Event peak flow dataset for spatial counterfactual events, Germany

This dataset comprises event peak flows, representing extreme floods at 516 stations in Germany. The data generation process involves several key steps. Initially, observed rainfall events associated with 10 historical flood disasters from 1950 to 2021 are undergone spatial shifts. These shifts involve three distances (20, 50, and 100 km) and eight directions (North, Northeast, East, Southeast, South, Southwest, West, Northwest), resulting in 24 counterfactual precipitation events. Including the factual (no shift) event, a total of 25 distinct shifting events are considered. Subsequently, these shifted fields are used as atmospheric forcing for a mesoscale hydrological model (mHM) set up and calibrated for the entire Germany. The model produces daily stream flows across its domain, from which the event peak flows are derived. This dataset is expected to provide a valuable resource for analyzing and modeling the dynamics extreme flood events in Germany.本数据集涵盖德国516个水文站点的事件洪峰流量，用于表征当地极端洪水事件。其数据生成流程包含以下关键步骤：首先，针对1950年至2021年间10场历史洪涝灾害的实测降雨事件开展空间位移操作；该操作涵盖3种位移距离（20、50、100千米）与8个方位（北、东北、东、东南、南、西南、西、西北），由此生成24个反事实降雨事件；连同未位移的真实事件，共计25种不同的位移事件。随后，将这些位移后的降雨场作为大气强迫数据，输入至针对全德国构建并校准的中尺度水文模型（mesoscale hydrological model, mHM）中；该模型可生成研究全域的日径流序列，进而从中提取事件洪峰流量。本数据集可为德国极端洪水事件的动力学分析与模拟提供极具价值的研究资源。

搜集汇总

背景与挑战

背景概述

该数据集提供了德国516个站点的极端洪水事件峰值流量数据，基于1950年至2021年10个历史洪水灾害的降雨观测，通过空间移位生成24种反事实降水情景，并结合中尺度水文模型模拟得出。数据集旨在支持德国极端洪水事件的动态分析与建模研究，重点关注反事实情景下的洪水峰值流量变化。