Location: Water Management and Systems Research2020 Annual Report
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.
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.
Objective 1. Sub-objective 1.1: ARS researchers at Fort Collins, Colorado, further developed the Unified Plant Growth Model (UPGM) to characterize response of crop phenology to water-stress across a range of genetic variation (grouped by variety maturity class) for 11 key crops: maize (corn), sorghum, soybean, dry (pinto) bean, winter wheat, spring wheat, winter barley, spring barley, sunflower, proso millet, and hay millet. Field data collected to test interactions between genotype, environment, and irrigation management (GEM) have been compiled for each crop. Preliminary calibrations enhanced both UPGM and the soil-water module of the coupled UPGM/Agricultural Ecosystems Services (AgES) model. Current work is finalizing a suite of UPGM parameter values. Revised UPGM parameters were immediately applied to design optimal sorghum planting dates to reduce the risk of frost before maturity for field experiments in a separate ARS research project. The simulation results increased the likelihood of ARS and collaborative field experiments producing useful research results and thus maximizing USDA investments in water management research. Objective 1. Sub-objective 1.2: Shared on-farm field data in Colorado and collaborated with Colorado State University to relate soil strength to spatial soil moisture estimates using the conceptual hydrology model called Equilibrium Moisture with Topography plus Vegetation and Soils (EMT+VS). Improved maps of expected soil strength under time-variable average soil moisture help stakeholders make tactical decisions about off-road trafficability. Additional sensors to measure meteorological and soils data were added to the spatial network. New data include air temperature, humidity, wind speed and direction, light multi-spectral reflectance, and soil-moisture variables. Sensor installations on the new 10-m tower and along transects spanning a range of ground elevations were designed to detect cold air drainages controlled by topography and atmospheric conditions. Detailed patterns of variations in space (within a field or small watershed) and time (within a day) will be related to variations in plant growth and status using both measurements and simulations. Objective 1. Sub-objective 1.3: Researchers at Fort Collins, Colorado, used the enhanced AgES watershed model (version 0.3.0) with field-level tile drainage to simulate daily hydrology and nitrogen cycling and transport in the South Fork Iowa River. AgES parameters were calibrated to fit daily streamflow rates and nitrate concentrations at the outlet. Adjustment of recommended fertilizer and swine manure application rates to match nitrate loads at the gauging station provided estimates of reduced mineral fertilizer and increased manure application. Researchers on the project were invited to present the results at a symposium of the Soil & Water Conservation Society’s annual meeting, July 2020. Objective 2: Researchers at Fort Collins, Colorado, used the AgES watershed model to simulate streamflow in the lower, agricultural portion of the Big Dry Creek Watershed (BDCW) near Denver, Colorado. Results from AgES are being compared with results from the Soil Water Assessment Tool (SWAT) version 2012 with particular attention to irrigation returns flows and their spatial and temporal distributions. The BDCW Association invited ARS and a university collaborator to present preliminary results at its annual meeting in 2019. Objective 3: Model calibration (fitting simulated output to measured data by adjusting model parameters) is essential but generally very challenging, time consuming, and computationally expensive. Existing methods used for AgES calibration are sequential (one generated set of parameter values run at a time) and can take weeks to months to find optimal values. Researchers at Fort Collins, Colorado, are developing new calibration methods that run multiple parameter sets (“particles”) in parallel to reduce this timeframe. A completely new approach combines concepts to implement Particle Swarm Optimization (PSO) with stepwise calibration of parameters that are grouped by biophysical processes. A new stepwise or multi-group PSO calibration service is being developed and tested running on a server with capacity to run tens of AgES simulations in parallel. Substantial enhancements to AgES were made in the newly released version (0.3.0). These critical improvements include improved simulation of soil water processes in layered soils, tile drainage, coupled nitrogen and plant water uptake, upstream and point-source discharges as inputs, and detailed water and nitrogen balance at multiple locations. AgES 0.3.0 can be downloaded and more easily installed for use on a local computer and for cloud computing. A sharable online notebook demonstrates how to run AgES online. Domestic universities in Colorado and Nebraska are applying AgES to explore water and nitrogen movement in watersheds influenced by irrigation and inputs from wastewater treatment plants. International collaborators are using AgES in Brazil to explore effects of reforestation of predominantly rangeland watersheds, and in China to explore effects of large-scale crop management (irrigation, drainage and nutrients) and rangeland contributions to lakes that are seasonally frozen. In support of AgES applications, ARS researchers and collaborators at Colorado State University are developing and deploying web-based tools and services for processing spatial data and generating required model inputs. The Catchment areas delineation (Cadel) tool analyzes the watershed topology using a Digital Elevation Model and other spatial layers (such as soil and land use) to partition a watershed into interconnected Hydrological Response Units and generate essential input files for AgES.
