Club Rd. Okeechobee, FL 34972
West Palm Beach, FL 33416-4680 tel. (813) 763-3345
tell (407) 686-8800
Information and Education
Vickie Hoge
CES
501 N.W. Fifth Ave.
Okeechobee, FL 34972
tel. (813) 763-6469
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>.6S
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WESTPORT RIVER WATERSHED - RCWP 15
Bristol County, Massachusetts
MLRA: R-145
H.U.C. 010900-04
I. Project's Major Contributions Toward Understanding the Effectiveness of NFS
Control Efforts
The project will make little contribution because it has a low level of implementation.
II. Project's Characteristics and Results
1. Project Type: RCWP
2. Timeframe: 1981-1991
3. Total Project Budget (includes funds for technical assistance, cost sharing, and information and
education, but excludes water quality monitoring funds and farmers' contributions): $ 656,643 (1986
Project Progress Report, RCWP-5)
4. Cost Share Budget:
a. Funds Allocated: $ 518,401 (1986 Project Progress Report, RCWP-5)
b. Total Farmers'Contributions: $172,799 (1986 Project Progress Report, RCWP-7)
5. Water Quality Monitoring Budget: SO
6. Watershed Area: 47,000 acres
7. Project Area: 47,000 acres (approximately one third of project area is not monitored). Part of project
area is in Rhode Island.
8. Critical Area: 473 acres on East Branch of Westport River
9. Project Land Use (equivalent to watershed land use): (ref. 4, pp. 21,22)
Use % project area
cropland 3
.pasture/range 10
woodland 80
urban/roads
other 7
10. Animal Operations in Project Area: (ref. 5, p. 10 & ref. 4, p. 22, except as noted):
a. Dairy: 14 farms with average 1593 cows (2230 a.u. - ref. 7, p. 3)
b. Beef: 5 cattle farms with average of 60 cattle (270 a.u.)
c. Swine: 12 farms with average of 83 pigs/hogs (175 a.u.)
d. Poultry: 1 farm with average of 60,000 chickens, etc. (270 a.u.)
e. Sheep: 1 farm with average of 100 sheep (50 a.u.)
f. Horse: 3 farms with average of 50 horses (110 a.u. - ref. 6, p. 10)
Animal operations in critical area
a. Dairy: 8 farms (ref. 7, p.5)
b. Beef: 7 farms (ref. 6, p.10)
2:69
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c. Swine: 6 farms (ref. 6, p.10)
d. Poultry: 1 farm (ref. 6, p.10)
e. Horse: 3 farms (ref. 6, p.10)
11. Water Resource Type:
There are wetlands and lakes in the upper section of the watershed which drain into the West Branch of
the Westport River. Both the East and West Branches of the river discharge into an estuary.
12. Water Uses and Impairments:
Ponds in the project area are used for recreation (limited to local residents) and for municipal water
supply. The Westport River supports commercial shellfishing (average of $425,000 annually from
1980-1984, $2,671,000 in 1985 due to extremely high scallop harvest), and public recreation. The main
use impairment is the closure of shellfishing beds in the estuary due to bacterial contamination. Other
reported impaired uses include boating, contact recreation, and fishery/
13. Water Quality at Start of Project: 1979 Coliform Bacteria Data for Station 6 at Hix Bridge, the
impaired tidal area: (ref. 4, p36, ref. 6, pp. 34-36)
pcf*i
log mean (#/100ml) 62 103 7
median (#/100mI) 36 91 7
% exeeedance 43/100 ml 28 7
% exceedance 23AOO ml 43 7
* U.S.EPA recommendations for shellfishing waters includes: a) median FC value should not exceed a
MPN of 14 per 100 ml and b) not more than 10% of the samples should exceed an MPN of 43 (Quality
Criteria for Water. 1976V !
* MA Water Quality Standards for shellfishing waters includes: a) median TC shall not exceed 70 MPN
per 100 ml and b) not more than 10% of the samples shall exceed 230 MPN per 100 ml.
14. Meteorologic and Hydrogeologic Factors:
a. Mean Annual Precipitation: 39-.S inches
b. USLE 'R' Factor: ~ 150
c. Geologic Factors: The project is located in the central lowland section of the New England
Physiographic Province. Topography is gently rolling. Soils are loamy and moderately to well drained.
Substrata are compact and permeability is slow. The surface drainage pattern is a series of wetland areas
connected by a system of streams and the river.
15. Water Quality Monitoring Program:
a.Timeframe: 1982-1990
b. Sampling Scheme:
1. Location and Number of Monitoring Stations: 10 sampling stations, 9 of which are along the fresh
water tributaries and streams and one is located in the tidal estuary.
2. Sampling Frequency: approximately 6 to 10 times per year
3. Sample Type: It appears that all stations except site 5 are monitored by grab samples. Site 5 has an
automatic sampler but grab samples are taken for bacterial analysis.
c. Pollutants Analyzed: temperature, pH, DO, TC, FC, FS, Chloride, TSS, TDS, NOs, NO2, TKN, TP,
DP, conductivity, and alkalinity.
d. Flow Measurements: For freshwater stream stations, the stage is to be measured and converted to
flow after hydraulic analysis is completed. None of these values (stage or flow) has yet been reported by
the project.
16. Critical Areas:
a. Criteria: The critical area was redefined to focus on dairy farms, which are the sources of bacterial
contamination. 8 dairy farms are in the critical area.
b. Application of Criteria: Practices are being implemented outside the critical area. Participation within
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Westport River RCWP, Massachusetts
the critical area is poor. Cultural barriers between project personnel and most dairy fanners, the USDA
Dairy Buy-Out program, and uncertainty in the dairy industry are factors in this project.
17. Best Management Practices:
a. General Scheme: RCWP BMPs approved for this project are 1-12,15, and 16. The main focus,
however, is on animal waste management.
b. Quantified Implementation Goals: To contract with all 8 dairies in critical area, and to treat all
agricultural land within the critical area. Four farms in the critical area have contracts.
c. Quantified Contracting/Implementation Achievements: As of Sept. 30,1986 (ref. 7, p. 6,13)
Location % under contract Tn implemented
project area NA 2
critical area NA 23
critical area dairies 50 NA
(crit. area dairies are sources of the bacterial contamination) -
project area dairies 36 NA ' '
d.CostofBMPs: -
Ave. Fanner Ave. RCWP
BMP Share CS1 Share (S\ Total Cost (SI
1 perm. veg. cover 124/ac. 115/ac. 239/ac.
2 animal waste mgmt. 18,200 ea. 23,900 ea. 42,100 ea.
4 terraces 2^7/ac 7.80/ac, 10.67/ac.
7 waterways 0.56/ac. 1.70/ac. 2.26/ac.
e. Effectiveness of BMPs: not reported to date
18. Water Quality Changes: no improvements have been reported
19. Changes in Water Resource Use: ,
.- Shellfish bed closures have continued in the Westport River area. The number of closed areas have
increased due to continued high bacteria levels, with the greatest impact on oyster production.
Commercial oyster harvests have decreased from 340 bushels in 1980 to 85 bushels in 1985. Harvests of
other shellfish have generally increased during the same time period despite high bacteria levels. The
amount of recreational shellfishing appears to be relatively steady, with 959 permits issued in 1985
compared to 814 permits in 1981.
20. Incentives:
a. Cost share rates: 75%
b. $ Limitations: $50,000 per landowner
c. Assistance Programs: None have been reported as part of the RCWP project other than I&E and
technical assistance. ACP funds have been used to establish cover crops within the watershed.
21. Potential Economic Benefits: __
a. On-farm: not evaluated
b. Off-farm:
1) Recreation: 0
2) Water Supply 0
3) Commercial Fishing: 0
4) Wildlife Habitat: unknown
5) Aesthetics: unknown
6) Downstream Impacts: 0
III. Lessons Learned
Criteria for selecting critical areas were not well established at the beginning of the project and did not focus
on the main cause of impairment. However, in 1986 the project redefined the critical area, focusing on dairy
runoff. Although an adequate water quality monitoring design was employed, it will do little good if sufficient
2.71
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and appropriate BMP implementation is not achieved.
There is a jurisdictionaJ problem on the Rhode Island state boundary concerning who should address bac-
terial contamination which has recently appeared downstream in the West Branch of the Westport River in
Massachusetts. There is also a communication problem concerning project activities because of cultural dif-
ferences between project personnel and Portuguese dairy owners within the project area.
IV. Project Documents
L Rose, D. and P. Fisher (ASCS), 1981. Westport River Watershed Application for USDA - RCWP Special
Project. Bristol County, MA 47 pp.
2. Westport River RCWP Project Local Coordinating Committee, 1981. RCWP Westport River Watershed
Project Plan of Work and Annual Progress Report. Westport, MA 50 pp.
3. Westport River RCWP Project Local Coordinating Committee, 1982. RCWP Westport River Watershed
Project Plan of Work and Annual Progress Report. Westport, MA 26 pp.
4. Westport River RCWP Project Local Coordinating Committee, 1983. RCWP Westport River Watershed
Project Plan of Work and Annual Progress Report. Westport, MA 108 pp.
5. Westport River RCWP Project Local Coordinating Committee, 1984. RCWP Westport River Watershed
Project Plan of Work and Annual Progress Report. Westport, MA 42 pp.
6. Westport River RCWP Project Local Coordinating Committee, 1985. RCWP Westport River Watershed
Project Plan of Work and Annual Progress Report. Westport, MA 51 pp.
7. Westport River RCWP Project Local Coordinating Committee, 1986. RCWP Westport River Watershed
Project Plan of Work and Annual Progress Report. Westport, MA 15 pp.
V. NWQEP Project Contacts
Land Treatment/Technical Assistance
Joanne Haracz.
District Conservationist
SCS
21 Spring St.
Taunton, MA 02780
tel. (617) 824-6668
Water Quality Monitoring
Larry Gil
Div. of Environmental Quality Eng.
- Water Pollution Control
Westboro Technical Services Branch
Lyman School
Westboro, MA 02790
tel. (617) 727-0437
2.72
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GARVIN BROOK - RCWP 16
Winona County, Minnesota
MLRA: M-105
H.U.C.: 070400-03
I. Project's Major Contributions Toward Understanding the Effectiveness of NFS
Control Efforts
. The project has demonstrated the use of a computer model to identify and evaluate of critical areas for sur-
face water problems. Critical areas for ground water problems were identified by extensive geologic mapping
and locating sinkholes and abandoned wells through which pollutants can easily enter the ground water.
The project demonstrates the effectiveness of nutrient management based on a nitrogen budget utilizing data
on crop yields, commercial fertilizer use, retention of N in soils, and animal waste application. This is impor-
tant to illustrate to farmers the value of animal waste as a nutrient resource.
II. Project's Characteristics and Results
Background: The project's original objective was to treat nonpoint sources of pollutants entering Garvin
Brook, a high quality trout stream. In 1985, after three years of work within the original surface watershed
area, the project shifted its emphasis to ground water quality. The change was made after analysis of samples
from 80 wells within the surface watershed showed that 21% of the wells had levels of NO>N exceeding the
10 mg/1 drinking water standard. In 1985, the Garvin Brook RCWP expanded its project area to include all of
the ground water watershed (approximately one-half is outside the surface watershed). While the full range
of BMPs was approved for cost share funding on the original project area, funding was restricted to BMPs 15
and 16 in the expanded area. Critical areas were redefined and the total number needing treatment in-
creased. .
1. Project Type: RCWP . . .. .
2.Timeframe: 1982-1991
3. Total Project Budget: (excludes water quality monitoring and farmer's contributions) $1,809,662
4. Cost Share Budget:
a. Funds Allocated: $1,077,022
b. Total Farmers' Contributions: $242,246
c. Winona County funds: $118,000
5. Water Quality Monitoring Budget: $270,500
$120,500 original monitoring budget
$150,000 for ground water monitoring (1987-1989)
6. Watershed Area: 46,516 acres (30,720 acres original surface watershed plus 15,796 acres in the expanded
ground water watershed)
7. Project Area: 46,516 acres
8. Critical Area: 20,255 acres (12,681 acres affect ground water quality and 7,574 affect surface water
quality)
Of the 20,255 acre critical area, 10,714 are listed as needing treatment (7,609 for ground water and 3,105
acres for surface water protection). All.acres needing treatment are in annual row crop production.
2.73
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9. Project Land Use:
Use % original area % expanded area % project area
pasture/range 12 ""5 "10
woodland 25 "5 ~18
urban/roads 2 53.
other (roads) 3-2
There are 218 farms, mostly small dairies, in the project area. There are 94 farms in the critical area.
Dairy and cash grain are the primary farm operations.
10. Animal Operations in Project Area:
Original area (ref. 7):
a. Dairy: 54 farms with average of 94 cows (5,100 a.u.)(ref. 8)
b. Beef: 9 farms with average of 170 cattle (1300 a.u.)
c. Swine: 13 farms with average of 335 pigs/hogs (880 a.u.)
e. Other 8 miscellaneous farms (85 a.u.)
Expanded area (ref. 8):
a. Dairy: 8 farms with average of 283 cows (2,265 a.u.)
11. Water Resource Type: streams and ground water
parvin Brook is designated a trout stream by the Minnesota Department of Natural Resources. The
Prairie du Chien-Jordan aquifer is the impaired ground water resource.
12. Water Use and Impairments: .
Current project area population is estimated at 2^00; most rely on domestic wells for water supply.
Approximately 25,000 people use Garvin Brook for recreation, primarily swimming and fishing.
The primary ground water impairment is decreased drinking water quality from high nitrate-
concentration and pesticide contamination. Use of Garvin Brook for trout fishing is reportedly impaired,
however, fishing impairments are not well documented. The primary pollutants in Garvin Brook are
bacteria, sediment, and turbidity. Pollutant sources include nitrogen fertilizers, animal operations1
(mostly dairy), and pesticides. *
13.Water Quality at Start of Project:
Garvin Brook: Turbidity levels exceeded standards (10 and 25 FTUs) 18-61 percent of the time. The FC
standard (200 counts per 100 ml) was also violated 45-89 percent of the time.
Ground water. Of the 80 wells in the original project area tested in 1983 and 1984, about 21% had
nitrate-N levels exceeding the drinking water standard of 10 mg/1. During the summer of 1985,64
additional wells in the expanded ground water watershed were tested for NOs-N. Forty-eight percent of
these wells had N03-N levels exceeding the 10 mg/1 standard. Measurable amounts of Alachlor and/or
Atrazine were found in 6 of 10 wells tested. Levels were below health advisory level.
14. Meteorologic and Hydrogeologic Factors:
a. Mean Annual Precipitation: 33 inches (75 % occurs April-Sept.) ~~
b. USLE 'R' Factor: 160
c. Geologic Factors: The watershed is characterized by karst topography. The bedrock is near-surface
fractured and cavernous Dolomitic limestone and Paleozoic sandstone with sinkhole development.
Sinkholes and rock fissures are direct channels for contaminated agricultural runoff to gain access to the
Prairie du Chien aquifer.
15. Water Quality Monitoring Program:
a. Timeframe: surface water monitoring 1981-1985 and 1990; ground water monitoring 1981-1990
b. Sampling Scheme: In FY1986, the monitoring program shifted its emphasis from surface water to
focus on ground water. Available funding was used predominantly for nitrate and pesticide monitoring
of private farm water supplies. The expanded ground water monitoring effort is intended to track the
effects of BMPs 15 and 16.
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Garvin Brook RCWP, Minnesota
1. Location and Number of Monitoring Stations: The current program emphasis is on monitoring the
original 80 well sites and 3 springs for nitrate in the surface water area and an additional 50 wells for
nitrate and pesticide (alachlor and atrazine) in the ground water area. Monitoring of 196 private water
supplies, including the above 50 wells, was reported in FY1986.
2. Sampling Frequency: ground water-yearly (May or June)
3. Sample Type: automatic
c. Pollutants Analyzed: ground water nitrates and pesticides (alachlor and atrazine) conductivity, pH
and Q
16. Critical Areas:
a. Criteria: The Agricultural Non-point Source Pollution Model I (AGNPS I) computer simulation
model, which predicts runoff rate and volume, eroded and delivered sediment, total nitrogen, total
phosphorus, and chemical oxygen demand was used to evaluate the surface watershed and designate
priority areas. Critical area affecting ground water was determined by identifying excessive nitrogen and
herbicide application areas and the location of sinkholes and abandoned wells.
b. Application of Criteria: Critical areas were substantially redefined in 1985 using new information
about the ground water problems both within and outside of the original surface watershed project area.
Redefinition of critical areas has resulted in expansion of critical acres needing treatment. These acres
are now defined to be the cropland acres annually planted to row crops within the critical area and any
sinkholes and abandoned wells. Only 1,423 acres reported as treated in the previously defined critical
area meet the new definition of acres needing treatment. Animal units are known to be significant
contributors to the pollution problem. However, animal waste systems were not accepted by farmers due
to depressed economic conditions in the project area.
17. Best Management Practices:
a. General Scheme: BMPs 2^3,4,5,9,10,15,16 are considered important. This project has increased its
emphasis on BMPs 15 and 16, including split nitrogen application, improved manure storage and
improved calibration of manure and fertilizer spreading equipment.
b. Quantified Implementation Goals:
treat 8,095 of 10,793 critical acres (75%) '. '
treat 33 of 44 dairies
fill 59 of 79 sinkholes
split N application on 8,036 of 10,714 acres
pesticide management on 15,169 of 20,255 acres
obtain 94 contracts
c. Quantified Contracting/Implementation Achievements:
The project has not reported the location of BMP activities with respect to critical areas. Quantified
achievements are as follows:
9 sinkholes filled
7,590 acres treated with split N application
14,812 acres treated with pesticide management
69 RCWP contracts
d. cost of BMPs: (from RCWP 4 table, ref. 12)
BMP
1 perm. veg. cover
2 animal waste mgmt
3 stripcropping
5 diversions
6 grazing land prot.
9 conservation tillage
10 stream protection
12 sediment retention,
erosion control struc.
(continued on next page)
Ave. Fanner
Share f<1
55/ac.
13,150 ea.
4.80/ac.
0.55/rt.
0.35/ft.
375/ac.
3.75/ac.
1,875/ac.
Ave. RCWP
Share (S\
175/ac.
39,350 ea.
14.50/ac.
1.70/ft.
1.05/ft.
1,125/ac.
11.25/ac.
5,625/ac.
Total Cost CS1
230/ac.
52^00 ea.
19.30/ac.
2^5/fL
1.40/ft.
1,500/ac.
15/ac.
7,500/ac.
2.75
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14 tree planting 3,425 ea. 10,275 ea. 13,700 ea.
15 fertilizer mgrnt 50/ac. ISO/ac. 200/ac
16 pesticide mgrnt. 5/ac. IS/ac. 20/ac
e. Effectiveness of BMPs: Under BMP IS (split N application), the amount of total N applied that was
applied early (fall or early spring) decreased from 64% for the 1985 growing season to 59% for the 1986
growing season (ref. 12). There was also a 20% reduction in the total N applied by farmers using split
application for the 1986 growing season.
Effectiveness of BMPs for controlling sediment, phosphorus, nitrogen, and COD reduction in the
project area is being evaluated by AGNPS I. The model is also being used to illustrate how livestock
producers, many of whom grow corn, would benefit by managing their manure as a fertilizer resource.
18. Water Quality Changes:
No trends in surface water quality are reported by the project. The project does not intend to evaluate
surface water quality trends until 1990. The expected effects of land treatment on surface water quality
are currently being modeled with AGNPS I. There is evidence of increasing NO3-N levels over four
years of ground water data collected from the 80 original wells in the surface water watershed from 1983
to 1986.
19. Changes in Water Resource Use:
Population growth in Winona County is slow, 2.1% from 1980 to 1984, therefore, ground water use has
probably changed little since RCWP began. Garvin Brook outlets to Pool 5A of the Upper Mississippi
River. Total recreational use of Pool 5A is about 159,000 users annually. However, the contribution of
sediment to Pool 5A from Garvin Brook is very small. Fishing use of the project area does not appear to
have changed since RCWP began.
20. Incentives:
a. Cost Share Rates: 90 percent (75 percent from RCWP and 15 percent from the Winona County Board
of Commissions) ' . ; .
b. $ Limitations: $50,000 RCWP funds plus $6,000 from Winona County per contract
._ c. Assistance programs: Extension service did nitrogen budgets for ^MP-15 and included the use of
legumes, and manure; public meetings; newsletter; split-N application demonstration farm; crop
scouting; free soil testing.
21. Potential Economic Benefits:
a. On-farm: not evaluated
b. Off-farm:
1) Reaction: 0
2) Water supply: $35,000 - $130,000 per year
3) Commercial fishing: 0
4) Wildlife habitat: unknown
5) Aesthetics: unknown but positive
6) Downstream impacts: unknown but positive .
III. Lessons Learned
Expensive structural BMPs (e.g. BMP-2) are difficult to sell in times of depressed economic conditions even
with cost sharing as high as 90%. Lower cost manure management alternatives should have been promoted
from the beginning of the project.
Critical area for treatment of surface water may differ from ground water critical area.
Development of nitrogen budgets for farmers' fields (accounting for N from manure and legumes) not only
keeps excess quantities of commercial fertilizer from being available for leaching, but also allows the farmer
to optimize the use of N from manure and legumes.
Off-farm benefits from improving or maintaining ground water quality are potentially large.
2.76
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Garvin Brook RCWP, Minnesota
IV. Project Documents
1. Garvin Brook Rural Clean Water Project Application. 12 p.
2. Minnesota Soil and Water Conservation Board. March 1982. Minnesota's Soil and Water Conservation
Program: A Process of Gaining Ground. Box 19, Centennial Office Building, St. Paul, Minnesota 55155.
56 p.
3. Balaban, NJH. and B.M. Olsen. 1984. Geologic Atlas Winona County, Minnesota. County Atlas Series
Adas C-2. Minnesota Geological Survey. University of Minnesota, St. Paul.
4. Annual Progress Report: Garvin Brook Rural Clean Water Project, Winona County, Minnesota. Novem-
ber 1982.
5. Annual Progress Report: Garvin Brook Rural Clean Water Project, Winona County, Minnesota. Novem-
ber 1982.
6. Payne, G.A.1983. Streamflow and Suspended-Sediment Transport in Garvin Brook, Winona County,
Southeastern Minnesota-Hydrologjc Data for 1982. U.S. Geological Survey. Open-File Report 83-212.
St. Paul, Minnesota. 22p.
7. Annual Progress Report: Garvin Brook Rural Clean Water Project, Winona County, Minnesota. Decem-
ber, 1984.20 p. .
Appendix A. Agreement Between the Agricultural Stabilization and Conservation Service and the Min-
nesota Pollution Control Agency.
Appendix B. Garvin Brook Watershed Water Quality: General Monitoring for the Rural Clean Water
Program. 1984 Annual Report. Minnesota Pollution Control Agency.
Appendix C. RCWP Garvin Brook Project Technical Report Update. September, 1984.29 p.
Appendix D. BMP - Fertilizer Management -Split Application.
Appendix E. Forms: ACP-305, RCWP-3, RCWP-5, RCWP-7, Contract locations.
Appendix F. Questionnaire
Appendix G. Summary of Trout Stream Habitat Improvement. 2 p.
Appendix H. Project Coordinator - Position Description. 1 p.
8. Annual Progress Report: Garvin Brook Rural Clean Water Project PN16, Winona County, Minnesota.
November 1985.
9. Garvin Brook Watershed Detailed Action Plan. April 1985.4 p. .
10. Supplement to Plan of Work. April 1985.3 p.
11. Method Used to Determine Nitrate Loading. April 1985.2 p.
12. Annual Progress Report: Garvin Brook Rural Clean Water Project PN16, Winona County, Minnesota.
November 1986.
V. NWQEP Project Contacts:
Water Quality Monitoring Land Treatment/Technical Assistance
David Wall MarkKunz
520 Lafayette Road USDA - SCS
Minnesota Pollution Control Agency Box 38
St. Paul, Minnesota 55155 Lewiston, Minnesota 55952
Telephone (612) 296-7360 Telephone (507)523-2171.
2.77
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LONG PINE CREEK - RCWP17
Brown and Rock Counties, Nebraska
MLRA: G-66
H.U.C. 101500-04
I. Project's Major Contributions Toward Understanding the Effectiveness of NPS
Control Efforts
The Nebraska RCWP combines an approach to both ground water and surface water problems. This project
has potential to demonstrate effects of nutrient and pesticide management, irrigation water management as
BMPs for surface and ground water quality protection.
II. Project's Characteristics and Results
1. Project Type: RCWP
2.Timeframe:1981-1995
i
3. Total Project Budget: (excluding monitoring and farmers' contributions) $2,233,231 (RCWP-5, ref. 13)
If the secondary RCWP-5 is approved, $4,297,075 would be budgeted. This would include additional
large sediment control structures.
4. Cost Share Budget: (ref. 13)
a. Funds Allocated: $1,386,975 ($3,317,475 if secondary RCWP-5 approved)
- b. Total Farmers' Contributions: $394,825 (as of 1995) ($414,325 if secondary RCWP-5 approved)
»' "
5. Water Quality Monitoring Budget: $297,850 "" :
6. Watershed Area: 293,100 acres
7. Project Area: 80,000 acres
8. Critical Area: 54,212 acres
9. Project Land Use: -
% critical
use area
cropland 25
- irrigated corn (23)
- irrigated alfalfa (2)
pasture/range 70
woodland 0
urban/roads 1
other 4
There are 130 farm or ranch units in the project area. Approximately 90 are thought to be critical.
10. Animal Operations in Project Area:
a. Dairy: 4 farms with 50 cows ave. (200 a.u.)
b. Beef: 11 farms with 2500 cattie (23,375 a.u.)
c. Swine: 1 farm with 500 ni
-------
Long Pine Creek RCWP, Nebraska
11. Water Resource Type: surface streams and ground water
Surface water: Long Pine Creek (drainage = 293,100 acres, average aggregate flow = 150 cfs at mouth);
major tributaries are Bone Creek, Sand Draw, and Willow Creek.
12. Water Use and Impairments:
Surface water The Long Pine Creek Recreation Area, a state park, is used by over 8,500 people each
season. The primary water use impairments are to recreation and fishing. Long Pine Creek is the longest
self-sustaining trout stream in the state. Relic populations of three species of fish, threatened in
Nebraska, can be found in the streams in this area. The primary pollutants are: sediment, bacteria, and
nutrients. Streambank erosion is the primary source of sediment. This is exacerbated by peak flow events
and excessive irrigation water imported from the basins. A secondary factor contributing to the
Streambank erosion is overgrazing. The other primary pollutants, bacteria and nutrients, are from
feedlot storm runoff and agricultural fertilizers.
Ground water Ground water is used for irrigation, stock watering and domestic and municipal water
supply throughout the project area. A stable population of about 3,200 people live within the project
area. There is potential for degradation of the drinking water supply from high nitrate and pesticide
contamination.
13. Water Quality at Start of Project:
See Reference #15 for a complete baseline documentation (1979-1985). Suspended solids data from 9
sample dates from July, 1979 to July, 1980 show that two tributaries, Bone Creek and Sand Draw,
contributed greatly to the turbidity problems in Lower Pine Creek (LP8). Station LP7 is located
upstream of the confluence of Sand Draw and Bone Creek with Long Pine Creek. Station LP8 is below
this confluence. Total suspended solids (TSS) at LP8 were fairly high, but were less at up stream LP1,
LP5,orLP7.
Surface monitoring, April, 1980 to September 23,1981 (n = ~ 13):,
. Tot. Su*. Solids (mg/1) Fecal Coliform (#/100mI)
Station*^ . Mean Ran^e Geometric Vfean
Long Pine (LP1) 13 1- 32 315
Long Pine (LP5) 11 1-40 .35
Long Pine (LPT) 20 1-50 90
Long Pine (LP8) 70 9-220 400
Bone Creek (BN) 1 1-1 670
Bone Creek (BN1) 95 1-620 4550
Bone Creek (BN2) 330 1-3590 1680
Bone Creek(BN3) 640 1-4360 1180
Sand Draw (SN1) 10 1-30 410 .
Sand Draw (SN2) 130 1-1000 500
*LP1, LP5, and LP7 are above confluences with tributaries. LP8 is below confluences.
b. Ground water: 23 domestic wells monitored in 1977-1978 show that 17 percent exceeded 10 mg/1
nitrate-N
14. Meteorologic and Hydrogeologic Factors:
a. Mean Annual Precipitation: 21.5 inches; about 14.5 inches of irrigation water are needed to
supplement precipitation to grow corn
b. USLE 'R' Factor: 100
c. Geologic Factors: The watershed is underlain by shale and sand stone. Topography is diverse, ranging
from nearly level to steep. Most of the watershed is covered by a blanket of eolian sand material. Soils in
the range area are predominantly silts and sands.
15. Water Quality Monitoring Program:
a. Timeframe: surface - July 1979 - September 1984 -1995 groundwater ~ 1979 -1995
b. Sampling Scheme: . '.
