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Preprints
https://doi.org/10.5194/egusphere-2022-760
https://doi.org/10.5194/egusphere-2022-760
21 Sep 2022
 | 21 Sep 2022

Bayesian calibration of a flood simulator using binary flood extent observations

Mariano Balbi and David Charles Bonaventure Lallemant

Abstract. Computational simulators of complex physical processes, such as inundations, require a robust characterization of the uncertainties involved to be useful for flood hazard and risk analysis. While flood extent data, as obtained from synthetic aperture radar (SAR) imagery, has become widely available, no methodologies have been implemented that can robustly assimilate this information source into fully probabilistic estimations of the model parameters, model structural deficiencies, and model predictions. This paper proposes a fully Bayesian framework to calibrate a 2D physics-based inundation model using a single observation of flood extent, explicitly including uncertainty in the floodplain and channel roughness parameters, simulator structural deficiencies, and observation errors. The proposed approach is compared to the current state-of-practice Generalized Likelihood Uncertainty Estimation (GLUE) framework for calibration and with a simpler Bayesian model. We found that discrepancies between the computational simulator output and the flood extent observation are spatially correlated, and calibration models that do not account for this, such as GLUE, might consistently mispredict flooding over large regions. The added structural deficiency term succeeds in capturing and correcting for this spatial behavior, improving the rate of correctly predicted pixels. We also found that binary data does not have information relative to the magnitude of the observed process (e.g. flood depths), raising issues in the identifiability of the roughness parameters, and the additive terms of structural deficiency and observation errors. The proposed methodology, while computationally challenging, is proven to perform better than existing techniques. It also has the potential to consistently combine observed flood extent data with other data such as sensor information and crowd-sourced data, something which is not currently possible using GLUE calibration framework.

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Journal article(s) based on this preprint

14 Mar 2023
Bayesian calibration of a flood simulator using binary flood extent observations
Mariano Balbi and David Charles Bonaventure Lallemant
Hydrol. Earth Syst. Sci., 27, 1089–1108, https://doi.org/10.5194/hess-27-1089-2023,https://doi.org/10.5194/hess-27-1089-2023, 2023
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.

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We proposed a methodology to obtain useful and robust probabilistic predictions from...
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