Dynamically adaptive data-driven simulation of extreme hydrological flows
KAUST DepartmentApplied Mathematics and Computational Science Program
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Earth Fluid Modeling and Prediction Group
Earth Science and Engineering Program
Physical Science and Engineering (PSE) Division
KAUST Grant NumberOCRF-2014-CRG3-62140389/ORS#2156
Online Publication Date2017-12-27
Print Publication Date2018-02
Permanent link to this recordhttp://hdl.handle.net/10754/626823
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AbstractHydrological hazards such as storm surges, tsunamis, and rainfall-induced flooding are physically complex events that are costly in loss of human life and economic productivity. Many such disasters could be mitigated through improved emergency evacuation in real-time and through the development of resilient infrastructure based on knowledge of how systems respond to extreme events. Data-driven computational modeling is a critical technology underpinning these efforts. This investigation focuses on the novel combination of methodologies in forward simulation and data assimilation. The forward geophysical model utilizes adaptive mesh refinement (AMR), a process by which a computational mesh can adapt in time and space based on the current state of a simulation. The forward solution is combined with ensemble based data assimilation methods, whereby observations from an event are assimilated into the forward simulation to improve the veracity of the solution, or used to invert for uncertain physical parameters. The novelty in our approach is the tight two-way coupling of AMR and ensemble filtering techniques. The technology is tested using actual data from the Chile tsunami event of February 27, 2010. These advances offer the promise of significantly transforming data-driven, real-time modeling of hydrological hazards, with potentially broader applications in other science domains.
CitationKumar Jain P, Mandli K, Hoteit I, Knio O, Dawson C (2018) Dynamically adaptive data-driven simulation of extreme hydrological flows. Ocean Modelling 122: 85–103. Available: http://dx.doi.org/10.1016/j.ocemod.2017.12.004.
SponsorsThe authors acknowledge the support of the King Abdullah University of Science and Technology Competitive Research Grant Program, award number OCRF-2014-CRG3-62140389/ORS#2156.