2.79
-------
1. Location and Number of Monitoring Stations: Baseline surface monitoring at 11 sites was collected
from July 1979 to September 1984. This is considered the pre-implementation phase. Except for station
LP8 (which will be sampled once per month after September 1984), surface water monitoring has been
discontinued until the last two years of the project (1994-1995). Fish were collected between April 1981
and June 1984.
Irrigation and domestic wells are sampled once per year in July or August (when the aquifer is used for
irrigation)." """"""
2. Sampling Frequency: Surface water monthly for baseline samples, composite samples during runoff
events, fish were collected 2-3 times per year. Ground water Once per year in July or August
(1982-1985)
3. Sample Type: grab
c. Pollutants Analyzed: (1) Surface water all 11 sites are sampled for TSS, FC, DO, and conductivity.
Seven of the sites include macroinvertebrate and periphyton sampling. Diurnal water temperatures are
also recorded. (2) Ground water Nitrate-N and pesticides.
d. Flow Measurements: Runoff event data is collected at six surface sites. Stream discharge is recorded.
16. Critical Areas:
a. Criteria: high erosion rates and proximity to waterways
b. Application of Criteria: consistent - Contracts are primarily being applied to the critical areas.
17. Best Management Practices: *
a. General Scheme:
Focus is on sediment control structures to trap sediment and control stream flow (BMP-12). The project
is currently emphasizing on-site components such as irrigation water management (BMP-13). One major
emphasis for this BMP is to install irrigation tailwater recovery (re-use) systems to minimize the total
water usage, thereby reducing infiltration to ground water with ultimate release in the creek. A
. secondary storage reservoir j(BMP-13) is being constructed using pooled funds from 10 RCWP
, cooperators within the Ainswqrth irrigation district and is scheduled for completion in fall 1987. The
reservoir will save 2,000 acre feet of water annually for 8,000 acres of cropland aritt reduce the amount of
irrigation waste water delivered to the creeks with an associated reduction in sediment delivered. Other
BMPs include fertilizer and pesticide management (BMP-15, BMP-16), diversion systems (BMP- 5),
grazing land protection systems (BMP-6), stream protection (BMP-10), permanent vegetative cover on
critical acres (BMP-11), Permanent vegetative cover (BMP-1), waterway system (BMP-7), Cropland
protective system (BMP-8), conservation tillage (BMP-9), and tree planting (BMP-14).
b. Quantified Implementation Goals: 75 percent of the critical area
c. Quantified Contracting/Implementation Achievements:
% under contract
. Location fend FY86> % implemented
project area S3 not reported
critical area ' 78 not reported
critical area farms 94 not reported
project area farms 85 not reported
d. Cost of BMPs: (from RCWP Table 4, Ref. 16)
Ave. Farmer Ave. RCWP
BMP Share (SI Share m Total Cost (SI
1 perm. veg. cover 20/ac. 60/ac. 80/ac.
2 animal waste mgmt. 3,750 ea. 11,250 ea. 15,000 ea.
5 diversions 0.30/ft. I/ft. IJO/ft.
6 grazing land prot. 0.75/ac. 2.25/ac. l_50/ft.
7 waterways 0.30/ft. I/ft. 1.30/ft.
Z crcplar.cJ pro:. 5/ac. 0 5/ac.
9 conservation till: 3.25/ac. 9.75/ac. 13/ac.
10 stream protection 0.40/ft. 1.10/ft. UO/ft.
11 perm. veg. on crit. area 125/ac. . 375/ac. 500/ac.
12 sediment retention 300 ea. ' 900 ea. 1,200 ea.
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Long Pine Creek RCWP, Nebraska
13 irrigation/water mgmt. 2,500 ea. 7,500 ea. 10,000 ea.
14 tree planting 75/ac. 225/ac. 300/ac.
15 fertilizer mgmt. 0.33/ac. 1/ac. 1.33/ac.
16 pesticide mgmt. 0-33/ac. Vac. 1.33/ac.
e. Effectiveness of BMPs: not documented
18. Water Quality Changes:
The surface and ground water samples reported for 1979 to 1984 are considered pre-implementation
(ref. IS). However, an increasing trend in nitrate concentrations in some of the irrigation wells has been
identified but no change has been observed in the domestic wells.
19. Changes in Water Resource Use:
Grouhdwater well samples in the project area showed 8.6% of the wells sampled have nitrate levels
above federal standards. As a result of high nitrate levels, some well water is blended with lower nitrate
level water to reduce health risks. Total domestic groundwater use has not changed since RCWP began.
Recreational use of the project area has been steady since 1976 and fishing continues to be impaired in
the project area by high sediment levels.
20. Incentives:
a. Cost Share Rates: 75%
b. $ Limitations: $50,000 per farmer
c. Assistance Programs: SCS develops water quality plans and provides technical assistance. The
Extension Service has a 50 acre demonstration farm to display conservation tillage. There are IPM
meetings and 4,519 acres were scouted in 1985.
d. Other Incentives or Regulations: RCWP cost share improvements to the feedlots have not been
approved in the past because they are considered point sources.
21.Potential Economic Benefits: -
a. On-farm: not evaluated
. b. Off-farm:
1) Recreation: $5,000 - $50,000 per year.
2) Water Supply: $15,000 - $50,000 per year.
3) Commercial Fishing: 0
4) Wildlife Habitat: unknown
5) Aesthetics: unknown but positive
6) Downstream Impacts: unknown
III. Lessons Learned
Opportunities exist to reduce fertilizer use by transferring manure from large feedlots (defined by the state
as point sources) to RCWP participating farms. Cost-shared improvement of feedlots has not been ap-
proved, however, in the past because of their legal designation as point sources.
. The ground and surface water monitoring Program used in this project aids in prioritizing portions of the
watershed for critical area definition. Emphasis on fertilizer and pesticide management is a key factor in deal-
ing with ground and surface water problems simultaneously.
IV. Project Documents
1. Long Pine Creek Nebraska: A Rural Clean Water Program Application. 1981.
2. Plan of Work - Long Pine Creek RCWP Project. October 1981.
3. Monitoring and Evaluation Plan. 1981.11 + p.
4. Report to Local Coordinating Committee Long Pine Creek Rural Clean Water Program. October 23,
1981. Program Planning Section, Nebraska Department of Environmental Control. 30 p.
2.81
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5. NDEC Long Pine Intensive Survey Water Quality Update. January 22,1982.
6. Jensen, D. January 1982. An Index for Assessing the Water Quality of Nebraska Streams. Program Plans
Section, Water and Waste Management Division, Department of Environmental Control, State of
Nebraska. 57 p.
7. Long Pine Creek Rural Clean Water Program Annual Report:FY-198£
8. Long Pine Creek RCWP Plan of Work (FY1983).
9. Long Pine Creek Rural Clean Water Program Annual Report:FY 1983.
10. Long Pine Creek RCWP Plan of Work (FY 1984).
11. Long Pine Creek Rural Clean Water Program Annual Report:FY 1984.
12. Long Pine Creek RCWP Plan of Work (FY 1985).
13. Long Pine Creek Rural Clean Water Program Annual Report: FY 1985.
14. Long Pine Creek Rural Clean Water Program: Plan of Work (FY 1986), revised November 1985.32 p.
15. Maret, T. December 1985. Water Quality in the Long Pine Rural Clean Water Project 1979-1985.
Nebraska Department of Environmental Control, P.O. Box 94877 - Statehouse Station, Lincoln, NE
68509,4877.194 p.
16. Long Pine Creek Rural Clean Water Program Annual Report: FY1986.
17. Long Pine Creek RCWP: Plan of Work (FY1987), revised November 1986.30pp.
V. Project Contacts:
Water Quality Monitoring
" Don Zaroban or Terry Macret - Surface Water
Dave Chambers - Ground Water
Nebraska Dept. of Environmental Control
301 Centennial Mall South
P.O. Box 94877
State House Station
Lincoln, Nebraska 68509-4877
tel. (402) 471-4432/4700
Land Treatment
Jerry Hardy or Diego Ayala
Soil Conservationist
USDA-SCS
Ainsworth Field Office
Ainsworth, Nebraska 69210
tel. (402) 387-2242
Information and Education
Bud Stolzenburg
Extension Agent
Long Pine Creek RCWP
BKR Cooperative Extension Service
Brown County Courthouse
Ainsworth, NE 69210
tel. (402) 387-2213
Land Treatment
RayStenka
ASCS Office
Ainsworth Field Office
R.R.2
Ainsworth, Nebraska 69210
tel. (402)387-2242
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TILLAMOOK BAY- RCWP18
Tillamook County, Oregon
H.U.C. 171002-03
I. Project's Major Contributions Toward Understanding the Effectiveness of NPS
Control Efforts
This project has made important contributions concerning the effectiveness of animal waste management for
improving water quality at the watershed Jevel. To date, the water quality monitoring shows a 40-50% reduc-
tion in mean fecal coliform concentration, attributed to bringing approximately 60% of the animal waste
produced in the project area under best management. A more thorough knowledge of the marginal water
quality benefits of increased manure management should be gained from this project as the total treatment
approaches the expected 90% level. The project appears to be cost-effective on a water quality basis.
Results from this project indicate that projects that address clearly defined impairments to high-valued
recreational resources are most likely to be cost-effective.
II. Project's Characteristics and Results
1. Project Type: RCWP
2. Timeframe:1981-1991
3. Total Project Budget (excludes water quality monitoring funds and farmers' contributions): $5,186,715
44 Cost Share Budget:
a. Funds Allocated: $4,383,278
. b. Total Farmers'Contributions: $2,191,600
5. Water Quality Monitoring Budget: $344,000 (approx.)
6. Watershed Area: 363,520 acres
7. Project Area: 23,540 acres
8. Critical Area: 9,200 acres
9. Project/Watershed Land Use:
% project % watershed
use . area area
cropland 0 0
pasture/range 98 7
woodland 1 89
urban/roads 1 ' 2
10. Animal Operations in Project Area:
a. Dairy: 115 farms with average of 75 cows (8,625 a. u.)
b. Beef: 95 farms with average of 75 cattle (6,056 a. u.)
11. Water Resource Type: streams, estuary, Tillamook Bay
2.83
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12. Water Uses and Impairments:
Water resources in the project area are used primarily for domestic consumption, recreation and
commercial shellfishing. Sport fishing throughout the watershed is a popular activity. Recreational
clamming and angling in Tillamook Bay account for approximately 70,000 user-days. Commercial
shellfishing in the Bay is a $1-5 million industry (annual gross sales).
The shellfish industry is impaired by excessive fecal coliform levels in the bay. Shellfish harvesting has
been closed down frequently during periods of high FC contamination and health hazards exist in
tributaries where water contact recreation is popular.
13. Water Quality at Start of Project:
The FC concentration standard for commercial shellfishing waters is a log mean of 14/lQOml with no
more that 10% of samples allowed greater than 43/100ml. The standards were consistently violated in
Tillamook Bay following moderate to large runoff periods. .
14. Meteorologic and Hydrogeologic Factors:
a. Mean Annual Precipitation; 90 -140 inches
b. USLE'R'Factor: 50
c. Geologic Factors: The watershed topography is extremely diverse, from the Coast Range in the east
followed by gently to steeply sloping rocky uplands, deeply incised canyons to flat to gently rolling
floodplains. The coastline is largely sand dunes, beaches and sedimentary rock outcrops alternating with
occasional rugged headlands of volcanic rock. Slopes range from 0 to 90%. Soils are varied, ranging
from deep, well-drained coarse-textured bottomland soils with high permeability and slow runoff to
well-drained, fine-textured upland soils with moderate permeability and medium to rapid runoff.
15. Water Quality Monitoring Program:
a. Timeframe: 1975 - 1990 .
b. Sampling Scheme:
1. Location and Number of Monitoring Stations: five-small tributary stations; five major river stations,
fourteen bay stations.
2. Sampling Frequency: varies, usually monthly, some intensive wet weather samplings
3. Sample Type: grab. : -
c. Pollutants Analyzed: fecal coliform bacteria /'
d. Flow Measurements: Flow measurements accompany all samples since 7/83. Before then only 2
stations have relatively complete flow records.
e. Other salinity and turbidity measurements taken in Bay
16. Critical Areas:
a. Criteria: distance to watercourse, present manure management practices; designated subbasins
b. Application of Criteria: Criteria used to prioritize dairy farms for cost sharing.
17. Best Management Practices:
a. General Scheme: All RCWP cost share funds have been focused on BMP-2, Animal Waste
Management. Unique BMP components are used in the animal waste systems such as: roofing and
guttering of manure storage areas, tidal dikes to prevent high tides from spilling into past'jres, and
pasture drainage systems to prevent water from standing in pastures where manure is applied.
b. Quantified Implementation Goals: 109 dairies; 8,723 acres
c. Quantified Contracting/Implementation Achievements:
location % under contract % implemented
project area 37 22
critical area . 93 52
cr«!'ca! area 94 54
farms
project area ~ 52 .36
farms
2.84
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Tillamook Bay RCWP, Oregon
ACostofBMPs:
Ave. Fanner Ave. RCWP
BMP Shared Share fS"> Total Cost fSI
2 Animal waste mgmt. 5,450-6,300 ea. 16,300-18,900 ea. 21,750-25,200 ea.
2 Subsurface drainage 140/ac. 420/ac. 560/ac.
2 curbing/guttering/
diversion 0.50-2.00/ft 1.44-5.90/ft 1.94-7.90/ft
10 fencing 0.13/ft. 0.40/ft 0.53/ft
IS.Water Quality Changes:
NWQEP analysis indicates that annual log-mean fecal coliform concentrations in both the streams and
Bay have decreased significantly since BMP implementation, especially when variations in streamflow
and Bay salinity are accounted for. See Tables 1 and 2 below. :
Table 1. Tillamook Log Mean Fecal Coliform Concentrations 1975-1981 vs. 1982-1985.
Sampling Log Mean Log Mean
Sites 1975-1981 1982-198S % Reduction
Bay 1 493 22.4 55
Bay 2 55 J 43 2. 22 +
Bay 3 823 46J 44
Bay 4 111.0 53.3 52 +
Bay5a 131.0 313 76'
Bay 6 36.7 20.6 44
Bay 7 33.1 14 5 56 +
Bay8 203 11.6 44
Bay 9 33.3 1Z7 62'
Bay 10 . 193 16.1 . » "
Bay 11 24.5 .. 13.5 45 +
Bay 12 . 153.0 123.0 20
Bay 13 - 23.S 11.7 50 +
Bay 14 . ' 49.3 20.0 59 +
Kilchis River 87.0 61.0 30
Miami River 276.6 . 60.7 78
Track River 168.0 63.4 _ 62'
Tillamook River 387.0 162.0 " 58
Wilson River 147.0 68.6 53'
* Statistically significant at p =» 0.05
+ Statistically significant at p - 0.10
19. Changes in Water Resource Use:
Due to the nonpoint source control project and associated changes in criteria for closing the bay to
commercial shellfishing, permanent closure does not appear likely. Commercial oyster production has
been steady after low production in 1979 and 1980. Recreational clamming is also likely to be affected
by reduced bacteria levels. However, no recreational use figures are currently available to indicate
changes attributable to RCWP.
20. Incentives:
a. Cost Share Rates: 75% on BMP-2
b. $ Limitations: $50,000 per landowner. Many animal waste management systems cost more than
$66,670. Farmers' share may, therefore, exceed 33%.
c Assistance Programs: ACP cost sharing has also been used to treat some problems. ACP has a limit of
$3,500/yr. for animal waste management systems. '
d. Other Incentives or Regulations:. Oregon allows a 50% tax credit for conservation measures which can
be spread over 10 years. Oregon also has regulations which allow the state to fine agricultural operations
that are obvious pollution sources.
2.85
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21. Potential Economic Benefits:
a. On-farm: not evaluated
b. Off-farm:
1) Recreation: $40,000 - $530,000 per year.
2) Water Supply: 0
3) Commercial Fishing: $20,000 - $50,000 per year.
4) Wildlife Habitat: unknown
. 5) Aesthetics: unknown
6) Downstream Impacts: 0
III. Lessons Learned
1. Animal waste management can improve water quality (reduced mean fecal coliform concentrations) when
implemented for the critical sources in a 23,000 acre project area.
2. Some measurable indicator of hydrologic state such as precipitation, stream flow, or salinity should be in-
cluded in water quality sampling programs to identify water quality trends.
3. Thorough records of land treatment accomplishments are essential to attribute water quality trends to
BMP implementation.
4. A pre-BMP water quality data base of at least 2 years duration greatly facilitates documenting water
quality effects of BMPs.
5. A high level of farmer participation can be achieved when agricultural and water quality agency personnel
work together closely on designing and publicizing the program.
6. The combination of financial incentive and environmental regulation is effective in achieving high rates of
participation.
7. Agricultural NPS control projects can be very cost- effective if they reduce an impairment to. a water
resource with high recreational value.
8. Recreational benefits from improved water quality are likely to outweigh commercial fishing benefits even
in a region where impaired commercial fishing is the primary concern. -
IV. Project Documents: _
1. Jackson, J. E. and E. A. Glendening. Oregon Dept. of Environmental Quality. Tillamook Bay Bacteria
Study: Fecal Source Summary Report." January 1982. ^
2. Tillamook County SWCP and Tillamook Bay Water Quality Committee. January 1981. Tillamook Bay
Drainage Basin Agricultural Nonpoint Source Pollution Abatement Plan".
3. Tillamook Bay RCWP Application. Tillamook County, Oregon. January 1981.
4. Tillamook Bay RCWP. Plan of Work. Tillamook County, Oregon. 1982.
5. Tillamook Bay RCWP Annual Report 1982.
6. Tillamook Bay RCWP Annual Report 1983.
7. Tillamook Bay RCWP Annual Report 1984.
8. Tillamook Bay RCWP Annual Report 1985.
9. Tillamook Bay RCWP Annual Report 1986.
10. Maas, R.P., M.D. Smolen, J. Spooner and A. Patchek. 1987. Benefit/cost analysis of nonpoint source con-
trol in the Tillamook Bay, Oregon watershed. Lake and Reservoir Management J., Vol. Ill, pp. 157-162.
2.86
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Tillamook Bay RCWP, Oregon
V. NWQEP Project Contacts
Water Quality Monitoring
Andy Schaedel
Oregon Dept. of Environmental Quality
811 S.W. Sixth Ave.
Portland, Oregon 97204
teL (503) 229-5878
Land Treatment/Technical Assistance
Mr. John Van Calcar
ASCS
U.S. Dept. of Agriculture
Portland, Oregon 97204
teL(503) 221-2741
2.87
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CONESTOGA HEADWATERS - RCWP19
Lancaster County, Pennsylvania
MLRA: S-148
H.U.C. 020503-06
I. Project's Major Contributions Toward Understanding the Effectiveness of NPS
Control Efforts
Project results come from two, intensively monitored field sites. Results are summarized below:
L. Terraces may reduce sediment and nutrient loadings to surface water by reducing the volume of runoff,
but in permeable soils with excess manure, terraces appear to increase nitrate transport to ground water and
may increase dissolved nutrient concentrations in surface runoff.
2. In this project manurial nitrogen generally exceeds crop needs. Thus, water quality benefits from animal
waste storage (e.g^ improved timing of applications) are partially offset because nitrogen that could have
been volatilized in storage is conserved and applied as a sludge to the soil.
3. Nutrient management BMPs (soil and manure testing, proper matching of application rates, and timing to
match plant needs) can reduce both ground and surface water nitrogen losses.
II. Project's Characteristics and Results
1. Project Type: RCWP Comprehensive Monitoring and Evaluation
2. Timeframe: 1981-1991 -
3. Total Project Budget: (excludes water quality monitoring funds and farmers' contributions) $2,015,513
4. Cost Share Budget:
a. Funds Allocated: $1,448,000 T
b. Total Farmers' Contributions: $285,264 through 1986
5. Water Quality Monitoring Budget: $1310,000
6. Watershed Area: 110,000 acres
7. Project Area: 110,000 acres
8. Critical Area: 16,000 acres
9. Project/Watershed Land Use:
% project % watershed
cropland 44 44
pasture/range 16.4 16.4
woodland 25 25
urban/roads 14 14
other 14 14
10. Animal Operations in Project Area: .
a. Dairy: 445 farms with average of 50 cows (22,098a.u.) and 39 heifers (8,722 a.a)
b. Beef: J.nOQ heef «ml« fnrmc anth avorum nf SI /v>»»1a M* 9« - A
"'" "' ~ f-i -> V ^^--^ -~ ^, ^.' . _ . - _ iH.vr.t.^.. -w
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Conestoga Headwaters RCWP, Pennsylvania
11. Water Resource Type: streams, ground water
12. Water Use and Impairments:
Public water supplies originate in the project area for approximately 175,000 people plus 2,000
commercial industries within and downstream from the Conestoga Headwaters (ref. 8). Water resources
also support fisheries and contact recreation. Streams used for these activities are impaired by bacteria
and sediment. Nitrates impair potable ground water supplies.
13. Water Quality at Start of Project: Two-thirds of wells had nitrate concentrations above 10 mg/1.
Maximum concentrations observed were over 100 mg/L
14. Meteorologic and Hydrogeologic Factors:
a. Mean annual precipitation: 42 inches
b. USLE'R'factor 175 .
c. Geologic Factors: The northeastern two-thirds of the project area lies in the Triassic Lowlands
underlain by .conglomerate, shale, sandstone and diabase. Average depth to the water table in this area is
15 to 35 feet. The southwestern one-third of the project area is in the Conestoga Valley underlain by
carbonate rocks, where average depth to the water table is 20 to 50 feet. Throughout the project area
soils are mainly well drained, deep or moderately deep silty loams that provide ample penetration of
surface runoff to groundwater supplies.
15. Water Quality Monitoring Program:
a. Timeframe: 1981-1991
b. Sampling Scheme:
1. Location and Number of Monitoring Stations: One 3000 acre watershed with 2 stream gauge sites and
5 additional baseflow sampling sites as well as 5 ground water sites. 2 field sites each with one surface
outlet site and seven ground water monitoring wells. '
2. Sampling Frequency:
L Gauged sites: all major storms
: ii. Baseflow sites: every 3~ weeks . "
iii. Ground water sites: quarterly (small watershed) monthly (field sites)
3. Sample Type: Grab and automatic
c. Pollutants Analyzed: TSS, nutrients, herbicides
d. Flow Measurements: continuously at gauged sites
16. Critical Areas:
a. Criteria: Small watershed experimental area, and land within carbonate area
b. Application of Criteria: Adherence to the criteria has been undermined by the lack of farmer
participation; however, I&E efforts have been focused to the identified critical areas.
17. Best Management Practices:
a. General Scheme: Revised implementation goals include securing 90 contracts to treat about 6,300
acres. New emphasis is on educational programs and nutrient management plans to encourage better
nutrient management instead of contracts with cost sharing.
b. Quantified Implementation Goals: The project was revised to emphasize management of animal waste
and reduction of commercial fertilizer use.
c. Quantified Contracting/Implementation Achievements (as of September 30,1986):
location % under contract °7e implemented
project area 5.3 3.8
critical area 36.0 11.4
critical area farms 25.0 not available
project area farms " 6.2 not available
2.89
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cLCbstofBMPs:
Average Installation Cost of RMPg
Earthen basin
(6 month, manure storage structure) $12,000
Steel tank
(6-month manure storage structure) $39,000
Contour strip cropping GO/acre
Winter cover and residue mgmt SO to S20/acre
Terrace systems S56/acre
Diversion systems
( with 20 ft wide filter strip) 510/acre
Sod waterway systems $7/acre
e. Effectiveness of BMPs: :
Results of CREAMS model on corn silage following corn silage, 5 percent slope, 30 tons/acre manure
application spread daily:
Nloss Nloss P loss
Soil Ground Surface
erosion water water
tons/acre ... |b*/acre- - . . .
No BMPs 11 50 68 31
Terraces 3 52 29 12
Reduced-till 6 SO 45 20
No-till 3 45 33 14
Multiple BMPs 1 54 14 5
In general, the project believes that nutrient loading reductions will be achieved by reducing nutrient
application rates. " . . '
f. Cost-effectiveness of BMPs:
Results of modeling continuous corn-grain on a 5 percent slope:
SAon of S/lb. of J/lb. of "
soil saved N saved P saved
Terraces 457 1.10 2.12
Animal waste systems NA 0.67 1-50
Diversions 2.06 0.41 0.78
Contouring 1.66 - QJ3 0.76
Crass waterways 0.99 0.24 0.45
Conservation tillage .76 0.17 034
18. Water Quality Changes:
Thus far, BMP implementation at the project area or small watershed level has not produced significant
water quality changes. BMPs have been applied on one field site and their effect on ground and surface
waters is being observed.
IP.Changes in Water Resource Use:
Only minor changes in water resource use are anticipated since the number of BMPs installed is small
relative to the large area affected by the nonpoint source pollution. Localized improvements in
individual drinking water wells may occur, however, these improvements will be isolated.
20. Incentives:
a. Cost Share Rates: 50% on animal waste management and soil/manure testing
b. $ Limitations: $ 50,000 maximum
c. Assistance Programs: Project has hired 2 nutrient management specialists and uses a mobile
soil/manure testing laboratory
2.90
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Conestoga Headwaters RCWP, Pennsylvania
21. Economic Benefits:
The educational gains associated with nutrient management practices have enhanced the work of the
Chesapeake Bay and other regional water quality programs. In the long run this may be the greatest
benefit from this project. Some on-site benefits are possible from practices that reduce runoff and
conserve nutrients for crop production. Off-site benefits associated with expected minor water quality
improvement include (discounted SO year):
Surface water improvements - $65,000 to $200,000
Groundwater improvements $0 to $85,000
Total improvements $65,000 to $285,000
III. Lessons Learned:
High cost share rates are needed to gain farmer participation when manure nutrients exceed crop needs and
manure has no value to the farmer.
There may be trade-offs between BMPs designed to improve surface and groundwater, complicating treat-
ment of impaired uses if both surface water and groundwater are impaired.
Conservation tillage, nutrient management, and grass waterways are the lowest cost alternatives. Extensive
implementation of these over other practices are expected to produce the most water quality results for a
given expenditure.
1. Where manure nutrients exceed crop requirements, waste management systems must be designed to
reduce the burden On surface and ground waters. Volatilization may be desirable.
2. When on-farm manure nutrients exceed crop needs, manure is a waste product not a resource. High cost
share rates, regulations, and export markets for manure should be considered.
3. Targeting is not effective in projects where farmer participation and interest are low.
IV. Project Documents
1. Conestoga Headwaters RCWP. 1982 Plan of Work. Lancaster County, Pennsylvania.
v - ",
2. Conestoga Headwaters RCWP. Comprehensive Monitoring Program. Revised, October 1982.
'*
3. Conestoga Headwaters RCWP. 1983 Progress Report.
4. Conestoga Headwaters Rural Clean Water Program. 1983 Progress Report. Appendix B, Water Quality
Data.
5. Conestoga Headwaters RCWP. 1984 Progress Report.
6. Conestoga Headwaters RCWP. 1985 Progress Report.
7. Crowder, B.M., and C.E. Young. 1986. An Economic Analysis of the Conestoga Headwaters RCWP
Project. Draft. Proposed ERS Technical Bulletin.
8. Conestoga Headwaters RCWP. Project Application. February, 1981. Lancaster County, Pennsylvania.
9. Conestoga Headwaters RCWP. 1986 Progress Report.
10. Conestoga Headwaters RCWP. Nutrient Management Plan. 1986.
11. Young, C. Edwin, Bradley M. Crowder, James S. Shortle, and Jeffery R. Alwang. "Nutrient
Management on Dairy Farms in Southeastern Pennsylvania." Journal of Soil and Water Conservation,
VoL 40, No. 6, Sept.-Oct., 1985. pp. 443-445.
2.91
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12. Crowder, Bradley M., and C. Edwin Young. Modeling Agricultural Nonpoint Source Pollution for
Economic Evaluation of the Conestoga Headwaters RCWP Project. Staff Report No. AGES850614.
Economic Research Service, USDA, Washington, D.C, 1985,70 pp.
13. Crowder, Bradley M., and C. Edwin Young. "Evaluating BMPs in Pennsylvania's Conestoga
Headwaters Rural Clean Water Program." Proceedings: Nonpoint Pollution Abatement Symposium.
Marquette University, Milwaukee, WI, 1985, pp. P-IH-A-1 - P-IJJ-A-11.
14. Alwang, Jeffery, R. "An Economic Evaluation of Alternative Manure Management Systems and
Manure Hauling." Unpublished Master of Science thesis, Department of Agricultural Economic and
Rural Sociology,. Pennsylvania State University, 1985.
15. Young, C. Edwin, Eugene Lengerich, James G. Beierlein, The Feasibility of Using a Centralized
Collection and Digestion System for Manure: The Case of lancaster County." (In) Proceedings of
Conference on Poultry Waste Conversion, (H. C. Jordan and R. E. Graves, eds.), Pennsylvania State
University, University Park, PA (1984), pp. 19-26.
