Number  52                                                    March 1992

Local Coordinating Committee
Tillamook Bay Rural Clean Water Program Project

Project Synopsis

The Tillamook Bay Drainage Basin is located in northwestern Oregon. It is bounded on the east by the Coast Range and on the west by the Pacific Ocean. Elevations range from sea level to 3,461 feet. The basin consists of five watersheds totaling 363,520 acres. Tillamook Bay at high tide is six miles long and, at some points, three miles wide, covering about 12 square miles.

Approximately 65 miles of rivers and tributaries wind through the project area (23,540 acres/critical area: 8,723 acres). Many miles of man-made ditches also exist. Soils in the basin range from well drained, fine-textured soils in the uplands to very poorly drained, extremely acid soils in the tidelands.

Eighty-four percent of the annual precipitation (90 to 150 inches) falls between October and May.

The primary agricultural industry is dairy farming, involving approximately 12,190 acres and 22,000 animals. Dairy farms are located primarily in the lowlands adjacent to the bay and the five rivers. An additional 11,350 acres of farmland are used for related production of hay and silage, and other types of livestock.

Tillamook Bay contains a shellfishing resource (oysters) of significant commercial value and provides important recreational clam digging, fishing, and boating.

Before the RCWP project was started, there were severe in-stream water quality problems. Major forest fires caused severe erosion. Streambank erosion had occurred along the five rivers that flow into Tillamook Bay. The seafood and commercial fishery industries had suffered as a result of sediment accumulation in the bay, which had smothered eelgrass, shellfish beds, and shellfish larvae and severely affected recreational boating.

A potential human health hazard existed due to high fecal coliform levels in the river systems and Tillamook Bay. The primary sources of fecal coliform were overloaded sewage treatment plants, failing septic tanks, and manure runoff from dairy operations. Shellfish harvesting had been closed down unexpectedly during periods of high fecal contamination and health hazards existed in the tributaries where water contact was very popular.

Livestock operations produced 322,500 tons of manure annually. The combination of this "never-ending" volume of manure, lack of manure handling and storage facilities, and the predominately wet climate created runoff and contamination not equaled anywhere else in Oregon.

Project Goals

Reduce fecal coliform bacteria entering project area water courses and Tillamook Bay by 70%.

Project Administration and Coordination

A Local Coordinating Committee including the following agencies was organized: Tillamook County Agricultural Stabilization and Conservation Service (ASCS) Committee; USDA-ASCS; USDA-Soil Conservation Service (SCS); USDA-Farmers Home Administration, Cooperative Extension Service; Tillamook County Creamery Association; Tillamook County Soil and Water Conservation District; Tillamook County Farm Bureau; Oregon Departments of Forestry, Environmental Quality, Fish and Wildlife; McMinnville Farmers Creamery Association.; and Tillamook Bay Water Quality Committee. Each agency had specific responsibilities. As lead agency, ASCS was responsible for problem solving and final decision making; however, efforts were made to involve all agencies in these decisions. Cooperation among federal, state, and local agencies and organizations was outstanding.

Best Management Practices (BMPs) Implemented

Permanent Vegetative Cover (BMP-1)
Animal Waste Management Systems (BMP-2)
Stream Protection Systems (BMP-10)
Grazing Land Protection Systems (BMP-6)
Permanent Vegetative Cover on Critical Areas (BMP-11)
Improving Irrigation and/or Water Management Systems (BMP-13)
Fertilizer Management (BMP-15)
Sediment Retention, Erosion, or Water Control Structure (BMP-12)

Selected Findings and Recommendations

• Partial success of the Tillamook project is due to the agricultural industry's strong involvement.
  

• Information and Education and water quality monitoring activities must be adequately funded for the life of the project. Both local and state budget cuts adversely affected the original program commitments and accomplishments.
  

• Farm visits are required to identify critical operations. Visits should be conducted during wet weather.
  

• A low interest loan program is needed to assist participants with their portion of the Best Management Practice (BMP) costs. This would accelerate BMP installation, decrease program length, and provide earlier water quality improvement trends.
  

