Using SWAT Model to Assess the Impacts of Land Use and Climate Changes on Flood in the Upper Weihe River, China
Yinge Liu, Yuxia Xu, Yaqian Zhao, Yan Long
Abstract
Flood disasters have occurred frequently in recent years, but there is no consensus on the mechanism and influencing factors. Taking the upper reaches of Weihe River Basin as a case in Western China, a soil and water assessment tool (SWAT) model was established to quantitatively simulate the impact of land use and climate change on runoff changes, while 4 extreme land-use scenarios and 24 temperature and precipitation scenarios assumptions were proposed to simulate the response of runoff to land use and climate changes. The SWAT simulation results showed that the sensitivity parameters affecting the model simulation were the CANMX, CN2, SOL_K, CH_N2, and SOL_AWC. The correlation index R2 and the efficiency coefficient ENS of the upper Weihe River were both in the range of 0.75–0.78, the relative error PS between the simulated results and the measured runoff was below 10%, suggesting the good applicability of the SWAT model in this study area. Using the improved SWAT model to simulate the peak runoff (flood) simulation value is generally smaller than the measured value, and the absolute value of the error is less than 6%. The expansion of wasteland increased the runoff by over 90% on average, the expansion of cultivated land increased the runoff by 8% on average, and the expansion of woodland and grassland increased the surface runoff by 6% on average. When the precipitation decreased by 25% and the temperature increased by 22%, the smallest runoff was obtained in the simulation. Accordingly, when the precipitation increased by 25% and the temperature decreased by 22%, the maximum annual runoff was obtained. By decomposing the contribution rate of human activities and climate change to runoff, it showed that the contribution rate of human activities to the reduction of runoff was greater than that of climate change. This study can provide scientific reference for the simulation and prediction of future floods.