Estimating irrigation water use from remotely sensed evapotranspiration data: Accuracy and uncertainties across spatial scales

Abstract

Irrigated agriculture is the dominant use of water globally, but most water withdrawals are not monitored or reported. As a result, it is largely unknown when, where, and how much water is used for irrigation. Here, we evaluated the ability of remotely sensed evapotranspiration (ET) data, integrated with other datasets, to calculate irrigation water withdrawals and applications in an intensively-irrigated portion of the central United States. We compared irrigation calculations based on OpenET data with reported groundwater withdrawals from a flowmeter database and hundreds of farmer irrigation application records at three spatial scales (management area, water right group, and field). We found that ET-based calculations of irrigation exhibited similar temporal patterns as flowmeter data, but tended to be positively biased with substantially more interannual variability than reported pumping rate. Disagreement between ET-based irrigation calculations and reported irrigation was strongly correlated with annual precipitation. Agreement between calculated and observed ET was better for multi-year averages than for individual years across all spatial scales. The selection of an ET model was also an important consideration, as variability in calculated irrigation across an ensemble of satellite-driven ET models was larger than the potential impacts of conservation measures employed in the region. Linking individual wells to specific fields was challenging, but uncertainties in calculating irrigation depths due to the above-mentioned factors exceeded potential uncertainty from irrigation status and field boundary mapping. From these results, we suggest key practices for working with ET-based irrigation data that include accurately accounting for changes in root zone soil moisture for within-season applications, such as irrigation scheduling, and conducting an application-specific evaluation of sources of uncertainty. Remotely-sensed approaches have a high potential for improving scientific research and water resource management through improved spatial and temporal characterization of irrigation, but uncertainties must be resolved to fully realize this potential.

Streamflow Depletion Caused by Groundwater Pumping: Fundamental Research Priorities for Management‐Relevant Science

“…While pumping well location data are available in some countries (Jasechko & Perrone, 2021), even in these settings information about pumping volume, timing, and rate are rarely available (Brookfield, Zipper, et al, 2023). Modeling and remote sensing approaches for estimating water use are promising (e.g., Majumdar et al, 2020;Melton et al, 2021;Shapoori et al, 2015), but require additional development and verification against measured pumping rates to reduce measurement errors and uncertainties to the point where they can be used operationally (Foster et al, 2020;Zipper et al, 2024). However, future uncertainty (particularly in the realm of management scenario assessment) is inevitable and unavoidable, and therefore impact monitoring and mitigation planning should be established as a precaution (Saito et al, 2021).…”