Dynamically adaptive data-driven simulation of extreme hydrological flows

Handle URI:
http://hdl.handle.net/10754/626823
Title:
Dynamically adaptive data-driven simulation of extreme hydrological flows
Authors:
Kumar Jain, Pushkar; Mandli, Kyle; Hoteit, Ibrahim ( 0000-0002-3751-4393 ) ; Knio, Omar; Dawson, Clint
Abstract:
Hydrological 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.
KAUST Department:
Physical Sciences and Engineering (PSE) Division; Earth Science and Engineering Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Applied Mathematics and Computational Science Program
Citation:
Kumar 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.
Publisher:
Elsevier BV
Journal:
Ocean Modelling
KAUST Grant Number:
OCRF-2014-CRG3-62140389/ORS#2156
Issue Date:
27-Dec-2017
DOI:
10.1016/j.ocemod.2017.12.004
Type:
Article
ISSN:
1463-5003
Sponsors:
The 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.
Additional Links:
http://www.sciencedirect.com/science/article/pii/S1463500317302019
Appears in Collections:
Articles; Applied Mathematics and Computational Science Program; Physical Sciences and Engineering (PSE) Division; Earth Science and Engineering Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorKumar Jain, Pushkaren
dc.contributor.authorMandli, Kyleen
dc.contributor.authorHoteit, Ibrahimen
dc.contributor.authorKnio, Omaren
dc.contributor.authorDawson, Clinten
dc.date.accessioned2018-01-16T11:05:26Z-
dc.date.available2018-01-16T11:05:26Z-
dc.date.issued2017-12-27en
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.en
dc.identifier.issn1463-5003en
dc.identifier.doi10.1016/j.ocemod.2017.12.004en
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.en
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.en
dc.publisherElsevier BVen
dc.relation.urlhttp://www.sciencedirect.com/science/article/pii/S1463500317302019en
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/en
dc.subjectData assimilationen
dc.subjectEnsemble Kalman filteren
dc.subjectAdaptive mesh refinementen
dc.subjectTsunamien
dc.subjectOkada modelen
dc.subjectShallow water equationsen
dc.subjectUncertainty quantificationen
dc.titleDynamically adaptive data-driven simulation of extreme hydrological flowsen
dc.typeArticleen
dc.contributor.departmentPhysical Sciences and Engineering (PSE) Divisionen
dc.contributor.departmentEarth Science and Engineering Programen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.contributor.departmentApplied Mathematics and Computational Science Programen
dc.identifier.journalOcean Modellingen
dc.eprint.versionPost-printen
dc.contributor.institutionDepartment of Aerospace Engineering and Engineering Mechanics, University of Texas at Austin, 210 E 24th St, Austin, TX 78712-1085, USAen
dc.contributor.institutionDepartment of Applied Physics and Applied Mathematics, Columbia University, 500 W. 120th St., New York, NY, 10027, USAen
dc.contributor.institutionInstitute for Computational Engineering and Science, University of Texas at Austin, 201 E 24th St. Stop C0200, Austin, TX, 78712-1229, USAen
kaust.authorHoteit, Ibrahimen
kaust.authorKnio, Omaren
kaust.grant.numberOCRF-2014-CRG3-62140389/ORS#2156en
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