University of Twente Student Theses


Data-driven uncertainty in SEBS based estimates of evapotranspiration

Agyapong, Albert Anning (2013) Data-driven uncertainty in SEBS based estimates of evapotranspiration.

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Abstract:Evapotranspiration (ET) is a vital component of the water budget and it accurate estimation. Surface Energy Balance System (SEBS) is a model that uses remotely sensed data in combination with meteorological data to estimate evapotranspiration. SEBS in the estimation of ET uses albedo, emissivity, land surface temperature, vegetation index; leaf area index and land cover type from MODIS. For this study, in situ meteorological data used include temperature, wind speed, sunshine hours coupled with other meteorological from the ECMWF products. Sources of uncertainty in any of the mentioned input data will have considerable effect on the output thereby hampering the ability to give informed decisions. Sources of uncertainty in the in situ meteorological data are the main focus of the study in the Black Volta Basin of Ghana. MATLAB was used in the estimation of ET from SEBS. The Monte Carlo simulation method was used in the analysis of uncertainty in the input data and how it affects the output ET. Monte Carlo method uses a set of probability distribution function generated from the input sources of uncertainty. The results provided evidence of uncertainty in the output data giving a range of input uncertainty from temperature, wind speed and sunshine hours. SEBS provided baseline actual ET estimates in the rate of 2.88mm to 6.44mm daily and the Monte Carlo simulation of SEBS gave a mean daily actual ET rate of 2.99mm to 6.44mm. Standard deviation ranges from 0.03mm to 1.38mm across stations. Actual ET estimates based on SEBS is affected by uncertainty in input in situ meteorological data. Keywords: Evapotranspiration, SEBS, MODIS, ECMWF, remote sensing, uncertainty, Black Volta Basin
Item Type:Essay (Master)
Faculty:ITC: Faculty of Geo-information Science and Earth Observation
Programme:Geoinformation Science and Earth Observation MSc (75014)
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