Assessing water resource vulnerability based on remote sensing data-enhanced SWAT+ and High-Resolution precipitation data

Abstract

Climate change and human activities have significantly impacted the global water cycle, increasingly threatening water security across various sectors. By improving the performance of hydrological models in simulating the water balance, including precipitation and evapotranspiration, we can better support decision-making for regional water resource management and the deployment of climate change adaptation measures. This study aims to enhance the simulation performance of the vegetation growth module in the SWAT + model and reduce its uncertainty by dynamically updating the model’s data files with satellite-derived leaf area index and phenology data. Additionally, this study modified the weather station allocation mechanism in SWAT + to fully utilize the high-resolution meteorological data grids. The simulation results show that the enhanced SWAT + model achieved a Nash-Sutcliffe Efficiency (NSE) between 0.84 and 0.90 and a PBIAS of less than 6 % for daily streamflow simulations at four hydrological stations in the Weihe River Basin. The enhanced SWAT + model’s water balance simulation results for the Weihe River Basin were used to analyze the vulnerability of water resources within the basin. The results indicate that the Qinling region, benefiting from its well-developed forest ecosystems, has the lowest green water vulnerability. The overall blue water vulnerability of the basin reached 1.12, suggesting that during periods of insufficient precipitation or intense evaporation, blue water resources may not meet the demands of agriculture, industry, ecology, and residential use. Among these, agricultural water use exhibits the highest vulnerability. Efficient irrigation technologies can be employed to improve irrigation water use efficiency, and the use of treated reclaimed water for agricultural irrigation can be promoted to reduce the demand for fresh blue water resources.