Location: Hydrology and Remote Sensing LaboratoryTitle: Significance of uncertainty in evapotranspiration estimates on water balance modeling in SWAT) Author
|Kustas, William - Bill|
Submitted to: Annual International SWAT Conference
Publication Type: Abstract Only
Publication Acceptance Date: 7/17/2013
Publication Date: 7/17/2013
Citation: Sadeghi, A.M., Beeson, P.C., Daughtry, C.S., Arnold, J.G., Anderson, M.C., Hain, C., Alfieri, J.G., Kustas, W.P. 2013. Significance of uncertainty in evapotranspiration estimates on water balance modeling in SWAT [abstract]. SWAT International Conference, Book of Abstracts. p. 60. Interpretive Summary:
Technical Abstract: In water quality models, such as the Soil and Water Assessment Tool (or SWAT), accurate forcing of potential evapotranspiration (PET) is crucial for producing reasonable predictions of water budget components, sediment and other pollutant loads from larger river basins. Methods and data, needed to compute PET, vary in space and time such as air temperature, vapor pressure, wind speed, and solar radiation. In SWAT, PET is required as an input and is either computed internally by the weather generator using available weather data by a choice of three different methods: i) Priestly-Taylor; ii) Penman-Monteith; and iii) Hargreaves methods, or calculated by an external source and provided to SWAT as an input. The actual ET (AET) is then calculated in SWAT based on available water, crop and soil moisture conditions. Most often, the modelers rely on the models to simply match AET annual means, provided by the literature values, when calibrating the models due to sparse data (both temporal and spatial). For this study, we used three methods to calibrate AET parameters: i) basin-wide/annual (using literature values); ii) subbasin/monthly (using Atmosphere-Land Exchange Inverse (ALEXI) model output); and iii) HRU/daily (using the NDVI/crop coefficient method from two in situ towers in corn and soybean fields). After calibration, most PET inputs produced accurate AET estimates on a basin-wide, annual basis; however, on finer spatial and temporal scales, PET computed from local or regional data performed the best. The weather generator inputs were only able to capture the annual averages, not the variation throughout the growing season affecting biomass and yield estimates. This research provides insight into the importance of correct estimations of both potential and actual ET for water balance components and for the application of physically-based watershed models.