Understanding flood probabilities is essential to making sound decisions about flood-risk management. Many people rely on flood probability maps to inform decisions about purchasing flood insurance, buying or selling real-estate, flood-proofing a house, or managing floodplain development. Current flood probability maps typically use flood zones (for example the 1 in 100 or 1 in 500-year flood zones) to communicate flooding probabilities. However, this choice of communication format can miss important details and lead to biased risk assessments. Here we develop, test, and demonstrate the FLOod Probability Interpolation Tool (FLOPIT). FLOPIT interpolates flood probabilities between water surface elevation to produce continuous flood-probability maps. FLOPIT uses water surface elevation inundation maps for at least two return periods and creates Annual Exceedance Probability (AEP) as well as inundation maps for new return levels. Potential advantages of FLOPIT include being open-source, relatively easy to implement, capable of creating inundation maps from agencies other than FEMA, and applicable to locations where FEMA published flood inundation maps but not flood probability. Using publicly available data from the Federal Emergency Management Agency (FEMA) flood risk databases as well as state and national datasets, we produce continuous flood-probability maps at three example locations in the United States: Houston (TX), Muncy (PA), and Selinsgrove (PA). We find that the discrete flood zones generally communicate substantially lower flood probabilities than the continuous estimates.

中文翻译：

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FLOod概率插值工具（FLOPIT）：提高空间洪水概率量化和交流的简单工具

了解洪水概率对于做出正确的洪水风险管理决策至关重要。许多人依靠洪水概率图来了解有关购买洪水保险，购买或出售房地产，房屋防洪或管理洪泛区开发的决策。当前的洪水概率图通常使用洪水区（例如100年洪水区中的1或500年洪水区中的1）来传达洪水概率。但是，这种沟通方式的选择可能会漏掉重要的细节，并导致风险评估有偏差。在这里，我们开发，测试和演示FLOod概率插值工具（FLOPIT）。FLOPIT会在水面高程之间插入洪水概率，以生成连续的洪水概率图。FLOPIT使用至少两个返回期的水面高程淹没图，并创建年度超标概率（AEP）以及新的返回水位的淹没图。FLOPIT的潜在优势包括开源，相对容易实施，能够从FEMA以外的机构创建淹没地图，并且适用于FEMA发布洪水淹没地图但没有洪水概率的地区。利用来自联邦紧急事务管理局（FEMA）洪水风险数据库以及州和国家数据集的公开数据，我们在美国的三个示例地点生成了连续的洪水概率图：休斯顿（TX），Muncy（PA），和塞林斯格罗夫（PA）。我们发现，离散洪水区通常比连续估计传递的洪水概率要低得多。