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ARS Home » Plains Area » Fort Collins, Colorado » Center for Agricultural Resources Research » Water Management and Systems Research » Research » Research Project #422726

Research Project: Spatial Modeling of Agricultural Watersheds: Water and Nutrient Management and Targeted Conservation Effects at Field to Watershed Scales

Location: Water Management and Systems Research

2016 Annual Report


1a. Objectives (from AD-416):
Objective 1. Develop and apply new watershed modeling tools to evaluate the long-term effects of innovative cropping, limited water, and nitrogen management on water quantity, water quality, and crop production in selected agricultural sub-basins in Colorado. [Contributes to Problem Area #1, Effective Water Management in Agriculture, Problem Statements 1.1.3 and 1.4.2 of the new National Program (NP) 211 Action Plan (FY 2011 – 2015)] Objective 2. Using data from Colorado and the Midwest, improve model components to quantify and assess spatially targeted agricultural conservation effects on water quantity and quality. [Contributes to Problem Area #4, Improving Watershed Management and Ecosystem Services in Agricultural Landscapes, Problem Statement 4.1 of the new National Program (NP) 211 Action Plan (FY 2011 – 2015)] Objective 3. Simulate the combined effects of projected climate change on crop production, water use, and nitrate transport, and assess potential cropping system adaptations at field to sub-basin scales in Colorado. [Contributes to Problem Area #4, Improving Watershed Management and Ecosystem Services in Agricultural Landscapes, Problem Statement 4.3 of the new National Program (NP) 211 Action Plan (FY 2011 – 2015)]


1b. Approach (from AD-416):
As population increases and climate changes, we face global crises of conserving and managing water quantity and quality for agricultural and urban demands. Distributed agro-hydrologic modeling tools are needed to address complex system interactions in space and time for different soils and climates. Impacts of water and nutrient management and of targeted conservation practices within and adjacent to fields must be assessed in terms of water quantity and quality at designated watershed outlets. This project focuses on developing simulation tools for evaluating and proposing solutions to critical emerging problems in diverse agricultural systems over scales ranging from approximately 50 to 50,000 ha under current and future conditions. The component-based AgroEcoSystem-Watershed (AgES-W) model, developed in the Object Modeling System (OMS) framework, explicitly simulates the hydrologic and agronomic responses from spatially distributed land use, management, and weather conditions across inter-connected ecosystem response units (ERUs). AgES-W will be enhanced for: 1) routing water and nutrients across a watershed, 2) diverse cropping system responses to water deficits, 3) model uncertainty analyses and scaling, and 4) plant responses to atmospheric CO2. New OMS tools will include ERU delineation, sensitivity analysis, spatial visualization, statistical analyses of outputs, and web-based cloud computing. Selected conservation practices will be evaluated under existing and projected climates in the semi-arid West (Colorado), and spatially targeted conservation will focus on the sub-humid Midwest (Iowa), resulting in new agricultural adaptation strategies. These case studies address agricultural water and nutrient management issues in the American West and Midwest, while providing component-based modeling tools globally.


