Exploring Critical Attributes of 3D Channels for Enhanced Probabilistic Flood Inundation Mapping

Objective:

Predicting river flooding across the continental United States requires accurate weather forecasts and flood models. A critical challenge to forecasting flooding arises from calibrating the physical relation between water flow and depth for each segment of river. Fully calibrating a flood inundation model at continental scale remains an elusive goal. The primary objective of this project is to incorporate the variability inherent within river systems to develop probabilistic flood inundation predictions within the NextGen National Water Model.

Approach:

This project utilizes calibrated flood models and high-resolution terrain to develop simple physically based probabilistic rating curves (the relation between flow and depth).

Impact:

Abstract:

Flood inundation mapping (FIM) represents an essential planning and risk assessment tool produced by the National Water Center and a key product from the National Water Model (NWM). During forecasted high flow events, FIM is generated parsimoniously through the use of the Height Above Nearest Datum and relies on a deterministic synthetic rating curve (SRC) to map flow to depth. This 2-year project will enhance the accuracy of FIM through the incorporation of hydraulic and river corridor terrain variability through the development of a probabilistic SRC, which will form the hydraulic basis for generating probabilistic FIM within the NextGen NWM. The incorporation of variability and uncertainty can highlight the potential range of reach-scale flooding within forecasted FIM and reduce the risk of natural flood hazards to the public.