Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 27, issue 5
Articles | Volume 27, issue 5
https://doi.org/10.5194/hess-27-1089-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-27-1089-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 27, issue 5
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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Cited articles

Alfonso, L., Mukolwe, M. M., and Di Baldassarre, G.: Probabilistic Flood Maps to Support Decision-Making: Mapping the Value of Information: Probabilistic Flood Maps To Support Decision-Making: VOI-MAP, Water Resour. Res., 52, 1026–1043, https://doi.org/10.1002/2015WR017378, 2016. a
Aronica, G., Bates, P. D., and Horritt, M. S.: Assessing the Uncertainty in Distributed Model Predictions Using Observed Binary Pattern Information within GLUE, Hydrol. Process., 16, 2001–2016, https://doi.org/10.1002/hyp.398, 2002. a, b, c, d, e, f, g
Balbi, M.: Code and Data Github repository for “Bayesian calibration of a flood simulator using binary flood extent observations”, Zenodo [code and data set], https://doi.org/10.5281/zenodo.7682138, 2022. a
Bates, P. D., Horritt, M. S., Aronica, G., and Beven, K.: Bayesian Updating of Flood Inundation Likelihoods Conditioned on Flood Extent Data, Hydrol. Process., 18, 3347–3370, https://doi.org/10.1002/hyp.1499, 2004. a
Berrett, C. and Calder, C. A.: Bayesian Spatial Binary Classification, Spat. Stat., 16, 72–102, https://doi.org/10.1016/j.spasta.2016.01.004, 2016. a, b
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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.
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