c-HAND: Near Real-Time Coastal Flood Mapping

The Science

The Texas Gulf Coast region contains significant centers of population, infrastructure, and economy and is threatened by coastal flooding. Hydrodynamic models are commonly used to map coastal flooding, but they are time and resource intensive and often run at coarser resolutions. Emergency managers need quick, high-resolution coastal flood estimates to support planning and response.

A team of researchers developed c-HAND, a modification of the Height Above Nearest Drainage (HAND) method for coastal environments. C-HAND is a new method that rapidly predicts coastal and compound fluvial-coastal inundation and has been validated against a state-of-the-art hydrodynamic storm surge simulation of Hurricane Ike using the ADCIRC model. C-HAND’s inundation map agrees well with the ADCIRC simulation while taking under 2% of the time currently needed to run ADCIRC on a supercomputer.

The Impact

Researchers combined c-HAND with the GeoFlood framework for fluvial flood forecasting to create a compound fluvial-coastal inundation mapping workflow to run in near real-time. C-HAND’s fast wall-clock time and low central processing unit requirements provide an accessible means to support emergency managers. The method provides timely and convenient access to information such as the locations of flooded roads and inundated coastal areas. C-HAND can be coupled with other efficient terrain analysis-based methods, enabling the production of a wide range of synthetic compound flooding scenarios at a low computational cost, which can further support compound flood preparedness.

Summary

By taking a simplified, reduced physics approach, underpinned by terrain analyses on lidar elevation data, c-HAND quickly generates high-resolution static coastal inundation estimates. The method forecasts coastal inundation on a 600 million–cell digital elevation model in under 20 seconds on the team’s office workstation. The method was implemented in Python and released as open-source software, along with a demonstration Jupyter notebook that can be run in the cloud: https://github.com/passaH2O/c-HAND.

Researchers validated c-HAND against an ADCIRC simulation of Hurricane Ike, a major storm surge event on the Texas Gulf Coast that occurred in 2008. C-HAND identified 99% of ADCIRC’s inundated areas while overpredicting inundation by a factor of about 27%, and its critical success index was 0.77. Overprediction of inundation is a common feature among static flood inundation models and could be addressed in future work by reducing flood inundation with a factor proportional to distance from the coast. The team also coupled c-HAND with GeoFlood, a terrain-based fluvial inundation model, and calculated a compound fluvial-coastal inundation map of Hurricane Ike.

Principal Investigator

Paola Passalacqua
University of Texas–Austin
[email protected]

Co-Principal Investigator

Mark Wang
University of Texas–Austin
[email protected]

Program Manager

Sally McFarlane
U.S. Department of Energy, Biological and Environmental Research (SC-33)
Urban Integrated Field Laboratories
[email protected]

Funding

Financial support for this work was provided by the National Science Foundation Graduate Research Fellowship Program (DGE-2137420), the National Oceanic and Atmospheric Administration Adaptation Sciences Program (NOAA-OAR-CPO-2021-2006389), the Cockrell School of Engineering, and Planet Texas 2050, a research grand challenge at the University of Texas–Austin. This material is based upon work supported by the Biological and Environmental Research program within the U.S. Department of Energy’s Office of Science under Award Number DE-SC0023216.

References