Neglecting Model Parametric Uncertainty Can Drastically Underestimate Flood Risks

Sanjib Sharma, Benjamin Seiyon Lee, Iman Hosseini-Shakib, Murali Haran, Klaus Keller

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Floods drive dynamic and deeply uncertain risks for people and infrastructures. Uncertainty characterization is a crucial step in improving the predictive understanding of multi-sector dynamics and the design of risk-management strategies. Current approaches to estimate flood hazards often sample only a relatively small subset of the known unknowns, for example, the uncertainties surrounding the model parameters. This approach neglects the impacts of key uncertainties on hazards and system dynamics. Here we mainstream a recently developed method for Bayesian inference to calibrate a computationally expensive distributed hydrologic model. We compare three different calibration approaches: (a) stepwise line search, (b) precalibration or screening, and (c) the Fast Model Calibrations (FaMoS) approach. FaMoS deploys a particle-based approach that takes advantage of the massive parallelization afforded by modern high-performance computing systems. We quantify how neglecting parametric uncertainty and data discrepancy can drastically underestimate extreme flood events and risks. Precalibration improves prediction skill score over a stepwise line search. The Bayesian calibration improves the uncertainty characterization of model parameters and flood risk projections.

Original languageEnglish (US)
Article numbere2022EF003050
JournalEarth's Future
Volume11
Issue number1
DOIs
StatePublished - Jan 2023

All Science Journal Classification (ASJC) codes

  • General Environmental Science
  • Earth and Planetary Sciences (miscellaneous)

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