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ARS Home » Plains Area » El Reno, Oklahoma » Grazinglands Research Laboratory » Agroclimate and Natural Resources Research » Research » Research Project #422757


Location: Agroclimate and Natural Resources Research

2014 Annual Report

1a. Objectives (from AD-416):
The long-term objective is to bridge the gap between farm management goals and landscape or watershed goals that are shared across farms and communities, using research watersheds as the primary outdoor laboratories to address these issues of global relevance. The project is structured around four objectives, namely: Obj 1: Improve watershed management and ecosystem services in mixed use agricultural watersheds by developing remote sensing and modeling tools and techniques for the selection and placement of conservation practices for maximum effectiveness. 1A: Assess potential impacts of conservation practice targeting strategies to meet desired environmental endpoints. 1B: Develop and evaluate a sequentially linked evapotranspiration, surface, and groundwater hydrology model system. 1C: Develop, evaluate, and refine new subsurface tile drainage and water table depth algorithms in SWAT to improve water budget and water quality predictions. 1D: Develop remote sensing-based techniques to quantify landscape variables to inform the selection or application of conservation practices in grazing areas. Obj 2: Quantify impacts of land management, land cover, and climate on the generation, movement, and fate of sediments and nutrients in watersheds. 2A: Quantify interactive effects of land cover, land management, and climate on reservoir sedimentation. 2B: Quantify impacts of changing land use on hydrologic model simulations. 2C: Quantify impacts of juniper removal on surface and groundwater resources in central Oklahoma. Obj 3: Develop climate-informed decision support tools for crop and forage management, natural resource conservation, and policy options assessments. 3A: Develop and maintain a fundamental climate database and statistical analyses covering two CEAP watersheds in Oklahoma. 3B: Generate synthetic weather patterns that are both spatially and temporally coherent with recent observations for use in hydrologic models. 3D: Develop multi-scale, multiple-objective optimization framework for agricultural production, conservation, and policy assessment. Obj 4. As part of the LTAR network, and in concert with similar long-term, land-based research infrastructure in Southern Great Plains region, use the SP LTAR to improve the observational capabilities and data accessibility of the LTAR network, to support research to sustain or enhance agricultural production and environmental quality in agroecosystems characteristic of the Southern Great Plains region, as per the LTAR site responsibilities and other information outlined in the 2011 USDA Long- LTAR Network Request for Information (RFI) to which the location successfully responded, and the LTAR Shared Research Strategy, a living document that serves as a roadmap for LTAR implementation. Participation in the LTAR network includes research and data management in support of the ARS GRACEnet and/or Livestock GRACEnet projects.

1b. Approach (from AD-416):
The Soil and Water Assessment Tool (SWAT) will be the primary hydrologic model used to address watershed scale studies. SWAT will be linked to the USGS groundwater model, MODFLOW, and will be coupled to an energy balance/evapotranspiration (EB_ET) model to fully address the project’s conservation targeting research objectives. Field studies will be conducted to provide relevant data to SWAT and to verify SWAT performance and accuracy, and to assess the impacts of climate variability and land cover/land use on reservoir sedimentation. New remotely sensed products will be evaluated for their ability to better characterize landscape variables needed for watershed-scale hydrologic simulations. Mathematical and statistical analysis of climate data will be conducted to generate more realistic climatologies (e.g., non-stationary conditions, extreme conditions) and to produce spatiotemporally coherent daily weather grids required by SWAT. Farm to watershed scale process modeling will be conducted in the context of the project’s research watersheds and will focus on identifying practices or policies that optimize economic enterprise and environmental goals across farm to landscape scales. In addition, the SP LTAR will be used to improve observational capacities and data accessibility of the LTAR network and to support research to sustainh or enhance agricultural production and environmental quality.

