Location: Water Management and Systems Research2019 Annual Report
1a. Objectives (from AD-416):
Objective 1: Quantify changes in agricultural production and fluxes of water and associated nutrients (N and P) and sediment from field to watershed scales over the next several decades at fine temporal resolutions in response to changes in water availability, land use, management practices, and climate. Sub-objective 1.1. Understand and quantify the effects of variable irrigation practices on crop production responses by assessing genotype x environment x limited-water management (GEM) interactions for different land use, management, and climate scenarios at field to watershed scales. Sub-objective 1.2. Improve estimates of water redistribution and storage by resolving spatial scale issues related to the measurement and simulation of soil moisture in cropland and grassland ecosystems at field to watershed scales. Simulate hydroecology within the SPRB and the Central Plains Experimental Range (CPER) Long-Term Agroecosystem Research (LTAR) site to extend experimental results to larger areas and different management scenarios. Sub-objective 1.3. Understand how the effectiveness of spatially distributed water conservation strategies and agricultural best management practices (BMPs) for nutrient and sediment control vary with landscape position, geographic/geologic characteristics of the field, farm, or watershed, and other factors. Objective 2: Assess key ecosystem services for projected water requirements and water quality targets in the South Platte River Basin, Colorado, at field to watershed scales in response to changes in water availability, land use, management, and climate. Objective 3: Develop and disseminate a web-based geospatial data management system as a repository of data, models, and tools for accelerating collaborative research and facilitating sustainable management of water, nutrients, and sediment.
1b. Approach (from AD-416):
Objectives 1 and 2 focus on enhancing scientific knowledge for incorporation into the Agricultural Ecosystems Services (AgES) distributed watershed model with subsequent testing and application of AgES. Objective 1 is divided into three sub-objectives integrated from smaller to larger scales, which focus on: (1.1) improved model components for plant modeling of GEM interactions, particularly for irrigated water management, (1.2) soil water modeling emphasizing spatial scaling of soil water and surface runoff in dryland cropping and rangeland systems, and (1.3) simulation of conservation effects over regional watersheds, primarily in Iowa where collaborators have been investigating and monitoring water quality impacts over decadal time scales. In Objective 2, the AgES model will be used to simulate a series of land use, management practice and climate scenarios for hydrologic and water quality ecosystem service indicators in eastern Colorado. Objective 3 involves development of a web-based Geospatial Portal for Scientific Research (GPSR) for technology transfer of geospatial information. GPSR will be used for dissemination of the results of the present project together with broader technology transfer by ARS and collaborators, such as experimental results generated from Long-Term Agricultural Research sites and Climate Hubs.
3. Progress Report:
Objective 1.1: The Unified Plant Growth Model (UPGM) has been converted to Java, incorporated within the Agricultural Ecosystems Services (AgES) watershed model, enhanced to improve plant parameters, carbon partitioning, and plant nitrogen responses, and tested for winter wheat plant phenological response to genetics-by-environment-by-management (GEM) interactions. The study showed that winter wheat phonology simulations improve when adding responses to water stress. Extensive enhancements of parameters to represent GEM responses for 11 crops has been incorporated into UPGM/AgES and verified with field climatological, management, and phenological data. Work continued with the Wind Erosion Predictions System (WEPS) modeling team on restructuring and redesigning UPGM so that it can more readily incorporate recent scientific concepts and approaches and replace the plant growth components of other agency models. Objective 1.2: The Agricultural Ecosystems Services (AgES) watershed model was developed by ARS scientists in Fort Collins, Colorado collaboratively with researchers at Colorado State University. AgES was used to explore spatial “scaling” of soil water measurements using data from the Drake Farm in Colorado under dryland cropping (primarily winter wheat-fallow rotations in the simulation period 2003-2008). Objective 1.2: Measurement of soil moisture in space and time at the Drake Farm continued using buried capacitance sensors at multiple depths and landscape positions, and was enhanced using a portable Time Domain Reflectometer (TDR) and two Cosmic Ray Neutron Sensors (CRNS) installed at summit and toeslope positions. Testing and evaluation of the CRNS stations contributes toward ARS application of this technology at multiple sites, including collaboration at the Long-Term Agroecosystem Research network site on the Central Plains Experimental Rangelands and related milestones in future years of this project. Objective 1.3: Components for simulating management practices in the Agricultural Ecosystems Services (AgES) watershed model were tested. The new tile drainage component has been applied to improve simulation of nitrate transport in the Southfork Iowa River Watershed, Iowa. This enhances ongoing collaboration with ARS scientists in Ames, Iowa. This collaboration has potentially high impact because simulations of conservation practices with AgES complement and build upon the Agricultural Conservation Planning Framework. Objective 2: Working with Colorado State University and the Big Dry Creek Watershed Association, various data have been assembled and processed to perform preliminary runs of the Agricultural Ecosystems Services (AgES) watershed model for the lower, agricultural portion of the Big Dry Creek Watershed. AgES was modified to allow inputs of daily streamflow and nitrate concentrations at an upstream location and inputs and diversions of water and associated nitrate at multiple locations related to return flows and transboundary flows from water treatment facilities and irrigation ditches. Objective 3: Substantial progress was made regarding linking the data portal, now named the ARS Agricultural Collaborative Research Outcomes System (AgCROS), to the USDA Ag Data Commons catalog. AgCROS is being tested, and funding was secured to expand its utility and applications (case studies as examples) for more rapid agency-wide adoption in the coming years. Overall Project Deliverables: Web-based tools and services are being developed and deployed in collaboration with Colorado State University. Essential tools for processing spatial data and generating model inputs were developed and are being tested: Catchment areas delineation (Cadel) analyzes the watershed topology using a Digital Elevation Model and other spatial layers (e.g., soil and land use) to partition a watershed into interconnected Hydrological Response Units (HRU) and generate the essential HRU and routing files for AgES.
