Abstract Floodplain mapping using hydrodynamic models is difficult in data scarce regions. Additionally, using hydrodynamic models to map floodplain over large stream network can be computationally challenging. Some of these limitations of floodplain mapping using hydrodynamic modeling can be overcome by developing computationally efficient statistical methods to identify floodplains in large and ungauged watersheds using publicly available data. This paper proposes a geomorphic model to generate probabilistic 100-year floodplain maps for the Conterminous United States (CONUS). The proposed model first categorizes the watersheds in the CONUS into three classes based on the height of the water surface corresponding to the 100-year flood from the streambed. Next, the probability that any watershed in the CONUS belongs to one of these three classes is computed through supervised classification using watershed characteristics related to topography, hydrography, land use and climate. The result of this classification is then fed into a probabilistic threshold binary classifier (PTBC) to generate the probabilistic 100-year floodplain maps. The supervised classification algorithm is trained by using the 100-year Flood Insurance Rated Maps (FIRM) from the U.S. Federal Emergency Management Agency (FEMA). FEMA FIRMs are also used to validate the performance of the proposed model in areas not included in the training. Additionally, HEC-RAS model generated flood inundation extents are used to validate the model performance at fifteen sites that lack FEMA maps. Validation results show that the probabilistic 100-year floodplain maps, generated by proposed model, match well with both FEMA and HEC-RAS generated maps. On average, the error of predicted flood extents is around 14% across the CONUS. The high accuracy of the validation results shows the reliability of the geomorphic model as an alternative approach for fast and cost effective delineation of 100-year floodplains for the CONUS.

中文翻译：

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美国本土 100 年洪泛区绘图的地貌方法

摘要 在数据稀缺地区，使用水动力模型绘制洪泛区是很困难的。此外，使用水动力模型在大河流网络上绘制洪泛区在计算上可能具有挑战性。通过开发计算效率高的统计方法，使用公开可用的数据识别大型和未测量流域中的洪泛区，可以克服使用水动力模型绘制洪泛区的这些局限性。本文提出了一种地貌模型，用于为美国本土 (CONUS) 生成 100 年泛滥平原概率图。所提出的模型首先根据与河床 100 年一遇的洪水相对应的水面高度将 CONUS 的流域分为三类。下一个，使用与地形、水文、土地利用和气候相关的流域特征，通过监督分类计算 CONUS 中的任何流域属于这三个类别之一的概率。然后将该分类的结果输入概率阈值二元分类器 (PTBC) 以生成概率 100 年洪泛区地图。监督分类算法使用美国联邦紧急事务管理局 (FEMA) 的 100 年洪水保险评级地图 (FIRM) 进行训练。FEMA FIRM 还用于验证所提议模型在未包含在培训中的领域的性能。此外，HEC-RAS 模型生成的洪水淹没范围用于验证缺乏 FEMA 地图的 15 个站点的模型性能。验证结果表明，由建议模型生成的 100 年洪泛区概率图与 FEMA 和 HEC-RAS 生成的地图匹配良好。平均而言，整个 CONUS 的预测洪水范围的误差约为 14%。验证结果的高精度表明，地貌模型作为一种替代方法的可靠性，可用于快速、经济地划定 100 年泛滥平原 CONUS。