3. Progress Report:
Bi-weekly collection of water samples is being continued in our research watershed. The first year of geomorphic and LiDAR data collected on the Ft. Cobb watershed as part of the OSU-led, USDA-NIFA NIWQP funded project titled "Implementation of in-stream, streambank, and riparian practices in conjunction with upland practices for conservation of water resources." Remotely sensed hyperspectral canopy reflectance, soil physical and hydrologic property, and vegetation (mass, height, LAI, and nutrient) data were collected at the AFRI Grazing-CAP field sites located on GRL property. Unmanned aerial vehicles equipped with three-band color and thermal band sensors were acquired for the use of pasture/range assessment and development of near-real time assessment of evapotranspiration. Two groundwater wells were drilled and instrumented under the multi-agency partnership developed last year to study the Rush Springs aquifer, which underlies most of our research watersheds. The partnership consists of ARS, the USEPA, USGS, the Oklahoma Water Resources Board, the Oklahoma Water Survey, the Oklahoma Mesonet, and others. The Oklahoma Mesonet is responsible for collecting and storing real-time groundwater elevations. Plans are underway to drill and instrument 7 more wells in the Fort Cobb Reservoir Experimental watershed (FCREW). The partnership is designed to maximize resource allocation, data collection, optimize research activities, and to test and validate the linked SWAT_MODFLOW model being developed in this project. As part of the research activities developed to better understand surface and groundwater interaction and to improve model capacities at the watershed scale, a series of collaborative activities were developed with other agencies: 1) USGS Water Science Center in Oklahoma to deploy 7 ISCO water samples in the Fort Cobb Reservoir (FCREW) and Little Washita River (LWREW) experimental watersheds to measure nutrients and sediments during special streamflow events. With new funding to support the Southern Plains site of the Long-Term Agroecosystem Research (LTAR) network, several new initiatives were put in place. In conjunction with Grazing CAP collaborators, an intensive field campaign contrasted carbon, water, energy, methane, and nitrous oxide fluxes in grazed native and introduced pastures. A new long-term experiment to focus on annual forages as part of the regional beef-grazing systems is being established in 9 catchments, each approximately 50 ha. The research will compare tilled and no-till management of 4-year wheat-canola systems. Flux stations will be established in two of the fields for the 2014/15 wheat and canola growing seasons to measure flux and balances of carbon, water, energy, and methane. Static chambers to measure soil greenhouse gases will be established pre-fertilization. Instrumentation and infrastructure on 8-unit source watersheds established in 1976 are being modernized and automated. A wireless station is being established to allow automated monitoring and downloading of data from field data loggers on the 6700 research property. In addition, an SCA was established with the University of Oklahoma to support high speed computing of linked surface and subsurface hydrologic systems. An additional SCA was established with a non-profit organization to develop an initiative in STEM education at K-12, undergraduate, and graduate levels to leverage resources of our LTAR research. New initiatives were developed in hydrologic model calibration, validation and uncertainty to improve model parameterization and model robustness assessment. ARS researchers at El Reno, Oklahoma, led efforts on a special collection of papers on model calibration and validation submitted for publication in the transaction of ASABE journal. This effort will result in the development of ASABE engineering practices standards in hydrologic model calibration and validation. The USDA Office of Environmental Marketing provided funding to support uncertainty assessment for the Agricultural Policy Environmental eXtender (APEX) model. This initiative has been developed in collaboration with USDA ARS scientists from Tifton, Georgia; Kimberly, Idaho; and Columbus, Ohio, using data from sub-watersheds within the CEAP Upper Big Walnut Creek Watershed (UBWCW), Ohio. This initiative will result in the development of guidelines for parameterization, calibration, and validation of APEX to support the nutrient tracking tool (NTT).

4. Accomplishments
1. Upper Washita River watershed data publication. While hydrologic processes and scientific investigations related to sustainable agricultural systems are based on universal principles, research to understand processes and evaluate management practices is often site-specific in order to achieve a critical mass of expertise and research infrastructure to address spatially, temporally, and ecologically complex systems. In the face of dynamic climate, market, and policy environments, long-term research is required to understand and predict risks and possible outcomes of alternative scenarios. ARS researchers at El Reno, Oklahoma, and their collaborators from the US Geological Survey and USDA Natural Resources Conservation Service, along with past ARS scientists, published a collection of data and research papers describing long-term research (1961 to present) in the Upper Washita River basin of Oklahoma. Data papers document datasets in detail (weather, hydrology, physiography, land cover, and sediment and nutrient water quality) and associated research papers present analyses based on those data. This living history of research is presented to engage collaborative scientists across institutions and disciplines to further explore complex, interactive processes and systems, including resilience to current and future climate pressures; sources, fate, and transport of contaminants at a watershed scale; linked atmospheric-surface-subsurface hydrologic processes; high spatiotemporal resolution analyses of linked hydrologic processes; and multiple-objective decision making across linked farm to watershed scales.

2. Seasonal sediment and nutrient transport patterns. It is essential to understand sediment and nutrient sources and their spatial and temporal patterns in order to design effective mitigation strategies. However, long-term data sets to determine sediment and nutrient loadings are scarce and expensive to collect. ARS scientists at El Reno, Oklahoma, used instantaneous suspended sediment (SS), total nitrogen (TN) and total phosphorus (TP) concentration, and discharge data measured at the Fort Cobb Reservoir Experimental watershed (FCREW) to develop water quality–discharge relationships, which were used to generate continuous long-term SS, TN, and TP data. The largest sediment and nutrient loads were estimated during the wet springs and summers. In addition, priority locations and seasons that required implementation of TN and TP conservation practices within FCREW were identified. Common practices to mitigate nutrients and suspended sediments include nutrient management, no-till, conversion of cultivated land to pasture, riparian buffers, and animal exclusion. The discharge–water quality relationships developed are a potential cost-effective alternative to generate the continuous long-term data needed to determine sediment and nutrient concentrations and loadings to water bodies.