• Tours for financial lending institution representatives are useful in assisting participants to obtain their portion of the BMP expenses.
  

• Future projects should encourage the use of the Agricultural Conservation Program (ACP) or other state and local program funding sources in conjunction with project funds.
  

• Strong local leadership (peer pressure) is a key element in correcting contract violations and project success.
  

• Considerable time was required to write the RCWP Ten-Year Report due to the format not having been established at the beginning of the program. Reporting format should be developed early. Required data can then be developed, collected, and evaluated as the project is implemented.
  

• Project participation requires an aggressive Information and Education Program. USDA-ASCS and Tillamook County Creamery Association (TCCA) newsletters were effective in reaching potential participants. Best results were achieved by a TCCA field representative, whose producer contacts resulted in a high rate of producer participation.
  

• Professionally developed slide talks and displays were effective in informing the general public about the RCWP.
  

• It is too early to make a final assessment of RCWP results. Water quality improvements are expected to occur for a number of years after all manure storage BMPs are installed and contracts completed. These changes will occur as operators develop and refine manure and commercial fertilizer management.
  

• In order to be successful, monitoring must receive adequate and stable funding and staffing throughout the life of the project.
  

• Sufficient baseline water quality data are needed prior to project implementation. In cases where baseline data are not available, an additional two years should be added to the project's start up.
  

• A minimum monitoring strategy and design should be established early in the project planning and should be adhered to throughout the project.
  

• Monitoring strategy should include regularly spaced sampling on a predetermined schedule, at least as a sub-set of indicator sites (i.e., monthly or even more frequent sampling).
  

• Regular monthly sampling should continue after the project to allow a more complete evaluation of project impacts at a later date.
  

• Additional intensive monitoring studies, particularly during summer/fall runoff episodes, would be useful both for evaluating installed practices and for identifying "hot spots" that need further work.
  

• Methods of data analysis should be decided upon early in project planning to ensure that data sufficient for the anticipated analysis are collected.
  

• Bacteria data are highly variable and not normally distributed, but can be useful for trend detection.
  

• Explanatory variables such as river levels and flood stage play an important role in specifying fecal coliform levels that may reflect the effects of BMPs.
  

• For future projects, agencies responsible for administration, planning, implementation, and collecting and analyzing water quality data should have computer networking capabilities.
  

• According to the participants, the Tillamook RCWP has resulted in reduced commercial fertilizer usage. Manure nutrients saved and utilized will offset manure management system implementation costs.
  

• Local decision makers must have flexibility in choosing BMPs for cost-share assistance. Traditional practices may not be adequate to solve all water quality problems.
  

• BMPs must be developed at the local level. The agricultural industry and potential participants must be involved in the BMP identification process. Support from the State Coordinating Committee was essential to get innovative BMPs approved at the national level.
  

• A BMP component system is more effective in solving agricultural pollution problems than a single BMP.
  

• In a high rainfall area, managing and/or diverting rainwater away from animal manure accumulation or storage areas is an important consideration in BMP planning and implementation.
  

• Roofing manure accumulation areas in a high rainfall area is more cost effective than collecting and storing contaminated rainwater.
  

• Proper management and maintenance of animal manure systems are long term commitments. Education is a continual process.
  

For Further Information

Bob Pederson
USDA-SCS
2204 4th St., Suite B
Tillamook, OR 97141
Tel: 503-842-2848 or

Betty Lissman
USDA-ASCS
Federal Building Rm 1524, 1220 SW Third Ave.
Portland, OR 97204
Tel: 503-326-2741

George R. Hallberg, Geological Survey Bureau, Iowa Dept. of Natural Resources
Gerald A. Miller, Iowa State University Cooperative Extension Service

Since 1982, a consortium of state and federal agencies have implemented an array of programs to improve the environmental performance of agriculture in Iowa. One of the primary objectives has been to improve nitrogen (N) management because nitrate contamination of water supplies has become a growing problem in the state. The consortium developed a comprehensive demonstration, education, and research program to address the N problem, building on efforts initiated in 1981 through the Big Spring Basin Demonstration Project (BSBDP). Agencies involved in the cooperative effort are Iowa State University (ISU) Cooperative Extension Service, the Iowa Department of Agriculture and Land Stewardship (IDALS), the Iowa Department of Natural Resources (DNR), USDA - Soil Conservation Service, ISU's Leopold Center for Sustainable Agriculture, ISU's Agricultural and Home Economics Experiment Station, and the Iowa Fertilizer and Chemical Association.

