Effects of land-use land-cover data resolution and classification methods on SWAT model flow predictive reliability
by Kwasi Asante; Mansoor D. Leh; Jackson D. Cothren; Mauro Di Luzio; John Van Brahana
International Journal of Hydrology Science and Technology (IJHST), Vol. 7, No. 1, 2017

Abstract: The aim of this study is to evaluate the predictive reliability of the soil and water assessment tool (SWAT) model based on LULC data spatial resolution and image classification methods. The predictive reliability of the model is primarily evaluated with two descriptive statistics; the p-factor and the r-factor. The p-factor quantifies the percentage of the observed data that a calibrated model captures whereas the r-factor quantifies the level of uncertainty associated with the calibrated model. The hypothesis is that a combination of GIS-based hydrologic modelling and the promise of high-resolution LULC data obtained through object-oriented image analysis (OOIA) significantly improves SWAT flow predictive reliability. Two SWAT models were setup and calibrated at a gauging station located within the study area. After both manual and auto-calibration, results showed that the low-resolution model had a slightly better predictive reliability. The impact of the classification methods is however unclear.

Online publication date: Tue, 13-Dec-2016

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