Evaluating the SWAT Model for Hydrological Modeling in the Xixian Watershed and a Comparison with the XAJ Model

Abstract

Already declining water availability in Huaihe River, the 6th largest river in China, is further stressed by climate change and intense human activities. There is a pressing need for a watershed model to better understand the interaction between land use activities and hydrologic processes and to support sustainable water use planning. In this study, we evaluated the performance of SWAT for hydrologic modeling in the Xixian River Basin, located at the headwaters of the Huaihe River, and compared its performance with the Xinanjiang (XAJ) model that has been widely used in China. Due to the lack of publicly available data, emphasis has been put on geospatial data collection and processing, especially on developing land use-land cover maps for the study area based on ground-truth information sampling. Ten-year daily runoff data (1987–1996) from four stream stations were used to calibrate SWAT and XAJ. Daily runoff data from the same four stations were applied to validate model performance from 1997 to 2005. The results show that both SWAT and XAJ perform well in the Xixian River Basin, with percentage of bias (PBIAS) less than 15%, Nash-Sutcliffe efficiency (NSE) larger than 0.69 and coefficient of determination (R2) larger than 0.72 for both calibration and validation periods at the four stream stations. Both SWAT and XAJ can reasonably simulate surface runoff and baseflow contributions. Comparison between SWAT and XAJ shows that model performances are comparable for hydrologic modeling. For the purposes of flood forecasting and runoff simulation, XAJ requires minimum input data preparation and is preferred to SWAT. The complex, processes-based SWAT can simultaneously simulate water quantity and quality and evaluate the effects of land use change and human activities, which makes it preferable for sustainable water resource management in the Xixian watershed where agricultural activities are intensive.