Iowa's programs have utilized a large and varied network of on-farm demonstration and implementation projects. These were coupled with an aggressive marketing and information delivery plan to accelerate voluntary adoption. The marketing scheme recognizes that altering agricultural management is a sociological process as well as a technical one and is designed to expand program impacts beyond the cooperating farmers. Demonstrations have been conducted in every county to provide local credibility of the benefits of improved N-management techniques.

The results of N-management programs can be evaluated from two perspectives: results of individual projects and impacts on the state and regional scale. Individual projects show significant improvements in N-management. For example, in the BSBDP, 52% of the 200 area farmers report reducing fertilizer nitrogen (FN) rates since 1981. Farm inventories show that these farmers reduced FN applied to corn by 21%, resulting in a reduction of N-loading in the basin of more than one million lbs/year. These reductions translate into a cost savings of about $200,000 per year to area farmers. In another project, 48 farmers in Butler County reduced their FN use by over 240,000 lbs in 1989 (with no reductions in yields) and through integrated crop management refinements increased their net returns by over $500,000 per year.

Various statewide demonstration efforts using a nitrate-soil test (calibrated as part of the N-management program) reduced N applications by 62% in 1989 following drought conditions and by 21% in 1990, with no difference in yields relative to usual N rates.

Data on statewide FN use, standardized to a crop acreage basis, show Iowa producers have reduced nitrogen use since 1985, despite declining fertilizer prices and contrary to trends in the adjacent cornbelt region. Reductions are calculated relative to 1985 rates and to regional time-series trends. Since 1986, these reductions total over 800 million lbs-N for use on corn, with no decline in yields. These reductions equate to cost savings for Iowa farmers of over $120 million and an energy savings equivalent to over 200 million gallons of diesel fuel.

While significant achievements can be noted in reducing overall environmental loading from agricultural practices in Iowa, there are substantial improvements yet to be made. Continued program support will be required, as will major efforts by Iowa agri-business to provide services to farmers that promote adoption of more efficient nitrogen use. Based on state surveys, considerable refinements are still feasible through use of realistic yield goals and appropriate crediting for rotation and manure benefits. Further refinement of new soil test methods will be needed. Past estimates suggesting that nitrogen use in Iowa could be reduced by $100 million per year clearly seem feasible.

For further information, contact:

George Hallberg, Water Quality Project Director
Geological Survey Bureau, Iowa Department of Natural Resources
123 North Capitol St., Iowa City, Iowa 52242
Tel: 319-335-1575

Gerald Miller, Extension Agronomist
Iowa State University Cooperative Extension Service
Ames, Iowa 50011
Tel: 515-294-1923

This is the first of a three-part article that continues a new series on nonpoint source (NPS) modeling started in the January, 1992, issue of NWQEP NOTES. Numerous models and geographic information systems (GIS) are being used for a wide range of NPS applications. Such applications include evaluating the effectiveness of best management practices (BMP) in controlling NPS pollution; identifying high priority areas for implementation of stormwater management techniques; estimating pollutant loadings for current and projected land use scenarios within watersheds; defining and mapping critical areas for NPS control projects; and many others. A range of models and applications will be discussed in this series, with the goal of introducing NOTES readers to some of the ways in which models and GIS are being used in NPS pollution research and control. In this three-part article, modeling as an approach to the evaluation of BMP effectiveness is examined (see NWQEP NOTES No. 53, May 1992) and NWQEP NOTES No. 54, July 1992) for parts two and three of this article). Types of models being used in NPS work will be discussed and specific models will be described. Readers interested in contributing to the series are encouraged to contact Judith Gale, NWQEP NOTES editor.

