Flood forecast of the Qingshandian Reservoir based on flood-based classification
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Abstract:
To enhance the accuracy of flood forecasting and mitigate prediction errors resulting from the utilization of a single parameter set across the entire hydrological model basin, various corresponding parameters were employed in distinct precipitation scenarios.The research focused on Qingshandian Reservoir, where historical floods were categorized into three levels: large, medium, and small. The Xin'anjiang model and SCE-UA parameter optimization algorithm were employed to investigate the correlation between cumulative rainfall and measured flood peak flow, utilizing the Pearson correlation coefficient. The parameter application index was determined as the maximum 6 h cumulative rainfall exhibiting the strongest correlation. Flood verification and prediction were subsequently performed utilizing the corresponding parameter set.The findings indicated that when floods were not classified, the overall pass rate for 38 floods stood at 92.1%, with an average deterministic coefficient of 0.82. Following the classification of floods into large, medium, and small categories, flood process simulation was conducted. Consequently, the pass rate for large, medium, and small floods reached 100%, accompanied by average deterministic coefficients of 0.92, 0.88, and 0.87, respectively, resulting in an overall average deterministic coefficient of 0.88. The classification demonstrated enhancements in both the pass rate and deterministic coefficient for each individual flood, contributing to an improved fitting effect. Furthermore, an analysis was conducted on the correlation between flood peak flow and maximum rainfall within 1 h, 3 h, 6 h, and 24 h, based on the Pearson correlation coefficient. Results revealed that the maximum 6 h rainfall exhibited the highest Pearson correlation coefficient with flood peak flow. Accordingly, the maximum 6-hour cumulative rainfall during the forecast period was employed as the criterion for determining large, medium, and small parameters.In the actual flood forecasting during the 2021 flood season, all flood forecast results were deemed satisfactory. Notably, four significant and medium-sized floods exhibited a strong fitting degree and high deterministic coefficient. The flood forecasting accuracy, predicated on flood classification, demonstrated a high level of reasonableness and feasibility, thereby offering valuable reference for reservoir flood control.
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