1. Developed framework for evaluating effects of irrigation management and climate change in the semi-arid Western U.S. In semi-arid regions such as the Western U.S., agricultural water has high value, as municipalities seek water security under growing populations and projected climate change and variability. Historical “return flows” from irrigated fields to streams and groundwater must be subtracted from the amount of irrigation water traded. ARS scientists in Fort Collins, Colorado, developed a framework for simulating spatially variable infiltration and derived distributions of groundwater recharge and nitrate leaching. This study used an agricultural system model to simulate groundwater recharge and nitrate leaching under irrigated corn in Colorado, U.S. Projected climate representing a period centered on the year 2050 increased baseline recharge by up to 58%, but the climate effect decreased with increasing spatial variability of applied irrigation. This study illustrates a framework for further evaluations of the potential combined effects of irrigation management and climate change on groundwater resources, which will be critical as the Western U.S. struggles to manage precious water resources to meet growing demands from food production and urban population growth.
2. Conducted global analysis of water sustainability and the role of groundwater for irrigation. Rapidly increasing water demand has led to overexploitation of water resources in many important food-producing regions. In particular, growing groundwater-based irrigation causes potential depletion in critical crop production regions. Food systems are increasingly globalized, leading to large export-oriented production. Much research has focused on quantifying the amount of water resources embedded in traded products, but less attention has been given to the role of groundwater use and the related sustainability of agriculture globally. Thus ARS scientists in Fort Collins, Colorado, analyzed the current knowledge of virtual water trade in light of groundwater use and sustainability. This study highlights critical challenges related to water sustainability in the coming decades.
3. Estimated soil moisture patterns from global datasets. Numerous scientific and resource management applications require fine-resolution soil moisture patterns, but most satellite remote sensing methods provide very coarse resolution soil moisture estimates (on the scale of tens of kilometers). The Equilibrium Moisture from Topography, Vegetation, and Soil (EMT+VS) model downscales soil moisture using fine-resolution topography, vegetation, and soil data. In previous applications, the model was calibrated using detailed ground-based soil moisture data, but very few regions have such data. ARS scientists from Fort Collins, Colorado, estimated soil moisture patterns from global datasets. This effort provides a technique for more reliable estimates of soil moisture patterns than simply using the coarse-resolution soil moisture, which should prove valuable to multiple agencies and stakeholders for use as input to decision support systems for trafficability of large equipment (defense and agriculture) as well as precision agricultural conservation and production.
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Lee, S., McCarty, G.W., Moglen, G.E., Lang, M., Sadeghi, A.M., Green, T.R., Yeo, I., Rabenhorst, M. 2018. Effects of subsurface soil characteristics on depressional wetland inundation within the Coastal Plain of the Chesapeake Bay watershed. Hydrological Processes. 33(2):305-315. https://doi.org/10.1002/hyp.13326.
McMaster, G.S., Edmunds, D.A., Marquez, R., Haley, S.D., Buchleiter, G.W., Bryne, P.F., Green, T.R., Erskine, R.H., Lighthart, N.P., Kipka, H., Fox, F.A., Wagner, L.E., Tatarko, J., Maragues, M., Ascough II, J.C. 2019. Winter wheat phenology simulations improve when adding responses to water stress. Agronomy Journal. 3:1-11. https://doi.org/10.2134/agronj2018.09.0615.
Senay, G.B., Schauer, M., Velpuri, N.M., Singh, R.K., Kagone, S., Friedrichs, M., Litvak, M.E., Douglas-Mankin, K.R. 2019. Long-term (1986-2015) water use characterization over the upper Rio Grande basin using Landsat-based evapotranspiration. Remote Sensing. 11(13):1-25. https://doi.org/10.3390/rs11131587.
Barnard, D.M., Germino, M.J., Arkle, R.S., Bradford, J.B., Duniway, M.C., Pilliod, D.S., Pyke, D.A., Shriver, R.K., Welty, J.L. 2019. Soil characteristics are associated with gradients of big sagebrush canopy structure after disturbance. Ecosphere. 10(6):1-12. https://doi.org/10.1002/ecs2.2780.
Shriver, R.K., Andrews, C.M., Arkle, R.S., Barnard, D.M., Duniway, M.C., Germino, M.J., Pilliod, D.S., Pyke, D.A., Welty, J.L., Bradford, J.B. 2019. Transient population dynamics impede restoration and may promote ecosystem transformation after disturbance. Ecology Letters. https://doi.org/10.1111/ele.13291.
Barnard, D.M., Germino, M.J., Pilliod, D.S., Arkle, R.S., Applestein, C.V., Davidson, B.E., Fisk, M.R. 2019. Cannot see the random forests for the decision trees: selecting predictive models for restoration ecology. Restoration Ecology. 10(6):1-11. https://doi.org/10.1111/rec.12938.
Gao, J., Sheshukov, A.Y., Yen, H., Douglas-Mankin, K.R., White, M.J., Arnold, J.G. 2018. Uncertainty of hydrologic processes caused by bias-corrected CMIP5 climate change projections with alternative historical data sources. Journal of Hydrology. 568:551-561. https://doi.org/10.1016/j.jhydrol.2018.10.041.