缺资料山区河流设计洪水推求及预警指标分析OA

Small and medium-sized rivers in mountain areas face challenges in setting flood warning thresholds due to complex terrain and a lack of historical flood data.This research proposes a method that integrates deep learning with hydrodynamic simulation to extract flood warning indicators.Firstly,a Time Series Generative Adversarial Network(TimeGAN)is introduced to augment high-fidelity flood sequences based on limited historical data,effectively preserving both statistical characteristics and temporal dynamics.Then,the design flood hydrographs and flood scenarios are derived using the Pearson Type Ⅲ(P-Ⅲ)distribution.Finally,a one-and two-dimensional coupled hydrodynamic model is constructed to perform flood analysis and determine warning indicators at control cross-sections of small and medium-sized rivers.Taking the Yongkang River in Zhejiang Province as an example,the study results show that the TimeGAN can effectively capture the temporal dynamics of flood processes,avoiding assumptions about historical sample distributions,and the average Pearson correlation coefficient and R2 between TimeGAN-generated sequences and historical flood sequences reach 0.86 and 0.79,respectively.Based on the simulated design flood scenarios,the flood protection standards at Xikou and Yongkang Hotel stations are determined to correspond to a 20-year return period,with warning water levels of 80.08 m and 84.7 m,and guaranteed water levels of 80.91 m and 85.5 m,respectively.These findings provide a scientific basis for the development of flood early warning systems in mountain river basins.