Extreme scenario generation for drought-flood abrupt alternation based on improved conditional diffusion model

Under the backdrop of global warming, abrupt transitions between drought and flood events have been rather frequent, posing severe challenges to flood-control, drought-resistance, and water-resource management in river basins. The scarce availability of extreme-event samples limits the ability of traditional generative models to accurately capture their abrupt characteristics. This study, focused on the Shuangjiangkou Reservoir, proposes an extreme-scenario-generation method for abrupt drought-flood transitions, based on an improved conditional diffusion model, validated using monthly runoff data from 1966 to 2025 (spanning 59 years). The results show that the generated sequences are highly consistent with the statistical distribution of historical observations, with a relative mean error of 3.3%, KL divergence of 0.1185, and Nash-Sutcliffe efficiency coefficient of 0.985 for the flow hydrograph; under monthly conditions, the correlation coefficient between the first-month flow of generated sequences and the historical mean of the same period reaches 0.995, thus accurately reproducing the full hydrological annual cycle from low-flow to flood periods; under intensity conditions, the mean absolute error between the generated events and target intensity is less than 0.5 levels, and the classification accuracy of abrupt events exceeds 89%. Thus, this method can generate statistically reasonable extreme scenarios beyond the limited historical record under conditional constraints, thereby providing a new technical approach for abrupt drought-flood risk assessment and reservoir-operation decision-making.