State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, Research Center on Flood and Drought Disaster Reduction, China Institute of Water Resources and Hydropower Research, Beijing 100038, China.
College of Land Science and Technology, China Agricultural University, Beijing 100193, China; Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Science and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China.
Sci Total Environ. 2021 Apr 1;763:142977. doi: 10.1016/j.scitotenv.2020.142977. Epub 2020 Oct 15.
China frequently suffers from considerable and disastrous flash floods with wide areal coverage and high frequency. Obtaining useful information to support flash flood management and decision-making is challenging for massive flash flood events that vary greatly in spatio-temporal characteristics. In this study, hydrological modelling approach (CNFF) and cluster analysis were integrated to assess simulation reliability of entire flash flood processes including both hydrographs and behavior characteristics in a manner of similarity classification, rather than at event scale. A total of 207 hourly events from 13 mountainous catchments with diverse physiographic and meteorological characteristics across China were selected for study. Representative flash flood types were classified using normalized hydrographs with diverse spatio-temporal patterns by k-means clustering algorithm. For individual flash flood types, simulation reliability of CNFF was assessed in capturing corresponding hydrographs, seven behavior metrics measuring flash flood magnitude, intensity, occurrence time, flood timescale, rates of change and variability, and their uncertainties. Results showed that three (fast, intermediate and slow) flash flood types were identified from all the flash flood events with overall average silhouette index of 0.45. Hourly hydrographs of three flash flood types were well reproduced by CNFF, with absolute average relative error of runoff within 15% and Nash-Sutcliffe Efficiency above 0.55. All the behavior metrics were the most accurately reproduced for slow flash flood type with the least average relative root-mean-square error (0.30), followed by intermediate (0.52) and fast (0.58) types. Moreover, the slow flash flood type had the most reliable but greatest uncertainty interval of both hydrograph and behavior metrics, with average relative interval length being 1.24 and 71.96%, and 93.10% and 100% of observations contained in 95% confidence interval, respectively. This study provided efficient and detailed information for flash flood management, and extended application scope of hydrological models to encompass flash flood types and behavior metrics.
中国经常遭受大面积和高频率的严重洪灾。对于时空特征差异很大的大规模洪水事件，获取有用信息以支持洪水管理和决策是具有挑战性的。在本研究中，水文模型方法（CNFF）和聚类分析被集成在一起，以评估整个洪水过程的模拟可靠性，包括以相似性分类的方式而不是在事件尺度上评估洪水过程的水文和行为特征。从中国各地的 13 个具有不同地形和气象特征的山区流域中选择了 207 个小时事件进行研究。使用具有不同时空模式的归一化水文图，通过 k-均值聚类算法对具有代表性的洪水类型进行分类。对于单个洪水类型，通过评估 CNFF 捕捉相应水文图的能力，评估其模拟可靠性，包括七个衡量洪水规模、强度、发生时间、洪水时间尺度、变化率和变异性及其不确定性的行为指标。结果表明，从所有洪水事件中确定了三种（快速、中间和缓慢）洪水类型，整体平均轮廓指数为 0.45。CNFF 很好地再现了三种洪水类型的小时水文图，径流的绝对平均相对误差在 15%以内，纳什-苏特克利夫效率大于 0.55。所有行为指标对缓慢洪水类型的再现最为准确，平均相对均方根误差最小（0.30），其次是中间类型（0.52）和快速类型（0.58）。此外，缓慢洪水类型的水文图和行为指标的可靠性最高，但不确定性区间也最大，平均相对区间长度分别为 1.24%和 71.96%，95%置信区间内包含了 93.10%和 100%的观测值。本研究为洪水管理提供了高效和详细的信息，并将水文模型的应用范围扩展到涵盖洪水类型和行为指标。
