Flash floods are a worldwide threat to humans, which is why they are being intensively studied using historical event records. As measurements and event data increase, databases are becoming increasingly important for flash flood research. However, the recent literature on flood databases lacks technical details as well as discussions about a suitable database design for scientific investigations. In this paper, we thus show how an event database for the investigation of heavy rain-induced flood occurrences can be created. Based on the HiOS dataset (a German dataset with ~ 23,800 flash flood and pluvial flood events), we exemplify the database design and explore the spatiotemporal characteristics of floods caused by heavy rain in Germany. We outline all aspects relevant to database setup: from database requirements and system architecture through table and attribute design to a key and relationship definition. Furthermore, we clarify why a spatial database with interfaces for GIS softwares should be chosen, why a damage-based event definition is preferable to a hydrometeorological definition, and how table attributes support differentiated analyses. By means of the database, we investigated frequency, temporal evolution, spatial distribution and patterns, fatalities and injuries, as well as the seasonality of heavy rain-induced floods in Germany. The results indicate that floods caused by heavy rain occur throughout Germany but with a tendency toward fewer events in the northern direction. Across the country, we identified seven hot spots in urbanized and mountainous regions. Although heavy rain-induced floods in Germany take place mostly between noon and late afternoon, most people are injured and killed in events starting in the evening. Our investigation indicates an increased incidence of flash flood and pluvial flood-related injuries and fatalities in the identified hot spots. Overall, we observe a pronounced summer seasonality of the heavy rain-induced flood events. This study highlights the importance of event databases for flash flood research and advances our understanding of heavy rain-induced flood occurrences in Germany.

山洪暴发是对人类的全球威胁，这就是为什么要使用历史事件记录对其进行深入研究的原因。随着测量和事件数据的增加，数据库对于山洪研究变得越来越重要。但是，有关洪水数据库的最新文献缺乏技术细节，也缺乏有关用于科学研究的合适数据库设计的讨论。因此，在本文中，我们展示了如何创建一个事件数据库来调查暴雨引发的洪水事件。基于HiOS数据集（德国数据集，其中包含约23,800次山洪和暴雨事件），我们以数据库设计为例，并探讨了德国大雨造成的洪水时空特征。我们概述了与数据库设置有关的所有方面：从数据库需求和系统架构到表和属性设计，再到键和关系定义。此外，我们阐明了为什么应该选择带有GIS软件接口的空间数据库，为什么基于损伤的事件定义比水文气象学定义更可取，以及表属性如何支持差异分析。通过该数据库，我们调查了德国的频率，时间演变，空间分布和格局，死亡和伤害以及大雨引起的洪水的季节性。结果表明，大雨造成的洪水在整个德国发生，但北向的事件趋于减少。在全国范围内，我们确定了城市化和山区的七个热点。尽管德国大雨引发的洪水多发生在中午至下午之间，但大多数人在傍晚开始的事件中受伤或丧生。我们的调查表明，在确定的热点地区，山洪泛滥和与洪水相关的伤害和死亡的发生率增加。总体而言，我们观察到大雨引发的洪水事件在夏季表现出明显的季节性。这项研究强调了事件数据库对于山洪洪水研究的重要性，并增进了我们对德国暴雨诱发洪水发生的了解。我们观察到大雨引发的洪水事件在夏季的明显季节性。这项研究强调了事件数据库对于山洪洪水研究的重要性，并增进了我们对德国暴雨诱发洪水发生的了解。我们观察到大雨引发的洪水事件在夏季的明显季节性。这项研究强调了事件数据库对于山洪洪水研究的重要性，并增进了我们对德国暴雨诱发洪水发生的了解。