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Evapotranspiration based on equilibrated relative humidity (ETRHEQ): Evaluation over the continental U.S.
Author(s) -
Rigden Angela J.,
Salvucci Guido D.
Publication year - 2015
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1002/2014wr016072
Subject(s) - evapotranspiration , fluxnet , environmental science , latent heat , land cover , data assimilation , relative humidity , sensible heat , meteorology , eddy covariance , atmospheric sciences , geography , land use , geology , ecology , civil engineering , biology , ecosystem , engineering
A novel method of estimating evapotranspiration ( ET ), referred to as the ETRHEQ method, is further developed, validated, and applied across the U.S. from 1961 to 2010. The ETRHEQ method estimates the surface conductance to water vapor transport, which is the key rate‐limiting parameter of typical ET models, by choosing the surface conductance that minimizes the vertical variance of the calculated relative humidity profile averaged over the day. The ETRHEQ method, which was previously tested at five AmeriFlux sites, is modified for use at common weather stations and further validated at 20 AmeriFlux sites that span a wide range of climates and limiting factors. Averaged across all sites, the daily latent heat flux RMSE is ∼26 W·m −2 (or 15%). The method is applied across the U.S. at 305 weather stations and spatially interpolated using ANUSPLIN software. Gridded annual mean ETRHEQ ET estimates are compared with four data sets, including water balance‐derived ET , machine‐learning ET estimates based on FLUXNET data, North American Land Data Assimilation System project phase 2 ET , and a benchmark product that integrates 14 global ET data sets, with RMSEs ranging from 8.7 to 12.5 cm·yr −1 . The ETRHEQ method relies only on data measured at weather stations, an estimate of vegetation height derived from land cover maps, and an estimate of soil thermal inertia. These data requirements allow it to have greater spatial coverage than direct measurements, greater historical coverage than satellite methods, significantly less parameter specification than most land surface models, and no requirement for calibration.