Location: Water Management and Systems Research2014 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) finished verification of irrigation scheduling and water conveyance science components in AgES-W; 2) modified AgES-W to better simulate surface runoff, changes in soil physical properties by tillage, reconsolidation, or other practices (using soil water retention functions and saturated hydraulic conductivity over time), and groundwater processes for improved prediction of conservation practices and systems; and 3) assembled AgES-W input/observed data files and commenced AgES-W evaluation for South Fork, Iowa watershed stream flow and nitrogen/sediment transport. Using data collected in Colorado, AgES-W was tested and calibrated to simulate daily soil water dynamics in vertical soil profiles at multiple depths. A model calibration tool called LUCA was applied using different approaches to calibrating layered soil parameters. The profile dynamics of soil water content compared well with field data, but optimal parameter sets were not unique and model results did not fully capture the measured dynamics. AgES-W simulations compared favorably with previous simulations at this site using a physically based model; thus the layered soil hydrology in AgES-W is suitable for three-dimensional watershed modeling. A semi-automated watershed delineation tool (WDAML) was ported to ArcGIS, tested, and implemented to delineate Hydrological Response Units (HRUs) at different scales within a field-scale (56 ha) watershed on a farm in Colorado. Factors used to distinguish HRUs included topography (surface flow paths), land use (crop strips and native grass), and mapped soil units (three types). The delineation algorithm was able to produce HRUs that follow the land use and soil boundaries, but adjustment of some of the inputs remains a trial-and-error process to avoid excessive aggregation and undesired spatial features. Simulations are being conducted using previously calibrated parameter sets applied across the watershed. The results will help guide HRU delineation and hydrological simulations in this and other semi-arid agricultural watersheds. The Object Modeling System (OMS) was enhanced to provide information from individual components needed for integration in a user interface. Commercial workflow software tools and systems were evaluated for their potential use and adaptation in model builders, and user interface components for creating simulations, model calibrations, and result visualization were implemented. OMS was also enhanced to better support parallelization of AgES-W. ARS collaborated with CSU and NRCS to develop the Cloud Services Innovation Platform (CSIP), which is a scalable infrastructure for cloud computing. AgES-W source code was added to the CSIP repository, the model was deployed in CSIP, and testing of model execution distributed across multiple cloud computers was initiated. The growing complexity of environmental models to represent real-world systems makes it increasingly difficult to fully comprehend model behavior, sensitivities and uncertainties. The Model Optimization, Uncertainty, and SEnsitivity Analysis (MOUSE) software application, an open-source, Java-based toolbox of visual and numerical analysis components, was developed for the evaluation of environmental models. MOUSE is model-independent and helps the modeler understand underlying hypotheses and assumptions regarding model structure, identify and select robust model parameterizations, and evaluate model performance and uncertainties. MOUSE offers well-established local and global sensitivity analysis methods, single- and multi-objective optimization algorithms, and various approaches to quantify model uncertainty. A robust graphical user interface provides visualization of the methods and algorithms described above. The simplicity of use and the ease with which model results can be visualized make MOUSE an effective tool that should dramatically reduce the time and effort spent on model calibration and sensitivity/uncertainty analysis. Grain sorghum is an important dryland crop in southeast Colorado, but expansion into northeast Colorado is thought to be limited due to the shorter growing season. This study tested whether sorghum production could be expanded into northeast Colorado using different maturity groups and agronomic management practices. Physiological maturity was reached under all environments and management practices, and acceptable yields were produced. Hybrid selection and seeding rate significantly impacted the thermal time to reach maturity, whereas row orientation and spacing did not influence maturity. Therefore, grain sorghum can be grown successfully in northeast Colorado, especially if planting early maturity hybrids using 0.76 m row spacing at a seeding rate close to 11 seeds m-2. These data will be used to improve the simulation of sorghum growth and development in the Unified Plant Growth Model (UPGM) being linked into the AgES-W model. NRCS funded a subordinate project to develop software tools that provide spatial data for conservation tools and process models. LAMPS (Landuse Agricultural Management Practices web-Service) is an online tool which automatically generates crop rotations and associated management (currently tillage, planting, and harvest) for any user-defined areas within the continental USA. LAMPS uses a web-service called CropScape by NASS to retrieve high-resolution (30 m grid) satellite-based annual crop types to determine an annual sequence of dominant crops within each area (e.g., field boundary). Then the crop sequences are matched to representative crop rotations in the NRCS Land Management Operations Database (LMOD). The output is spatially explicit and date-specific over the period of interest. LAMPS will provide the crop and tillage input data needed for process-based models, and is being prototyped for applications of the AgroEcoSystem-Watershed model in Colorado and Iowa. To date, LAMPS has been tested in Colorado on dryland and irrigated fields. LAMPS output based on LMOD can match ground-truth crop data even better than the sequences from CropScape. Collaborators at Colorado State University are interested in applying LAMPS to other projects, including erosion modeling. NRCS also funded a project to make a standard Hydrology Tool based on its three different software packages that implement the Curve Number method to obtain hydrograph characteristics, such as peak flow for different rainfall events. The Hydrology Tool is the computational engine, and NRCS will deploy it under their conservation desktop. The current project will deliver: 1) A consistent code based on TR20 in a non-proprietary software; 2) a web service of text output that can be used anywhere online; 3) implementation of the computational engine within the current graphical interface, WinTR20; 4) a web page with basic functionality that serves as a training and demonstration tool; and 5) implementation in a new version of the Engineering Field Tool desktop application.
