Field-scale crop water consumption estimates reveal potential water savings in California agriculture

Abstract

Efficiently managing agricultural irrigation is vital for food security today and into the future under climate change. Yet, evaluating agriculture’s hydrological impacts and strategies to reduce them remains challenging due to a lack of field-scale data on crop water consumption. Here, we develop a method to fill this gap using remote sensing and machine learning, and leverage it to assess water saving strategies in California’s Central Valley. We find that switching to lower water intensity crops can reduce consumption by up to 93%, but this requires adopting uncommon crop types. Northern counties have substantially lower irrigation efficiencies than southern counties, suggesting another potential source of water savings. Other practices that do not alter land cover can save up to 11% of water consumption. These results reveal diverse approaches for achieving sustainable water use, emphasizing the potential of sub-field scale crop water consumption maps to guide water management in California and beyond.

Abstract

This study introduces a novel framework for generating high-resolution, in-situ estimates of agricultural evapotranspiration (ET) using satellite-based ET data combined with machine learning. This approach is leveraged to assess the water-saving potential of various management strategies and in calculating irrigation efficiency across California’s Central Valley.