the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 25 Jul 2024
Assimilation of satellite flood likelihood data improves inundation mapping from a simulation library system
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.
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RC1: 'Comment on hess-2024-178', Anonymous Referee #1, 07 Sep 2024
To enhance the triggering accuracy of FbF, this manuscript (MS) puts forward a framework that utilizes satellite-derived flood likelihood data to update the flood map selection in the simulation library of a flood forecasting system. From the tested cases, it can be observed that by incorporating SAR data, it has the capability to enhance the selection accuracy of flood inundation forecasts in situations where the forecast system fails to trigger a flood map due to various limitations.
This MS holds particular value for natural disaster mitigation. However, it falls short of meeting the requirements for high-quality publication, which necessitates a significant scientific contribution to the hydrological community.
- The concept of "data assimilation" is introduced in this paper, but I believe it has been misapplied. While I do not suggest that the data assimilation method must adhere strictly to traditional approaches, such as 4D-Var or filtering techniques, I think the proposed method should effectively integrate both observed and simulated data well. In this manuscript, the proposed method just facilitates the selection of appropriate flood maps; thus, the term should be used with caution.
- Several intriguing aspects, such as the agreement scale, have already been discussed in other literature.
- The proposed framework could be better understood through the inclusion of a diagram.
Citation: https://doi.org/10.5194/hess-2024-178-RC1 - AC1: 'Reply on RC1', Helen Hooker, 11 Sep 2024
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RC2: 'Comment on hess-2024-178', Guy J.-P. Schumann, 21 Sep 2024
This paper describes the value and use of EO-derived flood maps, in particular SAR, to update the choice of a simulated forecast flood map from a library of possible maps.
The paper is well written and follows a clear structure. The methods and results are well described. I have only a few minor to moderate comments:
- I think it is a good idea to provide a diagram illustrating the assimilation filtering method applied. In my opinion this would help illustrate the methodology described in the text. Also, not exactly the same filter, but a very similar idea was proposed by Andreadis in 2014 using SWOT simulated data and a forecasting model: https://www.sciencedirect.com/science/article/abs/pii/S0309170814001158
- Using the Pakistan 2022 event is good but I think it is also an event where EO really worked well. It would have been valuable to illustrate the approach also for an area where EO yielded less "complete" or less spatially contiguous maps. The reason I say this is because one of the main issues of EO for floods still remains the inability of EO to observe flooding of various landscapes, such as flooded vegetation or flooded urban areas etc, for which models provide a more spatially complete map. In such cases it would be good to understand the limitation of the presented DA + EO approach to select and update the correct forecast map.
- It would be good to get the authors opinion what they believe could be the effect of using a EO flood map, representing all flood inundation processes on the image (pluvial+fluvIal) to decide between a set of model maps from fluvial processes only?
Citation: https://doi.org/10.5194/hess-2024-178-RC2 - AC2: 'Reply on RC2', Helen Hooker, 26 Sep 2024
Status: closed
-
RC1: 'Comment on hess-2024-178', Anonymous Referee #1, 07 Sep 2024
To enhance the triggering accuracy of FbF, this manuscript (MS) puts forward a framework that utilizes satellite-derived flood likelihood data to update the flood map selection in the simulation library of a flood forecasting system. From the tested cases, it can be observed that by incorporating SAR data, it has the capability to enhance the selection accuracy of flood inundation forecasts in situations where the forecast system fails to trigger a flood map due to various limitations.
This MS holds particular value for natural disaster mitigation. However, it falls short of meeting the requirements for high-quality publication, which necessitates a significant scientific contribution to the hydrological community.
- The concept of "data assimilation" is introduced in this paper, but I believe it has been misapplied. While I do not suggest that the data assimilation method must adhere strictly to traditional approaches, such as 4D-Var or filtering techniques, I think the proposed method should effectively integrate both observed and simulated data well. In this manuscript, the proposed method just facilitates the selection of appropriate flood maps; thus, the term should be used with caution.
- Several intriguing aspects, such as the agreement scale, have already been discussed in other literature.
- The proposed framework could be better understood through the inclusion of a diagram.
Citation: https://doi.org/10.5194/hess-2024-178-RC1 - AC1: 'Reply on RC1', Helen Hooker, 11 Sep 2024
-
RC2: 'Comment on hess-2024-178', Guy J.-P. Schumann, 21 Sep 2024
This paper describes the value and use of EO-derived flood maps, in particular SAR, to update the choice of a simulated forecast flood map from a library of possible maps.
The paper is well written and follows a clear structure. The methods and results are well described. I have only a few minor to moderate comments:
- I think it is a good idea to provide a diagram illustrating the assimilation filtering method applied. In my opinion this would help illustrate the methodology described in the text. Also, not exactly the same filter, but a very similar idea was proposed by Andreadis in 2014 using SWOT simulated data and a forecasting model: https://www.sciencedirect.com/science/article/abs/pii/S0309170814001158
- Using the Pakistan 2022 event is good but I think it is also an event where EO really worked well. It would have been valuable to illustrate the approach also for an area where EO yielded less "complete" or less spatially contiguous maps. The reason I say this is because one of the main issues of EO for floods still remains the inability of EO to observe flooding of various landscapes, such as flooded vegetation or flooded urban areas etc, for which models provide a more spatially complete map. In such cases it would be good to understand the limitation of the presented DA + EO approach to select and update the correct forecast map.
- It would be good to get the authors opinion what they believe could be the effect of using a EO flood map, representing all flood inundation processes on the image (pluvial+fluvIal) to decide between a set of model maps from fluvial processes only?
Citation: https://doi.org/10.5194/hess-2024-178-RC2 - AC2: 'Reply on RC2', Helen Hooker, 26 Sep 2024
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