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United States Department of Agriculture

Agricultural Research Service


Location: Grassland, Soil and Water Research Laboratory

2011 Annual Report

1a.Objectives (from AD-416)
The objectives of this study are to:.
1)Compile environmental, water quality, and agronomic data from the Leon River and Riesel watersheds and deliver to the STEWARDS data system in a compatible format;.
2)Measure and quantify hydrologic and water quality effects of conservation practices and management at the field, farm, and sub-watershed scale within the Leon River and Riesel watersheds;.
3)Validate, quantify uncertainties in model output, and conduct land use and climate analyses with the SWAT and ALMANAC models at field, farm, and watershed scales;.
4)Provide proper output and linkages from SWAT to economic models to ensure appropriate environmental and crop yield output at spatial scales compatible with selected economic models; and.
5)Extract relevant components from the ALMANAC and SWAT models for integration into the Object Modeling System (OMS) and assist in the verification of the ALMANAC and SWAT models for major agricultural regions.

1b.Approach (from AD-416)
For Objective 1 we will provide data to the STEWARDS data system from the Leon River and Riesel watersheds. Data will include environmental and agronomic data, measured water quality data, and SWAT output. Socio-economic data will not be collected. Our role in Objective 2 involves quantifying the effects of conservation practices (with emphasis on nutrient and manure management) in the Leon River and Riesel watersheds. It also involves quantifying nutrient and manure management of grasses and pastures for bio-fuels at three field sites in Texas. Although several models are considered in the overall CEAP Objective 3, our focus is solely on the SWAT and ALMANAC models. SWAT will be evaluated and uncertainty analysis will be performed on varying spatial scales in the Leon and Riesel watersheds. Model development will include:.
1)river basin scale processes in SWAT,.
2)plant growth and land management processes in ALMANAC, and.
3)linkage with remotely sensed data. Our role in Objective 4 is to provide proper output and linkages from the SWAT model to economic models. We will ensure appropriate environmental and crop yield output from SWAT at spatial scales that are compatible with the selected economic models. For Objective 5, we will extract relevant components from SWAT and ALMANAC model for integration into the Object Modeling System (OMS).

3.Progress Report
Data collection and public outreach have substantially advanced for two projects investigating application of poultry litter and proper fertilizer management. Experimental results on effects of conservation practices in the Leon River and Riesel watersheds have been analyzed. The compartmentalized version of the ALMANAC model was incorporated into the SWAT model to improve SWAT's plant simulation components. The ALMANAC model was extensively tested against NRCS Ecological Site Descriptions in the Intermountain West. Work is continuing with the SWAT and ALMANAC models, to provide linkages to models with different application and scales.

1. Improved model to simulate water quality in large river basins. The U.S. Environmental Protection Agency (US EPA) and state environmental agencies have identified approximately 15,000 water quality-impaired water bodies in the U.S. At the same time, USDA is mandated to conduct a thorough analysis of the risks and benefits of USDA's conservation programs to human health, safety, and environment; determine alternative ways of reducing risk; and conduct cost-benefit assessments. New algorithms were developed for a river basin scale model called SWAT (Soil and Water Assessment Tool) to simulate on-site septic systems, stream sediment routing, urban management practices, improved phosphorus fate and transport, and stream health. As part of the CEAP (Conservation Effects Assessment Project) National Cropland Assessment, SWAT was validated at more than 70 USGS stream gauges across the country to assure realistic simulation of stream flow, sediment, nutrient and pesticide (atrazine)loads. Final SWAT validation and scenario analysis was completed on the Upper Mississippi river basin, the Chesapeake Bay watershed, the Ohio-Tennessee river basin, and the Great Lakes watersheds; the final draft reports are under review by USDA-NRCS and are available on the CEAP website. Validation and scenario analysis has been completed for the Missouri, Arkansas-Red, and Lower Mississippi river basins, and reports are being developed. The scenario runs from this model are being used by USDA-NRCS to identify places where conservation practices such as conservation tillage, terraces, and CRP (Conservation Reserve Program) will be most efficient and provide the greatest benefits. This will help guide USDA conservation policy and Farm Bill debate. The model is also being used in more than 30 states by US EPA and is impacting the selection of land management alternatives to resolve water quality concerns.

Review Publications
White, M.J., Storm, D.E., Busteed, P.R., Smolen, M.D., Zhang, H., Fox, G.A. 2010. A quantitative phosphorus loss assessment tool for agricultural fields. Environmental Modeling & Software. 25(10):1121-1129.

Kiniry, J.R., Johnson, M., Mitchell, R., Vogel, K.P., Kaiser, J., Bruckerhoff, S.B., Cordsiemon, R.L. 2011. Switchgrass leaf area index and light extinction coefficients. Agronomy Journal. 103(1):119-122.

Johnson, M., Kiniry, J.R., Sanchez, H., Polley, H.W., Fay, P.A. 2010. Comparing biomass yields of low-input high-diversity communities with managed monocultures across the central United States. BioEnergy Research. 3:353-361.

Migliaccio, K., Harmel, R.D., Smiley, P.C. 2010. Chapter 5: Surface water quality sampling in streams and canals. In: Li, Y., Migliaccio, K., editors. Water Quality Concepts, Sampling, and Analyses. CRC Press. p. 51-72.

