Title: Effects of land-use land-cover data resolution and classification methods on SWAT model flow predictive reliability

Authors: Kwasi Asante; Mansoor D. Leh; Jackson D. Cothren; Mauro Di Luzio; John Van Brahana

Addresses: Environmental Systems Research Institute (ESRI), 3325 Springbank Ln # 200, Charlotte NC 28226, USA ' Department of Biological and Agricultural Engineering, University of Arkansas, Fayetteville AR 72701, USA ' Center for Advanced Spatial Technologies (CAST), University of Arkansas, 304 JBHT, Fayetteville AR 72701, USA ' Blackland Texas AgriLifeResearch and Extension Center, 720 Blackland Rd, Temple TX 76502, USA ' University of Arkansas, 346 Arkansas Avenue, Fayetteville AR 72701, USA

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.

Keywords: SWAT stream flow modelling; soil and water assessment tool; land use land cover; LULC; remote sensing; high resolution; low resolution; predictive reliability; GIS; hydrological modelling; object-oriented image analysis; OOIA; geographic information systems.

DOI: 10.1504/IJHST.2017.080956

International Journal of Hydrology Science and Technology, 2017 Vol.7 No.1, pp.39 - 62

Received: 22 Sep 2015
Accepted: 29 Dec 2015
Published online: 13 Dec 2016 *

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