16. Young, C. Edwin, Jeffery R. Alwang, and Bradley M. Crowder. Alternatives for Dairy Manure
Management. Staff Report No. AGES860422, Economic Research Service, USDA, Washington, D.C.,
1986,35pp.
17. Crowder, Bradley M. and C.Edwin Young. "Evaluating BMPs in Pennsylvania's Conestoga Headwaters
Rural Clean Water Program." Paper presented at Nonpoint Pollution Abatement Symposium,
Milwaukee, WI., April 23-25,1985.
--
18. Crowder, Bradley M. and C. Edwin Young. "Modeling the Cost Effectiveness of Soil Conservation
Practices for Stream Protection." Selected paper presented during the annual meetings, Amherst, MA,
June 24-25,1985.
19. Crowder, Bradley M. and C. Edwin Young. "Managing Nutrient Losses: Some Empirical Results on the
Potential Water Quality Effects." Northeast Journal of Agricultural and Resource Economics, Oct.
1986. pp 130-136.
20. Crowder, Bradley and C. Edwin Young. Managing Farm Nutrients Tradeoffs for Surface and
Groundwater Quality. Agricultural Economic Report Number 583, Economic Research Service,
USDA, Washington, DC. Jan. 1988. 22 pp.
V. NWQEP Project Contacts
Water Quality Monitoring
Ms. Patricia LJetman
U.S. Geological Survey
Water Resources Division
P.O. Box 1107
Harrisburg, FA 17108
tel. (717) 782-3860
Mary Jo Brown
PA Dept. of Environmental Resources
Bureau of Water Quality Management
One Ararat Blvd.
Harrisburg, PA mil)
Land Treatment/Technical Assistance
Richard Pennay
Agricultural Stabilization and Conservation Service
Federal Bldg.
Harrisburg, PA 17108
tei. (717) 782-4593
Economic Evaluation
C. Edwin Young
Economic Research Service
U.S. Dept. of Agriculture
1301 New York Ave. NW, Rm. 508
Washin"tcr DC 2Q005-478S
tel. (202)786-1401
2.92
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OAKWOOD LAKES - POINSETT - RCWP20
Brookings, Kingsbury and Hamlin Counties, South Dakota
MLRA 102-A
H.U.C. 101702-01,02
I. Project's Major Contributions Toward Understanding the Effectiveness of
NPS Control Efforts
The project started water quality monitoring relatively recently (1984). Preliminary analysis suggests that
agricultural fertilizer contributes nitrate to a holding area in the soil profile from which nitrate is leached to
ground water on a continual basis. In the future, the project will contribute more information on the
transport of nutrients and pesticides from the soil surface to ground water.
II. Project's Characteristics and Results
1. Project Type: RCWP, Comprehensive Monitoring and Evaluation Project
2.Timeframe: 1981-1991 (
3. Total Project Budget (excludes water quality monitoring funds and farmers' contributions): $1,720,753
4. Cost Share Budget:
a. Funds Allocated: $1,240,886
b. Farmers' Contributions: not available
5. Water Quality Monitoring Budget: not available
6. Watershed Area: Lake Poinsett - 7,868 acres draining 32,452 acres
Lake Albert - 2,400 acres within Lake Poinsett watershed
Oakwood Lakes - 2,184 acres draining 52,856 acres
7. Project Area: 106,163 acres (includes surface and groundwater area)
8. Critical Area: 79,450 acres of cropland and grassland
9. Project/Watershed Land Use: (ref. 7)
use % project area
cropland 61-5
grassland . 13-3
water 9.9
other . 153
There are 304 farms in the critical area.
10. Animal Operations in Project Area: (est. October 1985, ref. 7) (in the Priority 1 Critical Area)
a. Dairy: 830 a.u.
b. Beef: 2,167 a.u.
c. Swine: 1,350 a.u.
d. Sheep: 37.5 a.u.
11. Water Resource Type: three main lakes, ground water (portions of the Big Sioux aquifer)
2.93
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12. Water Uses and Impairments:
The project area has numerous lakes, sloughs and shallow ground water aquifers bordering on the Big
Sioux aquifer. The lakes are heavily used for recreation (e.g^ fishing, boating, swimming, water-skiing)
and stock watering. Over the past five years, recreational visitations to the lakes numbered 240,000 to
300,000 annually. Ground water is relied upon for drinking water and stock watering. Approximately
174,000 people live within fifty miles of the lakes.
Recreational activities are impaired by hypereutrophic conditions in the lakes. Algal blooms, excessive
aquatic weed growth, and DO depletion are common. Pesticides and excessive nitrates in ground water
are also of primary concern.
13. Water Quality at Start of Project: (ref. 7)
Groundwaten Water quality data (1977-1978 study) from 861 private wells in the project area showed
nitrate levels exceeding the federal drinking water standard (10mg/l) in 27% of the wells tested.
Total P Total N
Lake Poinsetc 0.12 4.0
Oakwood Lakes: 0.15 9.0
Tributaries: 0.50 3.2
14. Meteorologic and Hydrogeologic Factors:
a. Mean Annual Precipitation: 22 inches
b. USLE 'R' Factor ~ 100
c. Geologic Factors: The project area has typical glacial Pleistocene morphology with many alluvial
outwash deposits, lakes, potholes and shallow ground water resources. Soils are deep, silty, loamy and
well drained on rolling slopes. Generally, the water table is about 10 feet below ground level. Ground
water flow is active and a large aquifer, the Big Sioux, underlies a portion of the project area.
15. Water Quality Monitoring Program:
a. Timeframe: 1984 - (not specified in NWQEP documents)
b. Sampling Scheme:
1. Location and number of monitoring stations: There are seven field sites and one master
(experimental) site with nests of wells at each site. The sites are located in.different parts of the project
area at locations selected to represent predominant cropping practices on glacial till or outwash soils. A
control site is located on non- agricultural land.
2. Sampling frequency: ground water - monthly, surface runoff - storm event based
3. Sample type: automatic and grab
c. Parameters Analyzed:
ground water- NOa-N & NOa-N, NHs, organic N, TP, CL, SO4, pesticides, pH, conductivity, DO, TKN
surface runoff- ground water parameters plus ortho P, TDS and SS
Flow is measured with surface runoff samples. Ground water levels are measured on a weekly to monthly
basis.
d. Other: As an extension of the CM&E project, a special monitoring study of the Oakwood Lakes
began in 1987. The study has two years of funding to produce annual and seasonal sediment,
phosphorus and nitrogen budgets for the Oakwood lakes during 1987 and 1988. Six monitoring stations
are located on tributaries, three sites are between lake basins, and one site is at the lake outlet. At
tributary stations base flow is measured biweekly to monthly and water quality samples are taken
automatically after storm events. Parameters sampled are TP, ortho P, NOa-N & NOa-N, NHa, TKN
and SS. At in-lake sites, integrated samples are taken every two weeks from May to October and every
month from November to April. Parameters sampled are TP, ortho P, NQa-N & NOa-N, NHs, TKN,
pH, chl a, algal density, DO, temperature, and secchi disk transparency. Biological sampling offish
populations and zooplankton also takes place.
16. Critical Areas:
a. Criteria: The entire 79,450 acres of cropland and grassland are considered critical. The project area
was divided into three priority areas based on sediment delivery levels and the impact on ground water
2.94
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Oakwood Lakes/Poinsett RCWP, South Dakota
(e.g., regional ground water movement, distance from lakes or streams, drainage characteristics, and
thickness of overburden). The first priority area covers 59,500 acres. The second and third priority areas
cover 19,950 acres combined.
b. Application: The first priority area includes most of the livestock operations and encircles the lakes.
17. Best Management Practices:
a. General Schemer"
reduce nutrients and pesticides entering ground water using fertilizer and pesticide management
(BMP 15 & 16)
reduce sediment related pollutants entering waterways and lakes using conservation tillage (BMP 9)
reduce amount of animal waste entering waterways, lakes and ground water by applying waste
management systems
b. Quantified Implementation Goals:
fertilizer management on 70,000 acres (66% of project area)
pesticide management on 65,000 acres (61% of project area)
sediment control BMPs applied and/or maintained on 65,000 acres
waste management systems on 10 livestock operations
c. Quantified Contracting/Implementation Achievements:
%under contract % implemented .
project area 41.1 . not available {
critical area 55.0 not available
critical area
farms 49.0 not available
project area not available not available
Three feedlots have been brought under best management.
d. Cost of BMPs: Estimated cost of the three major BMPs being implemented are:
. Years
of life
': 3 +
4 +
3
Govt.
cost share
Conservation tillage 22.50
Fertilizer management 3.00
Pesticide management -0-
e. Effectiveness of BMPs:
soil savings
BMP ftons^
Perm. veg.cover 5,935
Strip cropping 125
Terrace systems 215
Waterways 6
Shelterbelt 620
Cons, tillage 160,700
Tech. Asst.
-cost-
1.09
72.00
4.29
j
applied units ;
1,025 ac.
132 ac.
7,491 ft.
3ac.
1,489 rod rows
24,677 ac.
Total Gov.
cost
23.59
3.72
4.29
18. Water Quality Changes:
Simulation with the AGNPS model indicates that all contracted BMPs implemented as of July 1986,
should reduce sediment and phosphorus loadings to the four major lakes by 5 to 12 percent compared
with pre-RCWP loadings. However, the model also indicates that water soluble nitrogen loadings
should increase 2 to 3 percent. The model provides no estimates of changes in nitrogen infiltration.
19. Changes in Water Resource Use:
The projected reductions in loadings to the lakes as a result of RCWP do not appear sufficient to affect
water quality and water use. No findings are yet available on ground water use.
2.95
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20. Incentives:
a. Cost Share Rates: 75%
b. $ Limitations: $50,000 maximum per farm
c. Other Incentives: I & E programs
21. Economic Benefits:
a. On-fann:
Participating farmers appear to benefit economically from reduced tillage costs, reduced fertilizer costs,
and perhaps slightly lower pesticide costs. Also there may be some short and long-term yield
improvement attributable to soil and moisture retention by conservation tillage.
b. Off-farm:
Recreational values are so high that reducing algae blooms could generate benefits as high as $3.5 to
$5.9 million annually. Actual recreational benefits attributable to RCWP will likely be much less,
however, because reductions in nutrient loadings to lakes will probably be small. Domestic water supply
' benefits could reach $100,000 annually if groundwater quality is maintained above public health
standards.
IV. Lessons Learned
The project is developing a method for aggregating ground water data. With only two full years of data,
analysis of potential changes in water quality as a result of BMP implementation remains limited. Currently,
monitoring data is providing the project with increased understanding of the complex hydrogeology in this
project area. The AGNPS model is being used to predict the effect of BMP implementation on sediment, P
and N loadings to surface waters, and the project is documenting the effect of fertilizer management on the
quality of ground water.
IV. Project Documents
1. Application for RCWP Funds, February 1981.
2. Comprehensive Monitoring and Evaluation Plan for the Oakwood Lakes - Poinsett RCWP, South Dakota
~ State Coordinating Committee, July 1982, '
3.1982 Annual RCWP Progress Report - Project 20, Oakwood Lakes - Poinsett, South Dakota.
4.1983 Annual RCWP Progress Report - Project 20, Oakwood Lakes - Poinsett, South Dakota.
5.1984 Annual RCWP Progress Report -Project 20, Oakwood Lakes - Poinsett, South Dakota.
6.1985 Annual RCWP Progress Report - Project 20, Oakwood Lakes - Poinsett, South Dakota.
7.1986 Annual RCWP Progress Report - Project 20, Oakwood Lakes Lakes - Poinsett, South Dakota.
8. Piper, Steve, Mark Ribaudo, and A. Lundeen. The Recreational Benefits from an Improvement in
Water Quality of Oakwood Lakes and Lake Poinsett South Dakota." North Central Journal of Agricul-
tural Economics, vol. 9, no. 2,1987. pp. 279-288.
V. NWQEP Project Contacts:
Water Quality Monitoring Land Treatment/Technical Assistance
Jeanne Goodman Dee Watson
SDDept. of Water & Natural Res. USDA-SCS
Water Resource Institute (WRI) 6195thAve.
So'Jth Dakota Stnts University Breakings, SD 57CG6
Box 2120 tel. (605) 692-8464
Brookings, SD 57007
tel. (605) 688-5025
2.96
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Oakwood Lakes/Poinsett RCWP, South Dakota
Information and Education
Charles H. Ullery
Water & Natural Res. Specialist
CES
229 Agricultural Engineering
South Dakota State University
Box 2120
Brookings,SD 57007
teL (605) 688-5141
Economic Evaluation
Richard Magleby
Economic Research Service/RTD
U.S. DepL of Agriculture
JL301_New York Ave. NW, Rm. 532
Washington, DC 20005-4788
teL (202)786-1435
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NANSEMOND-CHUCKATUCK - RCWP 21
City of Suffolk and Isle of Wight County, Virginia
MLRA: T-153A
H.U.C. 020802-08
I. Project's Major Contribution Toward Understanding the Effectiveness of NPS
Control Efforts
The project is not evaluating the effectiveness of individual BMPs but the water quality data and detailed
land treatment records should make possible the analysis of the project's impact on water quality.
II. Project's Characteristics and Results
1. Project Type: RCWP
2. Timeframe: 1981-1991
3. Total Project Budget $1,929,995
4. Cost Share Budget:
a. Funds Allocated: $1,721,000
b. Total Farmers' Contributions: $4,242,000 estimated as of 1991
5. Water Quality Monitoring Budget: $118,400
" 6. Watershed Area: 161,365 acres
7. Project Area: 161365 acres
8. Critical Area: 23,917 acres (expanded from 18,749 in 1985-ref.l7)
9. Project Land Use: (equivalent to watershed land use)
% project % watershed
use area area
cropland 29.1 29.1
pasture/range 2.8 2JJ
woodland 6Z5 SLS
urban/roads 1.2 ' 1.2
other 4.4 4.4
There are 825 farms in the project area.
10. Animal Operations in Project Area:
a. Dairy: 125 a.u.
b. Beef: 2315 a.u.
c. Swine: 7,200 a.u.
d. Poultry: 2£40 a.u.
11. Water Resource Type: 2 estuaries and 7 drinking water reservoirs
12. Water Uses and Impairments:
Reservoirs in the project area are sources of public wacer supply for the cities of Norfolk, Chesapeake
and Virginia Beach, Virginia. Chuckatuck Creek is a successful shellfish growing area and a tidal
tributary to the James River. Commercial and recreational fishing and shellfishihg are important water
2.98
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Nansemond/Chuckatuck RCWP, Virginia
uses. The reservoirs are becoming eutrophic due to sediment and nutrients. Tidal waters are impaired by
high fecal coliform levels.
13. Water Quality at Start of Project
Estuary: 3,000 acres of shellfish beds have been condemned, chl a concentrations exceed 40 ug/1, and
DO is frequently depleted. _
Reservoirs: Phosphate-P concentrations range from 0.05 to 0.20 mg/1 in fall and winter samples. Higher
concentrations have been associated with high fecal coliform densities in some tributaries to the
reservoirs.
14. Meteorologjc and Hydrogeologic Factors:
a. Mean Annual Precipitation: 48 inches
b. USLE'R'Factor 300
c. Geologic Factors: The project area is characterized by nearly level to gently rolling topography with
steep slopes adjacent to small tributary streams. Most soils have moderately low credibility factors.
Depth to groundwater is generally 25 feet or more.
15. Water Quality Monitoring Program:
a. Timeframe: sampling of reservoirs was initiated October 1982; regular sampling of estuary stations
initiated in June 1983.
b. Sampling Scheme:
1. Location and Number of Monitoring Stations: 19 sampling stations 4 in the Nansemond River
estuary, 3 in the Chuckatuck Creek estuary, and 12 stations in the upstream impoundments of the
Nansemond River system
2. Sampling Frequency: at each station is conducted monthly. .
3. Sample Type: grab -
c. Pollutants Analyzed: . . . .' ' ,
estuary: DO, salinity, TSS, NOa, dissolved OP, FC, BOD
impoundments: TS.TP, pH, FC, DO, BOD, algal species
d. Other: There are no flow measurements.
16. Critical Areas: /
a. Criteria: The boundary was originally specified to include the area one mile from the Nansemond
River or its impoundments and one mile from Chuckatuck Creek. This was expanded during 1985 to '
include most of the remaining project area (new boundary includes 1 mile radius from all tributaries). In
treating the expanded critical area, the project established a priority checklist for ranking. Weights are
based primarily on distance to live stream and less than optimal soil or animal waste management.
Animal waste operations are given twice the priority of croplands, and erosion problems are given the
same priority as pesticide and fertilizer management problems. Farms with animal operations and no
cropland treatment needs do not qualify.
b. Application of Criteria: Project reports do not contain appropriate detail to evaluate this.
17. Best Management Practices:
a. General Scheme: The project has concentrated primarily on animal waste management, for hog and
dairy operations, and conservation tillage with fertilizer and pesticide management.
b. Quantified Implementation Goals: The project seeks to treat. 17,931 acres and 115 animal operations.
These goals expanded from 14,055 acres and 51 animal operations in 1985, when the critical area was
expanded.
c. Quantified Contracting/Implementation Achievements: (ref. 18, p.21)
location % Under Contract % Implemented
project area 9.3 NA
critical area 62.8 NA
critical area farms NA NA
project area farms NA NA
2.99
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.cLCostofBMPs:
Ave. Farmer Ave. RCWP
BMP - Share (Vi Share ffl Total Cost (SI
1 Perm. Veg. Cover 33/ac. 100/ac. 133/ac.
2 Animal Waste Mgmt - . 6,670 ea. 20,000 ea. 26,670 ea.
5 Diversion System 0.33/ft. I/ft 1.33/ft.
6 Grazing Land Protection 1,670 ea. 5,000 ea. 6,670 ea.
7 Waterway System 83/ac 250/ac. 333/ac.
8 Cropland Protection 5/ac. 5/ac. 10/ac.
9 Conservation Tillage 7.30/ac. 22/ac. 29.30/ac.
11 Perm. Veg. on Crit. Areas 35/ac. 105/ac. 140/ac.
12 Sediment Retention Struc. 750 ea. 2^50 ea. 3,000 ea.
e. Effectiveness of BMPsr The project estimates that 45,108 tons of soil have been protected from
erosion annually, and 56,546 tons of manure produced annually (65% of production) have been put
under management.
18. Water Quality Changes:
Water quality data have not yet been analyzed. Improving trends in TSS and orthophosphorus have been
observed for Nansemond River when compared with reports from the 1960s. An improving trend in
NOs-N has been observed for Chuckatuck Creek. However, these trends may not be attributable to
RCWP work because they originated in the late 1960s after point sources removed from the project area.
Analysis of water supply lakes in the project area indicates high variability in water quality data and little
evidence of trends.
19. Changes in Water Resource Use:
Oyster production has decreased from a total of 214,000 pounds in 1980 to 95,400 pounds in 1985.
Lowdst production was in 1984 with 57,800 pounds. Three reservoirs in the project area are used for
domestic water supply, and water treatment has not changed since RCWP began. Fishing is the primary
recreational activity in the area, with approximately 30,100 user days per year, unchanged since 1980. Of
7,200 total shellfishing acres, 2,100 acres are condemned and 2,700 acres have been conditionally
approved. ' .
20. Incentives:
a. Cost Share Rates: 75% for most practices except cover crops and some waste application equipment
cost shared at 50%. Fertilizer and Pesticide management are not cost shared.
b. $ Limitations: $50,000 per contract (some contracts cover multiple tracts) c. No other assistance
programs or regulations are utilized to encourage participation.
21. Potential Economic Benefits:
a. On-farm: not evaluated
b. Off-farm: '
1) Recreation: 0
2) Water Supply: $10,000 - $130,000 per year
3) Commercial Fishing: $30,000 per year
4) Wildlife Habitat: unknown
5) Aesthetics: unknown
6) Downstream Impacts: unknown but positive
III. Lessons Learned
This project shows a high degree of coordination among agencies concerned with water quality and resour-
ces. The land treatment program is implemented by SCS. SCS keeps appropriate records to identify each
contract with respect to the water resource that it affects. Several water resource agencies are conducting
monitoring programs that are used to assess the effectiveness of the land treatment program. The monitoring
2.100
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.
Nansemond/Chuckatuck RCWP, Virginia
agencies interface with the land treatment program through a coordinator at the Hampton Roads Water
Quality Agency. The agencies appear to maintain effective communication.
IV. Project Documents
1. RCWP Local Coordinating Committee, County of Isle of Wight and the City of Suffolk, Southeastern Vir-
ginia. Nansemond-Chuckatuck Rural Clean Water Project, City of Suffolkand Isle of Wight County, -
Project Proposal. 1980. (includes the.following Appendices:
a. Presnell-Kidd Assoc., Inc. (for City of Norfolk, Va. Dept. of Utilities) Phase 1 Water Quality Manage-
ment Study Norfolk-Western Lakes Reservoir Systems, (no date)
b. Virginia State Water Control Board. Chuckatuck Creek Non-point Source Bacteriological Study. April
24,1980.
c. Virginia Department of Health. Notices of Shellfish Area Condemnation for Chuckatuck Creek dated: 28
June 1979; Nansemond River dated 16 August 1976,9 March 1972, and 6 November 1963.
d. Virginia State Water Control Board. State Water Quality Management Plan for the Hampton Roads Plan-
ning Area. Adopted March 23-25,1980.
e. Kilch, L.R. and B.R. Neilson. Field and Modeling Studies of Water Quality in the Nansemond River. A
report to the Hampton Roads Water Quality Agency. Special Report No. 133 in Applied Marine
Science and Ocean Engineering. Virginia Institute of Marine Science! Gloucester Point, Va. December
1977.
f. Hampton Roads Water Quality Agency. Hampton Roads Water Quality Management Plan. Executive
Summary. (Draft, no date)
g. City of Norfolk, Department of Utilities. Summary Report. Western Reservoir System Water Quality
Management Plan-Phase II. June 1980.
2. USDA-SCS and VPI&SU. Soil Survey of City of Suffolk, Va. June 1981. :
/'
3. RCWP Local Coordinating Committee. Nansemond-Chuckatuck Rural Clean Water Project Plan of
Work. October 1981. .
4. Cox, C.B. Nonpoint Pollution Control: Best Management Practices Recommended for Virginia. Special
Report No. 9. Virginia Water Research Center, Blacksburg, VA. November 1979.
5. VPI&SU Extension Division. Best Management Practices in Agriculture and. Forestry. Publication 4
WCB 1. Blacksburg, Va. January 1980.
6. VPI&SU Extension Division. Best Management Practices for the Urban Dweller. Publication 4 WCB 2.
Blacksburg, Va. April 1980.
7. VPI&SU Extension Division. Best Management Practices for Row-Crop Agriculture. Publication 4 WCB
3. Blacksburg, Va. June 1980.
8. VPI&SU Extension Division. Best Management Practices for Beef and Dairy Production. Publication 4
WCB 4. Blacksburg, Va. July 1980.
9. VPI&SU Extension Division. Best Management Practices for Swine Operations. Publication 4 WCB 5.
Blacksburg, Va. November 1980.
10. VPI&SU Extension Division. Best Management Practices for Tobacco Production. Publication 4 WCB
6. Blacksburg, Va. January 1981.
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11. VPI&SU Extension Division. Conservation Tillage a Best Management Practice. Publication 4 WC8 7.
Blacksburg, VA. January 1981.
12. VPI&SU Extension Division. Integrated Pest Management - a Best Management Practice Publication
390-409. Blacksburg, VA. November 1980.
13. Nansemond-Chuckatuck RCWP Best Management Practices, as approved by EPA in letter from Peter
Wise to Orin Hanson, May 14,1981.
14. RCWP Local Coordinating Committee. Nansemond-Chuckatuck RCWP 1982 Progress Rep. Nov. 1982.
15. RCWP Local Coordinating Committee. Nansemond-Chuckatuck RCWP 1983 Progress Rep. Nov. 1983.
16. RCWP Local Coordinating Committee. Nansemond-Chuckatuck RCWP 1984 Progress Rep. Nov. 1984.
17. RCWP Local Coordinating Committee. Nansemond-Chuckatuck RCWP 1985 Progress Rep. Nov. 1985.
18. RCWP Local Coordinating Committee. Nansemond-Chuckatuck RCWP 1986 Progress Rep. Nov. 1986.
19. Neilson, B J.'Nonpoint Source Sampling in the Hampton Roads Area. A report to the Hampton Roads
Water Quality Agency. Special Report No. 128 in Applied Marine Science and Ocean Engineering. Vir-
ginia InsL of Marine Sciences. March 1977.
20. Neilson, B J. Summary of the Hampton Roads 208 Water Quality Modeling Studies. A report to the
Hampton Roads Water Quality Agency. Special Report No. 170 in Applied Marine Science and Ocean
Engineering. Virginia Inst. of Marine Sciences. January 1978.
21. Bosco, C. and Neilson, BJ. Interpretation of Water Quality Data from the Nansemond and Chuckatuck
Estuaries with respect to Point and fclonpoint Sources of Pollution. A report to the Hampton Roads
Water Quality Agency. Virginia Inst; of Marine Sciences., May 1983.
22. Kerns, W.R. R.A. Kramer, W.T. McSweeney, R. Greenough, and R.W. Stavros. Nonpoiht Source
Management: A Case STudy of Fanners' Opinions and Policy Analysis. Unpublished ReportrVirginia
Polytechnic Inst. and State University. Blacksburg, Va. November 1982.
23. Kramer, R.A. and D.L. Faulkner. Income Tax Provisions Related to Agricultural BMPs. (Working
Draft) Agricultural Economics Department. Virginia Polytechnic Inst. and State University. Blacksburg,
Va. (no date)
V. NWQEP Project Contacts:
Water Quality Monitoring
Paul Fisher
Hampton Roads WQ Agency
The Regional Bldg.
723 Woodlake Drive
Chesapeake, VA 23320
tei. (804) 420-5364
Information and Education
Charlie Perkins
Virginia Coop. Ext. Service
P.O. Box 364
Windsor, VA 23487
tei. (80$) 242-6195
Land Treatment/Technical Assistance
Harry O. Dalton
SCS
200 N. Main St.
B.-19
Suffolk, VA 23434
tei. (804) 539-9270
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LAKE LE-AQUA-NA
Stephenson County, Illinois
MLRA: M-108
H.U.C. 070900-03
I. Project's Major Contribution Toward Understanding the Effectiveness of NPS
Control Efforts
This project has an integrated approach to watershed management that includes land use and in-lake treat-
ment. It has a strong probability of achieving its goal of cleaning up the lake, but it may not fully document
the effectiveness of the BMPs implemented in the watershed. It also has the potential to demonstrate the ef-
fectiveness of conservation tillage. Project organization at the local level contributed considerably to the suc-
cessful implementation of this project.
II. Project's Characteristics and Results
1. Project Type: Clean Lakes Program along with the Agricultural Conservation Program and the Illinois
Dept. of Conservation
2. Timeframe: Phase 11981-1983 Phase II1984-1987
3. Total Project Budget by Project Components:
Phase I Study (baseline water quality study): $51,750
Federal ACP Special Project (for implementation of conservation tillage): $38,000
Phase II (in-lake treatment and terracing of 2 parcels of land): $56,163
Matching funds $56,163
State ACP Special land treatment: $26,500
Supplemental Phase II: $36,465 -
Matching funds: $36,465
4. Cost Share Budget:
a. Funds Allocated: $118,760
b. Total Farmer's Contributions: $29,235
5. Water Quality Monitoring Budget: Phase I $23,000 Phase II 41,821 Total $64,821
6. Watershed Area: 2348 acres
7. Project Area: 2^48 acres
8. Critical Area: 1500 acres cropland, with 200 acres requiring practices in addition to conservation tillage.
9. Project Land Use: (equivalent to watershed land use) (ref. 2 p. 19)
% project
Use . area
cropland 66.9
pasture/range 7.8
woodland 17.7
urban/roads . 1.4
other 62
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10. Animal Operations in Project Area: (ref. 1)
There are 7 livestock operations in the watershed, with 5 having more th'an one type of livestock. Overall
there are:
a. Dairy: 145 cows (145 a.u.)
b. Beef: 30 heifers and 216 cattle (184 a.u.)
c. Swine: 630 pigs/hogs (189 a.u.)
d. Sheep: 20 sheep (4 a.u.)
11. Water Resource Type: Streams and impoundment, Lake Le-Aqua-Na
12. Water Uses and Impairments:
The lake's impairments are loss of lake capacity,, impaired fishing, boating and aesthetics due to nutrient
and sediment loading causing algal blooms, excessive aquatic macrophytes, dissolved oxygen depletion,
turbidity and sedimentation.
13. Water Quality at Start of Project: (ref. 2, p. 4)
1981 Mean Lake Concentrations:
Pollutant Concentration fmy/n
Total Phosphorus 0323
Dissolved Phosphorus 0-27
Inorganic Nitrogen 1-85
Chlorophyll a ranged from 2 to 243 ug/1 with mean = 89.4 ug/1; nuisance algal blooms dominated by
blue-green algae were present. During peak stratification, 51% of lake volume was anoxic. Several winter
fish kills have occurred. . .
