Flood-prone people and decision-makers are often unwilling to discuss and prepare for exceptional events, as such events are hard to perceive and out of experience for most people. Once an exceptional flood occurs, affected people and decision-makers are able to learn from this event. However, this learning is often focussed narrowly on the specific disaster experienced, thus missing an opportunity to explore and prepare for even more severe, or different, events. We propose spatial counterfactual floods as a means to motivate society to discuss exceptional events and suitable risk management strategies. We generate a set of extreme floods across Germany by shifting observed rainfall events in space and then propagating these shifted fields through a flood model. We argue that the storm tracks that caused past floods could have developed several tens of km away from the actual tracks. The set of spatial counterfactual floods generated contains events which are more than twice as severe as the most disastrous flood since 1950 in Germany. Moreover, regions that have been spared from havoc in the past should not feel safe, as they could have been badly hit as well. We propose spatial counterfactuals as a suitable approach to overcome society’s unwillingness to think about and prepare for exceptional floods expected to occur more frequently in a warmer world.

中文翻译：

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探索灾难性洪水的空间反事实

洪水易发人群和决策者往往不愿意讨论特殊事件并为其做好准备，因为此类事件对于大多数人来说很难感知且缺乏经验。一旦发生特殊洪水，受影响的人和决策者就能够从这次事件中吸取教训。然而，这种学习往往狭隘地集中于所经历的具体灾难，从而错过了探索和为更严重或不同的事件做好准备的机会。我们提出空间反事实洪水作为激励社会讨论异常事件和适当的风险管理策略的一种手段。我们通过改变太空中观测到的降雨事件，然后通过洪水模型传播这些改变的场，在德国各地产生了一系列极端洪水。我们认为，过去引发洪水的风暴路径可能已经发展到距实际路径数十公里的地方。生成的一组空间反事实洪水所包含的事件的严重程度是德国 1950 年以来最严重的洪水的两倍多。此外，过去未遭受严重破坏的地区不应感到安全，因为它们也可能受到严重打击。我们提出空间反事实作为一种合适的方法，以克服社会不愿意思考和准备应对在温暖的世界中更频繁发生的特殊洪水的情况。