Show simple item record

dc.contributor.authorKumar Jain, Pushkar
dc.contributor.authorMandli, Kyle
dc.contributor.authorHoteit, Ibrahim
dc.contributor.authorKnio, Omar
dc.contributor.authorDawson, Clint
dc.date.accessioned2018-01-16T11:05:26Z
dc.date.available2018-01-16T11:05:26Z
dc.date.issued2017-12-27
dc.identifier.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.
dc.identifier.issn1463-5003
dc.identifier.doi10.1016/j.ocemod.2017.12.004
dc.identifier.urihttp://hdl.handle.net/10754/626823
dc.description.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.
dc.description.sponsorshipThe 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.
dc.publisherElsevier BV
dc.relation.urlhttp://www.sciencedirect.com/science/article/pii/S1463500317302019
dc.rightsNOTICE: this is the author’s version of a work that was accepted for publication in Ocean Modelling. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Ocean Modelling, [, , (2017-12-27)] DOI: 10.1016/j.ocemod.2017.12.004 . © 2017. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectData assimilation
dc.subjectEnsemble Kalman filter
dc.subjectAdaptive mesh refinement
dc.subjectTsunami
dc.subjectOkada model
dc.subjectShallow water equations
dc.subjectUncertainty quantification
dc.titleDynamically adaptive data-driven simulation of extreme hydrological flows
dc.typeArticle
dc.contributor.departmentApplied Mathematics and Computational Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentEarth Fluid Modeling and Prediction Group
dc.contributor.departmentEarth Science and Engineering Program
dc.contributor.departmentPhysical Science and Engineering (PSE) Division
dc.identifier.journalOcean Modelling
dc.eprint.versionPost-print
dc.contributor.institutionDepartment of Aerospace Engineering and Engineering Mechanics, University of Texas at Austin, 210 E 24th St, Austin, TX 78712-1085, USA
dc.contributor.institutionDepartment of Applied Physics and Applied Mathematics, Columbia University, 500 W. 120th St., New York, NY, 10027, USA
dc.contributor.institutionInstitute for Computational Engineering and Science, University of Texas at Austin, 201 E 24th St. Stop C0200, Austin, TX, 78712-1229, USA
kaust.personHoteit, Ibrahim
kaust.personKnio, Omar
kaust.grant.numberOCRF-2014-CRG3-62140389/ORS#2156
refterms.dateFOA2019-12-27T00:00:00Z
dc.date.published-online2017-12-27
dc.date.published-print2018-02


Files in this item

Thumbnail
Name:
main_elsformat.pdf
Size:
4.667Mb
Format:
PDF
Description:
Accepted Manuscript

This item appears in the following Collection(s)

Show simple item record