1. Reduced spring precipitation and soil moisture have large impacts on the success of rangeland restoration. ARS scientists in Fort Collins, Colorado, developed a novel procedure that uses soil moisture availability to guide post-fire shrubland restoration efforts. A process-based soil moisture model and a generalized drought index from gridded climate data were used to predict rangeland shrub restoration success from seeding treatments after wildfire at over 600 locations across the Great Basin, U.S. Successful restoration sites experienced seven more wet-warm days than unsuccessful sites. Thus, seemingly small differences in soil moisture can significantly impact ecosystem restoration efforts. These results will help scientists clarify the impacts of drought on semi-arid ecosystems, which will increase the likelihood of success of critical rangeland restoration projects in the U.S. Great Basin.
2. Topography, vegetation, and soil characteristics predict fine-scale patterns of soil moisture. ARS scientists and university collaborators in Fort Collins, Colorado, enhanced the conceptual hydrology model called Equilibrium Moisture with Topography plus Vegetation and Soils (EMT+VS). New results show effects of non-uniform overland flow and improved soil characteristics, as well as a new stochastic component for estimating and mapping spatial patterns. Other researchers, action agencies, and the U.S. military rely on these results because their stakeholders concerned with precision agriculture and off-road vehicle mobility need improved methods for downscaling coarse-grid estimates of soil moisture. The U.S. Army Engineering Research and Development Center (ERDC) uses EMT+VS results for mobility assessments, while the Tank Automotive Research, Development and Engineering Center (TARDEC) is using these methods to develop a new mobility model for the North Atlantic Treaty Organization (NATO).
3. Remote sensing predicts occurrence of harmful algal blooms. ARS scientists in Fort Collins, Colorado, collaborated with the United Arab Emirates University to make important advances in satellite remote sensing and imaging to better predict occurrences of harmful algal blooms. This novel technique uses sea surface temperature, calcite concentration, photosynthetically active radiation, fluorescence, and wind speed to explain patterns of chlorophyll-a concentration over space and time. Fisheries and coastal services organizations need these results to build more accurate algal bloom prediction tools for different coastal regions. Additionally, this research will aid governmental decision-makers in preparing strategic plans for algal control and responding to environmental conditions that coincide with harmful algal blooms.
Deshon, J.P., Niemann, J.D., Green, T.R., Jones, A.S., Grazaitis, P.J. 2020. Stochastic analysis and probabilistic downscaling of soil moisture. Journal of Hydrology. https://doi.org/10.1016/j.jhydrol.2020.124711.
Ball, G.P., Douglas-Mankin, K.R. 2019. Geospatial scaling of runoff and erosion modeling in the Chihuahuan Desert. Applied Engineering in Agriculture. 35(5):733-743. https://doi.org/10.13031/aea.13275.
Messer, T.L., Douglas-Mankin, K.R., Nelson, N.G., Etheridge, J.R. 2019. Wetland ecosystem resiliency: Protecting and restoring valuable ecosystems. Transactions of the ASABE. 62(6):1541-1543. https://doi.org//10.13031/trans.13578.
Fox, G.A., Douglas-Mankin, K.R., Muthukumarappan, K., Zhu, J., Walker, J.C. 2019. Navigating the publication process: An ASABE journals’ perspective. Transactions of the ASABE. 62(5):1147-1153. https://doi.org/10.13031/aea.13275.
Pauly, M.J., Niemann, J.D., Scalia, J., Green, T.R., Erskine, R.H., Jones, A.S., Grazaitis, P.J. 2020. Enhanced hydrologic simulation may not improve downscaled soil moisture patterns without improved soil characterization. Soil Science Society of America Journal. https://doi.org/10.1002/saj2.20052.
O'Conner, R.C., Germino, M.J., Barnard, D.M., Andrews, C.M., Pilliod, D.S., Arkle, R.S., Shriver, R.K. 2020. The scale of ecological drought in dryland restoration success. Global Change Biology. https://doi.org/10.1088/1748-9326/ab79e4.
Fathelrahman, E.M., Hussein, K.A., Paramban, S., Green, T.R., Vandenberg, B.C. 2020. Chlorophyll-a concentration assessment using remotely sensed data over multiple years along the coasts of the United Arab Emirates. Emirates Journal of Food and Agriculture. 32(5):345-357.