A sub-seasonal to seasonal climate forecast informed irrigation scheduling tool for the Contiguous United States

Abstract

Irrigation accounts for a significant share of global freshwater use, and optimizing scheduling is crucial for improving water use efficiency. Current methods rely on short-term weather forecasts, limiting long-term planning. Additionally, most models are site-specific due to data constraints, and lacking national applicability. This study develops a real-time irrigation scheduling tool for cornfields across the Contiguous United States (CONUS). By integrating sub-seasonal to seasonal (S2S) climate forecasts with the Soil Water Atmosphere Plant (SWAP) model, the tool optimizes irrigation scheduling at any day in the season, balancing water cost and crop yield. A human-computer interaction framework provides real-time irrigation recommendations while incorporating farmer feedback. S2S-informed scheduling improves water use efficiency and net profit compared to default SWAP schedules. Various up-to-date CONUS-scale datasets helped to reduce dependence on in-situ observations and extend the applicability of the tool to diverse field conditions in the CONUS.