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Neglecting model parametric uncertainty can drastically underestimate flood risks
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  • Sanjib Sharma,
  • Benjamin Seiyon Lee,
  • Iman Hosseini-Shakib,
  • Murali Haran,
  • Klaus Keller
Sanjib Sharma
Pennsylvania State University, Pennsylvania State University, Pennsylvania State University

Corresponding Author:[email protected]

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Benjamin Seiyon Lee
George Mason University, George Mason University, George Mason University
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Iman Hosseini-Shakib
Pennsylvania State University, Pennsylvania State University, Pennsylvania State University
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Murali Haran
Pennsylvania State University, Pennsylvania State University, Pennsylvania State University
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Klaus Keller
Thayer School of Engineering at Dartmouth College, Thayer School of Engineering at Dartmouth College, Thayer School of Engineering at Dartmouth College
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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: (1) stepwise line search, (2) precalibration or screening, and (3) the new 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.