Investigating Forecast Proficiency of Hurricane-Induced Compound Flooding With a Discontinuous Galerkin Shallow Water Equation Solver

📅 2026-08-27
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本文使用非连续伽辽金浅水方程求解器结合参数化降雨模型,提高了飓风引发复合洪水预报的准确性。
📝 Abstract
Recent severe storms on the U.S. Gulf coast have demonstrated the challenges presented by compound flooding, such as the interactions between rainfall runoff and storm surge. Historically, many studies have neglected these nonlinear interactions, but we propose to use a discontinuous Galerkin shallow water equation solver, which allows for incorporation of rainfall inputs directly onto the finite element mesh. In this work, we analyze the use of parametric rainfall for forecasting scenarios, using Hurricane Beryl (2024) as a case study. Beryl led to extensive flooding due to rainfall and storm surge along the Gulf. We use a collection of the National Oceanic and Atmospheric Administration's short-term advisories along with the best track data to demonstrate the efficacy of the parametric rainfall model for forecasting. Results show that the parametric rainfall input allowed for much more accurate inundation. Areas with heavy rainfall and low surge were affected the most, with many areas peaking over 50 cm above the baseline surge model. Almost none of the available high water marks from Beryl were captured by the standard models, but the compound flooding models capture many of them, the majority of which show relative errors under 10 percent. Results from the advisory forecast simulations were shown to be much closer to the best track hindcast simulation when rainfall forcing was used, even while early forecasts predicted the storm's trajectory much less accurately. The advisory simulations improved even further as Beryl neared the Texas coast, with sampled peak elevations most closely approximating the best track at Advisory 38, a few hours before landfall.
Problem

Research questions and friction points this paper is trying to address.

compound flooding
hurricane
forecast accuracy
rainfall runoff
storm surge
Innovation

Methods, ideas, or system contributions that make the work stand out.

discontinuous Galerkin shallow water equation solver
parametric rainfall model
compound flooding
M
Matthew Scarborough
The Department of Data Science, The Norwegian University of Life Science, Drøbakveien 31, Ås, 1433, Norway
C
Chayanon Wichitrnithed
The Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, 201 E. 24th St. Stop C0200, Austin, 78712, Texas, United States of America
S
Shintaro Bunya
Coastal Resilience Center, University of North Carolina at Chapel Hill, Chapel Hill, 27517, North Carolina, United States of America; Institute of Marine Sciences, University of North Carolina at Chapel Hill, Morehead City, 28557, North Carolina, United States of America
E
Ethan Kubatko
The Department of Civil, Environmental, and Geodetic Engineering, The Ohio State University, 2070 Neil Ave, Columbus, 43210, Ohio, United States of America
S
Suranjan Nepal
The Department of Civil, Environmental, and Geodetic Engineering, The Ohio State University, 2070 Neil Ave, Columbus, 43210, Ohio, United States of America
Clint Dawson
Clint Dawson
university of texas at austin
E
Eirik Valseth
The Department of Mechanical Engineering and Technology Management, The Norwegian University of Life Science, Drøbakveien 31, Ås, 1433, Norway; Department of Scientific Computing and Numerical Analysis, Simula Research Laboratory, Kristian Augusts gate 23, Oslo, 0164, Norway