Development of a Benchmark Eddy Flux Evapotranspiration Dataset for Evaluation of Satellite-Driven Evapotranspiration Models Over the CONUS

Authors

John M. Volk, Desert Research Institute
Justin Huntington, Desert Research Institute
Forrest S. Melton, NASA Ames Research Center, California State University
Richard Allen, University of Idaho, University of Nebraska-Lincoln
Martha C. Anderson, USDA-ARS Hydrology and Remote Sensing Laboratory
Joshua B. Fisher, University of California Los Angeles
Ayse Kilic, University of Nebraska-LincolnFollow
Gabriel Senay, U.S. Geological Survey Earth Resources Observation & Science
Gregory Halverson, NASA Jet Propulsion Laboratory
Kyla Knipper, USDA-ARS
Blake Minor, Desert Research Institute
Christopher Pearson, Desert Research Institute
Tianxin Wang, University of California
Yun Yang, Mississippi State University
Steven Evett, USDA-ARS Conservation & Production Research LaboratoryFollow
Andrew N. French, USDA-ARS US Arid-Land Agricultural Research Center
Richard Jasoni, Desert Research Institute
William Kustas, USDA-ARS Hydrology and Remote Sensing Laboratory

Document Type

Article

Date of this Version

1-27-2023

Citation

Agricultural and Forest Meteorology 331 (2023) 109307. https://doi.org/10.1016/j.agrformet.2023.109307

Comments

Open access.

Abstract

A large sample of ground-based evapotranspiration (ET) measurements made in the United States, primarily from eddy covariance systems, were post-processed to produce a benchmark ET dataset. The dataset was produced primarily to support the intercomparison and evaluation of the OpenET satellite-based remote sensing ET (RSET) models and could also be used to evaluate ET data from other models and approaches. OpenET is a web-based service that makes field-delineated and pixel-level ET estimates from well-established RSET models readily available to water managers, agricultural producers, and the public. The benchmark dataset is composed of flux and meteorological data from a variety of providers covering native vegetation and agricultural settings. Flux footprint predictions were developed for each station and included static flux footprints developed based on average wind direction and speed, as well as dynamic hourly footprints that were generated with a physically based model of upwind source area. The two footprint prediction methods were rigorously compared to evaluate their relative spatial coverage. Data from all sources were post-processed in a consistent and reproducible manner including data handling, gap-filling, temporal aggregation, and energy balance closure correction. The resulting dataset included 243,048 daily and 5,284 monthly ET values from 194 stations, with all data falling between 1995 and 2021. We assessed average daily energy imbalance using 172 flux sites with a total of 193,021 days of data, finding that overall turbulent fluxes were understated by about 12% on average relative to available energy. Multiple linear regression analyses indicated that daily average latent energy flux may be typically understated slightly more than sensible heat flux. This dataset was developed to provide a consistent reference to support evaluation of RSET data being developed for a wide range of applications related to water accounting and water resources management at field to watershed scales.