Theo A. Dillaha, Department of Agricultural Engineering, Virginia Polytechnic Institute and Scientific University
Judith A. Gale, NCSU Water Quality Group

Introduction

Nonpoint source (NPS) pollution is a significant source of water quality problems nationwide. Pollution control programs are attempting to reduce agricultural NPS pollution with best management practices (BMPs) which are intended to minimize the negative environmental consequences of land use activities, while maintaining the productivity of the land.

With the increased use of BMPs for NPS pollution control, there is an obvious need for methods of assessing BMP effectiveness. Proper management of any system requires reasonable estimates of the impacts of alternatives being considered. This is particularly true with NPS pollution control, as planners face the often conflicting goals of minimizing pollution and maximizing economic return to the landowner. An effective plan can be developed only from good data.

Estimates of BMP effectiveness are essential for 1) selecting the most appropriate BMP for a particular problem and site; 2) estimating the benefits of BMP implementation; 3) ranking BMP alternatives in terms of cost- effectiveness; and 4) determining an optimum BMP program based upon program objectives.

Two approaches have been used to evaluate BMP effectiveness: monitoring and modeling. Water quality monitoring can be defined as any effort to obtain an understanding of the physical, chemical, and biological characteristics of water via statistical sampling. Monitoring is an important way to assess BMP effectiveness, but the usefulness of monitoring data depends on the design and implementation of the monitoring effort. Because there is tremendous variability in the soil, land use, topography, and weather factors influencing BMP effectiveness, a BMP may be highly effective for pollutant control at one site but ineffective at another site. Also, monitoring is time consuming and expensive. If the particular BMPs selected for a site are found to be ineffective, considerable time and resources will have been wasted and it may take many more years to develop an effective program. Such factors make it difficult to implement monitoring programs whose results can be generalized across the spectrum of BMP scenarios.

(Monitoring approaches are discussed in the paper by Dr. Dillaha on which this article is based, entitled Role of Best Management Practices in Restoring the Health of the Chesapeake Bay: Assessments of Effectiveness, published in Perspectives on the Chesapeake Bay, 1990, compiled and edited by Michael Haire and Elizabeth C. Krome and printed by U.S. EPA for the Chesapeake Bay Program.)

An alternative approach to BMP assessment uses mathematical models. Physically-based models attempt to describe the physical, chemical, and biological processes affecting the natural system being modeled. This allows consideration of site-specific soil, land use, topographic, and weather conditions that are critical to BMP effectiveness. Models of another class, called empirical models, are less suitable for BMP evaluation because they do not describe the physical processes controlling the system being simulated. They are simply statistically derived equations and should not be used beyond the range of data used in their development. Consequently, they are not good for simulating the site-specific nature of BMPs.

The goals of this three-part article are to 1) discuss modeling approaches to BMP effectiveness evaluation, 2) describe the types of models being used, 3) briefly summarize available NPS models, and 4) identify research needs for effective utilization of BMPs.

Modeling Approaches

Nonpoint source models used to evaluate the effectiveness of BMPs for NPS pollution control range in complexity from simple empirical models like the Universal Soil Loss Equation (USLE) (Wischmeier and Smith, 1978) to complex physically-based watershed-scale models like ANSWERS (Beasley and Huggins, 1982). Some models have also been developed for evaluating specific BMPs, such as filter strips. NPS models generally concentrate on the generation of pollutants and their transport across the land surface to waterways or through the soil profile to ground water. Water quality models, on the other hand, are more concerned with the transport and fate of pollutants during concentrated flow in streams, rivers, lakes, and other large water bodies. Because of these differences, NPS models are often called loading models, while water quality models are called receiving water models. Only a few models, such as HSPF (Johanson et al., 1981), combine both loading and receiving water aspects into one overall model.

Types of Models

NPS pollution models can generally be classified as either screening or hydrologic assessment models (Novotny, 1986). Screening models are usually relatively simple and are intended to identify problem areas within large drainage basins or to make preliminary qualitative evaluations of BMP alternatives. One of the most important uses of screening models is the identification of potentially critical sources of NPS pollution within watersheds. Numerous studies have indicated that, for many watersheds, a few critical areas are responsible for a disproportionate amount of the pollutant yield. Consequently, concentration of pollution control activities in these critical areas can maximize the improvement in downstream water quality achievable with limited funds (Storm et al., 1988). Because of the simple nature of screening models, their predictions are expected to be accurate only within an order of magnitude or so. Examples of NPS screening models include the USLE, GAMES (Madramootoon et al., 1988), and VirGIS (Shanholtz et al., 1989).

