Breaking Down the Computational Barriers to Real-Time Urban Flood Forecasting

Valeriy Y. Ivanov, Donghui Xu, M. Chase Dwelle, Khachik Sargsyan, Daniel B. Wright, Nikolaos Katopodes, Jongho Kim, Vinh Ngoc Tran, April Warnock, Simone Fatichi, Paolo Burlando, Enrica Caporali, Pedro Restrepo, Brett F. Sanders, Molly M. Chaney, Ana M.B. Nunes, Fernando Nardi, Enrique R. Vivoni, Erkan Istanbulluoglu, Gautam BishtRafael L. Bras

Research output: Contribution to journalArticlepeer-review

20 Scopus citations


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.

Original languageEnglish (US)
Article numbere2021GL093585
JournalGeophysical Research Letters
Issue number20
StatePublished - Oct 28 2021


  • estimation and forecasting
  • extreme events
  • floods
  • megacities and urban environment
  • uncertainty assessment

ASJC Scopus subject areas

  • Geophysics
  • General Earth and Planetary Sciences


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