Ensemble Estimation of Historical Evapotranspiration for the Conterminous U.S

Evapotranspiration (ET) is the largest component of the water budget, accounting for the majority of the water available from precipitation. ET is challenging to quantify because of the uncertainties associated with the many ET equations currently in use, and because observations of ET are uncertain and sparse. In this study, we combine information provided by available ET data and equations to produce a new monthly data set for ET for the conterminous U.S. (CONUS). These maps are produced from 1895 to 2018 at an 800 m spatial scale, marking a finer resolution than currently available products over this time period. In our approach, the relative performance of a suite of ET equations is assessed using water balance, flux tower, and remotely sensed ET estimates. At the observation locations, we use error distributions to quantify relative weights for the equations and use these in a modified Bayesian model averaging weighted ensemble approach. The relative weights are spatially generalized using a random forest regression, which is applied to wall‐to‐wall explanatory variable maps to generate CONUS‐wide relative weight maps and ensemble estimates. We assess the performance of the ensemble using a reserved subset of the observations and compare this performance against other national‐scale map products for historical to modern ET. The ensemble ET maps are shown to provide an improved accuracy over the alternative comparison products. These ET maps could be useful for a variety of hydrologic modeling and assessment applications that benefit from a long record, such as the study of periods of water scarcity through time. Plain Language Summary In the water cycle, most of the rain either evaporates or is converted to water vapor by plants. This combined amount of water is termed evapotranspiration (ET). Water resource managers need to be able to estimate ET to ensure that plants have adequate water supply, and to improve our understanding of the water remaining to replenish streams or groundwater. However, estimating ET over large areas and long time periods is a challenge. There are many simplified ET equations based on meteorological inputs that can be used, but each can give significantly different results. In this work, we use three national‐scale ET data sets to assess the performance of several equations and evaluate how performance depends on variables such as land cover type. We combine the strengths of different equations by producing an ensemble estim