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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

2017 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:
This is the final report for project number 3012-13660-008-00D which terminated in February 2017. The work continued under a bridging project during OSQR reviews until the new project 3012-13660-009-00D was approved on May 18, 2017. A key project team member and lead model developer died on December 1, 2016 which had a detrimental effect on the project, and his scientist position remains vacant. Even so, the main research and product delivery proceeded as much as possible. Substantial results were realized over the 5-year project in the understanding and spatial modeling of agricultural ecosystems services, particularly in assessing conservation effects for food and water security: 1) Systems research approaches were applied to develop a new computer simulator called the Agricultural Ecosystems Services (AgES) watershed model to evaluate the long-term effects of innovative cropping systems, limited-water conditions, nitrogen management, and agricultural conservation effects on water quantity and quality and crop production for watersheds in Colorado and the Midwest. AgES simulates the movement of water and nitrogen in three spatial dimensions with interactions between land units of any size at a daily time step. Water and nitrogen processes are linked with dynamic plant growth and development using the Unified Plant Growth Model (UPGM), which provides an alternative to the original plant growth component derived from the Soil and Water Assessment Tool (SWAT) model. UPGM was fully integrated into AgES in both the Object Modeling System (OMS) and a new Java Connection Framework (JCF). Integration in these two frameworks allows greater flexibility for component-based model development, maintenance and deployment. The new AgES has been archived (source code and project data) for open access. AgES is being applied regionally in the U.S. (mainly in Colorado and Iowa) and internationally (primarily by partners in Brazil and China)(Objectives 1 and 2). 2) The Agricultural Ecosystems Services (AgES) model was designed to explicitly account for interactions between simulated land areas within a watershed. Thus AgES can be used to investigate processes and simulated variables, such as soil moisture, over a range of scales. Detailed patterns of soil water in Colorado have been simulated with AgES and tested using a comprehensive data set collected by ARS researchers in Fort Collins, Colorado. The field site near Severance, Colorado is part of the Drake Farm. Detailed measurements collected since 2003 provide meteorological inputs and calibration/evaluation data for simulating the winter wheat cropping system with spatial soil water dynamics and surface water runoff. AgES has been used to estimate patterns of soil water dynamics from small landscape positions up to the whole field/watershed (56 ha or 140 acres). A manuscript on spatial scaling of soil water related to the model resolution and parameter complexity was drafted during this project cycle and will be published under the new project. Ongoing simulations of the field-scale watershed address watershed responses to changing land use (conversion to grassland) and climatic conditions (Objectives 1 and 3). 3) Development of the Unified Plant Growth Model (UPGM) provides an improved plant growth component for use in our hydrologic and agricultural system models by better simulating the growth and development of plants and thereby more accurately addressing interactions between Genetics, Environment and Management (GxExM) that confound crop management and impacts on water and food security. UPGM also provides improved simulation of plant phenology and responses to changing atmospheric carbon dioxide affecting photosynthesis (plant carbon assimilation) and water use. UPGM has been built as a component that can be used in a variety of agency models (Objectives 1, 2, and 3). In FY17 we made progress in the following areas: The three plant growth components available in AgES (Unified Plant Growth Model, UPGM; Wind Erosion Predictor System, WEPS; Soil Water Assessment Tool, SWAT) were tested for plant responses to atmospheric carbon dioxide (CO2), and AgES source code was modified to accommodate inputs of temporally changing CO2 concentrations at any discrete time interval. A default CO2 input file was provided for concentrations in the global atmosphere going back to the year 1800 (estimated from the literature) and since 1958 using monthly values from measurements at Mauna Loa, Hawaii. Users can modify the values and use projections of future climates for model inputs. (Objective 3) AgES was modified to include an explicit subsurface tile drainage component. Compared with in-house simulations using the previous version of AgES and reported simulations with SWAT, the new AgES model simulates streamflow dynamics very well at the Southfork Watershed in Iowa (working collaboratively with ARS colleagues in Ames, Iowa). Nitrate concentrations at the watershed outlet are also simulated reasonably well, and standardized fertilizer adjustment factors are being applied to improve simulations of nitrate loads coming out of the watershed. The calibrated inputs can be used to infer actual fertilizer application patterns in corn-soybean rotations and in continuous corn using conventional fertilizers and manure. A manuscript is being prepared for submission to a journal by the end of FY 2017 (Objective 2). We released Version 1.0 of AgES and accompanying User Document with a Quick-Start Guide (Objective 1 and overall).

4. Accomplishments
1. The Runoff Curve Number computational engine was deployed as a web service. The desktop computer application WinTR-20 remains a widely-used hydrologic modeling tool, and its use through the USDA Natural Resources Conservation Service (NRCS) is expected to continue for the foreseeable future. A web-service version of TR-20, the computational engine of WinTR-20 was developed and implemented to facilitate timely updates and revisions to enable online deployment through a partnership of ARS researchers at Fort Collins, Colorado, the NRCS, and Colorado State University. By implementing TR-20 as a web-service, this model is more accessible and can be reused and repurposed for other current and future applications. It is accessible by the general public using both direct access and through a website that provides a common, simple application of the web service.

