The lack of consistent, accurate information on evapotranspiration (ET) and consumptive use of water by irrigated agriculture is one of the most important data gaps for water managers in the western United States (U.S.) and other arid agricultural regions globally. The ability to easily access information on ET is central to improving water budgets across the West, advancing the use of data‐driven irrigation management strategies, and expanding incentive‐driven conservation programs. Recent advances in remote sensing of ET have led to the development of multiple approaches for field‐scale ET mapping that have been used for local and regional water resource management applications by U.S. state and federal agencies. The OpenET project is a community‐driven effort that is building upon these advances to develop an operational system for generating and distributing ET data at a field scale using an ensemble of six well‐established satellite‐based approaches for mapping ET. Key objectives of OpenET include: Increasing access to remotely sensed ET data through a web‐based data explorer and data services; supporting the use of ET data for a range of water resource management applications; and development of use cases and training resources for agricultural producers and water resource managers. Here we describe the OpenET framework, including the models used in the ensemble, the satellite, meteorological, and ancillary data inputs to the system, and the OpenET data visualization and access tools. We also summarize an extensive intercomparison and accuracy assessment conducted using ground measurements of ET from 139 flux tower sites instrumented with open path eddy covariance systems. Results calculated for 24 cropland sites from Phase I of the intercomparison and accuracy assessment demonstrate strong agreement between the satellite‐driven ET models and the flux tower ET data. For the six models that have been evaluated to date (ALEXI/DisALEXI, eeMETRIC, geeSEBAL, PT‐JPL, SIMS, and SSEBop) and the ensemble mean, the weighted average mean absolute error (MAE) values across all sites range from 13.6 to 21.6 mm/month at a monthly timestep, and 0.74 to 1.07 mm/day at a daily timestep. At seasonal time scales, for all but one of the models the weighted mean total ET is within ±8% of both the ensemble mean and the weighted mean total ET calculated from the flux tower data. Overall, the ensemble mean performs as well as any individual model across nearly all accuracy statistics for croplands, though some individual models may perform better for specific sites and regions. We conclude with three brief use cases to illustrate current applications and benefits of increased access to ET data, and discuss key lessons learned from the development of OpenET.

Accurate estimates of reference evapotranspiration (ET 0 ) are critical for estimating actual crop evapotranspiration and agricultural water use. This study uses observations from the Nevada Integrated Climate and Evapotranspiration Network (NICE Net) to validate forecasts of ET 0 and its driving variables from the National Weather Service's National Digital Forecast Database (NDFD). Daily NDFD ET 0 at lead times of 1 to 6 days were compared against 18 NICE Net stations. Correlations between NDFD and observations generally ranged between 0.4 and 0.9, with lower correlations at longer leads and a notable drop in skill during July and August. Systematic arid biases (high bias for temperatures and low bias for humidity) were found in NDFD with a strong warm minimum temperature bias and low vapor pressure bias most prominent during the growing season. Some of the largest relative biases were found in wind speed, although they were systematic and varied greatly by location. A case study revealed that NDFD consistently underestimates the variability found in observed minimum temperature, solar radiation, wind speed, and ET 0 . Cloudy days during summer were not well represented in the NDFD estimated solar radiation, which had a cascading impact on temperature, vapor pressure, and ET 0 estimates. A monthly ratio-based bias-correction was applied to NDFD ET 0 , which reduced the root-mean squared error by 5%-30% for most locations. Bias-corrected ET 0 forecasts from NDFD or other forecast systems show potential as a guide to develop weekly irrigation schedules for agricultural producers, with the ultimate goal of reducing applications of excess irrigation water.

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