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United States Department of Agriculture

Agricultural Research Service


Location: Grassland, Soil and Water Research Laboratory

2008 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
This report documents research for the project Development of Models and Conservation Practices for Water Quality Management and Resource Assessments. Data collection and public outreach are ongoing 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 are being compiled. Components of the ALMANAC (Agricultural Land Management Alternatives with Numerical Assessment Criteris) model have been compartmentalized and are being incorporated into the SWAT (Soil and Water Assessment Tool) model and other models, to improve their plant simulation components. Relevant sections of the ALMANAC model have been extracted and delivered for integration into the Object Modeling System. Work is progressing on the SWAT and ALMANAC models, to provide linkages to economic models. (NP211, Problem Areas 1, 5, and 6)

4. Accomplishments
1. Parameterization of the ALMANAC (Agricultural Land Management Alternatives with Numerical Assessment Criteria) Model for Grasses: Simulation of the growth and yield of grasses for conservation assessments and biofuels assessment requires an accurate, realistic simulation model that describes various grass types as well as competition among species when simulating complex grass mixtures. At the Grassland, Soil and Water Research Laboratory, Temple, TX, the ALMANAC model was parameterized for the major improved grass species and several common native grasses, including the major grasses for biofuel. This involved field measurements to determine potential leaf area index, optimum nutrient concentrations, and radiation use efficiency. The model has been validated with NRCS data for ecological sites in diverse regions. Additionally it was parameterized for switchgrass cultivars in Texas and in the Upper Midwest, and validated using measured data at a wide range of latitudes, from Texas to North Dakota and Wisconsin. Parameters derived for these important grass species, when incorporated into the ALMANAC model, provided realistic simulations of grass growth and productivity in a wide range of soils and climatic conditions. The model will be a useful tool assessing the impacts of grassland for conservation assessments and biofuels assessments for natural resource management in grasslands. (NP211, Problem Area 1)

2. Development of a River Basin Scale Water Quality Model: 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. Agricultural production has been identified as a major nonpoint source of water contamination. 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-benefits assessments. Models at various spatial scales, from field to river basin, are required to predict the impact of land management alternatives on the environment and agricultural production. At the Grassland, Soil and Water Research Laboratory, Temple, TX, we have developed a river basin scale model called SWAT (Soil and Water Assessment Tool) that integrates hydrology, soil erosion, plant growth, and nutrient cycling with off-site processes such as channel erosion/deposition, pond and reservoir processes, groundwater flow, and climate variability. New algorithms have been developed for bacteria fate and transport, and movement of heavy metals across the landscape. Numerous interfaces have been developed for the model to assist users in obtaining model inputs and interpreting model outputs. The model was calibrated and validated, and uncertainty analysis was performed on the CEAP Benchmark Watersheds and other watersheds around the world. In general, the model compared well with measured stream flow, sediment and nutrient loads and concentrations. The model is being used across the country by US EPA to assess water quality concerns and by USDA to assess the environmental impact of conservation programs. Scientists around the world are contributing to model development, and over 400 articles on SWAT development and application are found in the refereed literature. (NP211, Problem Areas 5 and 6)

3. Determination of Model Uncertainty: A novel method to incorporate data (measurement) uncertainty into model calibration and validation was developed. Prior to this development, modelers implicitly assumed that measured data were certain, and thus were faced with the less-than-ideal objective of reproducing data values that were in fact uncertain. While this method was designed for watershed models, it can be applied to any evaluation of paired model outputs with measured data. It has been applied in several such model evaluations and should continue to provide numerical goodness-of-fit estimates that consider measurement uncertainty. (NP211, Problem Area 5)

Review Publications
Kiniry, J.R., Evers, G.W. 2008. Radiation use efficiency of arrowleaf, crimson, rose, and subterranean clovers. Agronomy Journal. 100(4):1155-1160

Hudgeons, J.L., Knutson, A.E., Heinz, K.M., Deloach Jr, C.J., Dudley, T.L., Pattison, R.R., Kiniry, J.R. 2007. Defoliation by introduced Diorhabda elongata leaf beetles (Coleoptera: Chrysomelidae) reduces carbohydrate reserves and regrowth of Tamarix (Tamaricacceae). Biological Control. 43(2):213-221.

Drouet, J.L., Kiniry, J.R. 2008. Does spatial arrangement of 3D plants affect light transmission and extinction coefficient within maize crops? Field Crops Research. 107(1):62-69.

Green, C.H., Van Griensven, A. 2007. Autocalibration in hydrologic modeling: Using SWAT2005 in small-scale watersheds. Journal of Environmental Modeling and Software. 23:422-434.

Harmel, R.D., Bonta, J.V., Richardson, C.W. 2007. The original USDA-ARS experimental watersheds in Texas and Ohio: Contributions from the past and visions for the future. Transactions of the American Society of Agricultural and Biological Engineers. 50(5):1669-1675.

Di Luzio, M., Johnson, G.L., Daly, C., Eischeid, J.K., Arnold, J.G. 2008. Constructing retrospective gridded daily precipitation and temperature datasets for the conterminous United States. Journal of Applied Meteorology and Climatology. 47(2):475-497.

King, K.W., Balogh, J.C., Hughes, K., Harmel, R.D. 2007. Nutrient Load Generated by Storm Event Runoff from a Golf Course Watershed. Journal of Environmental Quality. 36:1021-1030.

King, K.W., Balogh, J., Harmel, R.D. 2007. Nutrient Flux in Storm Water Runoff and Baseflow from Managed Turf. Environmental Pollution. 150:321-328.

Last Modified: 2/23/2016
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