14. Meteorologic Factors:
a. Mean annual Precipitation: 3435 inches
b. USLE'R" Factor. ~ 175 .. .
15. Water Quality Monitoring Program:
a. Timeframe: 1981-1986
b. Sampling Scheme (location, number, frequency, and sample type):
1. One station located on a stream, just above discharge into the lake, is grab sampled after storm events
exceeding 2 inches in 48 hours, as well as monthly October - April, and biweekly May-September.
2. Three in-lake stations: grab sampled monthly from October to April and biweekly from May to
September.
3. One station downstream from dam - same sampling frequency as upstream station (#1).
c. Pollutants Analyzed:
1. Stream station: TSS, VSS, turbidity, TP, NHs, NO2 and NOs
2. In-lake stations: DO, temperature, pH, alkalinity, conductivity, Secehi, TSS, total dissolved solids,
VSS, turbidity, TP, dissolved phosphorus, NO: and NO3, NHs, TKN, chl'a, chl b, chl c, pheophytin
d. Flow Measurements: instantaneous discharge measurements during scheduled sampling visits
(monthly April - October, biweekly May - September)
e. Other: precipitation; sampling of lake benthic organisms; phytoplankton; mapping of macrophytes
16. Critical Areas:
a. Criteria: Criteria for selection were (1) distance to water course and (2) erosion rate
b. Application of Criteria: Appears to be consistent.
17. Best Management Practices: (ref. 3)
a. General Scheme: Consisted mainly of conservation tillage with some terracing, stripcropping,
waierSvays, scuimcni basins, and stream bank protection. Non-BMP, in-lake treatments include: (1) lake
destratifier, (2) macrophyte harvesting, (3) chemical algae control (CuSO4), and shoreline stabilization.
b. Quantified Implementation goals: The goal of the ACP project was to increase average ground cover
2.104 .
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Lake Le-Aqua-Na ACP/CLP, Illinois
from 20% to 40% on the tillable land and thereby reduce average soil loss on cropland 42%.
c. Quantified Contracting/Implementation Achievements: There appears to be land treatment on about
two-thirds of the watershed, which is all of the cropland. Most of this treatment is conservation tillage.
All of the 200-acre critical area has been treated with BMPs such as terracing, water and sediment
control structures, stripcropping, and streambank stabilization measures.
d. Effectiveness of BMPs: Erosion control estimated as 5,250 tons/year on cropland, 1,740 tons/year
prevented from reaching lake (57% reduction).
18. Documented Water Quality Changes:
No statistically significant changes were reported through 1984; however, visual improvement was
reported.
19. Incentives: -
a. Cost Share Rates: Cost share payment for conservation tillage varied with the amount of residue left.
Other practices received 80% cost share.
b. $ Limitations: $3,500 for ASCS LTA
c. Assistance Programs: Technical assistance for BMP implementation
III. Lessons Learned
Comparisons (two sample t-test) of 1981 to 1984 water quality data from both the stream and the lake sta-
tions showed no significant differences; however, the means of most parameters were lower in 1984 than in
1981. Use of stronger statistical analyses may verify significant decreases over this period. The effects of only
the BMP implementation on water quality may be difficult to document due to: (1) there is only one monitor-
ing station that is not effected by in-lake treatments and (2) extremes in precipitation variability occurred
during the monitoring period. Visual improvement in the appearance of the lake has been reported. In this
respect, the project may be successful with its lake protection/restoration program and may increase the
recreational benefits of the area whether or not improvement is verified by chemical monitoring.
IV. Project Documents .-'_
1. Kotlandaramar, V. and R. L. Evans (Illinois State Water Survey) 1983. Clean Lakes Program Phase I
Diagnostic/Feasibility study of Lake Le-Aqua-Na, Stephenson County, Illinois. 158 pp.
2. Davenport, T.E., D.F. Sefton, and S.K. Chick, 1985. Lake Le-Aqua-Na: A Cooperative Approach to
Resource Conservation. Presented at the EPA Region V Workshop (November 1985).
3. Sefton, D.F. and J.D. Mitzelfelt, 1987. Clean Lakes Program Phase II Project Report for Lake Le-Aqua-
Na, Stephenson County, Illinois. Illinois EPA. (in preparation)
V. NWQEP Project Contact
Donna Sefton
Illinois EPA
2200 Churchill Road
P.O. Box 19276
Springfield, IL 62794-9276
tel. (217) 782-3362
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2.106
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Blue Creek Watershed
Pike County, Illinois
MLRA
H.U.C. 071300-11
I. Project's Major Contributions Toward Understanding the Effectiveness of
NPS Control Efforts
The project demonstrated that land treatment practices can reduce soil erosion and improve water quality.
The project is unique in its attempt to relate changes in water quality to three periods of land use (1. fer- \
tilizer and seedbed establishment, April - June; 2. plant reproduction and maturation, July; November; and
3. plant residues, December - March).
The project used computer models to enhance the USLE, to determine the effectiveness of reduced tillage,
and to determine the relative off-site impacts of best management practices.
The project employed a carefully designed monitoring program to monitor the effects of BMP installation
on different areas of the watershed, from one specific practice to an entire sub-basin.
II. Project's Characteristics and Results
1. Project Type: ACP Special Water Quality Project
2. Tirnefrarne: 1979-1982
3. Total Project Budget: not available
4. Cost Share Budget:
a. Funds Allocated: $313,945
b. Farmers' Contributions: not available
5. Water Quality Monitoring Budget: not available
6. Watershed Area: 7,012 acres
7. Project Area: 7,012 acres
8. Critical Area: not available (approximately half the project area)
9. Project Land Use:
use % of pmjgrt area
cropland
(com, soybeans, wheat) 56.4
pasture/hayland 21.7
woodland 11.6
other (wildlife, farmstead,
feedlots, water) 10 3
10. Animal Operations in Project Area: 460 acres of hog and cattle feedlots on 21 farms.
11. Water Resource Type: Blue Creek and its .tributaries drain into Pittsfield City Lake, a multiple use
reservoir constructed in 1961.
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12. Water Uses and Impairments:
Use of Pittsfield City Lake was impaired for secondary contact recreation (fishing and boating) and for
public water supply for about 4,400 residents in the City of Pittsfield. Impairment was caused primarily
by sediment deposition in Pittsfield City Lake. Nutrients and pesticides were also of concern.
13. Water Quality at Start of Project:
Slightly more than one percent of lake capacity was lost to sedimentation annually. At this rate the lake
would have been completely filled in 92 years.
14. Meteorologic and Hydrogeologic Factors:
a. Mean Annual Precipitation: 3735 inches
b.USLE'R' Factor 175
c. Geologic Factors: Hilly terrain, steep slopes, and fine-textured soils which are almost entirely glacial
in origin. Topography ranges from nearly level to gently sloping.
15. Water Quality Monitoring Program:
a.Timeframe: 1980-1982
b. Sampling Scheme:
1. Location: Blue Creek was monitored at two locations (stations C & B) representing 50% and 70% of
the drainage area, respectively. A station (A) was also located at the watershed outflow and a station
(D) was located on a direct tributary to the lake to determine the relative contribution of a major
sub-basin. Two field stations (E & F), 38 and 79 acres, were monitored for comparative information on
field level conditions. One station (J) was maintained to monitor the effectiveness of a vegetative filter
strip installed to control runoff from a feedlot operation. Lake monitoring took place at three locations.
In addition, there were four biological monitoring stations in the watershed.
2. Frequency: daily - stations B,C / weekly - stations A,B,C / monthly - lake sites / event basis - all
stations
3. Sample Type: automatic and grab
c. Primary Pollutants Analyzed: TSS, TVS, NHa, TKN, TP, NOs, DP, BOD, COD
d. Flow Measurements: streamflow measured daily / flow measurements for ephemeral streams taken
only during rainfall-runoff events
e. Other Measurements: temperature, pH, conductivity, plus several chemical parameters
16. Critical Areas:
a. Criteria: locally designated (specific criteria not reported)
b. Application: not available
17. Best Management Practices:
a. General Scheme: Emphasis was placed on reducing soil erosion using the following practices:
contouring, stripcropping, no-till, reduced tillage, terracing, streambank protection, vegetative filter
strips, waterways, permanent vegetative cover and livestock exclusion.
b. Quantified Implementation Goals: The project's goal was to reduce soil loss by 23,587 tons. (Potential
gross erosion for the watershed was estimated to be 63,313 tons/year of which 99% was sheet and rill
erosion).
c. Quantified Contracting/Implementation Achievements:
% Project Area
Treated with cost share 283
Treated without cost share 8.S
d. Effectiveness of BMPs: As of October 1,1982,88% of the soil loss reduction goal had been achieved
soil erosion had been reduced by 20,674 tons per year.
18. Documented Water Quality Changes: (ref.9)
There is an overall trend of decreasing mean TSS in stream samples from 1979 -1982. Turbidity in
Pittsfield City Lake was still increasing but at a decreasing rate. Large incremental changes in the
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Blue Creek ACF, Illinois
overall lake water quality caused by implemented conservation practices were not measured within the
four year monitoring period. This was because of the high degree of inherent variability within the
watershed system and the long response time of the ecosystem to subtle changes in land use. The
project observed several changes in water quality parameters associated with the three periods of land
management. In general, lake water quality reflects the interaction of land management and
precipitation. Lake water quality is at its poorest during period one (fertilizer and seedbed
establishment) with excess rainfall and at its best during period three (plant residues) with no rainfall.
19. Incentives:
a. Cost Share Rate: not reported
b. $ Limitations: not reported *
c. Assistance Programs: Information and education programs led to 18% of the cropland in the project
area being treated without direct financial assistance. An intensive effort to make one-on-one personal
contacts with landowners was found to be very effective. .
Ml. Lessons Learned: (ref. 11)
Nonpoint source pollutants have greater impact on lakes than streams because of the hydraulic differences
between these two water resource systems.
The USLE alone is not adequate to measure sediment yield or to predict sediment movement off individual
fields. Additional modeling or calculations are needed to determine sediment delivery to the water resource.
Treating designated critical acres has a greater impact on reducing soil erosion than treating randomly
selected acres.
Over 80% of the annual sediment load was transported during 5% of the time.
IV. Project Documents
1. Davenport, T.E. and J J. Oehme. 1982. Soil Erosion and Sediment Delivery in the Blue Creek Watershed, .
Pike County, Illinois. IEPA/WPC/82-002.
2. Davenport, T.E. 1982. Economic and Physical Impacts on Individual Farm Management Units Under Al-
ternative Management Scenarios in the Blue Creek Watershed, Pike County, Illinois. IEPA/WPC/82-005.
3. Davenport, T.E. 1981. Blue Creek Watershed Project, Pike County, Illinois (May 1979 - October 1980).
64pp.
4. Davenport, T.E. 1983. Soil Erosion and Sediment Transport Dynamics on the Blue Creek Watershed,
Pike County, Illinois. IEPA/WPC/83-004. Illinois EPA, Springfield, Illinois. 212pp.
5. Davenport, T.E. 1982. Comparative Evaluation of Gross Erosion Assessment Techniques Used in the
Blue Creek Watershed, Pike County, Illinois. Illinois EPA, Springfield, Illinois. 39pp.
6. Davenport, T.E. and J J. Oehme. 1982. Soil Erosion and Sediment Delivery in the Blue Creek Watershed,
Pike County, Illinois. Preliminary Analysis. Illinois EPA, Springfield, Illinois. 35pp.
7. Davenport, T.E. 1982. Water Resource Data and Preliminary Trend Analysis for the Blue Creek Water-
shed Project, Pike County, Illinois. Phase I. IEPA/WPC/82-001. Illinois EPA, Springfield, Illinois. 109pp.
8. Davenport, T.E., et al. 1982. Water Resource Data and Preliminary Trend Analysis for the Blue Creek
Watershed Project, Pike County, Illinois. Phase II. IEPA/WPC/82-008. Illinois EPA, Springfield, Illinois.
161pp.
9. Davenport, T.E. 1983. Water Resource Data and Trend Analysis for the Blue Creek Watershed Project,
Pike County, Illinois. Phase III. IEPA/WPC/83-003. Illinois EPA, Springfield, Illinois. 264pp.
10. Lee, M.T., P. Makowski and W. Fitzpatrick. 1983. Assessment of Erosion, Sedimentation, and Water
Quality in the Blue Creek Watershed, Pike County, Illinois. SWS Contract Report 321. Surface Water
Section, Illinois State Water Survey, Champaign, Illinois. 191pp.
2.109
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11. Illinois Environmental Protection Agency. 1984. Blue Creek Watershed Project Executive Summary and
Recommendations. IEPA/WPC/84-008. Springfield, Illinois. 31pp.
12. Davenport, T.E. 1983. Comprehensive Monitoring and Evaluation of the Blue Creek Watershed.
V. NWQEP Project Contact
Tom Davenport
Nonpoint Source Coordinator
USEPA Region V
Water Quality Section (WQS-TUB-08)
230 S. Dearborn
Chicago, Illinois 60604
teL (312) 886-0148
2.110
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LaPIatte River Watershed
Chittendon County, Vermont
MLRA: R-142
H.U.C. 020100-03
I. Major Contributions Toward Understanding the Effectiveness of NPS Control
Efforts
This project will contribute knowledge on the effectiveness of manure management (timing and type of
spreading on fields) and practices to control barnyard and milkhouse waste runoff. The project found the
CREAMS model inadequate for simulating pollutant losses under northern United States climatic condi-
tions. The project also contributed to the verification, calibration and modification of four Vermont SCS
models that calculate phosphorus and sediment concentrations in cropland erosion runoff, and phosphorus
in barnyard runoff, milkhouse effluents and manure stacks runoff.
II. Project Characteristics and Results
1. Project Type: PL83-566 (USDA - SCS)
2. Timeframe: 1979 -1990
3. Total Project Budget (excludes water quality monitoring funds and fanners' contributions): not
available
4. Cost Share Budget: .
a. Funds Allocated: $680,507 (as of August 1987)
b. Farmers'Contributions: $292,842 (estimated)
5. Water Quality Monitoring Budget: $1,236,942 (ref. 2, p. H-29)
6. Watershed Area: 34,137 acres
7. Project Area: 34,137 acres
8. Critical Area: none designated
9. Land Use of Monitored Project Area: Monitored area is 25,981 acres, 76% of the project area.
Use % monitored area
agriculture 5l_
cropland ""24
pasture ~ 27
woodland 39
urban/roads 8.
other 2
There are 50 active farming operations in the project area.
10. Animal Operations in Project Area:
a. Dairy: 35 farms with a total of 3,999 a.u.
b. Beef: 2 farms
c. Horse: 1 farm
d. Sheep: 1 farm
2.111
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The Federal Dairy Termination Program (1985) and other livestock sales resulted in a net decrease in
total animal units of over 25% in less than 2 years.
11. Water Resource Type: LaPlatte River and tributary streams flowing into Shelburae Bay of Lake
Champlain.
12. Water Uses and Impairments:
Boating and aesthetics in Shelburne Bay and the LaPlatte River are impaired by phosphorus and
sediment
13. Water Quality at Start of Project: (Meals, 1985, personal communication)
Year 2 Data: Oct. 1979 - Sept. 1980
Station 1
Parameter
TSS
TP
TKN
( ~ 67% of watershed)
mgrt .
min mar median
1.16
0.113
0.47
4.8 8.95
1.406 0.327
3.52 1.02
Station 2
( ~ 12% of watershed)
min mar median
2.97
0.023
0.07
8J 15.5
0.424 0.90
3.35 0.79
14. Meteorologic and Hydrologic Factors:
a. Mean Annual Precipitation: 33.7 inches
b. USLE'R'Factor: '90
15. Water Quality Monitoring Program:
a. Timeframe: 1979 -1990
b. Sampling Scheme:
1. Location and Number of Monitoring Stations; Frequency, and Sample Type:
a) Three stream stations monitor small subbasins and a fourth monitors ~ 67% of the watershed. Sites
are automatically sampled every 8 hours and usually analyzed as 24-or 72-hour composites, except
during periods of high flow when samples are composited over shorter intervals,
b) The effluent of a sewage treatment plant in the watershed is automatically sampled as one 7-day
composite analyzed each month; two grab samples per month are analyzed for bacteria.
c) Pollutants Analyzed: OP, TP, TSS, VSS, turbidity, TKN, NHs, nitrate, DO and bacteria
d. Flow Measurements: continuous stage recorders
e. Other:
1. Precipitation measured by 3 gages in the watershed.
2. Temperature, pH, and conductivity measured.
3. Special projects: There are several special studies that add to the scope of this project: 1) a paired
watershed study to document the field-scale effects of best manure management; 2) a study to determine
the effectiveness of practices dealing with barnyards and milkhouses; 3) phosphorus attenuation in the
LaPlatte River downstream from a sewage treatment plant; 4) verification of the model CREAMS; and
5) monitoring changes in stream biota.
16. Critical Areas:
This project started prior to the RCWP, therefore, it had no requirement that critical areas be
determined and no strong precedent or criteria existed for defining critical areas. The project did
identify critical areas or prioritize individual farms for treatment. The project reports has estimated the
number of acres contributing nutrient and sediment loads as determined by models (ref. 11, p.25).
Location of these acres is not reported.
17. Best Management Practices:
a. General Scheme: The practices most emphasized in this project are animal waste management
(manure storage, barnyard and milkhouse waste treatment). Other practices contracted and
2.112
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LaPlatte River PL566, Vermont
implemented include conservation cropping, permanent vegetation, changes in crop rotation,
contouring, and streambank erosion control.
b. Quantified Implementation Goals: Implementation goals include the development of 412
conservation plans to treat: (1) 2,640 acres of cropland, grassland, and forestland, (2) 30 animal waste
systems, and (3) protection of 2^00 feet of critically eroding streambank (ref. 1).
c. Quantified Contracting/Implementation Achievements: (ref. 11, p.19)
Location % contracted % implemented
project area 19 95
project area farms 54 85.
Over half of the contracts have expired, however, there is no indication that farmers have abandoned
their BMPs.
d. Effectiveness of BMPs: Manure storage facilities treat over 48,000 tons of manure annually. Cropland
erosion control practices reduce soil erosion by an average of 2,100 tons per year, as estimated using the
USLE. Streambank erosion controls have reduced channel erosion and sedimentation by about 450 tons
annually.
Preliminary results from studies on the effectiveness of barnyard and milkhouse runoff management
follow:
Milkhouse Waste Studv: A filter strip reduced concentrations of TSS, TP and TKN in surface flow 92,86
and 79%, respectively. Concentrations of TSS, TP and TKN in localized groundwater were reduced 93,
92 and 91%, respectively. An effluent drainage ditch retained inputs of P and N during periods of
normal to low flow. A ditch of this type however, may become a source of nutrients and sediments
during periods of high flow,
Barnyard Runoff Study: The filter strip performed poorly due to channelized surface flow and a high
hydraulic loading rate. If paved livestock yards are not scraped frequently, runoff from these yards
contains significantly more sediment and nutrient than runoff from unpaved barnyards.
Manure Application to Hayland: This study used a paired watershed design to monitor the water
quality effects of winter spreading of manure as opposed to storing manure in winter for application and
incorporation'ih the fall. Fall application reduced SS concentrations but increased P concentrations and
discharge. Winter application increased P concentrations, decreased discharge, and did not change SS
concentrations. From manure applied in winter, 5% P was lost in runoff following. Only 2% was lost
from fall application. .
18. Water Quality Changes:
Higher precipitation and stream/low in year 8 made interpretation of long-term water quality trends
difficult. In watersheds 1,2 and 3, concentration and yield of sediment and nutrients were higher than in
the previous year, but generally lower than in other years of high flow.
A trend of decreasing N concentrations and export continued in year 8. Concentration and yield of all P
forms appears to be increasing on a long-term basis. A similar trend may be developing for sediment.
19. Incentives:
a. Cost Share Rates: 75% - agricultural waste management (including storage facilities) and streambank
protection.
60% - waterways, livestock exclusion, and pasture and hayland planting.
50% - diversions and troughs for pasture management.
b. $ Limitations: A maximum of $30,000 per treatment type (BMP) was allowed.
c. Assistance Programs: None have been reported other than the technical assistance of SCS for
installing practices.
d. Other Incentives or Regulations: None have been reported. There have, however, been some ACP
funds used for conservation practices, within the watershed, mostly prior to this project.
2.113
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III. Lessons Learned
The high variability of meteorologjc and hydrologic factors makes it difficult to establish significant trends in
water quality. The model CREAMS was found to be inadequate for predicting runoff, sediment and phos-
phorus export from two field sites. The project recommended that the model be modified and/or carefully
fitted with observed data to yield more accurate estimations of export under winter conditions in the north-
ern United States.
Project staff have noted that changes in project area land use may be related to observed localized water
quality trends. In one subwatershed, declining corn acreage and manure application may be related to an ob-
served decreasing trend in P and N concentration and yield from that area. Increasing corn acreage and
manure application in another subwatershed may be related to observed increases in sediment and nutrient
export. Careful tracking of land use changes is important in the overall project effort.
IV. Project Documents
1. Watershed Plan for LaPlatte River Watershed Vermont, 1979. Chittenden County, Vermont. 101 pp.
2. Cassell, E.A. and D.W. Meals, Jr., 1981. LaPlatte River Watershed Project Water Quality Monitoring and
Analysis Program, Program Report No. 1, Description of Watershed and Water Quality Program. Ver-
mont Water Resources Research Center, University of Vermont.
3. Cassell, E.A. and D.W. Meals, Jr., 1981. LaPlatte River Watershed Project Water Quality Monitoring and
Analysis Program, Program Report No. 2, Program Achievement Report - Year 1. Vermont Water
Resources Research Center, University of Vermont.
4. LaBar, G.W., 1982. LaPlatte River Fisheries/Benthos Evaluation Phase 11980-1981 final Report. Univer-
sity of Vermont. 40pp.
5. Clausen, J.C., D.W. Meals, E.A. Cassell, 1983. Supplement to the Program of Work, Water Quality
Monitoring and Analysis, February 1979. Vermont Water Resources Research Center. University of
Vermont. 14pp. :
6. Meals, D.W. Jr., 1983. LaPlatte River Waterhsed Water Quality Monitoring and Analysis Program
Report No. 5, Year 4,1981-1982, Executive Summary. Vermont Water Resources Research Center,
University of Vermont. 25pp.
7. Meals, D.W. Jr., 1983. LaPlatte River Watershed Water Quality Monitoring and Analysis Program,
Program Report No. 5, Year 4,1981-1982. Vermont Water Resources Research Center, University of
Vermont. 160pp.
8. Meals, D.W. Jr., 1984. LaPlatte River Watershed Water Quality Monitoring and Analysis Program,
Program Report No. 6, Year 5,1982-1983. Vermont Water Resources Research Center, University of
VermontT 133pp.
9. Meals, D.W. Jr., 1985. LaPlatte River Watershed Water Quality Monitoring and Analysis Program,
Program Report No. 7, Project Year 6,1983-1984. Vermont Water Resources Research Center, Univer-
sity of Vermont. 133pp.
10. Meals, D.W. Jr., 1986. LaPlatte River Watershed Water Quality Monitoring and Analysis Program,
Program Report No. 8, Project Year 7,1984rl985. Vermont Water Resources Research Center, Univer-
sity of Vermont. 154pp.
11. Meals, D.W. Jr., 1987. LaPlatte River Watershed Water Quality Monitoring and Analysis Program,
Program Report No. 9, Project Year 8,1985-1986.228pp.
2.114
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LaPlatte River PL566, Vermont
V. NWQEP Project Contact
Don Meals
Vermont Water Resources Research Center
School of Natural Resources
Unive'rsity of Vermont
Burlington, VT 05401-0088
Telephone (802) 656-4057
2.115
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Chapter Three Project Analysis
Saline Valley RCWP, Michigan
Agricultural Setting and Water Resource Problem
The Saline Valley RCWP project is located in southeastern Michigan in Washtenaw and
Monroe Counties. It includes 76,660 acres, about 70% of which is intensively cropped most-
ly in corn and soybeans. In addition, there are about 9,500 animal units in the project area.
Saline River and Macon Creek which drain the project area have been identified as dis-
proportionate contributors of phosphorus, on the basis of area, to the western basin of Lake
Erie. The 42,400 critical acres have an average erosion rate of 7.2 tons per acre per year, and
total phosphorus loading is estimated at 40 tons per year: 23 tons from commercial fertilizer
and 16 tons from animal waste. Two sewage treatment plants for the towns of Saline and Milan
are major phosphorus contributors in the project area.
Water Quality Monitoring Design ' , '"
The water quality monitoring design is very efficient and straightforward. Weekly grab
samples have been taken at seven stream sites without interruption since 1981. Each sample
includes an instantaneous stream flow measurement. The parameters measured include
suspended solids, soluble reactive phosphorus, total phosphorus, ammonia, nitrate, silica, pH,
and conductivity. "
Analysis of Water Quality Data: Preliminary Results
Although not all BMPs contracted under RCWP are installed, significant changes in farm
management have occurredjn the watershed, arid sufficient time has elapsed that it is plausible
that water quality changes due to BMPs could be occurring. The remainder of this section is
a critical examination of the water quality data drawing from a water quality report developed
by the project (Johengen, 1987).
The locations of the monitoring stations are shown in Figure 3.1. Stations 3-8 are in the
Saline River drainage although only stations 5 and 8 are actually on the Saline River. Station
9 is on Macon Creek. .
Cursory analysis of the water quality data reveals that the majority of phosphorus loading
from this project area is not from agricultural nonpoirit sources, but rather from the combina-
tion of urban nonpbint sources and point sources. This is evidenced by the fact that each
3.1
-------
Michigan
Project Boundary
Macon Creek Watershed Boundary
Monitoring Stations
3 Bridgevater Drain
3A Sallnt River at Feldkanp Road
4 Bauer Drain
5 Saline River at Dell Boad
8 Saline River at Blgelov Road
9 North Macon Creek at Ridge Road
6 Bear Creek
7 Wancy Drain
Figure 3.1 Saline Valley RCWP Project Area Map (after Johengen, 1987),
monitored agricultural subbasin to the Saline River shows much lower P concentrations than
station 8 on the Saline River below the outfalls of the Milan and Saline sewage treatment
plants. Station 5 on the Saline River above Milan and Saline also shows low P concentrations
relative to station 8. Table 3.1 shows that total P concentrations at station 8 decrease during
high flow periods (indicative of point sources) while all other stations show significant con-
Tabie 3.1 Comparison of Total Phosphorus Concentrations (mean TP hot flow weighted) at Upstream
Tributaries (stations 3,4,5.6,7) with the Watershed Outlet (station 8). (Data from 6/30/83 to
S/30/84).
Station 8
flow rate
(m3/sec)
Low
High
>5.0
n
30
9
[TP] (mg/I) contributed
from NPS upstream
(stations 3,4,5,6,7)
45.3
84.5
FTP] (mg/1) at
watershed outlet
(station 8)
387
214
Loading contribution
from upstream
NPS
12%
. 40%
3.2
-------
Project Analysis: Saline Valley RCWP
centration increases during high flow (indicative of nonpoint sources). The data suggest that
point sources are the major P source in both low flow and high flow periods.
Two important implications of these results are:
1. International Joint Commission P loading redaction goals (30%) for this water-
shed require point source P removal and NPS control.
2. Even in a predominantly agricultural watershed, small domestic sewage water
treatment plants, if present, are likely to be the main source of phosphorus.In this
case, the towns of Milan and Saline have a combined sewered population of less
than 6,000, yet sewage effluent-P outweighs P from nonpoint sources.
The project has done a considerable amount of analysis of the water quality data (Hol-
land et. al., 1985; Johengen, 1987). Johengen (1987) focused on comparing pollutant loads
between different years and different stations. Loads for stations 3, 4, 5, 6, and 7 were nor-
malized to a mean annual discharge given by the following equation:
Normalized Load = (Annual Load/Annual Discharge) x Mean Annual Discharge
It should be noted that loads calculated by this procedure are subject to bias because both
concentration and discharge estimates are based pn random weekly grab samples.
The goal of this experimental design is to observe significant changes in pollutant loads
between years and stations. Differences were tested for significance using one-way analysis
of variance except when the data failed to meet assumptions of normality or homogeneity in
which case nonparametric methods such as the Krushkal-Wallis procedure and Scheffe's mul-
tiple comparison tests were used.
Figures 3.2-3.5 show yearly loading time trends for various pollutants at the five stations
analyzed (Johengen, 1987). As shown in Figure 3.2, there was a substantial decrease in
suspended solids loadings in years 2 through 4 at stations 4 and 6 and an increase in year 5.
Stations 3 and 5 followed the same pattern except that both stations showed a dramatic in-
crease in year 3. The cause of the annual variations is unknown since the land use data are not
currently organized by subbasins. Some of the variotiort in loading may be attributable to varia-
tion in runoff. Analysis of concentration data considering instantaneous discharge as covariate
may reveal a different picture.
