Drought Propagation and Risk Assessment in the Naoli River Basin Based on the SWAT-PLUS Model and Copula Functions

With the intensification of global climate change, extreme weather events increasingly threaten water resources and agricultural systems. This study focuses on the Naoli River Basin, employing the Standardized Precipitation Actual Evapotranspiration Index (SPAEI), the Standardized Runoff Index (SRI), and the Standardized Surface Moisture Index (SSMI) to assess the spatiotemporal variability of meteorological, hydrological, and agricultural droughts. Drought events are identified based on travel time theory, and joint distributions of drought characteristics are modeled using optimized two- and three-dimensional copula functions. Lagged correlation and Bayesian conditional probability analyses are used to explore drought propagation processes. Key findings include (1) the SWAT model showed strong runoff simulation performance (R2 > 0.75, NSE > 0.97), while the PLUS model achieved high land use simulation accuracy (overall accuracy > 0.93, Kappa > 0.85); (2) future projections suggest continued forest expansion and farmland decline, with water areas increasing under SSP245 and urban areas expanding under SSP585; (3) five CMIP6 models with high skill (r = 0.80, RMSE = 26.15) were selected via a Taylor diagram for scenario simulation; (4) copula-based joint drought probabilities vary temporally, with meteorological drought risks increasing under long-term moderate-emission scenarios, while hydrological and agricultural droughts show contrasting trends; (5) and under extreme meteorological drought, the conditional probability of extreme agricultural drought doubles from 0.12 (SSP245) to 0.24 (SSP585), indicating heightened vulnerability under high-emission pathways. These results offer critical insights for regional drought risk assessment and adaptive management under future climate scenarios.