Location: Water Management and Systems Research2013 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:
The AgroEcoSystem-Watershed (AgES-W) model development team: 1) integrated science components for irrigation scheduling, water conveyance, and passive tile drainage into AgES-W to improve simulation of conservation practices and systems; 2) modified AgES-W to better simulate soil-water processes and provide automated calibration within the Object Modeling System 3 (OMS3); and 3) implemented further model improvements to simulate surface runoff at sub-daily time increments. Model code for calculating the effect of changes in soil physical properties by tillage, reconsolidation, or other practices were extracted from RZWQM2 and the new component tested under OMS3 for use in AgES-W or other models. The tillage component calculates the effects of tillage and soil reconsolidation on soil water retention functions and saturated hydraulic conductivity over time. AgES-W was evaluated for different processes across diverse geographic areas: (1) Model science components for crop growth, nitrogen (N) dynamics, and sediment transport were evaluated using measured water quantity/quality data from the Upper Cedar Creek Watershed (UCCW) in Indiana. (2) Soil moisture data in Colorado were assembled, and daily data were used for AgES-W evaluation at multiple locations and depths within a farm field. Surface runoff is currently being measured at edge-of-field within a small agricultural watershed as part of cooperative on-farm research in northern Colorado. (3) AgES-W was used to simulate forage growth and soil moisture on a grassland at the Central Plains Experiment Station, CO. These data and simulation results are being compared with GPFARM-Range model simulations. The results will guide future modifications to the AgES-W grass growth component. The Unified Plant Growth Model (UPGM) was evaluated for irrigated corn after incorporating PhenologyMMS Version 1.2 improvements for simulating seedling emergence, phenology, and canopy height. Subsequent progress has been made on evaluating additional crops under different irrigation practices and incorporating improvements from PhenologyMMS Version 1.3. Standalone UPGM code was restructured and modified to facilitate integration into OMS/AgES-W (UPGM input/output was identified, appropriate input files assembled, and input/output variables documented). UPGM has been compiled and tested under OMS3 and integration into AgES-W is on-going. OMS3 was enhanced to better support the FORTRAN and C++ programming languages. AgES-W was deployed as a prototype service to the Cloud Services Innovation Platform (CSIP), a collaborative effort between NRCS, CSU, and ARS to develop a scalable infrastructure solution for modeling using cloud computing. AgES-W source code was added to the CSIP repository and the CSIP computing infrastructure was extended to provide model calibration support. Initial testing for model execution distribution across multiple cloud computers was performed. Additionally, the set of Domain Specific Language (DSL)-based tools within OMS3 for calibration, sensitivity analysis, and uncertainty analysis was extended with addition of the “Particle Swarm” and “DREAM” methods and is currently being evaluated
Derner, J.D., Augustine, D.J., Ascough II, J.C., Ahuja, L.R. 2012. Opportunities for increasing utility of models for rangeland management. Rangeland Ecology and Management. 65(6):623-631.