Assessing the impacts of climate change and land use/land cover data characteristics on streamflow using the SWAT model in the Upper Han River Basin

The Upper Han River Basin (UHRB), a temperate monsoon region in central China. The combined impacts of multiple land use/land cover (LULC) datasets and future climate change on watershed runoff remain poorly understood. This work quantifies how LULC datasets, with spatial resolutions ranging from 10 m to 1000 m, influence SWAT streamflow simulations at daily and monthly scales. The approach integrates multi-resolution LULC data from different sources in an innovative manner, providing a robust framework for improving hydrological predictions under future climate scenarios. Our Analysis Reveals: (1) A comprehensive evaluation of LULC datasets across multiple spatial resolutions identified the FORM 10 m as yielding the most stable daily performance (NSE=0.68), with the CGLS 100 m exhibiting the highest monthly skill (NSE=0.87); (2) Based on the optimal LULC configurations, future projections suggest that the annual mean streamflow in the UHRB will increase by 28.8 % under SSP1–2.6 (low emissions) and 18.1 % under SSP5–8.5 (high emissions); (3) Seasonal shifts include a significant decrease in summer streamflow proportion from 44 % to 34–35 % accompanied by an increase in spring proportion from 18 % to 26 %, emphasizing potential challenges for water resource management during dry seasons. These findings demonstrate that LULC characteristics and climate drivers significantly affect SWAT accuracy and streamflow projections, offering insights for sustainable water resource management in temperate monsoon regions. • LULC datasets were evaluated for their effectiveness and the variability they introduced in streamflow simulations. • The impacts of LULC resolution and climate change on streamflow are quantified. • Future streamflow in the UHRB is predicted by the SWAT with optimal LULC scenario. • Climate change shifts UHRB streamflow seasonality and may increase annual runoff by up to 28.8 %.