3. Data screening procedure for data quality assurance. USDA-ARS Grazinglands Research Laboratory has been collecting climate and other types of data for quite a long time. However, no rigorous quality assurance had been conducted due to lack of software tools to handle large datasets and the corresponding quality assurance procedures. In this research ARS researchers at El Reno, Oklahoma, developed a procedure to screen the climate and soil moisture datasets (Micronet) measured within the USDA ARS benchmark watersheds in Oklahoma. Screening made it possible to detect incorrect data and to identify network operation problems that could lead to errors in measured data. These procedures were used to clean up the data, which was then made publicly available for research purposes. These procedures will contribute to ensuring good quality data for hydrologic and water quality modeling applications.

Review Publications
Moriasi, D.N., Gowda, P., Arnold, J.G., Mulla, D.J., Ale, S., Steiner, J.L., Tomer, M.D. 2013. Evaluation of the Hooghoudt and Kirkham tile drain equations in SWAT to simulate tile flow and nitrate-nitrogen. Journal of Environmental Quality. 42:1699-1710.

Sassenrath, G.F., Schneider, J.M., Schmidt, A.M., Corbitt, J.Q., Halloran, J.M., Prabhu, R. 2013. Testing gridded NWS 1-Day observed precipitation analysis in a daily irrigation scheduler. Agricultural Sciences. 4(12):621-627.

Guzman Jaimes, J.A., Chu-Agor, M.L., Munoz-Carpena, R., Kiker, G., Linkov, I. 2014. A simplified approach for simulating changes in beach habitat due to the combined effectgs of long-term sea level rise, storm erosion, and nourishment. Journal of Environmental Modeling and Software. 52:111-120.

Moriasi, D.N., Starks, P.J., Guzman Jaimes, J.A., Garbrecht, J.D., Steiner, J.L., Stoner, C.J., Allen, P.B., Naney, J.W. 2014. Upper Washita River experimental watersheds: Reservoir, groundwater, and stream flow data. Journal of Environmental Quality. 43:1262-1272.

Moriasi, D.N., Starks, P.J., Steiner, J.L., Guzman Jaimes, J.A., Allen, P.B., Naney, J.W. 2014. Upper Washita River experimental watersheds: Physiography data. Journal of Environmental Quality. 43:1298-1309.

Moriasi, D.N., Guzman Jaimes, J.A., Steiner, J.L., Starks, P.J., Garbrecht, J.D. 2014. Seasonal sediment and nutrients transport patterns. Journal of Environmental Quality. 43:1334-1344.

Starks, P.J., Fiebrich, C.A., Grimsley, D.L., Garbrecht, J.D., Steiner, J.L., Guzman Jaimes, J.A., Moriasi, D.N. 2014. Upper Washita River Eeperimental watersheds: Meteorologic and soil climate measurement networks. Journal of Environmental Quality. 43:1239-1249.

Starks, P.J., Steiner, J.L., Moriasi, D.N., Guzman Jaimes, J.A., Garbrecht, J.D., Allen, P.B., Naney, W.J. 2014. Upper Washita River experimental watersheds: Nutrient water quality data. Journal of Environmental Quality. 43:1280-1297.

Starks, P.J., Venuto, B.C., Dugas, W.A., Kiniry, J.R. 2014. Measurements of canopy interception and transpiration of openly-grown eastern redcedar in central Oklahoma. Environment and Natural Resources Research. 4(3): DOI: 10.5539/enrr.v4n3p103.

Steiner, J.L., Starks, P.J., Garbrecht, J.D., Moriasi, D.N., Zhang, X.J., Schneider, J.M., Guzman Jaimes, J.A., Osei, E. 2014. Long-term environmental research: The Upper Washita River experimental watersheds, Oklahoma, USA. Journal of Environmental Quality. 43:1227-1238.

Moriasi, D.N., Gowda, P., Arnold, J.G., Mulla, D.J., Ale, S., Steiner, J.L. 2013. Modeling the impact of nitrogen fertilizer application and tile drain configuration on nitrate leaching using SWAT. Agricultural Water Management. 130:36-43.

Ale, S., Gowda, P., Mulla, D., Moriasi, D.N., Youssef, M. 2013. Comparison of the performances of DRAINMOD-NII and ADAPT models in simulating nitrate losses from subsurface drainage systems. Agricultural Water Management. 129:21-30.

Tomer, M.D., Beeson, P.C., Meek, D.W., Moriasi, D.N., Rossi, C.G., Sadeghi, A.M. 2013. Evaluating simulations of daily discharge from large watersheds using autoregression and an index of flashiness. Transactions of the ASABE. 56(4):1317-1326.

Cosh, M.H., Starks, P.J., Guzman Jaimes, J.A., Moriasi, D.N. 2014. Upper Washita River experimental watersheds: Multiyear stability of soil water content profiles. Journal of Environmental Quality. 43(4):1328-1333. DOI: 10.2134/jeq2013.08.0318.