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.

Hydrometeorological changes profoundly influence runoff generation and evolution in river basins. It is of great significance to carry out runoff prediction research to ensure water resources security and improve disaster prevention and mitigation capabilities. In this paper, the Ganhe River Basin in Northeast China was taken as the research object. Based on the hydrometeorological and runoff data from 1980 to 2022, a variety of statistical methods were used to systematically study the climate change, runoff evolution characteristics and driving mechanism of the basin. Combined with BP neural network model and CMIP6 climate scenario data, the future runoff changes were predicted. The results showed that the precipitation and relative humidity showed a downward trend, while the temperature, sunshine and evapotranspiration showed an upward trend during the study period. The runoff showed a non-significant upward trend, and an abrupt change occurred in 2009. After the abrupt change, the runoff increased by 38.7% compared with the baseline period. The change in land use was the most significant from 1990 to 2000, and the area of cultivated land increased significantly. Correlation analysis showed that precipitation was the dominant meteorological factor affecting runoff change, and the contribution rate of human activities was 88.51%, which was much higher than that of climate change. The BP neural network model demonstrated satisfactory simulation performance, and the training set and test set R2 reached 0.88 and 0.82, respectively. In the future, both temperature and precipitation will increase under different SSP scenarios. On this basis, the BP neural network prediction results show that the runoff of the basin is generally increasing, and the increase is the most significant under the high emission scenario, and the risk of extreme hydrological events may be further aggravated. These findings provide scientific support for water resources management and ecological conservation in the Ganhe River Basin.