Location: Water Management and Systems Research
Project Number: 3012-13660-010-000-D
Project Type: In-House Appropriated
Start Date: Feb 6, 2022
End Date: Feb 5, 2027
Objective 1: Improve biophysical and ecohydrologic components of crop and ecosystem services models ranging in spatial scale from sub-field areas to watersheds by linking process-based modeling, data assimilation, and artificial intelligence (AI). Sub-objective 1.A: Develop ecophysiological model components for croplands. Sub-objective 1.B: Enhance modeling of crop phenology, yield, and ET in semi-arid conditions at daily to seasonal and plot to watershed scales. Objective 2: Inform precision agriculture (water and nutrient management in crop systems) and precision conservation within fields, across farms, and at regional watershed scales, using high-resolution process modeling and machine learning. Sub-objective 2.A: Improve the Agricultural Ecosystems Services (AgES) process model using components from Objective 1; develop and test a subdaily version of AgES. Sub-objective 2.B: Apply AgES to simulate on-farm precision conservation; train surrogate models for users; publish long-term data and results. Objective 3: Quantify current and future impacts of climate variability, land-use change, land disturbance (e.g., wildfire, insect infestation), and rehabilitation on water resources from source-water catchments in snow-dominated agricultural watersheds. Sub-objective 3.A: Develop and implement snow-process model components and ecosystem × hydrometeorology interactions. Sub-objective 3.B: Develop geospatial methods to analyze and model hydrologic function and response to change, from “fire to farm”. Sub-objective 3.C: Predict the ecohydrological impact of precision-conservation treatments in source-area catchments.
Agricultural productivity and ecosystem services are inextricably linked to water resources that are facing dual pressures of decreasing supply and increasing demand. In the western US, water resources are predominantly derived from the melting of seasonal high-elevation snowpack where disturbance (e.g., wildfire, insect infestation) and hydrologic and ecosystem functioning can directly impact water availability for agricultural production, i.e., “fire to farm”. Additionally, shifting precipitation patterns and increasing air temperatures are resulting in smaller and earlier peak snowpack water equivalents and advancing the timing of snowmelt and peak streamflow. Subsequent impacts of these changes to moisture availability affect natural ecosystem functioning, plant ecophysiological responses, and vegetation contributions to water cycling, in turn affecting ecosystem services and downstream water availability and quality. This project aims to improve the understanding of ecohydrological processes in semi-arid western US agricultural watersheds by considering the continuum of water resources from streamflow generation in the mountains through ecosystem controls of water cycling to impacts of farm-level water limitations on crop growth and productivity. To address these needs, we will use a variety of data-assimilation tools, process-based models, and artificial intelligence (AI) to better characterize the soil-plant-atmosphere pathway across landscape types. Model components and improvements resulting from this research will inform precision agriculture and conservation across spatiotemporal scales and improve quantification of water supply responses to climate, disturbance, and management (Fig. 1). By focusing on agricultural watersheds, this project will develop holistic tools and build broad and spatially resolved datasets that will improve crop production under limited water, operational forecasting of water supplies and ecosystem services, precision conservation of water quality, and broader earth-systems research to inform land surface models.