Harmel, R.D., Smith, P., Migliaccio, K. 2010. Chapter 12: Uncertainty in measured water quality data. In: Li, Y., Migliaccio, K., editors. Water Quality Concepts, Sampling, and Analyses. CRC Press. p. 227-239.

Johnson, M., Finzel, J.A., Spanel, D.A., Weltz, M.A., Sanchez, H., Kiniry, J.R. 2011. The rancher's ALMANAC. Rangelands. 33(2):10-16.

Bende-Michl, U., Volk, M., Harmel, R.D., Newham, L., Dalgaard, T. 2011. Monitoring strategies and scale-appropriate hydrologic and biogeochemical modelling for natural resource management: Conclusions and recommendations from a session held at the iEMSs 2008. Journal of Environmental Modeling and Software. 26:538-542.

Jeong, J., Kannan, N., Arnold, J.G., Glick, R., Gosselink, L., Srinivasan, R. 2010. Development and integration of sub-hourly rainfall-runoff modeling capability within a watershed model. Water Resources Management. 24(15):4505-4527.

Rabotyagov, S., Campbell, T., Jha, M., Gassman, P.W., Arnold, J.G., Kurkalova, L., Secchi, S., Feng, H., Kling, C.L. 2010. Least-cost control of agricultural nutrient contributions to the Gulf of Mexico hypoxic zone. Ecological Applications. 20(6):1542-1555.

White, M.J., Storm, D.E., Busteed, P.R., Stoodley, S., Phillips, S.J. 2010. Evaluating conservation program success with Landsat and SWAT. Environmental Management. 45(5):1164-1174.

Vadas, P.A., White, M.J. 2010. Validating soil phosphorus routines in the SWAT model. Transactions of the ASABE. 53(5):1469-1476.

Douglas-Mankin, K.R., Srinivasan, R., Arnold, J.G. 2010. Soil and Water Assessment Tool (SWAT) Model: Current developments and applications. Transactions of the ASABE. 53(5):1423-1431.

Bosch, D.D., Arnold, J.G., Volk, M., Allen, P.M. 2010. Simulation of a Low-Gradient Coastal Plain Watershed Using the SWAT Landscape Model. Transactions of the ASABE. 53(5):1445-1456.

Veith, T.L., Van Liew, M.W., Bosch, D.D., Arnold, J.G. 2010. Parameter sensitivity and uncertainty in SWAT: A comparison across five USDA-ARS watersheds. Transactions of the ASABE. 53(3):1477-1486.

Srinivasan, R., Zhang, X., Arnold, J.G. 2010. SWAT ungauged: Hydrological budget and crop yield predictions in the Upper Mississippi River Basin. Transactions of the ASABE. 53(5):1533-1546.

Chiang, L., Chaubey, I., Gitau, M.W., Arnold, J.G. 2010. Differentiating impacts of land use changes from pasture management in a CEAP watershed using the SWAT model. Transactions of the ASABE. 53(5):1569-1584.

Moriasi, D.N., Steiner, J.L., Arnold, J.G. 2011. Sediment measurement and transport modeling: Impact of riparian and filter strip buffers. Journal of Environmental Quality. 40:807-814.

Tuppad, P., Kannan, N., Srinivasan, R., Rossi, C.G., Arnold, J.G. 2010. Simulation of agricultural management alternatives for watershed protection. Water Resources Management. 24(12):3115-3144.

Parajuli, P.B., Douglas-Mankin, K.R., Barnes, P.L., Rossi, C.G. 2009. Fecal bacteria source characterization and sensitivity analysis of SWAT 2005. Transactions of the ASABE. 52(6):1847-1858.

Rossi, C.G., Srinivasan, R., Jirayoot, K., Le Duc, T., Souvannabouth, P., Binh, N., Gassman, P.W. 2009. Hydrologic evaluation of the lower Mekong River Basin with the Soil and Water Assessment Tool model. International Agricultural Engineering Journal. 18(1-2):1-13.

Tomer, M.D., Moorman, T.B., James, D.E., Hadish, G., Rossi, C.G. 2008. Assessment of the Iowa River's South Fork Watershed: Part 2. Conservation Practices. Journal of Soil and Water Conservation. 63(6):371-379.

Tomer, M.D., Moorman, T.B., Rossi, C.G. 2008. Assessment of the Iowa River's South Fork Watershed: Part 1. Water Quality. Journal of Soil and Water Conservation. 63(6):360-370.

Harmel, R.D., Smith, P.K., Migliaccio, K.W. 2010. Modifying goodness-of-fit indicators to incorporate both measurement and model uncertainty in model calibration and validation. Transactions of the ASABE. 53(1):55-63.

Harmel, R.D., Slade, R.M., Haney, R.L. 2010. Impact of sampling techniques on measured stormwater quality data for small streams. Journal of Environmental Quality. 39(5):1734-1742.

Medina-Garcia, G., Baez-Gonzalez, A.D., Lopez-Hernandez, J., Ruiz-Corral, J.A., Tinoco-Alfaro, C.A., Kiniry, J.R. 2010. Large-area dry bean yield prediction modeling in Mexico. Revista Mexicana de Ciencias Agricolas. 1(3):413-426.

Last Modified: 4/21/2014
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