4.4 Article

Revised Coefficients for Priestley-Taylor and Makkink-Hansen Equations for Estimating Daily Reference Evapotranspiration

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JOURNAL OF HYDROLOGIC ENGINEERING
卷 18, 期 10, 页码 1289-1300

出版社

ASCE-AMER SOC CIVIL ENGINEERS
DOI: 10.1061/(ASCE)HE.1943-5584.0000679

关键词

Evapotranspiration; Coefficients; Hydrologic models; Measurement; Reference evapotranspiration; Priestley-Taylor model; Makkink model; Evaporative coefficients; Evapotranspiration models

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Many applications require estimation of reference evapotranspiration (ETo) in areas where meteorological measurements are limited. Previous studies have shown that simple evapotranspiration models based on radiation and temperature perform relatively well in humid climates but underpredict ETo in drier and windier climates. In this paper, estimates of ETo based on existing simple models were compared with ETo calculated with the more comprehensive Penman-Monteith equation using meteorological measurements at 106 locations in the contiguous United States for a range of climates. Results showed that the simpler models were closest to the more comprehensive model at sites where the annual mean relative humidity (RH) was approximately 70% and annual 2-m wind speed (U) was less than 2ms-1. Equations for adjusting the model coefficients were developed based on annual averages of RH [or vapor pressure deficit (VPD)] and U to improve the performance of these models for drier and windier sites. Publicly available data sets of spatial distributions of annual RH and U were used to estimate local coefficients for the contiguous United States. The new coefficients were tested with additional data from 22 sites, not used for coefficient development. At the test sites, the performance of both tested models improved with the revised coefficients. Depending on the model, 63-90% of the stations had ETo within 10% of the Penman-Monteith ETo for the growing season. The revised coefficients can be used to improve estimation of reference ETo in data-limited applications such as remote sensing and distributed hydrologic modeling.

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