Climate and land use changes significantly affect runoff and hydrological drought, presenting challenges for water resource management. This study focuses on the Naoli River Basin, utilizing the SWAT model integrated with PLUS land use projections under the CMIP6 SSP245 and SSP585 scenarios to assess trends in runoff and drought characteristics from 2025 to 2100. The Standardized Runoff Index (SRI) and run theory are applied to analyze drought frequency and duration. Key findings include the following: (1) Under the SSP585 scenario (2061–2100), land use changes—specifically, a reduction in cropland and an increase in forest cover—resulted in a 12.59% decrease in runoff compared to the baseline period (1970–2014), with notable differences when considering climate-only scenarios. (2) The SSP585 scenario exhibits a significant rise in drought frequency and duration, particularly during summer, whereas SSP245 shows milder trends. (3) Based on the Taylor plot evaluation, the ensemble average MMM-Best (r = 0.80, RMSE = 26.15) has been identified as the optimal prediction model for the 2025–2100 period. Deviation analysis revealed that NorESM2-MM and IPSL-CM6A-LR demonstrated the greatest stability, while EC-Earth3 exhibited the largest deviation and highest uncertainty. (4) Land use changes under the SSP245 scenario help mitigate drought by enhancing water retention, although their effectiveness diminishes under SSP585 due to the dominant influence of climate factors, including increased temperature and precipitation variability. And (5) SRI-3 mutation analysis indicated that the mutation point occurred in July 2074 under the SSP245 scenario and in April 2060 under the SSP585 scenario (p < 0.05). The trend for SSP245 revealed significant fluctuations, with the number of crossover points rising to 40 following land use changes; conversely, the SSP585 trend remained stable with only seven crossover points, as high-emission scenarios predominantly influenced early mutations. These findings illuminate the interactive effects of land use and climate change, providing a scientific foundation for optimizing water resource management and developing effective drought mitigation strategies.

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.

Climate change has intensified extreme hydrological events in cold regions, threatening the stability of river ecosystems. The traditional monthly frequency method for calculating ecological flow assumes equal guarantee rates across all months, overlooking the complex nonlinear dependencies between interannual and intermonthly flows. This approach may result in flow values for certain months during low-flow years exceeding those of corresponding months in high-flow years, failing to align with actual hydrological patterns. This study integrates Copula functions with the monthly frequency method to establish an improved ecological flow calculation framework, accurately characterizing the statistical correlation between interannual and intermonthly flow variability. The Hailang River basin in Northeast China was selected as the study area. First, the SWAT model was employed to simulate natural runoff processes from 1956 to 1965. The calibration phase demonstrated excellent performance (R² = 0.84, NSE = 0.83), and the validation phase also met standards (R² = 0.82, NSE = 0.81). The improved method selected optimal Copula functions for each month through rigorous statistical tests (AIC, BIC, RMSE, and K-S test), establishing joint probability distributions for annual and monthly average flows. The results indicate that different Copula types better align with monthly hydrological seasonal characteristics: Gaussian Copula suits February, May, and July; t-Copula suits August; Clayton Copula from September to December; Gumbel Copula for January, March, April, and June. Through conditional probability relationships (P(X₀≥x₀, 90%) = 0.9), the monthly guarantee rate range determined by the improved method spans 81.83% to 90.08%, significantly outperforming the uniform 90% guarantee rate employed by traditional methods. Verification using the Tennant method confirmed that ecological flows throughout the year met “excellent” or higher standards. Ecological flows exhibited pronounced seasonal variation, ranging from 6.2 m³/s during winter to spring to 96.93 m³/s during summer to autumn, providing scientific basis for basin-scale ecological water management. This study establishes a reliable methodological framework for ecological flow management in cold-region rivers.