---
OA_place: publisher
OA_type: free access
_id: '22535'
abstract:
- lang: eng
  text: Flooding impacts are on the rise globally, and concentrated in urban areas.
    Currently, there are no operational systems to forecast flooding at spatial resolutions
    that can facilitate emergency preparedness and response actions mitigating flood
    impacts. We present a framework for real-time flood modeling and uncertainty quantification
    that combines the physics of fluid motion with advances in probabilistic methods.
    The framework overcomes the prohibitive computational demands of high-fidelity
    modeling in real-time by using a probabilistic learning method relying on surrogate
    models that are trained prior to a flood event. This shifts the overwhelming burden
    of computation to the trivial problem of data storage, and enables forecasting
    of both flood hazard and its uncertainty at scales that are vital for time-critical
    decision-making before and during extreme events. The framework has the potential
    to improve flood prediction and analysis and can be extended to other hazard assessments
    requiring intense high-fidelity computations in real-time.
article_number: e2021GL093585
article_processing_charge: No
article_type: letter_note
author:
- first_name: Valeriy Y.
  full_name: Ivanov, Valeriy Y.
  last_name: Ivanov
- first_name: Donghui
  full_name: Xu, Donghui
  last_name: Xu
- first_name: M. Chase
  full_name: Dwelle, M. Chase
  last_name: Dwelle
- first_name: Khachik
  full_name: Sargsyan, Khachik
  last_name: Sargsyan
- first_name: Daniel B.
  full_name: Wright, Daniel B.
  last_name: Wright
- first_name: Nikolaos
  full_name: Katopodes, Nikolaos
  last_name: Katopodes
- first_name: Jongho
  full_name: Kim, Jongho
  last_name: Kim
- first_name: Vinh Ngoc
  full_name: Tran, Vinh Ngoc
  last_name: Tran
- first_name: April
  full_name: Warnock, April
  last_name: Warnock
- first_name: Simone
  full_name: Fatichi, Simone
  id: cf8e546b-a9b0-11f0-a43b-aa89ed1b56d6
  last_name: Fatichi
- first_name: Paolo
  full_name: Burlando, Paolo
  last_name: Burlando
- first_name: Enrica
  full_name: Caporali, Enrica
  last_name: Caporali
- first_name: Pedro
  full_name: Restrepo, Pedro
  last_name: Restrepo
- first_name: Brett F.
  full_name: Sanders, Brett F.
  last_name: Sanders
- first_name: Molly M.
  full_name: Chaney, Molly M.
  last_name: Chaney
- first_name: Ana M. B.
  full_name: Nunes, Ana M. B.
  last_name: Nunes
- first_name: Fernando
  full_name: Nardi, Fernando
  last_name: Nardi
- first_name: Enrique R.
  full_name: Vivoni, Enrique R.
  last_name: Vivoni
- first_name: Erkan
  full_name: Istanbulluoglu, Erkan
  last_name: Istanbulluoglu
- first_name: Gautam
  full_name: Bisht, Gautam
  last_name: Bisht
- first_name: Rafael L.
  full_name: Bras, Rafael L.
  last_name: Bras
citation:
  ama: Ivanov VY, Xu D, Dwelle MC, et al. Breaking down the computational barriers
    to real‐time urban flood forecasting. <i>Geophysical Research Letters</i>. 2021;48(20).
    doi:<a href="https://doi.org/10.1029/2021gl093585">10.1029/2021gl093585</a>
  apa: Ivanov, V. Y., Xu, D., Dwelle, M. C., Sargsyan, K., Wright, D. B., Katopodes,
    N., … Bras, R. L. (2021). Breaking down the computational barriers to real‐time
    urban flood forecasting. <i>Geophysical Research Letters</i>. American Geophysical
    Union. <a href="https://doi.org/10.1029/2021gl093585">https://doi.org/10.1029/2021gl093585</a>
  chicago: Ivanov, Valeriy Y., Donghui Xu, M. Chase Dwelle, Khachik Sargsyan, Daniel
    B. Wright, Nikolaos Katopodes, Jongho Kim, et al. “Breaking down the Computational
    Barriers to Real‐time Urban Flood Forecasting.” <i>Geophysical Research Letters</i>.
    American Geophysical Union, 2021. <a href="https://doi.org/10.1029/2021gl093585">https://doi.org/10.1029/2021gl093585</a>.
  ieee: V. Y. Ivanov <i>et al.</i>, “Breaking down the computational barriers to real‐time
    urban flood forecasting,” <i>Geophysical Research Letters</i>, vol. 48, no. 20.
    American Geophysical Union, 2021.
  ista: Ivanov VY, Xu D, Dwelle MC, Sargsyan K, Wright DB, Katopodes N, Kim J, Tran
    VN, Warnock A, Fatichi S, Burlando P, Caporali E, Restrepo P, Sanders BF, Chaney
    MM, Nunes AMB, Nardi F, Vivoni ER, Istanbulluoglu E, Bisht G, Bras RL. 2021. Breaking
    down the computational barriers to real‐time urban flood forecasting. Geophysical
    Research Letters. 48(20), e2021GL093585.
  mla: Ivanov, Valeriy Y., et al. “Breaking down the Computational Barriers to Real‐time
    Urban Flood Forecasting.” <i>Geophysical Research Letters</i>, vol. 48, no. 20,
    e2021GL093585, American Geophysical Union, 2021, doi:<a href="https://doi.org/10.1029/2021gl093585">10.1029/2021gl093585</a>.
  short: V.Y. Ivanov, D. Xu, M.C. Dwelle, K. Sargsyan, D.B. Wright, N. Katopodes,
    J. Kim, V.N. Tran, A. Warnock, S. Fatichi, P. Burlando, E. Caporali, P. Restrepo,
    B.F. Sanders, M.M. Chaney, A.M.B. Nunes, F. Nardi, E.R. Vivoni, E. Istanbulluoglu,
    G. Bisht, R.L. Bras, Geophysical Research Letters 48 (2021).
das_tickbox: '1'
date_created: 2026-07-27T12:30:24Z
date_published: 2021-10-28T00:00:00Z
date_updated: 2026-08-07T06:50:54Z
day: '28'
doi: 10.1029/2021gl093585
extern: '1'
intvolume: '        48'
issue: '20'
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://doi.org/10.1029/2021GL093585
month: '10'
oa: 1
oa_version: Published Version
publication: Geophysical Research Letters
publication_identifier:
  eissn:
  - 1944-8007
  issn:
  - 0094-8276
publication_status: published
publisher: American Geophysical Union
quality_controlled: '1'
scopus_import: '1'
status: public
title: Breaking down the computational barriers to real‐time urban flood forecasting
type: journal_article
user_id: ba8df636-2132-11f1-aed0-ed93e2281fdd
volume: 48
year: '2021'
...