Hydrologic assessment models are much more complex than screening models and are intended for assessing current conditions or alternative management scenarios. The predictions of good hydrologic assessment models should be within a factor of 2 of observed values if model parameters are measured on site or if the model is calibrated. Otherwise, predictions should be within an order of magnitude of observed values.

Hydrologic assessment models can also be divided into field-scale and watershed-scale models. Field-scale hydrologic models attempt to describe the hydrologic processes within a single field or land resource unit with uniform soils, cropping, topography, and weather. They do not attempt to describe pollutant transport and fate beyond the boundaries of the field. Examples of field-scale hydrologic models include: CREAMS (Knisel, 1980); CNS and CPS (Haith and Loehr, 1982); GLEAMS Leonard et al., 1987); NTRM (Shaffer, 1985); and PRZM (Carsel, 1984) (specific models will be described in parts two and three of this article in NWQEP NOTES No. 53, May 1992) and NWQEP NOTES No. 54, July 1992).

Watershed-scale hydrologic assessment models attempt to describe pollutant transport in the field and between fields and receiving waters. Some are event-oriented (single storm predictions), while others are continuous simulation models. Several can be used to identify critical sources of NPS pollution in watersheds and to target BMPs. Most can be used to evaluate the cost-effectiveness of alternative BMP implementation scenarios. Important NPS watershed-scale hydrologic models include: AGNPS (Young et al., 1989); ANSWERS (Beasley and Huggins, 1982); ARM (Donigian and Davis, 1978); HSPF (Johanson et al., 1981); NPS (Donigian and Crawford, 1976), STORM (US Army Corps of Engineers, 1975); SWMM (Huber at al., 1983); and WEPP (Gilley et al., 1988) (see also next two parts of this article in NWQEP NOTES No. 53, May 1992) and NWQEP NOTES No. 54, July 1992)..

Advantages and Limitations of Modeling

Modeling was found to be an important element of the Rural Clean Water Program (RCWP) (Maas et al., 1988). Models used for targeting and BMP assessment in the RCWP included: CREAMS (Knisel, 1980); AGNPS (Young et al., 1987); ANSWERS (Beasley, 1987); and the USLE (Wischmeier and Smith, 1978). RCWP modeling experiences suggested that all models must be carefully calibrated for site-specific conditions even if it is claimed that a model requires no calibration. For example, CREAMS was found to be inaccurate in the northern climate of the Vermont RCWP, but minor modifications of the model greatly improved its accuracy and utility there (Clausen, 1985). A method for determining whether model predictions are within a prescribed factor of true values was developed and demonstrated using PRZM (Parrish and Smith, 1988).

An important obstacle in using models for BMP assessment is that the greatest modeling needs are in rural ungaged watersheds. Lumped parameter models requiring calibration, such as ARM, HSFP, and NPS are of limited value since there are few, if any, historical data available for calibration. Models such as AGNPS and ANSWERS that do not require calibration, however, require extensive amounts of information on watershed characteristics which may or may not be available. In general, physically-based, deterministic models, such as AGNPS, ANSWERS, and the watershed version of WEPP, are better able to simulate the effects of BMPs and are, therefore, recommended for evaluation of BMP effectiveness. At best, however, they and all other NPS models are accurate only to within a factor of 2 or 3, and their predictions should be used with full consideration of these uncertainties.

References

Beasley, D.B. 1987. Applying the ANSWERS Model to Assess the Impacts of Conservation Tillage on Sediment and Phosphorus Yields to Lake Erie: Final Report of the Modeling Component - Tri-State Tillage Project. Chicago, IL: U.S. Environmental Protection Agency, Region 5; EPA-905/2-87-003.

