Human-Driven Runoff Decline and Hydrological Drought Intensification in Semi-arid Regions in the Last 40 Years Revealed by a Hybrid Physics-Deep Learning Framework

Abstract. Amid accelerating global warming, the intensification of hydrological drought in semi-arid regions has become a critical threat to water security. In this study, we present an integrated framework that couples a physics-based WRF-Hydro model with a deep-learning module (LSTM-Attention) for error correction and attribution analysis. Focusing on the Xilin River Basin, a representative semi-arid catchment, this approach quantifies the relative contributions of climate change and human activities to runoff variations across interannual and intra-annual scales over the 1980–2020 period. We further systematically assess the impact of human activities on hydrological drought. Results reveal a significant runoff declining trend of -23.79×10⁴ m³/a, with an abrupt shift in 2001. During the post-shift period (2001–2020), hydrological drought frequency surged from 7.54 % in the baseline period to 54.58 %. Rapid warming (0.5 °C/10a) caused sustained increase of potential evapotranspiration, while snow water equivalent decreased significantly at 1.27 mm/a; these dual effects drove the overall runoff decline. April exhibited the most pronounced runoff reduction, accounting for 58.87 % of the annual decrease. In contrast, March runoff increased, primarily due to earlier snowmelt triggered by climate warming. Attribution analysis indicates that human activities were the dominant driver of runoff decline, contributing 61.04 %. These activities exerted dual effects on hydrological drought: alleviating it in 29.58 % of months but intensifying or triggering events in 38.34 % of months. The proposed integrated framework offers a robust tool for hydrological attribution analysis and underscores the critical role of human activities in sustainable water resource management.