The overall trend for total P (Figure 3.3) is similar to suspended sediment but with less
increase at station 3 during year 3. Johengen (1987) hypothesizes that BMPs improved water
quality in years 2-4 but lost their effectiveness in year 5. Given experiences with BMPs such
as animal waste management and conservation tillage in other RCWP projects, this seems un-
likely.
The soluble reactive phosphorus (SRP) data (Figure 3.4) show the same 'U'= shaped pat-
tern with only a peak in year 3 for station 6. This suggests that the total P peak in year 3 for
3.3
-------
CO
0.9
0.8
0.7
0.6
P
i
0.1
0.3
0.2 -
O.I
0.0
b.
^-7
&-
*-*
-H-
6.-*
*"-r-V-^Li.
1931
1982
1983
1904
198S
09
0.6
0.7
06
O.S
C/l
3
K n 4
O
n.
o
0.3
0.1
0.0
*-* 6
»^t 7
1981
srniiON
1982
3 !>-»«
1983
rrns
' I
1981
*»-* 6 «i>-« 7
1985
Figure 3.2 Discharge Normalized, unit Areal Loadings for
Suspended Solids (Johengen, 1987),
Figure 3.3 Discharge Normalized, Unit Areal Loadings for Total
Phosphorus (Johengen, 1987),
-------
0.35
CO
bi
a
1/1
,o
O-,'
£
no
_i
O
0.30
0.25
a. 20
0,15
0.10
o.os
0.00
S'lU'dN
1981
1985
o.oss
0.050
O.OSS
.0.010
0.035
u
X. 0.030
IT
5. 0.025
O.OJO
o.qis
0.010
' 0.005
0.000
-------- /
/
1
0)
"<
M.
(A
(D
U
3]
o
T)
1981
1982
1963
YEflH
sruffON
1961
6 «1»-«» 7
1985
Figure 3.4 Discharge Normalized, Unit Areal Loadings for
Soluble Reactive Phosphorus (Johengen, 1987),
Figure 3.5 Discharge Normalized, Unit Areal Loadings for
Nitrate-Nitrogen (Johengen, 1987).
-------
station 6 was almost entirely dissolved phosphorus because no increase occurred in suspended
solids to account for the change.
Nitrate loading shows a very different pattern than the other pollutants (Figure 3.5).
There was an overall increase in loadings at all stations over the five years. Also in direct con-
trast to the other pollutants, year 3 is the only year in which there was some decrease in nitrate
loading. Note that stations 6 and 7 show the highest nitrate loads (Figure 3.5) and the lowest
suspended solids loads (Figure 3.2). One explanation is that the BMPs being employed are
only effective in controlling surface losses of pollutants and actually enhance the transport of
leachabie pollutants such as nitrate through subsurface flow. More water quality data and bet-
ter land use data are required for a more definitive analysis.
It should also be noted in Figure 3.5 that the unit area! nitrate loadings reported for sta-
tions 6 and 7, about 40 to 100 pounds per acre, are some of the highest reported for an agricul-
tural watershed. For instance, the total N loadings from the Little Conestoga River watershed
in Pennsylvania, with over 2 animal units per acre combined with excessive fertilizer applica-
tions, is estimated at 44 pounds per acre. The other stations in the Saline River project area
show more typical agricultural loading rates of 4 to 30 pounds per acre per year. These high
values should be checked.
As more BMPs are installed in the project and a more complete post-BMP water quality
data base is developed, it should be possible to detect potential water quality changes with
greater sensitivity and tie them to land treatment activities.
LITERATURE CITED
Holland, RJE., A.M. Beeton, and D. Conley. 1985. Saline Valley Rural Clean Water Project Interim Report
on Monitoring. Michigan Sea Grant College Program, Great Lakes and Marine Water Center.
Johengen, T.H. 1987. Documenting the Effectiveness of Best Management Practices to Reduce Agricultural
Non-Point Source Pollution. Unpublished research paper to fulfill requirements at the University of
Michigan, Ann Arbor, Michigan.
3.6
-------
Chapter Four Project Analysis
Prairie Rose Lake RCWP, Iowa
ABSTRACT
The Prairie Rose Lake RCWP is located in west central Iowa. The 4,568 acre watershed includes a 215
acre impoundment. Prairie Rose Lake, and 3,648 acres of cropland. Use of the lake is impaired by excessive
sedimentation, turbidity, and eutrophic conditions. Agricultural runoff is the primary source of sediment and
nutrient loadings to the lake. The primary BMPs are terracing and nutrient management; contracts have been
made to treat 83 percent of the critical area. As of September 30,1986. eighty-nine percent of the contracts
were installed and 74 percent of the critical acreage were considered treated. The watershed is small and
land use is homogeneous. The project should be able to document water quality changes at the impaired
resource within the 10-year RCWP timeframe because it has consistent monitoring of lake water quality and
a large treated area.
Persistent turbidity after precipitation noted by project personnel prior to the RCWP is thought to have
decreased since RCWP began. However, this visual observation is not easily evaluated. Bathymetric map-
ping indicates that the rate of lake sedimentation may have decreased. The majority of BMP implementation
under RCWP occurred in 1982. The project estimates that the sediment delivered to the lake had been reduced
48% by the end of 1982. The project hypothesizes that reduction in sediment loads stimulated algal growth
by increasing light penetration.
Analysis of covariance, with adjustment for precipitation and chlorophyll a, shows that lake water clarity
was greatest during 1982 and 1983, shortly after the start of RCWP. In subsequent years, however, water
clarity has declined to 1981 levels.
Results of our analysis confirm some tradeoff of sediment turbidity for algal turbidity. A precipitation
covariate explained 6% of the variability in Secchi depth measurements. Even with adjustment for precipita-
tion, statistical analysis of water clarity parameters (Secchi depth, turbidity) suggested that water quality is
deteriorating over time. Adjustment for chlorophyll a explained an additional 26% of Secchi depth variability.
After correcting for both precipitation and chlorophyll a there is no significant trend overtime, neither improv-
ing nor deteriorating, in water clarity data.
In light of the above results, we offer two possible explanations for the relatively high turbidity levels from
1984 through 1986:
Unrecognized factors may be masking the expected improvement in water clarity. Such factors
may include resuspension of bottom sediments or other parameters not measured.
Rne sediment delivered to the lake has not been reduced to the extent estimated by the project.
The effectiveness of BMPs may have been overestimated and/or the sediment delivery ratio may
be greater than the estimated 0.32. Terracing may not be controlling erosion of fine soil particles
which cause turbid conditions when resuspended in the lake.
Considering the variability observed to date, the lake monitoring scheme should be able to document
a real change of approximately 20 percent in water clarity as measured by Secchi depth and-surface turbidity
with ten years of monitoring.
4.1
-------
INTRODUCTION
Background
Prairie Rose Lake is located in Shelby County in west central Iowa. Its watershed covers
4,568 acres, including the 215 acre impoundment surrounded by 433 acres of state parkland.
The project area eligible for land treatment covers 3,920 acres, primarily cropland (3,648 acres)
with highly erosive soils. The lake was constructed in 1962 and is a popular water resource for
fishing, swimming, and boating. Drinking water for park visitors is drawn from the lake.
When the RCWP project began the lake was impaired by excessive sedimentation, tur-
bidity, and eutrophic conditions. Sedimentation impaired lake storage volume and game fish
habitat. Turbidity and algal growth impaired swimming.
Pollution sources within the park have been treated adequately with the park's sewage
treatment facilities, permanent vegetation, and shoreline erosion control practices. Tributary
monitoring and erosion estimates have confirmed that agricultural land surrounding the park
is the primary source of sediment and nutrient loadings. Pesticides in agricultural runoff are
also a concern. Before RCWP, an estimated 62% of the cropland was eroding at an average
rate of 30 tons per acre. Most of the cropland is planted in either continuous corn or corn-
soybean rotations regardless of field slope. '-.-.
Project Perspectives .-.',...."
The following questions can be addressed by analyzing this project's water quality and
BMP implementation data. Analyses and discussion in this chapter address the first three of
these issues.
1. Can the project document a decrease in suspended sediment concentration or a
decrease in sedimentation rate in Prairie Rose Lake? If significant decreasing
trends exist, can the project relate them directly to BMP implementation, specifi-
cally terracing and conservation tillage under the RCWP? This is a small water-
shed with homogeneous land use and high BMP implementation, and it should be
a good setting to yield answers to these questions.
2. Can adjustment variables (e.g., precipitation, chl a, and TP) be used to correct for
some of the measured variations in pollutant concentrations?
3. If water quality trends exist in the lake, how long should monitoring be required to
document them? An important aspect of the RCWP experiment is to determine
if improvements in water quality can be measured directly at the impaired
resource. A long monitoring timeframe may be required due to the complex
hydrology of lakes.
4. Is terracing the most cost-effective BMP for reducing sediment delivered to
Prairie Rose Lake that this project could have implemented?
4.2
-------
Project Analysis: Prairie Rose Lake RCWP
Land Treatment Strategy
The eligible project area (entire watershed excluding parkland and lake) was identified
as critical area (3,920 acres) for land treatment under RCWP (Figure 4.1). Cropland covers
79% of the project area. Major project goals are to control excessive soil erosion on at least
80% of the critical area and to reduce the rate of sediment delivery to the lake by 60%. A sedi-
ment delivery ratio of 0.32 is assumed by the project (Progress Report, 1986). Achievement
of goals would reduce annual nutrient loadings by 59,290 Ibs. of phosphorus and 149,270 Ibs.
of nitrogen per year. The BMPs are primarily terracing and nutrient management.
N
Legend
agricultural land under BMP contract
Figure 4.1 Prairie Rose Lake RCWP. Shelby County, Iowa (after Monitoring Report, 1986).
Water Quality Monitoring Strategy
RCWP monitoring began in 1981. Biweekly grab samples are taken at three locations
(Figure 4,2) in the lake - site 1 (the upper reach), site 2 (mid-lake), and site 3 (the deepest
point near the dam). Surface and bottom samples are taken at each location. The depths of
bottom samples for sites 1,2. and 3 are 8.11, and 24 feet, respectively. Grab sampling is con-
ducted from May through September, yielding 10 samples per year per site. Parameters
analyzed are Secchi depth, turbidity, chlorophyll-a (chla), fecal coliform (FC), total phosphate
4.3
-------
(TP), orthophosphate (OP), nitrate- nitrogen (NOs-N), and ammonia plus ammonium-
nitrogen (NHs + NH4).
Samples are also taken at the drinking water intake within 24 hours of each event greater
than 2 inches of precipitation. These samples are analyzed for concentrations of pesticides and
heavy metals. Surface water samples are taken near the swimming beach 24 and 48 hours after
all events (maximum of 7 events) of greater than 1 inch of precipitation. These samples are
analyzed for fecal coliform bacteria levels. Analyses of bottom sediment and fish are also part
of the monitoring program. Bathymetric mapping of the lake bottom profile was performed
in 1971,1980, and 1986. A final survey will be made in 1991.
1 upper reach (8' deep)
2 mid-lake (11'deep)'-
3 dam (24* deep)
Prairie Rose Lake State Park
Figure 4.2 Prairie Rose Lake RCWP Monitoring Sites (after Monitoring Report, 1986).
BMP Implementation Achievements
Several BMPs were installed under other cost share programs prior to the RCWP. These
practices included contour farming on 1,000 acres, grassed backslope terraces protecting 528
acres, and two sediment control structures. The number of soil conservation practices imple-
mented increased substantially with financial incentives offered under the RCWP project.
4.4
-------
Project Analysis: Prairie Rose Lake RCWP
The project was well received by landowners, and contracting for BMP implementation
progressed quickly. By September 1983, 75% of the critical area was under contract. The
project estimates that, as of September 1986, soil losses were reduced 62% from 80,800 to
30,900 tons per year. Assuming a delivery ratio of 0.32, sediment delivered to the lake was es-
timated to have been reduced, from 26,300 to 9,900 tons (Progress Report, 1986). Most of this
reduction was attributed to BMP installation in 1982.
Contracting was completed in 1985, bringing the total critical acres under contract to
3,239, or 83% of the critical area (Table 4.1). Seventy-four percent of the critical area is con-
sidered treated (2,900 acres). The project estimates that the majority of cropland not under
conservation tillage is farmed using some form of reduced tillage.
Table 4.1 BMP Installation in Prairie Rose Lake RCWP as of October 1986 (Progress Report, 1986).
BMP No. Description
1 pasture seeding
4 terraces
7 waterway systems
9 conservation tillage
12 sediment control structures
15-16 fertilizer & nutrient management
Units Installed
32 acres
50.3 miles affecting 1,760 acres
10.1 acres
560 acres
13 structures under RCWP
10 farms affecting 828 acres
Commodity control programs which offer annual financial incentives to set aside corn
land have had a significant effect on land use within the project area. In 1983, about 20% of
the total cropland, in the project area was set aside under the Federal Payment In Kind (PIK)
program. Corn set aside data for years in which RCWP has been active are listed in Table 4.2.
Table 4.2 Corn Acreage Within the Prairie Rose Lake RCWP Set Aside Under Commodity Programs
(1981-1987) (Carter and Coenen. 1987).
Year
Corn Acres Set Aside
1981 20
1982 128
1983 757*
1984 175
1985 203
1986 565
1987 877
* PIK Program
4.5
-------
WATER QUALITY ANALYSIS
Objectives
This project has a consistent water quality monitoring program, and land use and land
treatment activities are well documented. Therefore, the data should allow a test of whether
sediment control practices can produce measurable improvement in lake water quality over
the 10-year RCWP monitoring timeframe.
Specific objectives of this analysis are:
1. Examine data from water quality monitoring (pre-RCWP and RCWP) and
bathymetric mapping to determine if any trends have been documented.
2. Perform covariate analysis of water quality data to determine if any statistically sig-
nificant trends can be documented. Precipitation and chl a covariates are ex-
amined for effects on variability in water quality monitoring data.
3. Examine association of land treatment and corn acreage set aside with water
quality measurements.
4. Calculate minimum detectable change (MDC) in water quality needed to docu-
ment significant improvement in water clarity of Prairie Rose Lake.
5..Examine effect of discontinuing monitoring until the last two years of RCWP on
the project's opportunity to show significant trends in water quality.
The project has observed persistent high turbidity levels following runoff events. The
project hypothesizes that if sediment delivered to the lake is decreased by BMPs, greater light
penetration triggers algal growth. Consequently, decreases in turbidity from lowered levels of
suspended solids (SS) may be compensated by increases in algal turbidity.
We selected covariate analysis to determine what portion of the variability in water clarity
(i.e., Secchi depth, turbidity) could be accounted for by algal growth and precipitation. This
approach allows for a better understanding of the true changes in water clarity after adjust-
ment for chl a and precipitation.
Minimum Detectable Change (MDC) in Secchi depth and turbidity over the monitoring
period, after correcting for algal concentrations and antecedent precipitation, is of particular
interest to the project. MDC is the amount of measured change in a water quality parameter
required before it is considered real and not an artifact of system variability.
The project would like to discontinue monitoring until the last two years of the RCWP
(1989-1990). This would leave one to three years of monitoring gap in their data set. The
MDC required in Secchi depth and turbidity, corrected for known explanatory variables such
as chl a and precipitation, is calculated for a gap in monitoring and compared to the MDC re-
4.6
-------
Project Analysis: Prairie Rose Lake RCWP
quired if the project monitored continuously through 1990. Such a comparison sheds light on
the opportunity for the project to document water quality trends using either monitoring
strategy.
METHODS
Inspection ofPre-RCWP Water Quality Data
We examined a summary of the available pre-RCWP water quality data for evidence of
water quality problems prior to 1981. The water quality data from lake sampling in the sum-
mers of 1974 and 1979 and tributary sampling following runoff events in 1979-1980 were used.
Inspection of Bathymetric Mapping Data
Bathymetric mapping surveys in 1971 and 1980 provide an indication of the lake sedimen-
tation rate prior to the RCWP. The 1985 survey data were examined for indications of a
reduced sedimentation rate under the RCWP.
Preliminary Inspection of RCWP Water Quality Data
We examined Secchi depth at the surface and turbidity, TP, OP, chl a, NO3-N,
NH3 + NH4 at both the surface and bottom of lake sites. Most of the attention was given to
Secchi depth, turbidity, TP, OP, and chl a. These data were retrieved from STORET and cor-
rected for discrepancies with the project's annual reports. The lower detection limit for inor-
ganic-N, OP, chl a, and NH3 + NH4 were reported when samples were below the detection
limit. When the STORET code indicated that the actual concentration was less than reported,
we divided the reported concentration by 2. This corresponds to one-half the detection limit.
The lake site monitoring data collected during the RCWP were examined for suitability
for parametric statistical analysis, i.e., residuals normally and independently distributed with
constant variance. Residuals in the analysis of site data over time were approximated by sub-
tracting the mean value for the site-depth-year from each observation. These residuals were
calculated on both original and log transformed scales for surface and bottom sampling data
(pooled over sites). The Kolomogorov-D test (sample size n>50) and the Shapiro-Wilk W
test (sample size n<50) (SAS Institute Inc., 1985) were used to test the normality of these
data, and log transformations were made as necessary.
Examination of Geometric Mean, Minimum, and Maximum for Each Site-Depth Over Time
The geometric mean and range of Secchi depth, turbidity, chl a, TP, and OP for each site,
depth, and year were plotted. The geometric mean is the antilog of the mean of the log values
and closely approximates the median value of the sample distribution. Visual inspection of the
plotted values are discussed in light of the information presented in the project's annual
reports.
We .performed analyses of variance to compare geometric means between years and/or
sites for surface and bottom samples. Interactions between years and sites were examined to
evaluate evidence that the differences between the sites differed over time. Orthogonal con-
4.7
-------
trasts were performed to test for evidence of linear, quadratic, and cubic behavior over time.
Similarities and differences between the sites are discussed. Evidence that the lake may act as
a large sediment trap is discussed.
Comparison of Precipitation Data With Water Quality Measurements
Antecedent precipitation is thought to be an important variable in the lake system and
may account for some of the variability in water quality monitoring data. We sought adjust-
ment of water quality data for precipitation to minimize the effect of such variability on
measurement of water quality trends.
The daily precipitation data, exceptfor 1981 and May 1983, were obtained from the Prairie
Rose Lake RCWP annual progress reports. The 1981 daily rainfall was estimated from the
Iowa Department of Natural Resources gage approximately 10 km west of Prairie Rose Lake
at Harlan, Iowa. Missing rainfall data for May 1983 were estimated from the gage at Audubon,
Iowa, about 20 km to the northeast.
Plots of daily precipitation were compared with surface Secchi depth, turbidity and chl a
data. The total precipitation occurring during sampling periods was compared with the general
lake water clarity measured under RCWP monitoring.
Devefopment of a Precipitation Index
An index to represent the effect of antecedent precipitation that could be matched with
water quality data was developed as described in Appendix 4.A, The precipitation index is a
function of the magnitude of antecedent rainfall and the number of days since rainfall. It served
as a regression covariate for analysis of covariance to test for differences in the average value
of a water quality parameter between levels of group variables (i.e., years and sites) after ad-
justing for antecedent precipitation (i.e., values of the precipitation index associated with each
sample).
Multivariate covariate analysis with terms for the magnitude of the last precipitation event
and the number of days this event occurred prior to sampling was performed for comparison
to the derived index. This technique is also outlined in Appendix 4.A.
We found the best index related to water clarity was the product of the magnitude of the
last rainfall event and e^~ /, where t is the number of days since precipitation. No improve-
ment was found using the multivariate model.
Adjustment of Water Quality Measurements for Antecedent Precipitation: Analysis of
Covariance With the Precipitation Index as a Covariate
The water quality parameters studied were Secchi depth, turbidity, chl a, TP, and OP. Sur-
face and bottom samples were analyzed separately.
The first step in the analysis of covariance is to determine the statistical model which best
represents the. true relationships between the water quality parameters under study. The
simplest model, the equal slopes model, would allow values of a specific parameter to be
regressed on the precipitation index with each year and site represented by separate, parallel
4.8
-------
Project Analysis: Prairie Rose Lake RCWP
lines. In this simple model, the response, or slope, of each regression line is the same but the
intercepts or means can be different.
The equal slopes model cannot be assumed, however, without testing the slopes for
homogeneity or interactions. If the slopes are found to be different, then each line has its own
slope and intercept, i.e., the response of parameters for each site and/or year can have a dif-
ferent relationship to the precipitation index. Interaction terms are eliminated from the model
in a stepwise fashion until the simplest, appropriate model is determined.
After the appropriate model was determined, we calculated the mean values adjusted for
the precipitation index (the least-squares means) to compare sites and years. Each yearly mean
was adjusted to an overall common value of the precipitation index to remove some of the ef-
fect of differing precipitation patterns. Tests were also performed to determine if site means
could be pooled to obtain a more powerful comparison over years. The adjusted least-squares
yearly means were compared to the yearly means unadjusted for precipitation.
Evidence for trends in the adjusted yearly means for Prairie Rose Lake data were tested
by using orthogonal polynomials (linear, quadratic, or cubic) given by Fisher and Yates (1943).
Adjustment of Water Clarity Measurements for Chi a and Precipitation
We assumed that water clarity is expressed by Secchi Depth and turbidity measurements.
The analysis of covariance technique presented above was performed to compare Secchi depth
and turbidity least squares means of sites and years after adjusting for the covariates of chl a
and precipitation. Evidence for trends over time in water clarity was examined.
Association of Water Clarity With Land Treatment
Estimates of sediment delivered to Prairie Rose Lake from 1981 to 1986 were obtained
from the project's annual reports (assuming a sediment delivery ratio of .32). These numbers
represent the tons of soil saved as a function of RCWP BMPs, i.e., terraces, conservation til-
lage, and sediment retention structures. An additional term for number of corn acres set aside
with cover crop under the Acres Conservation Reserve (ACR) annual federal commodity
reduction program was examined for its effect on variation in water quality parameters.
Linear association of water quality with land treatment was investigated. Analyses of
covariance were performedsimilar to the technique used with the precipitation index and chl
a covariates. A term for either sediment delivered to the lake or a term for set-aside com land
was used instead of the YEAR term. Then, a linear relationship between the yearly means of
water clarity parameters and of each land use parameter was tested using orthogonal polyno-
mials given by Fisher and Yates (1943). In addition, multiple regression models with both land
management terms were tested.
Minimum Detectable Change
Minimum Detectable Change (MDC) is the amount of change in the yearly geometric
means required over time to be statistically significant (Spooner et. al., 1987). MDC repre-
sents the change required over time to be considered real and not an artifact of system
variability. The procedure used to calculate MDC is given by Spooner et. al. (1987). ;
4.9
-------
After correcting for algal concentrations and antecedent precipitation, the MDC in Sec-
chi depth and turbidity over the monitoring period was calculated for two different monitor-
ing schemes: 1) ten years of continuous monitoring; or 2) continuous monitoring from 1981
to 1986 and again from 1989 to 1990, with no monitoring in 1987 and 1988. The effect of using
chl a concentrations and precipitation as adjustment covariates was examined. Also, the rela-
tive efficiency of using linear regression versus t-test as a statistical technique to determine
water quality trends was examined.
MDC is a function of the variability in water quality parameters within sites and between
years. This variability is measured by the mean square error (MSE) obtained from an ap-
propriate regression model. A better estimate of the variability, one with more degrees of
freedom and better accuracy, can be obtained; using the MSEs from a model that pools
residuals across sites. However, to use such a model the variance within each site must be
similar for all sites. To test for similar variance among sites, we ran regression models over
time to obtain MSE values (variances) for each site and depth. Bartlett's homogeneity of
variance test (Snedecor and Cochran, 1967) was performed on these MSE values. The surface
sites did not have statistically different MSEs and their residuals were pooled for calculation
of MSEs used to determine MDCs. The same procedure was performed with the same results
for bottom sites. The MSEs obtained using this procedure approximate a weighted average of
the variance within each site over time while allowing the variation due to site differences to
be excluded.
The next step in this technique is to determine the MSE values needed to calculate MDC
values for each parameter at both surface and bottom locations. For each parameter, the
regression model included a Slit term with 2 (number of sites - 1) degrees of freedom, a
linear year term with 1 degree of freedom. Terms for precipitation and chl a were selectively
included as adjustment covariates. The MSE from these models were obtained and the MDC
calculated for monitoring schemes with 8 and 10 years of monitoring data.
The magnitude of observed changes in water quality parameters measured from 1981 to
1986 was calculated and compared to the calculated MDC values. The observed change for a
given water quality parameter was expressed as a percent change in the predicted geometric
mean values from 1981 to 1986 relative to the predicted geometric mean value in 1981. The
predicted geometric mean values were calculated from the linear regression equations used
to estimate the MDC values. The predicted values for 1981 and 1986 were calculated by sub-
stitution of the mean values of the precipitation covariate and chl a into the regression equa-
tions containing only the precipitation covariate and also from the regression equations
containing both the precipitation and chl a covariates terms.
RESULTS AND DISCUSSION
inspection ofrre-RCVv'F Wafer Quality Data
Pre-RCWF water quality data are available from lake monitoring studies conducted in
1974 and 1979. RCWP lake monitoring began in 1981. Monitoring sites in 1974 and 1979 were
4.10
-------
Project Analysis: Prairie Rose Lake RCWP
not the same as the RCWP sites. The arithmetic means of parameters sampled in 1974,1979
and 1981 are listed in Table 4.3.
Table 4.3 Lake Surface Arithmetic Mean Values (sample number in parantheses beside mean value).
Parameter 19741 19792 19813
Secchi depth (inches) 36.7(3) 23.6(5) 22.1(10)
Chla(ug/l) 17.3(3) 38.6(8) 19.6(10)
Turbidity (JTU) NA. 14.1 (8) 10.0 (10)
TP(mg/lP) 0.06(3) 0.32(9) 0.08(5)
NO3-NO2 (mg/1) ' 0.52(3) 0.64(2) 0.13(5)
NH3 + NH4(mg/l) 0.05(3) 0.12(2) 0.69(1)
1 two sampling sites
2 ona sampling site
3 two sampling sites - average from RCWP surface sampling at sites 2 and 3
In 1974, Prairie Rose Lake was sampled three times at two sites as part of the National
Eutrophication Survey (Monitoring Report, 1981). The water was reported to be turbid. Mean
values in 1974 do not depict a water quality situation worse than in 1981 when RCWP began
(Table 4.3). In the summer of 1979, a few samples were taken from the upper mixed zone of
the lake for the Clean Lakes Classification Study. The mean values are very similar to those
measured in 1981 at the start of the RCWP. During 1979-1980, water quality monitoring was
conducted at the Elm Creek Tributary (Figure 4.2) during five rainfall-runoff events. Average
storm flow turbidity was 2,031 JTUs. The average TP and OP concentrations were 1.65 and
0.72 mg/1 P, respectively (Monitoring Report, 1981). Stream flows ranged from 1 to 20 cfs. Un-
fortunately, monitoring at this site stopped in 1981. The project estimates that the nonpoint
contribution from Elm Creek represents approximately 13% of the entire watershed area.
Relative loading sources from other water inputs have not been reported by the project.
Inspection of Bathymetric Mapping Data _
Lake volumes calculated from the bathymetric mapping surveys are shown in Figure 4.3.
The pre-project data show that sedimentation caused a loss of 18.7% (381 acre-feet) of the
lake volume to occur between 1971 and 1980. The rate of lake sedimentation between 1980
and 1985 was less than the rate estimated during the period 1971 to 1980. The bathymetric
mapping scheduled near the project's completion will hopefully confirm an observed reduced
sedimentation rate under the RCWP.
4.11
-------
1972 197H 1976 1978 1980 1982 198f 1986
YEflR
Figure 4.3 Prairie Rose Lake Volume Estimated by Bathymetric Mapping.
Preliminary Inspection ofRGWP Water Quality Data
The RCWP water quality data were inspected for applicability to parametric statistics
with and without the logarithmic transformation (Table 4.4). For surface sample data pooled
over sites, the log transformation was required to meet normality distribution requirements
for turbidity, Secchi depth, and OP. The data were normal for both original and log trans-
formed values for TP and chl a. The original scale for NOs-N was normal but the log trans-
formed scale was not. From inspection, it appears that NOs-N follows a uniform distribution.
For bottom sample data, all the log transformed variables were normal except NHs + NH4
which were not normal but closer to normal than with the original scale. On the original scale,
T? and NOs-N also exhibited normal properties.
The log transformation for aii variables was used in the subsequent analysis. Results were
presented on the original scale as geometric means.
4.12
-------
Project Analysis: Prairie Rose Lake RCWP
Table 4.4 Kolomogorov-D Tests for Normality of the Water Quality Measurements for both the Original
Scale and Log Transformed Values (pooled over sites)
Location
in Water
Column
Surface
Original Scale-
Visual Inspection
of Cumulative
Parameter D-Stat. Prob > D Distribution
Secchi Depth .13 <.01
Turbidity .15 <.01
Chl-a .07 .13
TP .14 <.01
OP .07 .05
NOa .09 .02
NHs + NH4 .13 * <.01
poor
poor
good
poor
ok
good
ok
D-Stat.
