Distinguishing hydraulically-distinct floodplain types from high resolution topography with implications for broad-scale flood routing (data)

Floodplains can have a significant impact on the routing of flood waves across the landscape, yet their representation in broad-scale water resource and flood prediction models are limited. To identify hydraulically-relevant floodplains at scale, we develop a workflow that automates the extraction of reach-averaged morphologic features from high resolution topographic data hypothesized to define a zone within the floodplain that conveys floodwaters distinctly from the surrounding landscape. This zone is identified from departures in hydraulic geometry with stage. Working in the topographically diverse Lake Champlain Basin in Vermont, USA, we apply the workflow to 2,629 reaches and use the extracted features to cluster settings similar in their proposed ability to route floodwaters. In total we identified eight clusters of reach types, two that were pre-sorted and largely lack a floodplain, and six that reflect variability in floodplain features, which were parsed out from the K-medoids clustering analysis. Clusters of floodplain types had distinct impact on the routing of synthetically-derived hydrographs, evaluated using the Muskingum-Cunge routing model. From these clusters we propose a Hydraulic Floodplain Classification, which is comparable to other geographically-defined systems but unique in its focus on the potential of the landscape to influence flood routing. The automated workflow may be repeated in other regions with high resolution topographic datasets, offering an improvement in the functionality of continental to global floodplain mapping efforts. Identification of hydraulically-effective zones has implications for improved watershed management to meet flood resiliency goals, and to improve flood predictions and warnings.

漫滩（Floodplains）对景观内洪水波的演进传播具有显著影响，但在大尺度水资源与洪水预报模型中，其表征方式却极为有限。为规模化识别具有水力学意义的漫滩，本研究开发了一套自动化工作流，可从高分辨率地形数据中提取河段平均地貌特征——这些特征被假设用于界定漫滩内与周边景观在洪水输送功能上存在显著差异的区域，该区域可通过水力几何形态随水位的偏离特征进行识别。本研究以美国佛蒙特州地形复杂多样的尚普兰湖（Lake Champlain）流域为研究区，将该工作流应用于2629个河段，并基于提取的特征，按照洪水演进能力对河段进行聚类分组。本研究通过K中心点聚类（K-medoids）分析共得到8类河段类型：其中2类为预先分类且基本无漫滩的河段，剩余6类则反映了漫滩地貌特征的差异。基于马斯京根-康吉（Muskingum-Cunge）洪水演进模型对合成过程线进行评估后发现，不同漫滩类型的聚类组别对洪水演进过程具有显著不同的影响。基于上述聚类结果，本研究提出了水力学漫滩分类体系（Hydraulic Floodplain Classification），该体系可与其他基于地理分区的分类系统相媲美，但其独特之处在于聚焦于景观影响洪水演进的潜在能力。这套自动化工作流可推广应用于其他拥有高分辨率地形数据集的区域，有助于提升大陆乃至全球尺度漫滩制图工作的功能与精度。识别具有水力学效能的漫滩区域，对于优化流域管理以实现洪水韧性目标、改进洪水预报与预警工作均具有重要意义。