汉江上游白河流域洪水类型辨识与模拟

Identification and simulation of flood types in Baihe basin of the upper Han River

构建多维度洪水行为特征指标刻画洪水过程,结合主成分分析、K均值聚类和水文模型等方法,辨识并模拟白河流域主要洪水类型及其行为特征。结果表明:白河流域主要洪水类型可分为矮胖型中等洪水、剧烈变化型大洪水、尖瘦型中等洪水和单峰突发性小洪水4类;洪水类型的空间分布具有显著的区域差异,干流以矮胖型中等洪水类型为主,支流以单峰突发性小洪水类型为主;时变增益水文模型能够准确捕捉不同类型洪水的行为特征,特别对尖瘦型中等洪水和单峰突发性小洪水的量级、涨落变化等特征模拟效果较好。研究成果可为流域防洪减灾和水资源区域化科学管理等提供科学支撑。

As climate change and human activities intensify,the frequency and intensity of heavy rainfall events keep rising,resulting in increased occurrences of extreme flood disasters.Due to the influence of climate change and topography, the flood processes exhibit significant spatial heterogeneity. The Baihe River basin in the upper reaches of the Han River is rich in water resources, but experiences uneven spatial and temporal distribution of rainfall, leading to frequent occurrences of flood disasters. Identifying and simulating representative flood types in the Baihe River basin are of great significance for regional flood management. The daily runoff processes of seven hydrological stations in the Baihe River basin were analyzed for the period from 2007 to 2020. In order to accurately separate flood events by comprehensively considering their magnitude and morphological characteristics, a method that couples with the Peaks Over Threshold and the Sliding Variance Threshold was constructed. A system of behavioral characteristic indicators was constructed to comprehensively describe flood events, considering six dimensions: magnitude, duration, fluctuation variation, peak distribution, peak volume relationship, and morphology. Using this system of behavioral characteristic indicators, the types of floods in Baihe River basin were identified by combining principal component analysis and the K-mean clustering algorithm. Finally, flood events and their behavioral characteristics in the Baihe River basin were simulated by the Time Variant Gain Model. Based on the constructed flood event separation method, 249 flood events were accurately identified in the Baihe River basin. These flood events in the Baihe River basin were classified into four representative types, accounting for 41.0%, 4.0%, 23.3%, and 31.7% of the total flood events, respectively. The four flood types are characterized as follows: continuous short and fat medium floods, extreme variable large floods, sharp and thin medium floods with strong catastrophic effects, and single-peaked flash floods. The spatial distribution of the flood types varied significantly. While floods in mainstreams were dominated by short and moderate-magnitude floods, floods in tributaries were dominated by single-peaked flash floods. This distribution pattern could be attributed to several factors in the tributaries, including smaller basin areas, steep channel slopes, narrow valleys, short flood response times, and the relatively weaker water storage capacity of water control projects. These conditions make it easier for the tributaries to generate rapidly changing, short-duration, and small flash floods, which are challenging to evolve into rare and extreme floods. The Time Variant Gain Model demonstrates high accuracy in capturing diverse characteristics of different flood types. It is particularly effective in simulating the characteristics of sharp and thin medium floods and single-peaked flash floods prevalent in the source and tributaries of the Han River basin. The correlation coefficients of indicators for these two flood types range from 0.78 to 0.98 and 0.80 to 0.99, respectively. The accuracy of results can be attributed to the model, which comprehensively considers basin characteristics, aligning well with the actual conditions. In conclusion, the multidimensional flood behavioral characteristic indicator system can comprehensively depict flood events and the main flood types in the basin. The Time Variant Gain Model performs high accuracy in simulating the behavioral characteristics of different flood types. Valuable insights into the mechanisms of flood formation and the dynamics of flood process changes are provided. The findings are crucial for informing the development of effective flood control planning and management policies.