Global flood models integrate flood maps of constant probability in space, ignoring the correlation between sites and thus potentially misestimating the risk posed by extreme events. Stochastic flood models alleviate this issue through the simulation of flood events with a realistic spatial structure, yet their proliferation at large scales has historically been inhibited by data quality and computer availability. In this paper, we show, for the first time, the efficacy of modeled river discharge reanalyses in the characterization of flood spatial dependence in the absence of a dense stream gauge network. While global hydrological models may show poor correspondence with absolute observed river flows, we find that the rate at which they can simulate the joint occurrence of relative flow exceedances at two given locations is broadly similar to when a gauge‐based statistical model is used. Evidenced over the United States, flood events simulated using observed gauge data from the U.S. Geological Survey versus those generated using modeled streamflows have similar (i) distributions of site‐to‐site correlation strength, (ii) relationships between event size and return period, and, importantly, (iii) loss distributions when incorporated into a continental‐scale flood risk model. Extremal dependence is generally quantified less accurately on larger rivers, in arid climates, in mountainous terrain, and for the rarest high‐magnitude events. However, local‐scale errors are shown to broadly cancel each other out when combined, producing an unbiased flood spatial dependence model. These findings suggest that building accurate stochastic flood models worldwide may no longer be a distant aspiration.

中文翻译：

---

走向全球随机河流洪水模型

全球洪水模型整合了空间中概率恒定的洪水图，而忽略了站点之间的相关性，因此有可能错误估计了极端事件带来的风险。随机洪水模型通过模拟具有真实空间结构的洪水事件来缓解此问题，但是历史上，大规模洪水的扩散受到数据质量和计算机可用性的抑制。在本文中，我们首次展示了在缺乏密集的流量表网络的情况下，模型化的河流流量再分析在表征洪水空间依赖性方面的功效。虽然全球水文模型可能显示出与观测到的绝对河流流量的对应性差，我们发现，在两个给定位置，他们可以模拟相对流量超限的联合发生的速率与使用基于量规的统计模型大致相似。在美国的证据显示，使用美国地质调查局观测的轨距数据模拟的洪水事件与使用模拟流量产生的洪水事件具有相似的（i）站点间相关强度分布，（ii）事件大小与恢复期之间的关系，重要的是，（iii）纳入大陆规模洪水风险模型时的损失分布。在较大的河流，干旱气候，山区和罕见的高强度事件中，对极端依赖的量化通常较不准确。但是，局部误差在组合时显示出可以相互抵消，产生无偏洪水空间依赖性模型。这些发现表明，在全球范围内建立准确的随机洪水模型可能不再是遥不可及的愿望。