2. Forage availability and timing in rangelands changes under warming temperatures and elevated atmospheric carbon dioxide (CO2). The length of the growing season (which influences forage availability and timing) in rangelands is lengthening with climate warming and requires adjusting management, but little is known about how increasing atmospheric CO2 will interact with warmer temperatures to influence the growing season length. ARS scientists and Colorado State University collaborators at Fort Collins, Colorado studied the effects of both elevated CO2 and temperature on the development of temperate grassland species under a Free-Air CO2 Enrichment (FACE) system on the growing season length. Elevated CO2 further extended the growing season observed under higher temperatures (due to earlier spring growth by some species and delayed cool-season senescence of other species) by conserving water, which enabled most species to remain active longer. These experimental results have been placed into a data repository for public access and incorporated into the AgES model to more accurately simulate rangeland plant growth under ambient and projected elevated CO2 concentrations and warming to provide information for ranchers in managing forage availability and timing.

3. Application of the Agricultural Ecosystems Services (AgES) watershed model in southeast Brazil. Recent drought conditions that have impacted the conservation of watersheds and the management of water for agricultural and urban demands. The Ribeirão das Posses watershed has become a focal headwater of the Cantareira Reservoir Complex in the state of São Paulo. Collaborating via the Brazilian Science without Borders program, we used AgES developed by ARS scientists in Fort Collins, Colorado to simulate water movement and storage among land areas undergoing reforestation and agricultural management changes for water conservation. The baseline results will help with simulations of different land-use scenarios to guide programs of payments for ecosystem services.

4. Soil moisture is converted from large to smaller scales of interest using fine-resolution topographic, vegetation, and soil data. Soil moisture can be estimated at coarse resolutions (>1 km) using satellite remote sensing, but that resolution is poorly suited for many applications. Downscaling with the Equilibrium Moisture from Topography, Vegetation, and Soil (EMT+VS) model captured the main features in the spatial patterns. The skill of estimating fine-resolution (10-30 m) soil moisture was improved using new precipitation and evaporation downscaling methods. The downscaled soil moisture is important for flood forecasting, drought monitoring and wildfire prediction, precision agricultural management, malaria outbreak modeling, and estimating soil mechanical stability for trafficability and land rehabilitation.

5. Theories for improved electrical sensors to measure soil water and electrical conductivity. Automated methods of sensing soil variables have been advancing, but improved theory is needed for more accurate and reliable estimates under different conditions. ARS researchers in Lubbock and Bushland, Texas and Fort Collins, Colorado collaborated to design a Frequency Domain Probe for improved measurements of soil moisture over a broad range using high frequency sensors. Other collaboration between ARS researchers in Fort Collins, Colorado and Ames, Iowa with the Central China Normal University led to an improved theory of Time Domain Reflectometry for measuring soil electrical conductivity. The theory provides accurate measurements without fitting model parameters and accounts for variable cable lengths. This accomplishment is valuable to all scientists measuring soil salinity.

Review Publications
Coleman, M.L., Green, T.R., David, O., Merkel, W.H., Quan, Q.D., Rojas, K., Niemann, J.D. 2016. Deploying the Win TR-20 computational engine as a web service. Applied Engineering in Agriculture. 32(5):601-608. doi:10.13031/aea.32.11258.
Cowley, G.S., Niemann, J.D., Green, T.R., Seyfried, M.S., Jones, A.S., Grazaitis, P.J. 2017. Impacts of precipitation and potential evapotranspiration patterns on downscaling soil moisture in regions with large topographic relief. Water Resources Research. 53(2):1553-1574. doi:10.1002/2016WR019907.
Reyes-Fox, M.A., Steltzer, H., Lecain, D.R., McMaster, G.S. 2016. Five years of phenology observations from a mixed-grass prairie exposed to warming and elevated CO2. Scientific Data. doi:10.1038/sdata.2016.88.
Pelletier, M.G., Schwartz, R.C., Holt, G.A., Wanjura, J.D., Green, T.R.Frequency domain probe design for high frequency sensing of soil moisture. Agriculture. 6(4):60-72. 2016.
Hoehn, D.C., Niemann, J.D., Green, T.R., Jones, A.S., Grazaitis, P.J. 2017. Downscaling soil moisture over regions that include multiple coarse-resolution grid cells. Remote Sensing of Environment. 199:187-200.
Wu, Y., Shi, X., Li, C., Zhao, S., Peng, F., Green, T.R. 2017. Simulation of hydrology and nitrate transport in the Hetao irrigation district, Inner Mongolia, China. Water. 9(3):169. doi:10.3390/w9030169.
Cruz, P., Green, T.R., Figueiredo, R.O., Pereira, A.S., Kipka, H., Saad, S.I., Da Silva, J.M., Gomes, M.F. 2017. Processing data for a spatial watershed model to evaluate hydrological responses of the Ribeirão das Posses, Brazil. Ambiente & Aqua, An Interdisciplinary Journal of Applied Science. 12(3). doi:10.4136/ambi-agua.2073.
Fathelrahman, E.M., Gheblawi, M., Muhammad, S., Dunn, E., Ascough II, J.C., Green, T.R. 2017. Optimum returns from greenhouse vegetables under water quality and risk constraints in the United Arab Emirates. Sustainability. 9(5):719. doi:10.3390/su9050719.
Tatarko, J., Van Donk, S.J., Ascough II, J.C., Walker, D.G. 2016. Application of the WEPS and SWEEP models to non-agricultural disturbed lands. Heliyon. 2(12):e00215. doi:10.1016/j.heliyon.2016.e00215.