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

Journal Article · · Geophysical Research Letters
DOI:https://doi.org/10.1029/2021gl093585· OSTI ID:1877171
 [1];  [1];  [1];  [2];  [3];  [1];  [4];  [4];  [5];  [6];  [7];  [8];  [9];  [10];  [11];  [12];  [13];  [14];  [15];  [16] more »;  [17] « less
  1. Univ. of Michigan, Ann Arbor, MI (United States)
  2. Sandia National Lab. (SNL-CA), Livermore, CA (United States)
  3. Univ. of Wisconsin, Madison, WI (United States)
  4. Univ. of Ulsan (Korea, Republic of)
  5. SRI International, Ann Arbor, MI (United States)
  6. National Univ. of Singapore (Singapore)
  7. Eidgenoessische Technische Hochschule (ETH), Zurich (Switzerland)
  8. Univ. of Florence (Italy)
  9. Consulting Engineer, Minneapolis, MN (United States)
  10. Univ. of California, Irvine, CA (United States)
  11. Princeton Univ., NJ (United States)
  12. Department of Meteorology Federal University of Rio de Janeiro (Brazil)
  13. University for Foreigners, Perugia (Italy)
  14. Arizona State Univ., Tempe, AZ (United States)
  15. Univ. of Washington, Seattle, WA (United States)
  16. Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
  17. Georgia Inst. of Technology, Atlanta, GA (United States)
+ Show Author Affiliations

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.

Research Organization:
Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)
Sponsoring Organization:
USDOE National Nuclear Security Administration (NNSA)
Grant/Contract Number:
NA0003525
OSTI ID:
1877171
Alternate ID(s):
OSTI ID: 1826419
Report Number(s):
SAND2022-9647J; 708286
Journal Information:
Geophysical Research Letters, Journal Name: Geophysical Research Letters Journal Issue: 20 Vol. 48; ISSN 0094-8276
Publisher:
American Geophysical Union (AGU)Copyright Statement
Country of Publication:
United States
Language:
English

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Related Subjects

54 ENVIRONMENTAL SCIENCES
Floods
estimation and forecasting
extreme events
megacities and urban environment
uncertainty assessment