3. Progress Report:
ARS researchers in Fort Collins, Colorado and university collaborators comprising the Agricultural Ecosystems Services (AgES) model development team: (1) Developed the Java Connection Framework (JCF) for improved connectivity of AgES core science components and debugging of model code. (2) Developed new AgES components for simulation of solar radiation balance and snowmelt processes. (3) Modified AgES to better simulate surface runoff, nitrate fluxes, sediment transport, tile drainage, and groundwater processes for improved prediction of conservation practice effects and systems. (4) Evaluated the three plant growth components available in AgES (i.e., Soil Water Assessment Tool, SWAT; Wind Erosion Prediction System, WEPS; and Unified Plant Growth Model, UPGM) for simulating phenological responses to varying water deficits. Based on the evaluation results, further enhancements were made to the UPGM plant growth component to better simulate genotype by environment (G x E) interactions. (5) Evaluated new components in AgES that simulate conservation effects, hillslope soil erosion, and infiltration (two additional methods). (6) Added a Quick-Start Guide to the draft AgES User Manual and Technical Documentation. (Objective 1) AgES auxiliary tools were updated and enhanced: (1) The Geospatial Modeling Interface (GMI) was upgraded through the addition of new features for two- and three-dimensional visualization of AgES (or other model) input/output data and expanded options for controlling the display of model output. Efforts continued on comprehensive integration of the new modular version of the Soil and Water Assessment Tool (SWAT) model as one of the suite of models currently operating under the GMI. (2) The Model Optimization, Uncertainty, and SEnsitivity Analysis (MOUSE) software, an open-source, Java-based toolbox of visual and numerical analysis components, was enhanced through the addition of Dynamically Dimensioned Search (DDS) parameter estimation methodology and new features for visualization of optimization and sensitivity/uncertainty analysis results. In addition, MOUSE was used to calibrate sensitive AgES input parameters for streamflow, nitrate (NO3-N), and sediment transport model output responses for the South Fork Watershed in Iowa. (3) Code modifications, integration within an ArcGIS software framework, and evaluation of the semi-automated Watershed Delineation Arc Macro Language (WDAML) tool was completed. WDAML has been used to delineate Hydrologic Response Units (HRUs) at different scales for watersheds in Iowa, Colorado, Brazil and China (Inner Mongolia) where field boundaries, hydrogeology, topography, management units, and soil types were the primary geospatial inputs used for delineation. (Objective 1) Using measured water quantity/quality data collected from the South Fork Watershed (SFW) in Iowa, AgES was evaluated and calibrated to simulate hydrologic (streamflow) fluxes, nitrate (NO3-N) fluxes, and sediment transport at the watershed outlet. A combination of manual and automated calibration approaches was used to calibrate sensitive AgES parameters for streamflow, NO3-N, and sediment transport model output responses. AgES model performance for SFW streamflow and NO3-N/sediment dynamics was assessed using standard statistical evaluation methods, showing that AgES performed as well or better than other watershed models found in literature. (Objective 2) AgES was used to simulate soil moisture under dryland (wheat-fallow rotation) management in a small agricultural watershed (56 ha or 140 acres) monitored since 2003. The model was calibrated at different spatial resolutions, then used to test concepts of upscaling (from probes to a whole field) and downscaling (from large areas to smaller areas) soil moisture. Downscaling error decreased by including greater spatial variability in the model parameter estimation, while upscaling results were similar regardless of the calibration complexity, and upscaled soil moisture could be estimated within the range of sensor measurement error or data uncertainty. These results help inform simulation efforts for future research and applied model use, where estimates of spatially variable and lumped watershed-level soil moisture are needed over time. (Objective 1) Sorghum is an important crop in semiarid southeastern Colorado as it tends to be productive and more economical to grow than crops such as corn. Expansion of sorghum production into northeastern Colorado has been thought to be limited by the shorter growing season and cool evening temperatures, restricting the crop reaching maturity and therefore limiting yields. The Phenology Modular Modeling System (PhenologyMMS) was enhanced to better simulate different crop (and genotype) phenological responses to varying water deficits using genetic information. PhenologyMMS was then applied to estimating the probability of sorghum reaching maturity before the first frost for ten locations in northeastern Colorado using historical weather data for a variety of genotypes and agronomic practices. A manuscript has been submitted and fact sheets are being prepared for distribution to farmers and extension. (Objective 1)


4. Accomplishments
1. Linking genetics and physiology for improved understanding of Genetics by Environment (G x E) interactions in agricultural models. Incorporating G x E interactions into agricultural models is hindered partly by limited understanding of the linkage between genetics and physiology, and also by uncertainty of how to use genetic information for estimating plant parameters. ARS researchers in Fort Collins, Colorado have partnered with university researchers in the Triticeae Coordinated Agricultural Project (TCAP) hard winter wheat association mapping panel that is addressing G x E interaction of phenological responses to varying water deficits and providing data for inclusion into agricultural models. Improvements to the Unified Plant Growth Model (UPGM) and Phenology Modular Modeling System (PhenologyMMS) models have been made using these data. Incorporating this information into models helps address agricultural problems such as selecting “best” varieties for specific production environments and irrigation practices.

