Improved Representation of Floodplains and Natural Features for Channel Routing

Floodplains play an important role in the attenuation of floods, influencing river forecasts and flood inundation predictions, but they are poorly represented in the National Water Model (NWM). This project aims to improve our understanding and modeled representation of the influence of floodplain-channel connectivity on flood celerity and flood routing processes. Our initial use case is situated in the Northeastern US, comprising over 3000 river reaches in the Vermont portion of the Lake Champlain basin. We use high-resolution topographic data to develop river reach morphological signatures in cross section to characterize floodplain types. A supervised machine-learning algorithm was used to cluster reaches based on their hypothesized influences on flood attenuation. The workflow for topographic signature extraction is publicly available on a GitHub repository, with future improvements planned as we integrate planform complexity into our characterization. Future work in project years 2-3 will involve development and testing of hydrodynamic models to validate hypothesized differences in routing for distinct floodplain classes, and for river reaches in our northeast testbed sites, instrumented with water level and inundation tracking sensors. As an outcome of the hydrodynamic modeling, we aim to identify the reach types for which the simpler representation of flood wave routing (i.e., Muskingum Cunge) is appropriate and which settings may require a more computationally expensive approach to optimize efficiency for enhanced performance of river stage forecasts and inundation extent predictions. Products developed through this project will be helpful for the National Water Center (NWC) and its partners (e.g., USGS) in the development of the Next Generation Water Resources Modeling Framework (NextGen) and geospatial data that support national hydrologic modeling applications.