Articles | Volume 27, issue 5
https://doi.org/10.5194/hess-27-1089-2023
https://doi.org/10.5194/hess-27-1089-2023
Research article
 | 
14 Mar 2023
Research article |  | 14 Mar 2023

Bayesian calibration of a flood simulator using binary flood extent observations

Mariano Balbi and David Charles Bonaventure Lallemant

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Latest update: 29 Jun 2024
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Short summary
We proposed a methodology to obtain useful and robust probabilistic predictions from computational flood simulators using satellite-borne flood extent observations. We developed a Bayesian framework to obtain the uncertainty in roughness parameters, in observations errors, and in simulator structural deficiencies. We found that it can yield improvements in predictions relative to current methodologies and can potentially lead to consistent ways of combining data from different sources.