21 Sep 2022
21 Sep 2022

Bayesian calibration of a flood simulator using binary flood extent observations

Mariano Balbi1 and David Charles Bonaventure Lallemant2 Mariano Balbi and David Charles Bonaventure Lallemant
  • 1Laboratorio de Materiales y Estructuras, School of Engineering, Universidad de Buenos Aires, Argentina
  • 2Earth Observatory of Singapore, Nanyang Technological University, Singapore

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.

Mariano Balbi and David Charles Bonaventure Lallemant

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2022-760', Anonymous Referee #1, 23 Oct 2022
    • AC1: 'Reply on RC1', Mariano Balbi, 25 Nov 2022
    • AC2: 'Reply on RC1', Mariano Balbi, 25 Nov 2022
  • RC2: 'Comment on egusphere-2022-760', Anonymous Referee #2, 27 Oct 2022
    • AC3: 'Reply on RC2', Mariano Balbi, 25 Nov 2022

Mariano Balbi and David Charles Bonaventure Lallemant

Model code and software

Code repository Mariano Balbi

Mariano Balbi and David Charles Bonaventure Lallemant


Total article views: 385 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
288 80 17 385 6 3
  • HTML: 288
  • PDF: 80
  • XML: 17
  • Total: 385
  • BibTeX: 6
  • EndNote: 3
Views and downloads (calculated since 21 Sep 2022)
Cumulative views and downloads (calculated since 21 Sep 2022)

Viewed (geographical distribution)

Total article views: 372 (including HTML, PDF, and XML) Thereof 372 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
Latest update: 27 Jan 2023
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 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.