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Linear models for estimating annual and growing season reference evapotranspiration using averages of weather variables
Author(s) -
Cristea Nicoleta C.,
Kampf Stephanie K.,
Burges Stephen J.
Publication year - 2013
Publication title -
international journal of climatology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.58
H-Index - 166
eISSN - 1097-0088
pISSN - 0899-8418
DOI - 10.1002/joc.3430
Subject(s) - evapotranspiration , wind speed , environmental science , climatology , linear regression , meteorology , growing season , relative humidity , regression analysis , atmospheric sciences , geography , statistics , mathematics , ecology , biology , geology , botany
We develop linear regression equations to estimate location‐specific average annual reference evapotranspiration (ET o ) using one or more of annual averages of: incoming solar radiation ( R s ), air temperature ( T ), relative humidity (RH), and wind speed ( U ). We also provide two sets of equations to estimate growing season ET o , either using one or more of annual averages of R s , T , RH, and U , or using growing season averages of the same variables. The equations are developed using the FAO‐56 Penman–Monteith model ET o estimates as a reference. Supporting weather data to develop the regression equations were from 102 locations (494 station‐years) across the contiguous United States. The models were tested with additional data from 32 stations (114 station‐years). To illustrate potential applications of the regression models, we estimate spatial patterns of annual ET o and growing season ET o across the contiguous United States using existing spatial datasets of annual averages of R s , T , RH, and U. Other applications of the models provided may include rapid assessments of historical annual and growing season ET o , evaluation of past ET o trends, or evaluation of ET o projected trends based on output from global climate models. Copyright © 2012 Royal Meteorological Society