1. Mapped patterns of soil erosion and deposition correlate with carbonate distribution. Knowledge of spatial patterns of erosion and deposition can help guide land management. ARS researchers at Fort Collins tested the hypothesis that variation in calcium carbonate concentration in the topsoil could be used to estimate soil erosion and deposition. Soil samples were collected from 185 landscape positions within a wheat field in Colorado in 2001 and in 2012. The expected relationships between changes in elevation and carbonates held, but were only significant over the summit and toe-slope positions. Complex spatial patterns of changes in elevation over time indicated erosion and deposition via both water and wind, which were affected by terrain and management. Thus crop rotation and tillage have substantial impacts on the occurrence and spatial distributions of water and wind erosion, providing the opportunity to reduce erosion through improved management in space and time.
2. New methodology for identifying sensitive parameters and calibrating environmental models. The most sensitive parameters of the Soil and Water Assessment Tool watershed model (SWAT) must be assessed for streamflow, combined nitrate and nitrite fluxes, and total phosphorous (TP) at different gage stations in an agricultural watershed. ARS scientists and Colorado State University collaborators in Fort Collins, CO developed a computational framework to incorporate disparate information from observed hydrologic responses at multiple locations into the calibration of watershed models. Sensitivity of SWAT parameters for different likelihood functions was highly variable, but the sensitivities of streamflow and TP were highly correlated. This study demonstrated the importance of hydrologic and water quality data availability at multiple locations. The use of multi-objective approaches is recommended for proper calibration of watershed models that are used for pollutant source identification and watershed management.
3. A Unified Plant Growth Module (UPGM) for Watershed Models. Many of the current watershed models use various versions of the EPIC plant growth module. We need a Unified Plant Growth Model (UPGM) to improve the AgroEcoSystem-Watershed (AgES-W) and other hydrologic watershed-scale models. ARS researchers at Fort Collins, CO evaluated the UPGM model developed earlier for growing corn under unstressed conditions in northeastern Colorado. Results were compared to the plant growth model in the Wind Erosion Prediction System (WEPS). In almost all instances, simulated plant and yield components were improved by using UPGM. Thus UPGM will improve simulation of plant growth and development over the EPIC-based plant growth model currently used in WEPS, AgES-W, and other watershed models.
4. Elevated carbon dioxide (CO2) further lengthens growing season under warming conditions. Climate warming is changing forage availability on rangelands, which requires better understanding to adjust management. ARS scientists and Colorado State University collaborators at Fort Collins evaluated the effects of both elevated CO2 and temperature on the development of temperate grassland species under a Free-Air CO2 Enrichment (FACE) system to better understand the effects on growing season length. Warming led to a longer growing season through earlier leaf emergence by the some species to leaf and constant or delayed senescence by other species. Elevated CO2 further extended the growing season, but not reproductive season, by conserving water, which enabled most species to remain active longer. These experimental results will make model parameters and simulation of phenology more accurate in different plant growth and hydrologic watershed models under ambient as well as projected elevated CO2 concentrations and warming.
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