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 three inter-related objectives that enhance hydrologic simulation tools, develop new understanding and technologies to characterize climate and landscapes, elucidate landscape and hydrologic function, and build toward an optimization framework for assessment of conservation targeting, land management, and climate scenarios. More specifically, the research objectives are: 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 or application of conservation practices on the landscape 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 to help identify alternative agricultural management practices to mitigate water quality problems and enhance water use efficiency through better surface/groundwater management. 1C: Develop, evaluate, and refine new subsurface tile drainage and water table depth algorithms in SWAT to improve water budget predictions for increasing accuracy of water quality simulations. 1D: Develop remote sensing-based techniques to quantify phytologic, geomorphic, and other landscape variables to inform the selection or application of conservation practices in grazing lands and watersheds. 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, for natural resource conservation, and to support assessments of policy options. 3A: Develop and maintain a fundamental climate database and statistical analyses covering the Fort Cobb Reservoir Experimental Watershed and Little Washita River Experimental Watershed to support CEAP-related analyses and modeling. 3B: Generate daily grids of synthetic weather that are both spatially and temporally coherent and replicate recent climate statistics for use in hydrologic and agronomic models. 3C: Develop decision support tools for cool season forages based on sub-monthly weather statistics that accurately predict the direction of variations in productivity. 3D: Develop multi-scale, multiple-objective optimization framework for agricultural production, conservation, and policy assessment.
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.
3. Progress Report:
Bi-weekly collection of water samples is being continued in our research watershed. Ground-truth data collected in 2012 is being compiled to develop a 2012 land use map for our research watersheds. Satellite data for our multi-decade land use survey has been analyzed and completed. The aerial black and white photographs of the research watersheds have been collected and about 95% assembled into mosaics. A protocol was developed to retrieve core samples from the reservoirs for age dating of sediments as well as chemical analysis of the cores. A full-time temporary Physical Science Technician was hired in June 2013 to help assemble and analyze LiDAR, NAIP, aerial photographs, and satellite data. The Technician will also help collect field data to address research objectives and will be trained to run selected hydrologic models. A farm cluster analysis was conducted by Tarleton State University collaborators for Upper Washita River Basin hydrologic unit, based on the USDA-NASS 2007 Agricultural Census data. Planning was initiated for a Fort Cobb Reservoir Experimental Watershed workshop with producers and conservationists to demonstrate the Nutrient Tracking Tool, to gain information on preferences and perspectives on agricultural conservation and to develop improved understanding of producer management goals, strategies and practices. This information will be used in scenario development for an optimization decision-support tool for conservation planning and placement. A multi-agency partnership has been developed 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 partnership is designed to maximize resource allocation and data collection, optimize research activities, and test and validate the linked SWAT_MODFLOW model being developed in this project. The ARS and USEPA partnership will improve the groundwater monitoring capacity (ground water levels and water quality) at the FCREW at several USDA-ARS Micronet sites. This improved monitoring capacity will help ARS to better understand groundwater and surface water interactions. Linked modeling framework partnership: As part of the ARS and USEPA surface-groundwater monitoring cooperation, the two agencies are planning to integrate a groundwater transport model into the SWAT-MODFLOW modeling framework to improve its hydrologic modeling capacity.
1. Climate products and agricultural decision-making. Accurate, long-term precipitation data are needed for climate-informed decision support tools for agriculture, but few such records exist. Gridded estimates of precipitation from 1895 through the present were created by the PRISM Climate Group, but the accuracy of the gridded PRISM product required validation. Precipitation data gathered over several decades at the Grazinglands Research Laboratory, El Reno, Oklahoma, were used to check the PRISM product. The monthly gridded PRISM precipitation estimates are close to the observed data in terms of averages (smaller by 3 to 4.5%) and probability distributions (within approx. 4%), but with variability less for PRISM than for gauge data. For agricultural decision support, the PRISM estimates might be useful for probabilistic applications, such as downscaling climate forecasts or driving weather generators. However, because many monthly estimates differed from observed data by greater than 1.2", the PRISM estimates are not suited for retrospective month-by-month studies of interactions between climate, crop management, and productivity.
2. Landscape predictors of stream phosphorus concentrations. Agricultural land uses have been identified as one of the greatest contributors to impairment of water quality in the US, and in many regions high phosphorus (P) concentration has been identified as the most limiting factor related to impaired water quality. Based on long term studies in the Fort Cobb Reservoir Experimental Watershed, a Conservation Effects Assessment Project Benchmark Watershed, ARS scientists at El Reno, Oklahoma; Watkinsville, Georgia and College Station, Texas, identified spatial patterns in P in streams associated with landscape metrics during wet and dry periods. Stream P concentrations were 3 to 5 times higher during wet periods than dry periods. Lateral metrics (topography, soil, geology, management) were better predictors than in-stream metrics for P concentrations in streams. During the wet period, metrics indicative of rapid surface and subsurface water movement were associated with higher P stream concentrations. The ability to identify portions of the landscape more vulnerable to P losses is an essential first step in developing better strategies for targeting conservation practices and sites within a watershed.
3. A tool for analysis of big hydrologic datasets. There is a need in water science for computational tools to perform data manipulations and analyses of large spatially distributed datasets in one application, but few such tools exist. A conceptual data model and analysis tool, SPELLmap, was developed at the Grazinglands Research Laboratory, El Reno, Oklahoma, to rapidly process, manipulate, analyze, visualize, and provide data metrics for large geo-located datasets. SPELLmap has the capacity to represent three- or four-dimensional problems using a layer-data structure that can be used to assess data quality, perform statistical analyses, support modeling changes in land use, and perform spatial and temporal computations within integrated environmental modeling problems. This tool can advance hydrological research because high spatio-temporal resolution datasets best represent the spatial and temporal variability of hydrological processes and the associated transport of nutrients and contaminants at the watershed scale.
Arnold, J.G., Moriasi, D.N., Gassman, P.W., Abbaspour, K.C., White, M.J., Srinivasan, R., Santhi, C., Harmel, R.D., Van Griensven, A., Van Liew, M.W., Kannan, N., Jha, M.K. 2012. SWAT: Model use, calibration, and validation. Transactions of the ASABE. 55(4):1491-1508.