Beasley, D.B. and L.F. Huggins. 1982. ANSWERS - Users Manual. Chicago, IL: U.S. Environmental Protection Agency, Great Lakes Program Office; 53 p. EPA-905/9-82-001.

Carsel, R.F., C.N. Smith, L.A. Mulkey, J.D. Dean and P.P Jowise. 1984. Users Manual for the Pesticide Root Zone Model (PRZM): Release 1. Athens, GA: U.S. Environmental Protection Agency; EPA-600/3-84-109.

Clausen, J. C., 1985. The St. Albans Bay watershed RCWP: A case study of monitoring and assessment: In: Perspectives on Nonpoint Source Pollution: Proceedings of a National Conference, May 19-22, 1985; Kansas City, MI. Washington, DC: U.S. Environmental Protection Agency; p. 21-24; EPA-440/5-85-001.

DeCoursey, D.G. 1985. Mathematical models for nonpoint water pollution control. J. Soil Water Conserv. 40(5):408-413.

Donigian, A.S. and N.H. Crawford. 1976. Nonpoint Pollution from the Land Surface. Athens, GA: U.S. Environmental Protection Agency; EPA- 600/3-76-083.

Donigian, A.S. and H.H. Davis. 1978. User's Manual for Agricultural Runoff Management (ARM) Model. Athens, GA: U.S. Environmental Protection Agency; EPA-600/3-78-080.

Gilley, J.E., L.J. Lane, J.M. Laflen, A.D. Nicks, an W.J. Rawls. 1988. USDA-water erosion prediction project: New generation erosion prediction technology. In: Modeling Agricultural, Forest, and Rangeland Hydrology: Proceedings of the 1988 International Symposium, December 12-13, 1988, Chicago, IL. St. Joseph, MI: American Society of Agricultural Engineers; p. 260-263.

Haith, D.A. and R.C. Loehr. 1982. Effectiveness of Soil and Water Conservation Practices for Pollution Control. Athens, GA: U.S. Environmental Protection Agency; EPA-600/3-82-024.

Hayes, J.C., B.J. Barfield, and R.I. Barnhisel. 1979. Filtration of sediment by simulated vegetation II. Unsteady flow with non-homogeneous sediment, Trans. of the ASAE 22(5):1063-1067.

Huber, W.C., J.P. Heaney, S.J. Nix, R.E. Dickinson, and D.J. Polmann. 1983. Storm Water Management Model User's Manual: Version III. Gainesville, FL: Univ. of Florida, Dept. of Environmental Engineering Sciences; 505 p.

Johanson, R.C., J.C. Imhoff, H.H. Davis, J.L. Kittle, and A.S. Donigian. 1981. User's Manual for Hydrologic Simulation Program-Fortran (HSPF): Release 7.0. Athens, GA: U.S. Environmental Protection Agency.

Knisel, W.G., ed. 1980. CREAMS: A Field-Scale Model for Chemical, Runoff, and Erosion from Agricultural Management Systems. Washington, DC: U.S. Dept. Agric., Science and Education Administration; 640 p. Conservation Research Report No. 26.

Lee, D., T.A. Dillaha, and J.H. Sherrard. 1989. Modeling phosphorus transport in grass buffer strips, J. of the Environ. Engr. Div. 115(2):408-426.

Leonard, R.A., W.G. Knisel, and D.A. Still. 1987. GLEAMS: groundwater loading effects of agricultural management systems, Trans. of the ASAE 30(5):1403-1418.

Maas, R.P., S.L. Birchford, M.P. Smolen, and J. Spooner. 1988. Agricultural nonpoint source control: Experiences from the rural clean water program, J. Lake and Reservoir Management 4(1):51-56.

Madramootoo, C.A., L. Laperriere, and S.F. Barrington. 1988. Modeling runoff and sediment yields from Quebec watersheds. In: Modeling Agricultural, Forest, and Rangeland Hydrology: Proceedings of the 1988 International Symposium, Chicago, IL. St. Joseph, MI: American Society of Agricultural Engineers; p. 264-270.