.06
.05
.09
.09
.08
.10
.12
Logarithmic Scale
Visual Inspection
of Cumulative
Prob > P Distribution
.13
.15
.02
.02
good
good
good
good
good
poor
ok
Bottom
Turbidity
Chl-a
TP
OP
NOa
NH3 + NKt
.26
.08
.15
.10
.08
.16
<.01
.03
<.01
<.01
.02
<.01
poor
ok
poor
ok
good
poor
.06
.08
.05
.05
.08
.10
.11
.04
>.15
>.15
.06
<.01
good
good
ok
good
good
ok
Examination of Geometric Mean, Minimum, and Maximum for Each Site-Depth OverTime
Figure 4.4 shows the yearly geometric means for Secchi depth, turbidity, chl a, TP, OP,
NOs-N, and NHs + NH4 for each site-depth from 1981 to 1986, Depth refers to location in the
water column, surface or bottom. Figure 4.5 shows the same geometric means with the mini-
mum and maximum values measured per site-depth-year. From examination of Figure 4.5, it
can be seen that there is considerable variability within each year. This variability makes sig-
nificant trends difficult to detect over a short timeframe. However, the variance within a site-
depth-year for a given water quality parameter is similar between sites. For example, the
variability at site 1 is not greater relative to sites 2 and 3.
Trends over time:
Figure 4.4 displays the concentration values unadjusted for known influencing factors
such as rainfall or land treatment. The data show that water quality was poor in 1981 but im-
proved dramatically in 1982 and 1983. It then deteriorated until 1986 to near 1981 levels for
chl a, turbidity, Secchi depth, and OP. TP had relatively high levels with a decrease in 1982
only. NHs + NH4 levels appear to be stable after a decrease from 1981 values; but the frequen-
cy of monitoring for this parameter has increased substantially, so the direction of NHs + NH4
trend is difficult to estimate. Nitrate-N has been high throughout the monitoring period.
4.13
-------
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80 1981 1982 ' 1903 1981 1905 1986 . , |9BO 1961 1982 1963 I98i 1985 1986
YEWt tE(W
Figure.4.5 (continued)
-------
10
1000
5
U
5
g
3
u
10
1.0
1980
' I''
1981
SURFACE
' I1' ''
1982
1981
1981
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1985
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1986
1000
Figure 4.5 (continued)
*x
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25 100
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-------
Analysis of variance verified statistically that the data exhibit cubic behavior (i.e., fit a
cubic polynomial). For all variables in surface and bottom samples at least one of the years
differed from one or more of the other years (p < 0.01). From examination of mean values,
years 1981,1985 and 1986 are similar but statistically different from 1982 and 1983.
The project believes that the poor water quality in 1981 is due primarily to runoff events
and resuspension of bottom sediment by carp. No algal blooms or submergent aquatic weed
growth were observed in this year, but chl a was high. The project attributes water quality im-
provements in 1982 to sediment retention BMPs as well as proper nutrient management prac-
tices. The lower algal productivity in 1982 as compared to 1981 may be due to decreased
phosphorus concentrations. The project noted growth of submerged vegetation in 1982 and
attributed this to improved water clarity in that year. .
In 1983, the project noted low turbidity in the first half of the sampling season and high
turbidity in the second half..They attribute the increased turbidity to a greenish coloration of
the water from the algal populations. The chl a, TP, and OP concentrations were similar to the
1981 values, but there was an algal bloom reported for 1983, the first since the RCWP began.
This suggests that as light penetration improved, algal growth may have become phosphorus
limiting. Algal assays confirm this scenario. If phosphorus is limiting, further BMP implemen-
tation efforts should emphasize the use of practices effective in reducing P delivery to the lake.
Aquatic weed growth was noted along the entire shoreline in June and first week in July, 1983.
Compared to 1981 data, there was no substantial improvement in 1984, 1985, or 1986
values for all of the water quality parameters. In fact, surface turbidity was higher in 1985 and.
1986 as compared to 1981,' and mean Secchi depths were comparable to 198-1. In 1984, there
were no reported algal blooms. In 1985, algal blooms and aquatic weed growth were limited
to shallow coves and a marsh area. High turbidity levels were recorded at the upper reach of
the lake and near the dam after a rainfall-runoff event on July 18 and 19, 1985. In 1986, in-
creases in algal populations started in July, with heavy growth in shallow coves after rainfall.
Chl a concentrations increased in July. There was no aquatic weed growth in 1986, likely due
to stocking of white amur (fish that eat aquatic weeds).
The project hypothesizes that sediment delivery to Prairie Rose Lake has decreased. They
report that sharp increases in turbidity levels noted after heavy rainfall before RCWP are no
longer_a problem. This is a visual observation which is investigated statistically below. The
project presents the scenario that improved water clarity allows increased algal growth which,
in turn, causes an increase in turbidity that masks the effects of land treatment as measured at
the lake. The accuracy of this scenario is investigated for statistically significant documenta-
tion below.
Comparison of lake sampling sites:
One would expect that water quality at site 1, the shallowest site (8 feet) at the upper reach
of the lake (inlet), would respond more rapidly to changes in the quality of lake inflow than
water quality at site 3, the deep area (24 feet) near the dam (outlet). There is no evidence for
any parameter, however, that the relative behavior of site means changed for any of the years
of monitoring; the magnitude of the difference between site 1 and sites 2 and 3 remains fair-
4.22
-------
Project Analysis: Prairie Rose Lake RCWP
ly constant. In fact, the low significance level for the YEAR'SITE interaction term is evidence
that the relative relationships of the geometric means between sites was the same in every year.
The project reports that Prairie Rose Lake acts as a sediment trap (i.e., water clarity should
be greatest at the deepest part of the lake where sediment has settled to the bottom). Our
analysis of differences in parameter concentrations between sites supports this theory. The
statistical tests for differences in water quality between sites were significant only for Secchi
depth, turbidity at the surface, and chl a at the bottom.
Water clarity, as measured by Secchi depth, increased with proximity to the dam and with
increasing lake depth. Mean yearly Secchi depth was lowest at site 1, in all years (Figure 4.4).
Surface turbidity measured at the upper reach of the lake (site 1) is greater than near the dam
(sites 2 and 3). The highest turbidity levels occurred at site 1 and were related to relatively
large rainfall events prior to sampling (Monitoring Report, 1986).
Although the lake does appear to act as a sediment trap, this description is most ap-
propriate when sediment is the primary source of lake turbidity. The project has observed lit-
tle difference in Secchi depth between sites 1 and 3 after rainfall-runoff events. The project
feels that water clarity is affected by algae or finer materials such as clay, which remain
suspended in the water column, rather than solids, which have a tendency to settle out
(Monitoring Report, 1986). It is important to note that water clarity may not be influenced
solely by sediment contributed from the watershed. .
Although not statistically significant, chl a at the surface is slightly higher for site 1 rela-
tive to sites 2 and 3; but this difference is enhanced significantly in the bottom samples, imply-
ing a relatively greater photosynthetic production level at site 1.
Differences are expected between sites due to mixing within the water column, proximity
to inlets and outlets, and lake depth. Mean turbidity values at the bottom for sites 2 and 3 are
much greater than at the surface for all years. This may indicate resuspension of bottom sedi-
ments. Site 1 exhibits the same pattern but to a lesser extent during 1981 to 1983. During 1984
to 1986 mean surface and bottom turbidity levels were similar at site 1, indicating a more
uniform water column due to mixing. The mean Secchi depth and surface turbidity measure-
ments indicate that sites 2 and 3 were clearer than site 1 at the surface. This may be due to
greater mixing in the shallow upper reach of the lake (site 1) relative to the outlet (site 3), or
a greater algal population, or sediment entering the lake, or a combination of these.
In 1981 and 1982, the mean chl a concentrations for surface and bottom samples were
similar. This phenomenon continued in 1983 at site 1. In the remaining years, chl a at site 1
was slightly higher at the surface than the bottom. This is surprising since much higher con-
centrations would be expected in the surface zone where light penetration occurs. The smal-
lest differences between surface and bottom chl a concentrations occurred at the relatively
shallow site 1. From 1983 to the present, chl a was much lower at the bottom than the surface
for sites 2 and 3. In contrast, turbidity at sites 2 and 3 was higher at the bottom than the sur-
face. This implies that high turbidity at the bottom may be due to resuspension of lake bottom
sediment, at least since 1983,
4.23
-------
Typically, levels of TP and OP vary with inputs from the watershed, resuspension of
nutrient-rich sediments on a lake bottom, and uptake and release from aquatic plants. There
was no statistical evidence of differences among sites for TP and OP data for Prairie Rose
Lake.
Within sites, phosphorus concentration was greater at the bottom then the surface for
sites 2 and 3, but similar for both depths at site 1. The project speculates that there is greater
suspension of phosphorus laden sediment and/or algae at site 1, as indicated by the nearly
equal concentrations throughout the water column. They suggest that this results from wave
action or fish stirring up bottom sediments at sampling site 1 near the shallow upper reach of
the lake (Monitoring Report, 1981). .
Comparison of Precipitation With Water Quality Measurements
Figure 4.6 shows annual precipitation recorded during the RCWP sampling periods (May
- September) from 1981 to 1986. Values for surface Secchi depth, turbidity, and chl a are
plotted for comparison. From visual inspection, there is a relationship between high rainfall
events and low.water clarity. Furthermore, there is strong similarity between sites 1,2, and 3
in their magnitude and direction of change for any given parameter, high variability within a
year, and no apparent trend over time.
The total precipitation during the monitoring period for each year is given in Table 4.5.
Precipitation in 1983 was lower than in the other years. Note that 1983 had the highest water
clarity (as measured by Secchi depth). Water clarity in 1982 was relatively high despite rela-
tively high total rainfall during the monitoring period: The response time of Prairie Rose Lake
wajer quality to precipitation is thought to be rapid, and the precipitation occurring within a
few days of monitoring may have a large effect on the value of measured parameters. Thus,
use of a precipitation index paired with each water quality sample in the analysis of coyariance
may be informative.
The yearly mean value of the precipitation index was calculated (Table 4.5). The mean
value was at a minimum in 1983 but also low in 1982 and 1984. Conversely, it was high in 1981.
Table 4.5 Total Precipitation During RCWP Sampling Period (May - September) and Yearly Mean Value
of Precipitation Index from 1981 to 1986.
Year Total Precipitation finches) Precipitation Index Mean
1981 15.95 .65
1982 18.96 .25
1983 7.34 .20
1984 17.25 .23
1985 10.7 .36
1986 21.75 .45
4.24
-------
5 3
s
'< 2
Project Analysis: Prairie Rose Lake RCWP
1.0 4-
1000 4-
1.0 4-
1000 4-
L '.
1.0 --
1981
1982
1983
1984
1985
1986
Figure 4.6 Measured Precipitation, Secchi Depth, Surface Turbidity, Surface Chi a for Sites 1, 2,
and 3. Each sampling year is May 1 - September 30.
4.25
-------
Adjustment of Water Quality Measurements for Antecedent Precipitation
Measurements of water quality in Prairie Rose Lake are highly variable. Some of this
variation has been shown above to be due to rainfall events prior to sampling. Ideally, we would
like to test if sediment contribution to Prairie Rose Lake is greater in years of events of higher
rainfall before sampling; and if the data are adjusted for antecedent precipitation, did the sedi-
ment contribution to the lake decrease over time? The latter is difficult to answer because the
sediment content of the water column was not measured directly. Instead, Secchi depth and
turbidity were measured and are assumed as surrogates for water clarity.
Analyses of covariance were performed using the independent variables of the water
quality parameters and the precipitation index as the covariate. The question asked in this
analysis is: Are the yearly mean concentrations different between years and sites after adjustment
for an index of prior rainfall?
Selection of the appropriate statistical model:
1 The appropriate statistical model was selected by testing the interactions between years
and sites, years and the precipitation index, and sites and the index for statistical significance.
For all the water quality parameters at both surface and bottom, there was no significant in-
teraction between the years and sites. This implies there was no evidence that the sites dif-
fered in their relative behavior from year to year. The SITE and YEAR terms were kept in
the model, allowing each to have its own intercept or mean in the regression of the water quality
parameters on the precipitation index. .
__ . ;»- ,^^__
For Secchi depth, turbidity, TP, and OP, there was no evidence that the response to an-
tecedent precipitation was different between years. However, there was significant evidence
that the response of surface chl a concentration to rainfall was not consistent from year to year.
Therefore, the slope for chl a plotted against the precipitation index was allowed to be dif-
ferent between years for surface samples. A common slope response to rainfall for each year
was used for the other parameters and depths.
The relationship of surface chl a was examined for each year. The slope was significant
and positive for 1983 and 1985, but not significant and negative in the other years. This implies
that chl a concentration at the surface increased after rainfall events in 1983 and 1985, but was
not affected by rainfall events in the other monitoring years^
For ail the parameters except surface turbidity, there was no indication that the sites
responded differently to precipitation levels. This was tested by the SITE*INDEX interaction.
In surface turbidity, the significance level was low (p < .06). The slope of log surface turbidity
vs. the precipitation index was 0.28,0.14, and 0.11 for sites 1, 2, and 3, respectively. This sup-
ports the theory that prior rainfall has a greater influence on surface turbidity at the inlet to
the lake. For simplicity and due to the low significance level, a common slope response for
each site was assumed for all parameters.
4.26
-------
Project Analysis: Prairie Rose Lake RCWP
In summary, the appropriate statistical model for surface chl a was a regression of chl a
against the precipitation index allowing a different slope and mean for each year
(YEAR'INDEX interaction term), allowing each site to have a unique mean (Silt term),
but keeping the slope relationship of chl a vs. precipitation index constant over the sites (no
SITE*INDEX interaction term). For all the other water quality parameters, the same model
was used, except the slope relationship of the parameter vs. precipitation index is kept con-
stant over years (no interaction terms).
Interpretation of the statistical models:
Table 4.6 lists the appropriate terms used in the analysis of covariance models and the
significance level of each of these terms. The R2 values are also given as an indication of the
percentage of the total variation in each of the water quality parameters that was explained by
the year-to-year, site-to-site, and precipitation variations. The last column is the R values ob-
tained from similar models which did not include the precipitation index term. This allows a
comparison to determine the amount of variation in the water quality measurements explained
by the addition of precipitation information.
Table 4.6 Analyses of Covarjance Models That Examine the Adjustment of Water Quality Measurements
for Antecedent Precipitation. The Significance levels of Each of the Model Terms and the R2
Values are Given.
Location
in Water
Coiumn
Surface
Bottom
Significance of appropriate terms a
Parameter Year Site PPT-lndex Year* Index -B2
Secchi Depth . **
Turbidity **
Chl-a **
TP **
OP **
Turbidity **
Chl^a . **
TP **
OP **
**
**
ns
+
ns
ns
**
ns
ns
**
**
ns
ns
**
**
ns
**
**
na
na
«*
na
na
na
na
na
na
.54
.62
.44
.24
.26
.41
.28
.28
.32
R2 -
(without
PPTb
.49
.56
.37
.24
.20
.29
.28
.22
.25
B4(YEAR»INDEX) + Error
* The analysis of covariance model:
log(y) - Bo + Bi(YEAR) + Bj(SrrE) + B3(PPT-INDEX)
Where: log(y) = log (water quality parameter)
Bo ». Intercept
B, - Coefficient on YEAR term with 5 (# of years-1) df
82 o Coefficient on SITE term with 2 (# of sites-1) df
83 = Coefficient on INDEX term with 1 df
B4 - Coefficient on YEAR'INDEX interaction term with 5 df
** There is significance evidence (p < .01) that at least one geometric mean was diferent from the other.
In the case of the YEAR'INOEX term, this indicates a significant interaction.
* There is significance evidence (p < .05) that at least one geometric mean was diferent from the other.
+ There is significance evidence (P< .10) that at least one geometric mean was diferent from the other.
ns There was no evidence that the means were different.
na Not applicable for this parameter.
b This R2 is for a model without the precipitation index term and is given for comparison to the R2 from the model with precipita-
tion to show the percent of variation in the water quality parameters that can be explained by the additional information supplied
by prior precipitation events.
4.27
-------
As can be seen in Table 4.6, the sites were found to be different for Secchi depth, surface
turbidity, surface TP, and bottom chl a. There was a significant difference between years for
all parameters. Secchi depth, and surface and bottom turbidity and OP all had a significant
relationship to precipitation. TP in the bottom samples appeared to be correlated with rain-
fall. Chl a at the bottom and TP at the surface were not correlated with rainfall. Chl a in sur-
face samples had a positive correlation with rainfall for 1983 and 1985 only, however, no
correlation was evident in the other years.
The addition of the precipitation index term explained an additional 5 to 12 percent of
the measured variation for the parameters that showed a correlation to this index term (Table
4-6).
Adjusted yearly means for a common precipitation index value:
Each yearly mean was adjusted to an overall common value of the precipitation index.
The mean values for site 1 differ from sites 2 and 3, however, the relative differences remain
constant over the monitoring period. Therefore, the least squares means were calculated for
a given year pooled over sites. Comparing means pooled over the 3 sites between years gives
a stronger and simpler representation of the changes from year to year. Figure 4.7 shows the
adjusted least squares means compared to the yearly means unadjusted for precipitation.
Adjusted means were found to be more appropriate (relative to unadjusted means) for
comparison over time because some of the differences in water quality that may be due to dif-
ferences in precipitation patterns, but not related to land treatment and other influences, are
included in th$ model. :-.-_
*
This procedure showed that the addition of precipitation does not strongly influence the
relative behavior of the parameters over time. There is an apparent conflict in that the addi-
tion of precipitation explained a significant amount of the variation in water quality data within
each site and year, but the adjusted yearly means with and without this covariate were not very
different (Figure 4.7).
4.28
-------
LOCflr=SURFflCE
Si
o
c
0.
Ul
Q
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O
O
til
(f\
1983 198H
YEflR
1985
15*6
COVARIATE:
None
PPT. INDEX
Figure 4.7 Comparison of Adjusted Yearly Means for Common
Precipitation Index to Unadjusted Yearly Means.
- LOCflT=SURFflCE
SO 4
Q
m
or
1981 198^1 1983 198H 1985 193d
YEHR
LOCftT=BOTTOM
03
o:
3-
ri "Vf r~pnr i
1931 1935 1983 198S 1985 19P6
YEflR
1
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tt
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XT
-------
LDCnT=SURI:RCE
LOCflT-SURFflCE
!£.
q
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55 4
5-
"--*-*! None
.- - g PPT.' INDEX.
1981 1982 1983 1980 1935 1986
YErtR
0.20 n
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0.01 -
1981 1982 1983 1984 1985 1986
YEflR
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r
a
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'pfTT'ri-i
1981
l.]Cm=BOTTOM
1982
1903 1981
YEflR
1S85
1986
o>
O
a
10
o
i
a.
0.20 -I
0.01 -
LOCflT-BOTTOM
1981 1982 1983 1981 1985 1986
YEflR
Figure 4.7 (continued)
-------
LOORT=SURFflCE
0.01
o
.005 -
1981
COVRR «*--+-* None
O Q O PPT. INDEX
1982
1983
1981
i '
1985
'I
1986
CO
YEflR
LOCflT=BOTTOM
0.01 ^
Figure 4.7 (continued)
Ul
X
a.
in
o
£
o
§
.005 -
1981
1982
1983
1981
1985
1986
YEflR
3
§
91
«<
a
i
?
-------
Precipitation probably was an important factor in water quality values, but there were
other sources of unexplained variability (i.e., terms not in the statistical model) that were in-
fluencing the system, also. For illustration of this point, we looked at the mean values for Sec-
chi Depth (log transformed) and the precipitation index for each year (pooled over sites). For
each year, a relative Secchi depth was calculated by subtracting the mean Secchi value over all
the years from each year's value. In a year with relatively good water clarity, this relative Sec-
chi Depth was positive. The same procedure was used to calculate a relative precipitation value
for each year. The relative Secchi depths and precipitation values for each year are plotted in
Figure 4.8.
Q ^
i
3J !
o £
uj S
£, 3
o
t gs
oe I-
0 &
cc. Z
e
-0:3 -
-0.2 \
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0.2 -
0.3 -
. 1
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.
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x|C '82
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lllll.,.'-I.II.M.I...II!ll,lll :
-0.3 -0.2 -0.1. -0:0 -0.1 0.2
RELflTIVE PPT (IKCREASING> )
0.3
Figure 4.8 Relative Yearly Mean Values of Water Clarity and Precipitation Index.
Note that water clarity was relatively poor in 1981,1984,1985, and 1986, but the range in
relative precipitation was very large between these years. The relative water clarity for 1982
and 1983 was good, but the relative precipitation value was not much different than 1984. The
large range in precipitation values over years of similar water clarity values does not allow for
large adjustments in the yearly mean values for precipitation. Therefore, the adjusted mean
values were not much different from the mean values unadjusted for varying precipitation.
Trend Inspection:
Visual inspection (Figure 4.7) suggested a nonlinear, or cyclic, relationship. The least
squares means were tested for polynomial linear, quadratic, and cubic behavior. The sig-
nificance levels of these polynomial terms are given in Table 4.7. The direction of the linear
trend (if significant) is also given. There was evidence of a linear trend in all the parameters
4.32
-------
Project Analysis: Prairie Rose Lake RCWP
except bottom turbidity and TP. This trend indicates a degradation of water quality over time.
These same trends were observed when the precipitation index was not in the regression
model, but the magnitude of the water quality degradation is less after correction for precipita-
tion.
Table 4.7 Evidence of Linear, Quadratic, and Cubic Trends Over Time in the Water Quality
Measurements. Trends are from models with (a) no covariate, (b) precipitation index
covariate, (c) with both precipitation and chl a covariates. The direction of significant linear
trends are given.
Location
in Water ;
Column Parameter
-Orthogonal Contrasts
Linear Quadratic Cubic
Significance of Terms-
-Covariates2-
PPT
Surface
Bottom
Secchi Depth
Turbidity
Chl-a
TP
OP
Turbidity
Chl-a
TP
OP
**(ns)
**(ns)
**
*
**
ns
**
ns
**
**
**
ns
*
ns
**(ns)
ns
ns
ns
**
**
**
**
**
**
**
**
**.
None PPT PPT & CHL a
Direction of Linear Trend
ns
+ + ns
(ns)
ns
+
ns
ns
+
ns
ns
'. Significance of terms in models with (a) no covariate, (b) precipitation index covariate, (c) with both precipitation and chl a
covariates. Significance for model (c) is expressed in parentheses if different from model (b). ..
** There is significance evidence (p<.01) for a linear trend over timo.
* There is significance evidence (p < .05) for a linear trend over time.
ns There was no evidence of a linear trend.
2. Covariates used in the models.
+ The mean parameter values are increasing over time.
The mean parameter values are decreasing over time..
ns There was no evidence of changes over time in the mean parameter values.
Adjustment of Water Clarity Measurements for Chl a and Precipitation
The project has documented a significant relationship between chl a and water clarity as
measured by surface turbidity and Secchi depth (Monitoring Report, 1984). This supports the
theory that, although suspended sediment levels were decreased by BMPs, increased light
penetration triggered algal growth and, thus, Secchi readings or surface turbidity have not im-
proved to the extent predicted by suspended sediment removal.
To investigate this theory, analyses of covariance were performed to compare Secchi
depth and turbidity least squares means of sites and years after adjustment for precipitation
and chl a. Adding chl a to the model should determine: What portion of the variation in the Sec-
chi depth measurements and turbidity can be accounted for by the algal growth and precipitation?
Is there a trend over time for water clarity after making these adjustments?
4.33
-------
Selection of the appropriate statistical model:
The interaction terms between the sites, years, precipitation index, and chl a were ex-
amined for significance and, if not significant, eliminated in a stepwise fashion until valid statis-
tical models were obtained. There was no evidence that the three-way interaction between
YEAR, SITE and CHL a or the interaction between SITE and YEAR was significant for any
of the parameters studied. There was slight evidence that the relationship between surface tur-
bidity and chl a, and between Secchi depth and chl a differed between sites (p<0.10).
However, this difference appeared to be small, and the interaction between sites and chl a was
not included in the statistical model. There is also no statistical evidence of the influence of
precipitation on turbidity decreasing over time.
The appropriate covariance model for Secchi depth and surface turbidity included YEAR
and SITE terms adjusted for chl a, the precipitation index, and the interaction between YEAR
and CHL a. This interaction term was left in the model because there was evidence that the
relationship between surface clarity and chl a was not the same between years. Specifically,
there was a significant relationship between Secchi depth and chl a only in 1982, 1983, and
1984. There was a significant relationship between surface turbidity and chl a only in 1981,
1983,1984, and 1986. This interaction was not present for the bottom turbidity.
Interpretation of the statistical models:
Table 4.8 lists the appropriate terms in the analysis of covariance models and the sig-
nificance levels of each of these terms. The R values are also given as an indication of the
percentage of the total variation in each of the water quality parameters that was explained by:
the year-to-year, site-to-site, chl a and precipitation variations.
Surface and bottom turbidity and Secchi depth were related to chl a measurements. An
additional 21 and 26 percent of the variation in surface turbidity and Secchi depth, respective-
ly, was accounted for by adding chl a (Table 4.8 compared to Table 4.6). An increase of 11 per-
cent was noticed for the bottom turbidity. From models without the precipitation term, but
with the chl a term, the precipitation term was determined to contribute 4 to 11 percent pf the
total variability in water clarity measurements after chl a was included. Therefore, botti chl a
and precipitation were considered to be important parameters in the water clarity measure-
ments.
4.34
-------
Project Analysis: Prairie Rose Lake RCWP
Table 4.8 Analyses of Covariance Models that Examine the Adjustment of Water Quality Measurements
for Antecedent Precipitation and Chi a. (The significance levels of each of the model terms
and the R2 value is given.)
Location
in Water
Column Parameter
YEAR
.....^inn'rfi
SITE
cance of Apr.
CHI a
YEAR* CHI a PPT-lndex R2
Surface Secchi Depth ** ** ** ** ** .80
Turbidity ** ** ** ** ** .83
Bottom Turbidity ** ns ** na ** ,, .52
* The analysis of covariance model: . '
log(y) - Bo -I- Bi(YEAR) + Ba(SITE) + Ba(CHLA) + B4(YEAR*CHLA) + Bs(PPT-INDEX) + Error
Where: log(y) - log (water quality parameter)
Bo =» Intercept
Bi » Coefficient on YEAR term with 5 (# of years-1) df
82 = Coefficient on SITE term with 2 (# of sites-1) df
83 - Coefficient on CHLAterm with 1 df
B4 - Coefficient on YEAR'CHLA interaction term with 5 df
89 - Coefficient on PPT-INDEX term with 1 df
** There is significance evidence (p < .01) that at least one geometric mean was diferent from the other.
In the case of the YEAR'CHLA term, this indicates a significant interaction.
* . There is significance evidence (p < .05) that at least one geometric mean was diferent from the other
ns There was no evidence that the means were different.
na Not applicable for this parameter.
Adjusted yearly means for common precipitation index and chl a values:
The least squares means for each year corrected for chl a and precipitation were calcu-
lated. That is, the least squares means adjusted the yearly means to the same mean value for
chl a and the precipitation index. These values are plotted in relation to the models without
these adjustment terms (Figure 4.9). The adjustment for chl a and precipitation was minimal
for the bottom turbidity, but significant for the surface turbidity and Secchi depth. After ad-
justing the yearly means of turbidity and Secchi depth for these parameters the differences per
year are decreased relative to the unadjusted means.
Trend inspection:
There was no significant linear trend over time for Secchi depth and turbidity after cor-
rection for precipitation and chl a (Table 4.7). This is in contrast to the model without chl a
correction where water clarity was decreasing over time. The absence of a linear trend" over
time after adjustment for chl a and precipitation could be attributed to several causes: chl a is
not a completely accurate surrogate of algal growth; there is sampling error in the measure-
ments and sampling; there is significant bottom resuspension; there has been a varied amount
of land treatment over the project; the sediment delivered to the lake has not been reduced
to the extent estimated by the project; or the effectiveness of BMPs may have been overes-
timated. The water quality monitoring is limited to the lake which does not allow direct
measurement of nutrient or sediment inputs to the lake from runoff.
The lack of documented trends over time does not mean that the sediment delivery to the
lake is not decreasing, rather this monitoring scheme has not demonstrated significant trends.
4.35
-------
LOCflT=SURFflCE
LOCRT=SURFflCE
154
in
4J
O
c
I
a
o
5
o
o
Ul
55
1981
.V*"
1982
1983 1981
YEflR
198S
1986
10 +
fc
m
a:
3-
1981 1982 1983 1981 1985 1986
YEflR
. \
COVARIATEiS:
«K-+--* None
a D O PPT 4 CHLfl
10
o
5
ce
3-
1981
LOCRT=BOTTOM
1982
1983 1981
YEflR
1985
1986
Figure 4.9 Comparison of Adjusted Yearly Means for Common Precipitation Index and Chlorophyll a Values to Unadjusted Yearly Means
(pooled over sites).
