Preprints
https://doi.org/10.5194/hess-2024-178
https://doi.org/10.5194/hess-2024-178
25 Jul 2024
 | 25 Jul 2024
Status: this preprint is currently under review for the journal HESS.

Assimilation of satellite flood likelihood data improves inundation mapping from a simulation library system

Helen Hooker, Sarah Dance, David Mason, John Bevington, and Kay Shelton

Abstract. Mitigating against the impacts of catastrophic flooding requires funding for the communities at risk, ahead of an event. Simulation library flood forecasting systems are being deployed for forecast-based financing (FbF) applications. The FbF trigger is usually automated and relies on the accuracy of the flood inundation forecast, which can lead to missed events that were forecast below the trigger threshold. However, earth observation data from satellite-based synthetic aperture radar (SAR) sensors can reliably detect most large flooding events. A new data assimilation framework is presented to update the flood map selection from a simulation library system using SAR data, taking account of observation uncertainties. The method is tested on flooding in Pakistan, 2022. The Indus River in the Sindh province was not forecast to reach flood levels, which resulted in a non-trigger of the FbF scheme. We found that the flood map selection could be triggered in four out of five sub-catchments tested, with the exception occurring in a dense urban area due to the simulation library flood map accuracy here. Thus, the analysis flood map has potential to be used to trigger a secondary finance scheme during a flood event and avoid missed financing opportunities for humanitarian action.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
Helen Hooker, Sarah Dance, David Mason, John Bevington, and Kay Shelton

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2024-178', Anonymous Referee #1, 07 Sep 2024
    • AC1: 'Reply on RC1', Helen Hooker, 11 Sep 2024
  • RC2: 'Comment on hess-2024-178', Guy J.-P. Schumann, 21 Sep 2024
    • AC2: 'Reply on RC2', Helen Hooker, 26 Sep 2024
Helen Hooker, Sarah Dance, David Mason, John Bevington, and Kay Shelton
Helen Hooker, Sarah Dance, David Mason, John Bevington, and Kay Shelton

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Short summary
This study introduces a method that uses satellite data to enhance flood map selection for forecast-based financing applications. Tested on the 2022 Pakistan floods, it successfully triggered flood maps in four out of five regions, including those with urban areas. The approach ensures timely humanitarian aid by updating flood maps, even when initial triggers are missed, aiding in better disaster preparedness and risk management.