2. Evolutionary algorithm optimization framework for optimal crop and irrigation water allocation. Evolutionary algorithms (EAs) have been used extensively for optimization of water resources problems; however, application of EAs to real-world problems presents multiple challenges. One of these is the generally large size of the search space, which may limit the ability to find globally optimal solutions in an acceptable time period. ARS researchers in Fort Collins, Colorado worked with university partners in Australia to develop an ant colony optimization (ACO) framework for allocation of crops and water to irrigated areas. The framework reduces the search space and increases computational efficiency by including a unique dynamic decision variable adjustment during the optimization process. A case study used to evaluate the framework indicated that the ACO approach was able to find better crop and water allocation solutions in less time compared to other optimization methods. The substantial reduction in computational effort should be a major advantage in the optimization of real-world problems using complex crop simulation models. Ultimately, the ACO framework will provide farmers, extension personnel, and action agencies with a decision support tool for crop and water use optimization.


5. Significant Activities that Support Special Target Populations:
Cooperative on-farm research in northern Colorado is being performed on a “small farm” (family owned and operated) with regular communication and transfer of information to the producer.


Review Publications
Formetta, G., Capparelli, G., David, O., Green, T.R., Rigon, R. 2016. Integration of a three-dimensional process-based hydrological model into the Object Modeling System. Water. 8(1). doi:10.3390/w8010012.
Nguyen, D.H., Maier, H.R., Dandy, G.C., Ascough II, J.C. 2015. Framework for computationally efficient optimal irrigation scheduling using ant colony optimization. Environmental Modelling & Software. Environmental Modelling & Software 76:37-53.
Yen, H., White, M.J., Ascough II, J.C., Smith, D.R., Arnold, J.G. 2016. Augmenting watershed model calibration with incorporation of ancillary data sources and qualitative soft data sources. Journal of the American Water Resources Association. 52(3):788-798. doi:10.1111/1752-1688.12428.
Grogan, S., Brown Guedira, G.L., Haley, S., Mcmaster, G.S., Reid, S., Smith, J.H., Byrne, P. 2016. Allelic variation in developmental genes and effects on winter wheat heading date in the U.S. Great Plains. PLoS One. 11(4):e0152852.
Grogan, S.M., Anderson, J., Baenziger, P.S., Frels, K., Guttieri, M.J., Haley, S.D., Kim, K., Liu, S., Mcmaster, G.S., Newell, M., Prasad, P.V., Reid, S.D., Shroyer, K.J., Zhang, G., Akhunov, E., Byrne, P. 2016. Phenotypic plasticity of winter wheat heading date and grain yield across the U.S. Great Plains. Crop Science. 56:2223-2236. doi:10.2135/cropsci2015.06.0357.
Kipka, H., Green, T.R., David, O., Garcia, L., Ascough II, J.C., Arabi, M. 2015. Development of the Land-use and Agricultural Management Practice web-Service (LAMPS) for generating crop rotations in space and time. Soil & Tillage Research. 155:233–249. doi:10.1016/j.still.2015.08.005.
Nguyen, D.H., Dandy, G.C., Maier, H.R., Ascough II, J.C. 2016. Improved ant colony optimization for optimal crop and irrigation water allocation by incorporating domain knowledge. Journal of Water Resources Planning and Management. doi.org/10.1061/(ASCE)WR.1943-5452.0000662.
Timlin, D.J., David, O., Green, T.R., Fleisher, D.H., Kim, S., Ahuja, L.R. 2016. Proposed standards for peer-reviewed publication of computer code. Agronomy Journal. 108:1-5. doi: 10.2134/agronj2015-0481
Lin, H., Drohan, P., Green, T.R. 2015. Hydropedology – The last decade and the next decade. Soil Science Society of America Journal. 79(2), 357-361. 2015.
Green, T.R. 2016. Linking climate change and groundwater. Chapter 5. In: Jakeman, A.J., et al. Integrated Groundwater Management. Switzerland. Springer international Publishing. pp. 97-141. 2016 doi:10.1007/978-3-319-23576-9.