-------
Project Analysis: Prairie Rose Lake RCWP
Association of Water Clarity With Land Treatment
The estimated sediment delivered to Prairie Rose Lake for 1981 to 1986 is shown in Figure
4.10. The number of corn acres set aside under ACR is shown in Figure 4.11. We believe that
the sediment delivery calculations include only the RCWP management effects and that the
com set aside acreage is an additional management factor in the watershed.
27500 -I
1981
1382
1983
198H
1985
1986
YEflS
Figure 4.10 Estimated Sediment Delivery to Prairie Rose Lake from the RCWP Project Area.
900 -
1982
1983 198"*
.YEflR
1985
1986
Figure 4.11 Corn Acres Set Aside Under Annual Federal Commodity Programs for 1981 to 1986.
4.37
-------
There was no evidence of a linear association between water clarity (as measured by sec-
chi depth or surface and bottom turbidity) and sediment delivered to the lake. A very small
percentage (< 2 percent) of the year-to-year variation was explained by the magnitude of es-
timated sediment delivered to the lake.
Reduction of corn acreage through annual set-aside programs, however, exhibited a sig-
nificant linear relationship with secchi depth and surface turbidity (p < .01 and p < .05, respec-
tively). This relationship explained about 10 percent of the year-to-year variability in secchi
depth and surface turbidity measurements. No association was found with bottom turbidity.
When terms for both land management factors are included in the model, there is some
evidence "(p < .10) that after accounting for the corn set aside acres, there is a linear relation-
ship between the surface water clarity measurements and sediment delivered to the lake.
Minimum Detectable Change
There has been some evidence from visual observations of Prairie Rose Lake that the
lake turbidity due to sediment is decreasing and the turbidity due to algal activity is increas-
ing. As discussed above, this is not supported by statistical examination of the grab sample
data. The latter suggest that although water clarity is affected by algal growth to a large extent,
it is also significantly affected by precipitation. There is no statistically significant trend in water
quality measurements for turbidity and Secchi depth in the 1981 to 1986 timeframe.
In the remaining years of the RCWP (1988-1990), this project has the potential to docu-
ment a water quality improvement in a lake using a simple grab sample monitoring scheme
because the project has a large amount of BMP implementation and a consistent water quality
monitoring program. From covariate analysis of the water quality data, it is evident that
variability in water quality parameters measured within monitoring sites and between years is
large. Therefore, in order to document water quality improvements we must find a significant
improving trend in water quality data of a great enough magnitude to be detected above sys-
tem variability.
To successfully document a change, the current monitoring scheme must be continued to
the project's end in order for the analysis to overcome the wide variations in parameters within
and between years. If monitoring is stopped until 1989 and 1990, several interpretation
problems occur. One or two years of monitoring data do not necessarily depict an accurate as-
sessment of the conditions of the water resource. For example, 1982 and 1983 lake samples
showed tremendous improvement in water quality as compared to 1981. However, these 2
years did not tell the whole picture when viewed in light of the next few years of monitoring.
In addition, the statistical power of determining real trends over time increases greatly with
continuous sampling records due to the ability to perform regression analyses (instead of t-
tests) and gain additional degrees of freedom.
Mean square error (MSE) calculation:
The MSE and R2 values from three models are given in Table 4.9. These models are
similar to those discussed in previous sections, except the YEAR term has only one degree of
freedom, i.e., a linear term only. This allows the year-to-year variation to be included in the
4.38
-------
Project Analysis: Prairie Rose Lake RCWP
MSE. Several observations can be noted. The R2 values are much lower than when the year-
to-year variation was accounted for in the models shown in Tables 4.6 and 4.8. This implies
that differences between the years did not exhibit a strong linear behavior. The strong influence
of both precipitation and chl a on Secchi depth and surface and bottom turbidity was still evi-
dent.
Table 4.9 Mean Square Error and R2 Values Obtained from Regression Models* With and Without
Precipitation and Chl a Covariates.
Location
in Water
Column Parameter
Surface Secchi Depth
Turbidity
Chl a
TP
OP
Bottom Turbidity
Chl a
TP
OP
Model
MSE
.065
.107
.157
.098
.136
.160
.135
.081
.197
'a2
.13
.29
.20
.04
.08
.01
.16
.01
.07
Model
MSE
.054
.088
.157
.098
.129
.124
.135
.078
.179
2
a2
.29
.42
.21
.04
.13
.24
.16
.06
.16
Model
MSE
.028
.041
_
-
.094
.
. -
"
3
a2'
.64
.74
_
-
.43
.
- :
1 The 3 regression models used to calculate MSE values were:
1.log(y) - Bo + Bi(YEAR) + B^SITE) + Error ." *
2. log(y) - Bo + BifYEAR) + Ba(SlTE) + BsfPPT). + Error
3.log(y) - Bo + BifYEAR) + B2(SITE) + BofPPT) + B4(CHL) + BsfYEAR'CHL)) + Error
Where: The year term had only 1 degree of freedom. The site term had 2 degrees of freedom.
Calculation of MDC:
The MSE values were used to calculate an MDC estimate for the following monitoring
schemes. The MDC is expressed as a percent change relative to the baseline concentrations.
The MDC was calculated for 3 scenarios:
1. How much-change is required over the entire 10 years of RCWP to be statistically
significant if a linear trend over time is tested (i.e., using a linear regression)
2. How much change is required from the pre-BMP period to the post-BMP period
where the pre-BMP period is denoted by 1981-1986 (6 years) and the post-BMP
period is denoted by 1987-1990 (4 years) (i.e., using a t-test to compare the pre-
. and post-BMP period means).
3. How much change is required from the pre-BMP period to the post-BMP period
where the pre-BMP period is denoted by 1981-1986 (6 years) and the post-BMP
period is denoted by 1989-1990 (2 years) (i.e., using a t-test to compare the pre-
and post-BMP period means). .:
4.39
-------
The first two schemes utilize monitoring data from 1981 to 1990. The only difference is
in the statistical analysis employed. The BMPs were installed over several years, therefore a
linear trend over time in water quality improvements is expected to be a more accurate depic-
tion of the physical system and a linear regression should be a more powerful statistical test
relative to the t-test.
If the water quality monitoring is notperformed in 1987-1988 and continued in 1989-1990,
then the linear regression approach is not valid and the third scheme would be appropriate.
Table 4.10gives the MDCfor each of these questions. The effect of adding the appropriate
covariates to determine the system MSB can be also seen from this table. The MDCs for linear
regression test were adjusted in scale for comparison with MDCs estimated using the t-test.
The MDC calculated from a linear regression is the amount of change required between the
two extreme years of monitoring; the MDC calculated using the t-test is the change required
between the pre- and post- period means. If a linear trend is assumed then the calculated
change between the two extreme years corresponds to a smaller change between the midpoint
of the pre-period to the midpoint of the post-period. This adjustment in scale to compare the
regression MDCs to the t-test MDCs is:
MDCadj = MDCreg * (N/2)/(N-1)
Where: N = number of sampling years = 10
The MDC values decrease by 3 to 5 units if monitoring is performed for 10 years instead
of only 8 years. This means that the sensitivity in detecting a real change increases with longer
monitoring timeframes. Assuming a linear trend over time, the linear regression technique
adds an additional 3 to 5 percent sensitivity to detection of change. The addition of the
precipitation index covariate and the chl a covariate each increases the ability to detect a real
change by an additional 3 to 5 percent.
The MDCs required from 1981 to 1990, assuming a linear trend over time and the use of
both the precipitation index and chl a covariates, were calculated to be 18 and 22 percent for
secchi depth and surface turbidity, respectively (see footnote 1, Table 4.10).
Magnitude of observed changes in water quality parameters:
The percent change in predicted geometric mean values from 1981 to 1986 are given in
Table 4.11. They have been predicted from linear regressions over time with adjustment for
the precipitation and/or chl a covariates. The magnitude of these changes with correction for
precipitation were large and indicate a decrease in water clarity. After correction for chl a the
magnitude of change in Secchi depth and Turbidity was much less and not statistically different
from zero. It should be noted that the change in Secchi depth from 1981 to 1986 indicated an
improvement in water clarity, although not statistically significant. The surface turbidity indi-
cated a decreased water clarity in these 6 years. However, for only 6 years of monitoring, this
was not sufficient to be considered statistically real. The measurements in the water quality
parameters is very large and it may require at least 10 years of monitoring to detect any real
changes that are real and not artifacts of the system variability.
4.40
-------
Table 4.10
Project Analysis: Prairie Rose Lake RCWP
Minimum Detectable Change (MDC) Required between the Pre- and Post- Periods to be
Considered Statistically Significant. (The effect of 8 vs. 10 years of monitoring, statistical test,
and use of precipitation index and chl a covariates are examined).
Location
in Water
Column Parameter
MDC
10 Years Monitoring
Regression1 T-test2
Pprrent C.hanni
8 Years Monitoring
T-testa
a
A. No Covariate Adjustments:
Surface
Bottom
Secchi
Turbidity
Chl a
TP
OP
Turbidity
Chl a
TP
OP
14
18
21
17
20
21
20
16
23
B. Adjustment for Precipitation Index Covariate:
Surface .Secchi 13
Turbidity . 17
Chl a - 21
TP 17
OP 19
Bottom
Turbidity
Chl a -
TF*
OP
19
20
16
22
18
23
27
22
25
27
25
20
29
17
21
27
22
24
24
25
20
28
C. Adjustment of the Precipitation Index and Chl a Covariates:
Surface Secchi 10 12
Turbidity 12 15
Bottom
Turbidity
17
21
22
28
32
27
31
33
30
24
35
20
25
32
27
30
29
.30
24
34
15
18
26
\ The MDC required over then entire 10 years of monitoring (1981 compared to 1990) would be these MDC values given
multiplied by 9/5, a number almost twice as large. The values given here are for comparison to the t-test results and
represent the equivalent percent change between pre- and post- period means. For example, the MDCs required from 1981
to 1990, assuming a linear trend over time and the use of both the precipitation index and chl a covariates, would be 18 and
22 percent for Secchi depth and surface turbidity, respectively.
2. The MDC required between pre- and post- period geometric means, where the pre-period was 1981 to 1986 and the post
period was 1986 to 1990.
3> The MDC required between pre- and post- period geometric means, where the pre-period was 1981 to 1986 and the post
period was 1989 to 1990.
4.41
-------
Table 4.11 The Percent Change in Predicted Geometric Mean Values from 1981 to 1986.
Location
in Water
Column
Surface
Bottom
Parameter
Secchi
Turbidity
Chi a
TP
OP
Turbidity
Chi a
TP
OP
Observed
Percent
Change
(adjusted for ppt)
-33
256
225
26
97
24
117
9.2
122
Observed
Percent
Change
(adjusted for ppt. and chl a)
12
76
na;
na
na
-22
na
na
na
LITERATURE CITED
-~
Carter, J. and R. Coenen. 1987. Shelby County ASCS Office. Harlan, Iowa. Personnel communication.
Fisher, R.A. and F. Yates. 1943. Statistical Tables for Biological, Agricultural and Medical Research. Oliver
and Boyd Ltd, London. , .
Monitoring Report. 1981. Prairie Rose Lake Monitoring RCWP Project-Year 1 (1981). March, 1982.
Monitoring Report. 1984. Prairie Rose Lake Monitoring RCWP Project. November 30,1984.
Monitoring Report. 1986. Prairie Rose Lake Monitoring RCWP Project - Year 6.
Progess Report. 1986. Prairie Rose Lake Monitoring RCWP Project - Year 6.
SAS Institute, Inc. 1985. SAS Users' Guide: Basics, Version 5 Edition. Gary, NC: SAS Institute Inc. 1290 pp.
Snedecor, G.W. and W.G. Cochran. 1967. Statistical Methods. 6th Edition. The Iowa State University Press,
Ames, Iowa.
Spooner, J., R.P. Maas, M.D. Smolen, and C.A. Jamieson. 1987. Increasing The Sensitivity Of Nonpoint
Source Control Monitoring Programs. In: Symposium On Monitoring, Modeling, and Mediating Water
Quality. American Water Resources Association, p. 243-257.
ACKNOWLEDGMENTS
The author would like to acknowledge Joann Carter and Rosie Coenen, Shelby County ASCS, Iowa.
4.42
-------
APPENDIX 4.A
DEVELOPMENT OF A PRECIPITATION INDEX.
Definition of a Precipitation Index
We developed an index variable to represent the magnitude and influence of antecedent
precipitation events for each sampling date as follows:
Each precipitation event received an index value defined as the product of the precipita-
tion amount (inches) and an exponential multiplier to adjust for the number of days before
the sampling date that rainfall occurred. The equation is:
Precipitation index = (Precipitation) * e^'
i Where: k = a constant value selected from the following list: 1, .5, .4, .3, .2, or .1
t = days since sampling date that precipitation occurred, i.e., t=0 for
precipitation on the sampling date.
Smaller values of k give greater influence to antecedent precipitation events occurring farther
away from the sampling date because the index has a negative exponential multiplier.
Selection Procedure
*' .
-." - -
Twelve different indexes were calculated and matched with each sampling date's water
quality data. Six of the indexes include only^the most recent rainfall events using one of the 6
values of k (1, .5, .4, .3, .2, or .1). Another set of six indexes were computed as the cumulative
index values from precipitation events since the last sampling date.
These 12 possible precipitation indexes were potential candidates for covariance vari-
ables that could help explain the observed variation in water quality parameter values. To ob-
tain an index that would explain the most water quality variation, an analysis of covariance
similar to that used in Chapter 4 was performed. Water quality parameters (log of Secchi depth,
turbidity, TP, OP, arid chl a), were regressed against values of each index assuming a separate
intercept and regression line for each year with a common slope among years. This regression
model had a YEAR term with (number of years - one) degrees of freedom^ allowing for pool-
ing of samples over years while correcting for different means between years. The analysis of
covariance was performed for each of 3 sites and 2 depths. In addition, the 3 sites were pooled
and the analysis was performed for each depth.
Results of. the analyses were examined to determine the index that explains the most varia-
tion in each of the water quality parameters for each site and depth, pooled over years. To
evaluate the relative.effectiveness of each index, the R2 term and the significance level for the
index coefficient in the regressions were compared for each water quality variable and statis-
tical model. The R values represent the percentage of the water quality variation that can be
attributed to both year-to-year variation and the precipitation index covariate.
4.43
-------
In order to determine if the precipitation index could be valuable in the overall water
quality analysis, R2s were compared between models with and without an index. If the index
covariate is not included in the model, the statistical analysis is equivalent to an analysis of
variance (ANOVA) in which parameter means are compared between years. If the index
covariate is determined to be important, R2s are compared between regression models using
the different indexes. If the R2 increases as the values of 'k' decrease, the increased influence
of rainfall events farther from the sampling date is indicated.
We found that Secchi depth was highly related to the precipitation index, especially if the
sites were pooled. Turbidity at the surface and bottom was also highly related to rainfall; at
the surface, site 1 exhibited the most significant relationship with precipitation, implying that
the upper reach of the lake, site 1, is affected more by recent rainfall than sites 2 and 3. Chi a
does not seem to be influenced by precipitation for any site or depth.
For TP, the precipitation index did not explain variations measured in the surface samples.
There was, however, some indication that precipitation influenced the bottom samples
(p < .05). The precipitation index covariate became highly significant when bottom samples
were pooled over sites. Surface OP for sites 1 and 2 and bottom OP for all sites seemed to be
influenced by prior rainfall events. The index covariate became highly significant when the
sites were pooled for both the OP surface and bottom samples.
Given the suggested relationship between precipitation and water quality parameter
values, the selection of the best index covariate was determined by comparison of R s (see
Table 4.12). In general, when k = 1, the index performed poorly for all parameters and depths.
When k < 0.5, the index was more significant relative to when k = 1. This implies that
precipitation events removed from the sampling date were important in explaining variation
in the water quality measurements and should not be ignored.
The optimal k value was slightly different for each parameter. For Secchi depth, k values
of .4, .3, and .2 for the most recent rain or the cumulative index were equivalent. Using the
cumulative index gave a slightly better model. For turbidity, k = .5, .4, .3, or .2 for the most
recent or cumulative index were equivalent. For TP and OP, the index from the most recent
rainfall event was better than the cumulative index and the index value with k = .2 was best.
However, values of k = .1 or .3 were almost as good. Overall, k values from .2 to .5 were al-
most equivalent when sites were pooled. The R values from the regression models with and
without the index term are given in Table 4.12. Models based on each site and depth were also
examined with similar results, except k = .l gave an inferior fit relative to k .2, 3,.4, or .5.
Values of k = .3 were slightly better than other k values for most parameters, thus, the index
chosen for use in subsequent analyses was k = .3 using only the most recent rainfall. This index
was matched with each sampling date for the analyses in the Chapter 4.
4.44
-------
Appendix 4.A
Table 4.12 R Values from Covariate Analyses to Document the Effect of the Precipitation Index
Covariate (pooled over sites).
Location
~ln~water
Column Parameter
-R Values from Covariate Analyses
0).
No Precip.Terms
Surface
Bottom
Secchi
Turbidity
Chi a
TP
OP
Turbidity
Chi a
TP
OP .
.49
.56
.37
.24
.20
.29
.28
.22
.23
1 The analysis of covariance model:
log(y) - Bo + Bi(YEAR) + BjfSITE) + BsflNOEX) + Error
Where: log(y) - log (water quality parameter)
Bo ~ Intercept
81 - Coefficient on YEAR term with 5 (# of years-1) df
Ba - Coefficient on SITE term with 2 (# of sites-1) df
63 - Coefficient on INDEX term with 1 df
ns
The precipitation index term was significant (p < .05)
The precipitation index term was not significant (P < .1) _.
With Precip. Index Terms
K = .1 k = .2 k= 3 k = .4 k = .5 k = 1
.54
.62
.37
.25
.26
.54
.62
.37
.25
.26
.54 (*)
.62 (*)
.37 (ns)
.25 (ns)
26 (*)
.54
.62
.37
.24
.25
.54
.62
.37
.24
.25
.53
.61
.37
.24
.22
.40
.28
.28
.33
.41
.28
.28
.33
.41 (*)
.28 (ns)
.28 (*)
.32 (*)
.41
.28
.28
.31
.41
.28
.27
.31
.39
.28
.24
.27
Multivariate Covariate Analysis as an Alternative to the Precipitation Index Covariate
Multivariate covariate analysis with separate terms for the magnitude of the last precipita-
tion event (PPT) and the number of days (DAY) prior to sampling is an alternative to develop-
ing a precipitation ind£x . This method has the advantage of simplicity, but adds more terms
to the analysis of covariance model decreasing the degrees of freedom for the model error
term.
Three types of multicovariate models were investigated: polynomial, logarithmic, and
square root transformations on the terms in the models. For each of these, a full model was
tested and stepwise elimination of non-significant terms was performed until all remaining
variables were significant. This was denoted as the 'best' model.
All of the models tested contained the YEAR and SITE terms. The full model for the
polynomial model included linear, quadratic, and cubic terms for PPT and DAY and all the
appropriate interactions. The full model for the logarithmic model contained linear and log
transformed values of the PPT and DAY terms and all the possible interaction terms (this is
called the transcendental transformation). The full model for the square root model contained
linear and square root transformations of the PPT and DAY terms and all their possible in-
teractions.
4.45
-------
^ ^^
The R values from the 'best' model are given in Table 4.13. For chl a at the surface, none
of the PPT or DAY terms for any model were significant. There was no evidence that the
logarithmic or square root transformations were superior to the polynomial models or the
precipitation index model discussed above for most of the variables. The exception was sur-
face TP, where a very complex full model with log or square root terms was significant. This
model may have very little physical meaning because of its complexity.
The precipitation index model developed was selected over the multivariate covariate
analysis for the analyses in Chapter 4.
Table 4.13 R2 Values for the 'best' Models from Multivariate Covariate Analyses to Document the Effect
. of the Precipitation and DAY Terms as Statistical Model Covariates.
Location
in water
Column
Surface
Bottom
-
Parameter
Secchi
Turbidity
Chl a
TP
OP
Turbidity
Chl a
TP
OP
9
R \/£)li 10*5 frrim
Polynomial Model
.56 (a)
61 (c)
.
.25 (c)
.31 (b)
.43 (b)
.31 (b)
27 (C)
.36 (b)
Loo Model Square Root Model
.57(3)
-
.31 (1) .32 (1)
.35(2)
.45(4) .44(4)
. T- .
* ' ' .
-
A. The analysis of covariance models: (Models 1-4 are for either log or square root models.)
1.log(y) - Bo + ^fYEAR)
Ba(log(PPT)»DAY) +
2.log(y) - Bo + BifYEAR)
Ba(log(PPT)»DAY) +
3. log(y) - Bo + Bi(YEAR)
B8(log(PPT)-DAY) +
4.log(y) = Bo + Bi(YEAR)
a.log(y) = Bo + Bi(YEAR)
D. iog(y) = Bo + BiCrEAR)
c.log(y) = Bo + Bi(YEAR)
B2(SITE) + B3(PPT)
B4(PPT)
B4(DAY)
B4(DAY)
B4(DAY)
B3(log(PPT)) H- B8(log(DAY))
B5(log(PPT)) + B8(log(DAY))
Bs(log(PPT))+ Be(log(DAY)) -
B7(PPT«OAY) +
-t-B9(PPT1og(DAY))
Bio(log(PPT)
BrfPPT'DAY) +
+89(PPTtog(DAY)) + Error
B7(PPT«DAY) +
-(-Bror
B<(DAY)
B4
-------
Chapter Five Project Analysis
Taylor Creek - Nubbin Slough RCWP, Florida
ABSTRACT
The Taylor Creek - Nubbin Slough RCWP Project area is located directly north of Lake Okeechobee in
southern Florida. The watershed covers 110,000 acres of which 63,109 acres have been identified as critical
agricultural sources of phosphorus entering Lake Okeechobee. These sources are primarily improved pas-
tures and dairies.
The project has an extensive pre-BMP water quality data base for statistical comparison with post-BMP
data. In addition, most of the BMP implementation occurred in 1985,1986, and 1987, allowing for 4 to 5 years
post-BMP water quality monitoring before the end of the project. Therefore, this project should be able to
document land treatment effects on water quality.
We analyzed water quality, monitoring data from in-stream sampling to determine the magnitude of
measured concentration change (minimum detectable change, MDC) inTP and OP required to say with con-
fidence that the change is real. High variability in the hydrologic system contributes to a high MDC. The im-
pact of adjustments for precipitation, seasonality, upstream concentrations, and ground water levels on reduc-
ing the MDC were investigated. The MDC for TP ranges from 1010*59 percent over 9 years of monitoring after
adjustments for available covariates. MDC was found to be a function of subwatershed size and variability in
covariates such as antecedent precipitation, ground water levels, season, and upstream concentrations.
The RCWP land treatment in the watershed emphasizes stream protection, animal waste management,
vegetative cover, and grazing land protection. We found a significant decreasing trend for TP in three sub-
watershed and at the outflow from the project area. These trends appear to be related to RCWP land treat-
ment under RCWP and to dairy closures.
/
INTRODUCTION
Background
The Taylor Creek - Nubbin Slough Basin is located directly north of Lake Okeechobee
in southern Florida. The watershed covers 110,000 acres of flat land with generally coarse tex-
tured soils. The water table is high and standing water occurs in low areas during the summer
months, May to October. Water flow from the basin enters Lake Okeechobee through a flow
control structure, S-191 (Figure 5.1).
Lake Okeechobee is a Class I water resource covering 480,000 acres. The lake is a primary
water supply for five cities along its shoreline and a secondary water supply for the eastern
coastal metropolitan area from West Palm Beach south to Miami. The lake supports commer-
cial fishing (valued at $6.3 million annually), sport fishing (valued at $22 million annually)
(Bell, 1987), a significant tourist industry, and habitat for many migratory as well as endemic
5.1
-------
bird species. Water from the lake is also used to irrigate about 500,000 acres of vegetable crops,
row crops, sugar cane and pasture.
High phosphorus (P) concentrations in Lake Okeechobee promote eutrophic conditions
that impair all water uses. Agricultural NFS pollution has been documented as a significant
water quality problem in the Taylor Creek - Nubbin Slough (TCNS) watershed (Allen et al.,
1982). The TCNS Basin contributes 27% of the external phosphorus load but only 4% of in-
flowing water to the lake (Frederico, 1981).
The Lake Okeechobee Technical Advisory Committee (LOTAC) (1986) has recom-
mended a 40% reduction in all phosphorus loadings to the lake to protect long term water
quality using the Vollenweider Model. From a management perspective, P loadings from the
TCNS basin would need to be reduced by 75% to 90% for achievement of this objective. Can-
field (1988) suggests that 40% reduction of P loadings to the lake may have a minor impact on
the short term quality, reflected by the P-concentration in the lake, because the lake has a sub-
stantial P reserve. Although changes in Lake Okeechobee's P impairment may be undetec-
table over a short timeframe, we emphasize that monitoring of external loadings provides
valuable information that can be used to project long term impacts of land treatment in sur-
rounding watersheds.
Land use in the watershed is primarily agricultural. There are 24 dairy bams with ap^
proximately 28,000 cows. There are 56 beef cattle ranches grazing about 25,000 head on im-
proved pastures that are ditched and fertilized. Citrus groves occupy approximately 1,400 acres
and require extensive drainage and irrigation. The main sources of high phosphorus loads in
the watershed are thought to be stock animals (dairy cows and beef cattle) excreting while
wading in streams to relieve heat stress and runoff from improved pastures (Stanley et al.,
1986). Streambank erosion from animals lounging in the streams is also thought to be sig-
nificant.
Project Perspectives
The primary objective of the project is to reduce phosphorus loading to Lake Okeechobee
by installing BMPs. Analysis of the project's water quality data and findings can be used to ad-
dress the following questions.
1. Can BMPs decrease the contribution of phosphorus to the lake from pastures lo-
cated on sandy coastal plain soils that are heavily grazed by dairy cows and beef .
cattle?
2. Can hydrological adjustment variables such as depth to ground water and
precipitation be used to correct for some of the measured variation in pollutant
concentration in a biweekly grab sample monitoring program?
3. What magnitude of measured water quality pollutant changes over time are re-
quired to be considered real, and not artifacts of hydrologic system variability?
4. Are we capable of detecting real changes in this highly variable hydrologic system?
5.2
-------
Project Analysis: Taylor Creek - Nubbin Slough
Figure 5.1 Talylor Creek - Nubbin Slough Basin. Water Quality Trend Stations'and Ground Water
Wells are Indicated.
Land Treatment Strategy f
About 63,109 acres have been identified as critical areas needing treatment. This includes
all dairy farms, all beef cattle ranches that have been extensively drained, and all areas within
one quarter mile of a waterway. The project has the following three treatment goals: 1) reduce
phosphorus and nitrogen loadings from the project area to Lake Okeechobee by 50%; 2) have
at least 75% of the critical area under contract for BMP implementation; and 3) have all dairy
farms in the project area under contract (Stanley et al., 1986).
The general treatment strategy was to install BMPs which exclude dairy cows and beef
cattle from waterways and control wastewater runoff from dairy barns. Principle BMPs used
are animal waste management systems, reduction of barn waste by improving water use ef-
ficiency and better effluent disposal such as spray irrigation, diversion systems, grazing land
protection systems, stream protection systems, permanent vegetative cover, sediment reten-
tion; erosion or water control structures, and improving irrigation and/or water management
5.3
-------
systems. Dairy closures independent of RCWP activities may also affect water quality within
the basin.
Studies by Heatwole et al. (1986,1987a) have employed water quality models (CREAMS-
WT and BASIN) to evaluate the cost effectiveness of basin-wide BMP implementation
projects. Heatwole et al. (1987b) used the BASIN model to give an estimate of the expected
long-term average annual response of the TCNS basin to a hypothetical 'maximum' BMP
scenario. They predicted reductions of about 50% in the annual phosphorus loads from this
basin.
BMP Implementation Achievements
The BMP contracting period ended in 1986 and the project clearly has achieved its con-
tracting implementation goals. All dairies in the project area are under contract and 89% of
the total critical area is under contract. BMP implementation is complete on 78% of the criti-
cal acres under contract (Stanley et al., 1986). Management BMPs are being used ori 51,396
critical acres and installed BMPs are being used on 24,368 critical acres. Most of the implemen-
tation occurred in 1985,1986, and 1987. This allows for a baseline pre-BMP period of 4-6 years.
RCWP BMP implementation by subwatershed is given in Table 5.1. Dairy cow numbers, BMP
emphasis, and land use changes are given in Table 52. Beef numbers and the acres involved
for both dairy and beef are currently being compliled by the project.
Water Quality Monitoring Strategy
Grab samples are taken biweekly at 23 stream stations (Figure 5.1), sortie monitored since
1978. Samples are analyzed for total-P, ortho-P, nitrate-N, nitrite-N, ammonia, total kjeldahl
nitrogen, pH, conductivity, turbidity (NTU), and color. Flow measurements have been taken
at five stations since 1978 and at the remaining stations since 1983. Precipitation and hourly
ground water levels have also been monitored at sites in close proximity to stations 01,03,06,
09,11, and 23. Monitoring under the RCWP will continue until 1991. The monitoring design
allows for comparison of the pre-, during-, and post- BMP implementation periods. There are
many pairs of upstream- downstream monitoring stations to adjust for pollutant concentra-
tions originating above the BMP implementation sites.
