Comparison of techniques for estimating evaporation from an irrigation water storage. Issue 3 (12th March 2013) - McJannet, D. L.; Cook, F. J.; Burn, S.
Key Points: Techniques for estimating evaporation were compared to measured values Penman‐Monteith model performed well with local site data and wind function Model predictions are sensitive to local variation in meteorological data Abstract : [1] With the emergence of water supply and food security issues as a result of increasing population and climate change pressures, the need for efficient use of available water supplies is paramount. Management of available resources and improved efficiency require accurate specification of evaporation, which is a major water loss pathway, yet evaporation remains difficult to accurately quantify. This study uses scintillometry‐derived measurements of evaporation to test the performance of water balance, pan coefficient, and combination modeling techniques, which might commonly be used by resource managers. Both pan coefficient and water balance techniques performed poorly, but the Penman‐Monteith model with local site data and site‐specific wind function produced estimates within 2% of those measured. Recognizing that such a model parameterization would rarely be a possibility in most environments, further testing involving the range of data sets that might be available for a location was undertaken. Modeling using over‐water measurements and, generally, applicable wind functions from the literature produced estimates 26% greater than those measured. Estimates within 12% of those measured were made for the equivalent model setup using over‐land meteorological data; however, when data from the nearest meteorological station was used, this difference increased to 27%. The different evaporation estimation techniques tested were shown to produce a range of estimates of water availability, which varied by nearly 30%. The large differences between measured and predicted evaporation highlight the uncertainty that still exists in evaporation estimation and the sensitivity of predictions to the source of input data.