Novotny, V. 1986. A review of hydrologic and water quality models used for simulation of agricultural pollution. In: Giorgini, A. and F. Zingales, eds. Agricultural Nonpoint Source Pollution: Model Selection and Application. New York: Elsevier Publishers; p. 9-35.

Parrish, R.S. and C.N. Smith. 1988. A Method for Testing Whether Model Predictions Fall Within a Prescribed Factor of True Values, with an Application to Pesticide Loading. Athens, GA: U.S. Environmental Protection Agency, Environmental Research Laboratory.

Shaffer, M.J. 1985. Simulation model for soil erosion- productivity relationships, J. Environ. Qual. 14(1):144-150.

Shanholtz, V.O., J.M. Flagg, S. Mostaghimi, and C.D. Heatwole. 1989. Agricultural Pollution Potential Database for the Peanut Soil and Water Conservation District: Interim Report. Blacksburg, VA: Virginia Polytechnic Institute and State University, Dept. of Agricultural Engineering, 40 p.; ISSL-89-5.

Storm, D.E., T.A. Dillaha, S. Mostaghimi, and V.O. Shanholtz. 1988. Modeling phosphorus transport in surface runoff, Trans. of the ASAE 31(1):117-127.

U.S. Army Corps of Engineers. 1975. Urban Storm Water Runoff- STORM. Davis, CA: Hydraulic Engineering Center.

Wischmeier, W.H., and D.D. Smith. 1978. Predicting Rainfall Erosion Losses - a Guide to Conservation Planning. Washington, DC: U.S. Department of Agriculture, Science and Education Administration; 58 p. Agriculture Handbook No. 537.

Young, R. A., C.A. Onstad, D.D. Bosch, and W.P. Anderson. 1987. AGNPS, Agricultural Non-Point-Source Pollution Model: A Watershed Analysis Tool. Washington, DC: U.S. Dept. of Agriculture, Agricultural Research Service; 80 p. Conservation Research Report 35.

Young, R.A., C.A. Onstad, D.D. Bosch, and W.P. Anderson. 1989. AGNPS: A nonpoint source pollution model for evaluating agricultural watersheds, J. Soil and Water Conserv. 44(2):168-173.

J. Spooner, J.A. Gale, S.L. Brichford, S.W. Coffey, A.L. Lanier, M.D. Smolen, and F.J. Humenik. 1991. NWQEP Report: Water Quality Monitoring Report for Agricultural Nonpoint Source Projects - Methods and Findings from the Rural Clean Water Program. National Water Quality Evaluation Project, NCSU Water Quality Group, Biological and Agricultural Engineering Department, North Carolina State University, Raleigh, NC. 164 p.

This interim water quality monitoring report highlights different water quality and land treatment monitoring designs used by the 21 RCWP projects. Emphasis is on water quality monitoring designs. The report includes a description of how each project monitored water quality and tracked land use, how the two data bases were linked together, and what lessons have been learned to date relating to water quality and BMP effectiveness. The report does not include information from the 10-year Project Reports or NWQEP on-site evaluations of RCWP projects. (The NWQEP reports scheduled for 1992 will include updates of the information presented in the 1988 Annual Report and the NWQEP Water Quality Monitoring Report, incorporating new information.) Chapter 4 provides an overview and discussion of water quality monitoring network designs with considerations of system variability. The report is currently under peer review by the U.S. EPA Office of Research and Development for consideration as a U.S. EPA-ORD publication.

Copies of the report may be ordered from the NCSU Water Quality Group using the enclosed publications order form (WQ-71). Cost per copy is $11.

NWQEP NOTES is issued bimonthly. Subscriptions are free (contact: Publications Coordinator at the address below or via email at wq_puborder@ncsu.edu). A list of publications on nonpoint source pollution distributed by the NCSU Water Quality Group is included in each hardcopy issue of the newsletter.

I welcome your views, findings, information, and suggestions for articles. Please feel free to contact me.

Judith A. Gale, Editor
Water Quality Extension Specialist
North Carolina State University Water Quality Group
Campus Box 7637
North Carolina State University
Raleigh, NC 27695
Tel: 919-515-3723
Fax: 919-515-7448
email: notes_editor@ncsu.edu