Assessing Calibration Uncertainty and Automation for Estimating Evapotranspiration from Agricultural Areas Using METRIC

• Published in: Journal of the American Water Resources Association Vol. 49; no. 3; pp. 549 - 562
• Main Authors: Morton, Charles G., Huntington, Justin L., Pohll, Greg M., Allen, Richard G., McGwire, Kenneth C., Bassett, Scott D.
• Format: Journal Article
• Language: English
• Published: Middleburg Blackwell Publishing Ltd 01-06-2013
• Subjects: Algorithms; Automated; Automation; Calibration; Estimates; Evapotranspiration; Financing; Investment; Irrigation; Remote sensing; surface energy balance; Uncertainty; uncertainty analysis; United States > US
• ISSN: 1093-474X; 1752-1688
• DOI: 10.1111/jawr.12054

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 Nevada 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.