UPPER AUGLAIZE WATERSHED AGNPS MODELING PROJECT FINAL REPORT

Authors: Bingner, Ronald - Ron; CZAJKOWSKI, KEVIN - UNIVERSITY OF TOLEDO; PALMER, MICHAEL - UNIVERSITY OF TOLEDO; COSS, JAMES - UNIVERSITY OF TOLEDO; DAVIS, STEVE - USDA-NRCS; STAFFORD, JIM - USDA-NRCS; WIDMAN, NORM - USDA-NRCS; THEURER, FRED - USDA-NRCS; KOLTUM, GREG - USGS; RICHARDS, PETE - HEIDELBERG COLLEGE; FRIONA, TONY - USACE

Submitted to: Laboratory Publication
Publication Type: Government Publication
Publication Acceptance Date: 4/3/2006
Publication Date: 4/3/2006
Citation: Bingner, R.L., Czajkowski, K., Palmer, M., Coss, J., Davis, S., Stafford, J., Widman, N., Theurer, F., Koltum, G., Richards, P., Friona, T. 2006. Upper Auglaize Watershed AGNPS Modeling Project Final Report. USDA-ARS National Sedimentation Laboratory Research Report. No. 51. 103 pp.

Interpretive Summary: Effective tools are needed to evaluate the impact of agricultural production practices on water quality. An interagency study was conducted to evaluate the effect of agricultural conservation practices on runoff and sedimentation within the Upper Auglaize River Watershed in Ohio using the capability of the USDA watershed model, AnnAGNPS, to estimate the these effects and the source of pollutants. The study determined that an application of 17 percent new no-till acres and eight percent new grassland acres, when randomly applied to the watershed, reduced loadings at the mouth 82 percent. Ephemeral gullies were found to be the primary source of sediment loading (73 percent). The model established that while many conservation incentive programs treat tile drainage as a production practice, significant erosion and sediment control benefits are provided by the practice in comparison to cultivation in an undrained state. The average sediment load of all alternative agricultural practices for drained loadings was 89.2 percent of the load for the corresponding undrained loadings. By applying this watershed model to agricultural areas that may contribute to non-point source pollution problems downstream, alternative conservation practices can be evaluated before their actual implementation to demonstrate the approach that best reduces any downstream impact on the watershed system. This technology provides a valuable tool to state or federal personnel involved in watershed management planning.

Technical Abstract: The Upper Auglaize Watershed agricultural non-point source modeling project was an interagency effort to use a Geographic Information System (GIS)-based modeling approach for assessing and reducing pollution from agricultural runoff and other non-point sources. This project applied the U.S. Department of Agriculture (USDA), Agricultural Research Service’s AGricultural Non-Point Source (AGNPS) suite of models to the Upper Auglaize River Watershed, a major watershed within the Maumee River Basin. This modeling project was conducted by an interagency team consisting of a partnership between the: (1) USDA, Agricultural Research Service (ARS); (2) USDA, Natural Resources Conservation Service (NRCS); (3) U.S. Army Corps of Engineers (USACE); (4) U.S. Geological Survey (USGS); (5) Ohio State University; (6) University of Toledo (UT); (7) Heidelberg College; (8) Ohio Department of Natural Resources (ODNR), Division of Soil and Water Conservation; (9) Ohio Environmental Protection Agency (OEPA); and (10) Allen, Auglaize, Van Wert, and Putnam Soil and Water Conservation Districts. The partnership was the first step in a process to eventually apply the model in a portioned subset of watersheds for the Maumee Basin, and then to link them to form a comprehensive basin-wide model This work was performed under the authority of Section 516(e) of the Water Resources Development Act (WRDA) of 1996, as amended, for the purpose of assisting State and local watershed managers with their evaluation, prioritization and implementation of alternatives for soil conservation, sediment trapping and non-point source pollution prevention in the Upper Auglaize River watershed. The project team, working in a cooperative effort, used the models to determine sediment sources, contributing locations, and the effect of application of best management practices (BMPs) on rates of sediment delivery to the mouth of the watershed. The results will be used to guide conservation incentive and land treatment programs. The team relied heavily on Geographic Information System (GIS)-based applications to expedite the application of the model. The results of the analysis demonstrated that the application of BMPs would have a positive effect on reducing the loadings of sediment leaving the mouth of the Upper Auglaize Watershed. An application of 17 percent new no-till acres and eight percent new grassland acres, when randomly applied to the watershed, reduced loadings at the mouth to 82 percent of the simulated existing condition loadings. No-till, conversion of cropland to grassland, other uses including grass buffers, and reforestation of parts of the watershed, were all shown by the model to have a measurable effect on reducing sediment loads. Conversion of all of the cropland in the watershed to no-till would reduce the average unit load (tons of sediment per acre) leaving the mouth of the watershed to a level that is 42 percent of the simulated existing condition load. Ephemeral gullies were found to be the primary source of erosion (72 percent), sediment yield (73 percent), and sediment loading (73 percent). Controlling sediment load means controlling gully erosion and possibly trapping sediment yield before it reaches the stream system. Most BMPs (e.g., no-till, conversion of cropland, etc.) that reduce sheet and rill erosion and its sediment yield will also reduce gully erosion and its sediment yield. However, grassed waterways, which have no effect on sheet and rill erosion, are frequently an effective BMP to prevent ephemeral gullies. And, of course, riparian vegetation and sediment traps would reduce the delivery ratios of all types of landscape erosion. New techniques were developed by the team to quantify the ephemeral gully erosion within the model. When calibrated to available stream gage data the model suggests that more (73% in the existing