Cross-mode Stabilized Stochastic Shallow Water Systems Using Stochastic Finite Element Methods
Chen Chen, Clint Dawson, Eirik Valseth

TL;DR
This paper introduces an efficient stochastic shallow water model with cross-mode stabilization techniques, enabling rapid and reliable predictions of hurricane storm surges under uncertainty, verified through extensive tests and real hurricane hindcasts.
Contribution
It develops a novel cross-mode stabilization method for stochastic shallow water models, improving stability and efficiency in uncertainty quantification for hurricane surge prediction.
Findings
Successfully stabilizes stochastic shallow water simulations
Accurately predicts hurricane storm surges with uncertainty quantification
Demonstrates effectiveness on Hurricanes Ike and Harvey
Abstract
The development of surrogate models to study uncertainties in hydrologic systems requires significant effort in the development of sampling strategies and forward model simulations. Furthermore, in applications where prediction time is critical, such as prediction of hurricane storm surge, the predictions of system response and uncertainties can be required within short time frames. Here, we develop an efficient stochastic shallow water model to address these issues. To discretize the physical and probability spaces we use a Stochastic Galerkin method and a Incremental Pressure Correction scheme to advance the solution in time. To overcome discrete stability issues, we propose cross-mode stabilization methods which employs existing stabilization methods in the probability space by adding stabilization terms to every stochastic mode in a modes-coupled way. We extensively verify the…
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Taxonomy
TopicsProbabilistic and Robust Engineering Design · Model Reduction and Neural Networks · Hydrology and Drought Analysis
