Location: Grassland Soil and Water Research Laboratory
Project Number: 3098-13610-009-003-A
Project Type: Cooperative Agreement
Start Date: Jun 1, 2026
End Date: Apr 30, 2027
Objective:
Objectives are to 1) evaluate and improve SWAT+ model simulations of groundwater recharge and evapotranspiration to facilitate irrigation and water resource assessments, 2) evaluate options for NAM weather input data and assess its ability for accurate representation of reference evapotranspiration, 3) obtain estimates of actual evapotranspiration and standardized reference evapotranspiration from the OpenET remote sensing platform for evaluation of NAM evapotranspiration simulations at various scales, 4) utilize the SWAT+ water allocation module for evaluating NAM-simulated water transfers and reservoir storage, and 5) evaluate outcomes of water management scenarios for achieving groundwater sustainability in the western United States.
Approach:
The Soil & Water Assessment Tool (SWAT+) is a watershed modeling system used internationally to estimate impacts of land management practices on water quality in complex watersheds. Among other applications, it has been used extensively within the USDA Conservation Effects Assessment Program (CEAP) for evaluating the effectiveness of conservation practices to improve water quality in U.S. watersheds. Serving as the basis for modeling efforts in CEAP, the National Agroecosystem Model (NAM) is an implementation of SWAT+ for the continental U.S., which considers field-scale hydrologic, nutrient, and plant production processes as well as routing of water and nutrient flows through the national watershed system. An important goal of the present project is to evaluate and apply the SWAT+ NAM model to address questions on sustainability of water resources and food production systems across the western United States. Specific activities will involve the acquisition of satellite-based estimates of evapotranspiration across the continental U.S., improvement of evapotranspiration algorithms in SWAT+ code, and development of machine learning methods for calibrating SWAT+ water balance using evapotranspiration input data.