5.4
-------
Table 5.1
Project Analysis: Taylor Creek - Nubbin Slough
BMP Implementation Under the RCWP by Subwatershed and Year for the Taylor Creek -
Nubbin Slough RCWP Project Area.
Subwatershed
NW Taylor Creek
Acres
Critical 11.865
Contracted 10,916
Total 12,203"
Year
1978
1979
1980
1981
1982
1983
1984
1985
1986
/**«*
Installed
BMPs
0
0
0
0
0
0
2010
3070
4488
Management
BMPs
0
0
0
0
0 -
8237
6956
6956
10092
% Critical
Acres
Implemented
0
0
0
0
0
4.6
16
24
63
Acres
Implemented
0
0
0
0
0
546
1856
2838
7532
Little Bimini
Acres
Critical 4.050
Contracted 4,050
Total 3.7T6
Onef Creek
(incl. E. Otter Crk.)
Acres . '
Critical 10,753
Contracted 10,487
Total 10,753
Main Taylor Creek
Acres
Critical 6,464
Contracted 4,809
Total 11.031
1978
1979
1980
1981
1982
1983
1984
1985
1986
1978
1979
1980
1981
1982
1983
1984
1985
1986
1978
1979
1980
1981
1982
1983
1984
1985
1986
0
0
0
0
539
671
1055
1717
2113
0
0
0
0 '
830
1952
2744
4768
5530
0
0
0
0
0
88
584
2709
4430
0
0
0
0
155
3487
3514
3789
3966
0
0
0
0
0
6000
7172
10372
10080
0
0
0
0
0
675
1426
2972
4521
0
0
0
0
13
18
26
44
91
0
0
0
0
6.8
15
24
44
75
0
0
0
0
0
0
8.9
39
67
0
0
0
0
527
729
1053
1782
3686
0
0
0
0
734
1573
2622
4719
8075
0
0
0
0
0
0
577
2549
4328
* Otter Creek, NW Taylor Creek, and Little Bimini are not perfectly defined hydrologically. There is-an additional 8077 acres in the
Taylor Creek Headwaters defined by these 3 subwatersheds, but these are not critical.
(Table 5.1 continued on next page)
5.5
-------
Table 5.1 (continued)
(acres) % Critical
Subwatershed
Williamson Ditch
Acres
Critical 9.774 »
Contracted 9,689 .
Total 21,026
.
Mosquito Creek
Acres
Critical 4,101
Contracted 3,663
Total 12.836
"
9
Nubbin Slough
Acres . .
Critical 7.091
Contracted 6,978
Total 11,934"
Henry Creek
Acres
Critical 4,255
Contracted 2,445
Total 10,049
Lettuce Creek
Acres
Critical 4.756
Contracted 2,743
Total 16,247
The total area my be
Year
1978
1979
1.980
1981
1982
1983
1984
1985
1986
1978
1979
1980
1981
1982
1983
1984
1985
1986
1978
1979
1980
1981
1982
1983
1984
1985
1986
1978
1979
1980
1981
1982
1983
1984
1985
1986
1978
1979
1980
1981
1982
:1983
1984
1985
19S6
Installed
BMPs
0
0
0
0
0
0
636
2696
3296
0
0
0
0
0
0
0
392
809
0
. 0
0
d
- ' 0
545
864
1264
2993
0
0
0
0
0
0
367
367
506
' 0
0
0
0
0
0
0
199
208
Management
Acres
Acres
BMPs Implemented Implemented
0
0
0
0
0
2309
8431
8431
8220
0
0
0
0
0
0
0
3044
3124
0
0
o
0
0
2791
3850
5156
6617
0
0
0
0
0
2240
1896
1896
1938
0
0
0
0
0
0
1353
966
2661
larger by approximately 2800 acres with non-critical
0
0
0
0
0
1.0
6.9
25
99
0
. 0
0
0
0
0
0
5.8
45
0
0
0
0
0
0
1-1-
28
80
0
0
0
. 0
0
0
5.2
5.2
56
0
0
0
0
0
0
0
2.9
5.8
acreage
0
0
0
0
0
100
678
2422
9689
0
0
0
0
0
0
0
239
1832
0
0
0
0
d
0
768
1954
5652
0
0
0
0
0
0
220
220
2396
0
0
0
0
0
0
0
137
274
east of Mosquito Creek.
5.6
-------
Project Analysis: Taylor Creek - Nubbin Slough
Table 5.2 BMP Emphasis. Dairy Cow Numbers, and Land Use Changes by Subwatershed and Year in the
Taylor Creek - Nubbin Slough RCWP Project Area from 1978 to 1987.
Subwatershed &
BMP Emphasis
N. W. Tavlor Creek
Fencing,
Pasture Mgt.
; .
Little Bimini
Fencing,
Pasture Mgt.
Otter Creek
Fencing,
Diversion,
Waste water
utilization,
Pasture Mgt.
Main Taylor Creek
Fencing,
Pasture Mgt,
Ponds.
Williamson Ditch
Fencing,
Nutrient Mgt.,
Pasture Mgt.
Year
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1978
1979
1980
_ 1982
1982
1983
1984
1985
1986
1987
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
Dairy Cow
Numbers
5500
5500
5500
5500
6300
6300
6500
6500
6500
6500
3173
3639
4607
4476
4472
4702
4784
4966
4794
5039
8336
8121
8058
8214
8300
8303
8792
8097
7966
7759
2343
2350
2248
2361
3093
3987
2908
2978
3223
3138
2000
2000
. 2000
2000
2000
2000
2000
2000 -
2000
2000
Other Land Use Changes
- . _ . - -_-, . . -
'81 McArthur Farms changed from 600-600 beef cows to 400-500
dairy heifers & changed to high P concentration feed.
'82 1 stock watering pond installed outside a calf-
heifer operation.
'85 New dairy under construction.
'86 Improvement will depend on handling pasture runoff
from high intensity areas located in the headwaters drainage.
' * -r
June 25,1980 start F&R dairy shutdown, Aug 25 complete
shutdown (dairy was between stations 03 and 04).
~: -
July-Oct'83 maintenance operations by dragline in Otter Ck.
causing increased drainage runoff throughout Otter Ck.
1984-85, Wilson dairy (500-700 cows) buy out upstream of stn.
,23 their lagoon had discharged into Stn. 23.
'86 1 new dairy, 1 dairy closure due to DTP program.
Watershed has low intensity beef compared to other watersheds
with high intensity dairy.
'84 Construction of a new sewer treatment plant w/ a spray field.
5.7
-------
Table 5.2 (Continued)
Subwatershed &
BMP Emphasis
Mosauito Creek
Fencing, Pasture Mgt.
Bam waste water Mgt
Improved effluent disposal
at 500 calf operation
Improved existing seepage
fields plugging off direct
drainage to Mosquito Ck
Reshaping high intensity
pasture, eliminated many
ori-farm drainage ditches
to Mosquito Creek.
Nubbin Slough
Fencing,
Pasture Mgt.
Henry Creek
Improvements in 2nd
lagoon and calf
operation runoff,
Pasture Mgt,
Fencing.
Lettuce Creek
Spray irrigation from
Pasture Mgt.,
Fencing.
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1978
1979.
1980
1981
1982
1983
1984
1985
1986
1987
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
Dairy Cow
Numbers
7578
7528"
7110
8425
7143
5700
5635
5307
7152
5604
4307
4331
Oth'er Land Use Changes
4917
4957
4418
5428
5097
6124
6990
1805
1590
1492
1501
1346
1061
1138
1390
1800
1687
2000
2048
2014
1977
2091
1984
2041
2011
2000
2268
'81 Shutdown of 2 dairies (from 5 previously),
decrease from 5000 to 2000 a.u.
'85 2 dairies back into production.
'86 Lots of BMPs on 3 dairies upstream of stn.15.
78 All the dairies ate between stations 14 & 17.
Oct.02'81 Introduced dairy heifers upstream of station 17
Summer'86, breach of sediment basin downstream from stn. 14
directly influenced by dairy effluent 700 yards upstream.
~ 82-86, problem with secondary lagoon effluent & high
. intensity runoff from calf operation.
5.8
-------
Project Analysis: Taylor Creek - Nubbin Slough
METHODS
Total phosphorus (TP) concentrations from 15 of the Taylor Creek - Nubbin Slough water
quality monitoring stations were examined using parametric statistical analysis. Initially, the
standard distribution assumptions on the residuals were investigated. The residuals are the
differences between the predicted and the observed values from the planned statistical
analyses. Assumptions included normality, independence, and constant variance over time.
Residuals were approximated by subtracting the mean value for each station- year from each
observation. The project had previously noted that the concentration data were not normally
distributed and that the empirical distribution is controlled by a few outlier values (Ritter and
Flaig, 1987). This was supported by the Kolomogorov-D test (SAS, 1985a) on the ap-
proximated residuals. We used log transformation in all the analyses to minimize the violation
of the assumptions.
The TP concentrations measured at the water quality monitoring stations were examined
visually to establish the magnitude of the water quality problem. Discussion of the relation-
ship of TP concentrations between stations is included. Project staff believe TP concentrations
in the tributaries are related to the water table depth and antecedent precipitation (Ritter and
Flaig, 1987). Variability in the water table depths and rainfall is also discussed.
Several hydrological and meteorological indexes were developed to correspond with the
biweekly grab samples: 1) average water table depth one day, two days, three days, or seven
days prior to sampling, 2) a moving average of the last seven days precipitation-giving more
influence to larger magnitude events closer to the sampling date, and 3) an indicator variable
to separate the wet season (May 15 - October 15) from the dry season. This latter index could
be used for all stations and was not limited to those that had water table depth and precipita-
tion measurements. ""
We define the minimum detectable change (MDC) as the minimum change required in
a pollutant concentration over a given period of time to be considered real and not an artifact
of hydrologic system variability. The MDC is expressed as a percent decrease relative to the
initial geometric mean concentration. To clarify, MDC is the percent change over all years,
not a per year change, and depends on the number of monitoring years considered. The MDC
is also a function of the statistical tests employed, the covariates used in the analyses to 'adjust
or explain' the variability in the measured data, the presence of autocorrelation, the number
of samples taken per year, and the variability of the measured observations.
The MDC was calculated for each station assuming a linear trend over time as described
by Spooner et al. (1987). The standard deviations on the slope over time from linear regres-
sion models were utilized to calculate the MDC required. The Durbin Watson Test for
autocorrelation was performed to determine if TP concentrations were related to previous
measurements (SAS, 1985b). If autocorrelation is present, the following occur: (1) Standard
errors on the coefficients calculated by ordinary least squares without paying attention to
autocorrelation are not valid; (2) The true standard errors are. not those indicated by ordinary
least squares computer programs because ordinary least squares does not take into account
the presence of the missing lag variable(s); (3) The standard errors calculated by generalized
5.9
-------
least squares regressions which account for autocorrelationtire valid and are smaller than those
for the true standard errors. Note that neither the true standard errors from a model without
the autocorrelation term or from the correct autocorrelation model correspond to.the stand-
ard errors calculated by ordinary least squares computer programs; (4) The standard errors
on the slope calculated by the correct autocorrelation model will often be larger than those
incorrectly calculated by ordinary least squares. Thus, to be truly significant, a change must be
larger than is indicated by ordinary least squares. If autocorrelation is significant, it must be
accounted for in the regression models for the appropriate calculation of the standard devia-
tion of the slope over time. Autocorrelation was significant at every station. Therefore, we cal-
culated MDC after correcting for autocorrelation by the use of an autoregressive model of
order 1 (SAS,1985b). ' - . '
The MDC was also calculated after the addition of a covariate index for seasonality (wet
or dry). For the water quality monitoring stations in close proximity of water table depth and
precipitation data, the indexes for these variables were added to the autoregressive models
and a new MDC was calculated. In addition, for the Otter Creek, Mosquito Creek, and Nub-
bin Slough subwatershed, upstream TP concentrations were also added as a covariate in
autoregressive models on downstream TP concentrations. At station 06; covariates for water
table depth, precipitation, and upstream TP concentrations were used to calculate MDC
values. Although stream flow may have been a meaningful covariate, flow data was not avail-
able. Ground water table depth is thought by the project to be an surrogate covariate for the
project area hydrology.
The magnitude of observed changes in TP measured from 1978 to 1986 was calculated
and compared to the calculated MDC values. The observed change was expressed as a per-
cent change in the predicted geometric mean value from 1978 to 1986 relative t<5 the predicted
geometric mean value in 1978. The predicted geometric mean values were calculated from the
linear regression equations used to estimate the MDC values. Specifically, the predicted values
for 1978 and 1986 were calculated by substitution of the mean values of the covariates into the
regression equations.
Tests for significant changes over time (i.e. changes greater than the MDC) at each sta-
tion were performed. The significance and direction of the TP concentration change is dis-
cussed in light of land treatment in each subwatershed.
5.10
-------
Project Analysis: Taylor Creek - Nubbin Slough
RESULTS AND DISCUSSION
Total phosphorus and orthophosphate-phosphorus concentrations measured at the water
quality monitoring stations were examined visually to establish the magnitude of the water
quality problem. The OP and TP concentrations were of similar magnitude at all stations, in-
dicating that most of the phosphorus is in the dissolved phase. The TP and OP concentrations
at the outflow from the project area to Lake Okeechobee are plotted in Figure 5.2. Concentra-
tions of TP are scattered around 1 mg/1 with an apparent slight decreasing trend over time.
The TP concentrations measured in Taylor Creek (stations 18 and 11) range from 0.25
to 5 mg/1 with a majority around 1 mg/1. Northwest Taylor Creek (station 01) has TP values
ranging from 0.01 to 1.75 mg/1. This subwatershed has very little dairy activity and is used
primarily to raise beef cattle. Williamson Ditch and Lettuce Creek also exhibit moderate TP
concentrations ranging from 0.01 to 1.75 mg/1. The remaining watersheds exhibit much higher
TP concentrations. For example TP ranges from 0.5 to 7.5 mg/1 in Otter Creek at stations 03
and 06 (Figure 5.3). TP concentrations at station 23 on Otter Creek are commonly above 10
mg/1, although the total phosphorus load is relatively small due to low discharge. Outlets from
the subwatersheds Nubbin Slough, Little Bimini, Mosquito Creek, and Henry Creek have high
TP concentrations, varying around 4 mg/1. The concentration of TP at the project outlet (Sta-
tion S191) are lower than those in the tributaries upstream in the watershed probably due to
dilution and phosphorus removal mechanisms in the watershed (Figure 5.4).
The project believes that TP concentrations in the tributaries are related to water table
depth and antecedent precipitation (Rittef and Flaig, 1987). By their scenario, when the water
table rises to within 2 feet of the land surface, runoff occurs, increased TP concentrations in
the surface water. Monthly minimum, mean, and maximum water table depths are depicted
in Figure 5.5 for the Judson well monitoring station, close to monitoring stations 03, 06, and
23. The data show large variability in water table depths. In addition, a high water table occurs
during the wet season from May to October. The variability in monthly precipitation can be
also be seen in Figure 5.5. Low concentrations of TP occur when the water table depth is rela-
tively deep. This is a significant relationship (r = .35), although the scatter is substantial, im-
plying that water table depth does influence TP concentrations but is not the only factor.
5.11
-------
SITE=S191
01
IV)
en
Q.
O
a.
D
OH
O
I
Q-
(/)
O
I
CL
01JRN78 01JRN79 01JRN80 01JRN81 01JRN82 01JflN83 . OUflNSI 01JRN85 01JRN86 01JRN87
DRTE
Figure 5.2 Total Phosphorus and Orthophosphate Concentrations for Station S191, the Outflow to Lake Okeechobee
-------
Project Analysis: Taylor Creek - Nubbin Slough
OTTER CREEK - UPSTREflM
SITE-03
o>
a.
o
3-
2-
0-
-t-
CL
O
i
0.
(/>
i
Q.
01JRN78 01JRN79 01JRN80 01JHN81 01JflN82 01JHN83 01JflN8<» 01JRN8S 01JFIN86 01JRN87
OflTE -
OTTER CREEK - DOWNSTREflM"'.
SITE-06 .'"-..-
8-1
7:
6-
5-
3-
2-
-t-
-t-
Figure 5.3
.01JflN78 01JflN79 01JRN80 01JRN81 01JRN82 01JRN83 OUflNSf 01JRN85 01JRN86 01JRN87
DflTE
Total Phosphorus and Orthophosphate Concentrations for the Upstream/Downstream
Otter Creek Water Quality Stations.
5.13
-------
Ol
o>
O
X
CL
(/)
O
I
Q.
Q.
C1JRN78 01JRN79 OlJflNSO 01JRN81 01JRN82 01JRN83 01JRN84 01JflN85 01JRN86 01JRN87
DRTE
Figure 5.4 Total Phosphorus for Station S191 and Downstream Otter Creek Station 06 from 1978 to 1986.
-------
WELL-JUDSON
ui
u.
UJ
K.
UJ
01
_L
01
Q.
Ul
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to
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z
I
6-
01JRN78 01JRN79 01JRN80 01JRN8.I 01JRN82 01JRN83 OUflNBH 01JflN85 01JflN86 01JflN87
25-
20 -
15 -
10 -
5-
0-
OUfl
i
lll.l
». _(_ _.
Illnl
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illlillh.i.
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DflTE
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3
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«<*
w
w'
fit
O
(D
rlnsa nrovlmitv to wa'pt nuai'tw ctatior^ 03 OR ««r>H 9?
-------
The MDC for each of the water quality monitoring stations determined from the variability
in the data is shown in Table 5.3. It should be noted that these MDC values were calculated
for the biweekly sampling design and are expressed as a percent change over all years con-
sidered. MDC calculated in this fashion is not a yearly value, but a function of the number
years monitored. The autocorrelation term was used in all models including covariate models
with meteorological and hydrological indexes. Seasonality decreased the MDC values at some
stations; seasonality was a statistically significant covariate at stations S191,11,18, and 09. The
water table depth covariate was significant at all stations where data were available and
decreased the MDC at all sites except station 23. The addition of the precipitation covariate
was significant only at stations 06 and 23, and was not as effective as the ground water table
variable in decreasing the MDC values. Adjustments for these variables.should allow for a
more meaningful comparison between years with varying amounts of precipitation.
Table 5.3 Minimum Detectable Change Required in the Initial Geometric Mean Concentration of Total
Phosphorus at Each Water Quality Monitoring Station over a 9 Year Monitoring Scheme. All
data were adjusted for autocorrelation.
Tributary
(Station)
Water
- Table
None Seasonality Depth
Water Table
Depth &
Precip. Precip.
Upstream
Cone.
Water Table
Depth & Up- .
stream Cone.
Henry(39)
Little Bimini(02)
Lettuce Creek (40)
Mosquito Creek(13)
Mosquito Creek(1 5)
Nubbin-Slough(14)
Nubbin-Slough(l7)
N.W. Taylor Cr. (01)
LOkeechobee(Si9l)
Otter Creek(03)
Otter Creek(06)
Otter Creek(23)
Taylor Creek(11)
Taylor Creek(l 8)
Williamson Dt.(09)
54
54
66
28
27
25
35
37
11
41
32
50
32
39
35
53
- 47
59
28
28
25
35
33
10*
40
31
49
28*
35*
29*
' .
,
.
.
.
321
29'
25'
5V
27'
.
331
Percent Decrease
15*
27
33
40
32*
49*
29
33
31*
29*
25*
49*
27*
32*
19*
19*
* The covariate (s) was significant in the regression model. In the case where both water table depth and precipitation were
covariates, both covariates were significant for all stations examined except stations 01 and 09 where the precipitation covariate
did not add significant information to the models.
5.16
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Project Analysis: Taylor Creek - Nubbin Slough
The use of an upstream covariate was statistically significant and decreased the MDC
value substantially at the downstream stations 13 and 06, however, this was not the case at sta-
tion 14. The upstream concentrations represent incoming pollutant concentrations from
natural and agricultural sources upstream from agricultural areas where BMPs are imple-
mented.
Relatively small MDC values were obtained for site S191. This may be due to buffering
capacity, inertia, or ponding effects at this station. S191 represents a large watershed. Although
the MDC values may be relatively small, the time to achieve a significant change will not only
depend on the amount of effective land treatment, but also on" the amount of buffering capacity
in the water system at this site. In contrast, first order stream sites such as 23, 39, 40, 02 ex-
hibited high variability in the TP measurements and have relatively high MDC values. It ap-
pears that the variability of the measured observations, and therefore the MDC,.is a function
of several factors including watershed size, land use, hydrology, and meteorology.
The magnitude of observed changes (after adjustment for covariates) in TP measured
from 1978 to 1986 was calculated and compared to the calculated MDC values. The observed
change was expressed as a percent change in the predicted geometric mean value from 1978
to 1986 relative to the predicted geometric mean value in 1978 (Table 5.4). Tests for significant
changes over time (i.e. changes greater than MDC) at each station were performed and are
reported in Table 5.4. The. direction and significance of the linear trends are summarized in
Table 5.5.
Table 5.4 The Percent Change in Predicted Geometric Mean Values from 1978 to 1986 Measured at
the Water Quality Monitoring Stations After Adjustment for the Appropriate Covariates.
Tributary
(Station)
None Seasonality
Water
Table
Depth
v^uvai iciica
Water Table
Depth &
Precip. Precip.
Upstream
Cone.
Water Table
Depth & Up-
stream Cone.
Henry Creek(391)
Little Bimini(02)
Lettuce Creek(401)
.Mosquito Creek(13)
Mosquito Creek(lS)
Nubbin-Slough(14)
Nubbin-Slough(17)
N.W. Taylor Cr.(Ol)
L Okeechobee(Sl9l)
Otter Creek(03)
Otter Creek(06)
Otter Creek(23S)
Taylor Creek(11)
Taylor Creekh a2)
Williamson DL(09)
67*
-30
55
-36*
-51*
27*
262*
63*
-29
-43*
-69*
-16
-38*
-37
-21
67*
-29
53
-36*
-51*
27*
256*
64*
-29
-43*
-69*
-17
-38*
-40*
-21
m
t
m
f
f
t
55
f .
-41
-61
-21
-se
t
-Y<
Percent Decrease
-13
21
65*
-42*
-69*
-19
-38*
-21
54*
-41*
-68*
-21
-36*
-10
-62*
-62*
1. Percent change calculated from available 1981 to 1986 data (6 years).
2. Percent change calculated from available 1979 to 1986 data (8 years).
" Observed changes were sufficient to be statistically significant.
5.17
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Table 5.5 Linear Trends Over 9 Years for the TP Concentrations Measured at the Water Quality
Monitroing Stations.
Tributary
Henry Creek
Little Bimini
Lettuce Creek
Mosquito Creek
Mosquito Creek
Nubbin-Slough
Nubbin-Slough
N.W. Taylor Creek
Lake Okeechobee
Otter Creek
Otter Creek
Otter Creek Trib.
Taylor Creek
Taylor Creek
Williamson Ditch
Station Significance of Trend
39
02
40
13
15
14
17
01
S191
03
06
23
11
18
09
+
ns
ns
*
**
ns
**
.. *
- **
+
»*
ns
*
*
ns
Direction of Trend
Increasing
.
.
Decreasing
Decreasing
.
Increasing
Increasing
Decreasing
Decreasing
Decreasing
-
Decreasing
Decreasing
-
ns
Significant at the P =. 1. level
Significant at the P = .05 level
Significant at the P-.01 level
Not significant -
It should be noted that this project has had a high rate of BMP implementation, most of
which occurred in 1985 and 1986. Thus, a very extensive pre-BMP water quality data base ex-
ists for statistical comparison with post-BMP data.
Phosphorus concentrations in Otter Creek decreased significantly. However, there is
strong evidence that two dairy closures (in 1981 and 1985) in that subwatershed may be the
cause of this trend (Ritter and Flaig, 1987). Data from Mosquito Creek, a subwatershed with
intensive BMP implementation, also show a significant decrease inTP (Ritter and Flaig, 1987).
In contrast, increased animal densities and use of animal feeds with high P concentrations ap-
pears to have degraded water quality in the N.W. Taylor Creek subwatershed (Ritter and Flaig,
1987).
At station S191, the watershed outlet, an overall decreasing trend in TP concentrations
is shown. The project postulates that this trend is largely a function of the dairy closures in the
Otter Creek subwatershed and the large number of BMPs implemented in the Mosquito Creek
subwatershed. Fencing, manure management, and fertilizer management are thought to be
significant practices related to decreasing total phosphorus concentration. It should be noted
that the majority of BMP implementation did not occur until 1985,1986, and 1987, so major
improvements may not be documented until a few years of post-BMP data is collected.
5.18
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Project Analysis: Taylor Creek - Nubbin Slough
CONCLUSIONS
Water quality monitoring data from the Taylor Creek - Nubbin Slough RCWP agricul-
tural NFS control project were examined for significant trends in TP and OP. The statistical
analysis employed calculation of the minimum detectable change (MDC) required to say with
confidence that changes in pollutant concentrations over time were real. The MDCs forTP at
monitoring stations in this NFS project range from 10 to 59 percent after adjustments for
precipitation, seasonality and ground water level. MDC was found to be a function of sub-
watershed size and variability in covariates such as antecedent precipitation, ground water
levels, season (wet or dry), and upstream concentrations.
We found that three subwatersheds as well as the outflow from the project area show real
decreasing trends for TP (P = .05). These trends appear to be related to land treatment under
RCWP and dairy closures. RCWP is a 10-year experiment and the project should be able to
document further significant decreases in TP concentrations over time.
There are many confounding factors in water quality analysis and it is difficult to identify
and account for all of them. However, by using the MDC, it is possible to evaluate with con-
fidence real changes in pollutant concentrations over time. This technique can contribute to
improved analysis of the effectiveness of NPS control efforts.
LITERATURE CITED
Allen, L.H., Jr., J.M. Ruddell, GJ. Ritter, F.E. Davis, and P. Yates. 1982. Land use effects on Taylor Creek
Water Quality. Pages 67-77 In Proc. Specialty Conference on Environmentally Sound Water and Soil
Management. American Society of Civil Engineers, New York, New York.
Bell, F.W. 1987. Economic Impact and Valuation of the Recreational and Commercial Fishing Industries of
Lake Okeechobee, Florida. Department of Economics, Florida State University, Tallahassee, Florida.
Canfield, D.E., Jr. 1988. The Eutrophication of Lake Okeechobee: An Alternative Viewpoint. J. Lake and
Reservoir Management, Volume 4.
Frederico, A.C., K.G. Dickson, C.R. Kratzer, and F.E. Davis. 1981. Lake Okeechobee water quality studies
and eutrophication assessment. Tech. Pub. 81-2. South Florida Water Management District, West Palm
Beach, Florida. 270 pp.
Heatwole, CD., A.B. Bottcher, and L.B. Baldwin. 1986. Basin Scale Model for Evaluating Best Manage-
ment Practice Implementation Programs. Trans, of the ASAE. 29(2):439-444.
Heatwole, C.D., A.B. Bottcher, and L.B. Baldwin. 1987a. Modeling Cost-effectiveness of Agricultural Non-
point Pollution Abatement Programs on Two Florida Basins. Water Res. Bull. 23(1):127-131.
Heatwole, CD., A.B. Bottcher, and K.L Campbell. 1987b. Basin Scale Model Water Quality Model for
Coastal Plain Flatwoods. Trans, of the ASAE. 30(4): 1023-1030.
The Lake Okeechobee Technical Advisory Committee (LOTAC). 1986. The Overall review of South
Florida Water Management District Lake Okeechobee Research Final Report to South Florida Water
Management District. Arthur D. Little, Inc.
5.19
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Ritter, G J. and E.G. Flaig. 1988. Water quality report: 1987 Annual report, Rural Clean Water Program.
Technical Memorandum. South Florida Water Management District, Water Quality Division. West
Palm Beach, Florida. 71 pp.
SAS Institute, Inc. 1985a. SAS User's Guide: Basics, Version 5 Ed. Gary, NC: SAS Institute Inc. 1290 pp.
SAS Institute, Inc. 1985b. SAS/ETS User's Guide, Version 5 Ed. Gary, NC: SAS Institute Inc. 738 pp.
Spooner, J., R.P. Maas, M.D. Smolen, and C.A. Jamieson. 1987. Pages 243-257, In Symposium on Monitor-
ing, Modeling, and Mediating Water Quality. American Water Resources Association.
Stanley, J., V. Hoge, and L. Boogs. 1986. Taylor Creek-Nubbin Slough Project Rural Clean Water Program
Annual Progress Report. Okeechobee County, Florida.
ACKNOWLEDMENTS
.
The authors would like to acknowledge the following persons for their help on this chapter: Dr. David A.
Dickey, Department of Statistics, North Carolina State University; Gary Ritter and Eric Flaig, South Florida
Water Management District; Lynn Hester, USDA-SEA-AR, Southeast Watershed Research Laboratory, Tifton,
Georgia.
5.20
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