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Assessing Calibration Uncertainty and Automation for Estimating Evapotranspiration from Agricultural Areas Using METRIC
Author(s) -
Morton Charles G.,
Huntington Justin L.,
Pohll Greg M.,
Allen Richard G.,
McGwire Kenneth C.,
Bassett Scott D.
Publication year - 2013
Publication title -
jawra journal of the american water resources association
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.957
H-Index - 105
eISSN - 1752-1688
pISSN - 1093-474X
DOI - 10.1111/jawr.12054
Subject(s) - evapotranspiration , metric (unit) , calibration , environmental science , water balance , statistics , precision agriculture , mathematics , hydrology (agriculture) , computer science , agriculture , geography , engineering , operations management , ecology , geotechnical engineering , archaeology , biology
Agricultural irrigation accounts for a large fraction of the total water use in the western United States. The Mapping Evapotranspiration at high Resolution with Internalized Calibration ( METRIC ) remote sensing energy balance model is being used to estimate historical agricultural water use in western N evada to evaluate basin‐wide water budgets. Each METRIC evapotranspiration ( ET ) estimate must be calibrated by a trained user, which requires some iterative time investment and results in variation in ET estimates between users. An automated calibration algorithm for the METRIC model was designed to generate ET estimates comparable to those from trained users by mimicking the manual calibration process. Automated calibration allows for rapid generation of METRIC ET estimates with minimal manual intervention, as well as uncertainty and sensitivity analysis of the model. The variation in ET estimates generated by the automated calibration algorithm was found to be similar to the variation in manual ET estimates. Results indicate that uncertainty was highest for fields with low ET levels and lowest for fields with high ET levels, with a seasonal mean uncertainty of approximately 5% for all fields. In addition, in a blind comparison, automated daily and seasonal ET estimates compared well with flux tower measurement ET data at multiple sites. Automated methods can generate first‐order ET estimates that are similar to time intensive manual efforts with less time investment.