Project Number: 3070-13000-011-00-D
Project Type: Appropriated
Start Date: Feb 26, 2012
End Date: Feb 15, 2017
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.
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.