the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 11 Jan 2024
Flood Occurrence and Impact Models for Socioeconomic Applications over Canada and the United States
Abstract. Large-scale socioeconomic studies of the impacts of floods are difficult and costly for countries such as Canada and the United States due to the large number of rivers and size of watersheds. Such studies are however very important to analyze spatial patterns and temporal trends to inform large-scale flood risk management decisions and policies. In this paper, we present different flood occurrence and impact models based upon statistical and machine learning methods over 31,000 watersheds spread across Canada and the US. The models can be quickly calibrated and thereby easily run predictions over thousands of scenarios in a matter of minutes. As applications of the models, we present the geographical distribution of the modelled average annual number of people displaced due to flooding in Canada and the US, as well as various scenario analyses. We find for example that an increase of 10 % in average precipitation yields an increase of population displaced of 18 % in Canada and 14 % in the U.S. The model can therefore be used by a broad range of end-users ranging from climate scientists to economists who seek to translate climate and socioeconomic scenarios into flood probabilities and impacts measured in terms of population displaced.
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Notice on discussion status
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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Preprint
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
Journal article(s) based on this preprint
Interactive discussion
Status: closed
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RC1: 'Comment on egusphere-2023-3039', Anonymous Referee #1, 26 Feb 2024
Revision of the manuscript number “egusphere-2023-3039” entitled “Flood Occurrence and Impact Models for Socioeconomic Applications over Canada and the United States”.
This manuscript contributes by modelling flood occurrences and their impacts using statistical and machine learning methods. The paper demonstrates high-quality research based on the methodology's effectiveness in modeling floods and predicting not only affected areas but also the displacement of populations. It presents important characteristics related to flood occurrence and impact that will inspire future research, especially when extrapolating the methodology to other regions. The paper is well-presented overall, containing crucial information that is carefully provided.
Additional comments:
- Check for spacing between numbers and units throughout the paper to correct numerous typos in this regard.
- Provide a more detailed explanation of what is meant by "Modelling flood occurrence is akin to a classification problem." Be specific.
- Represent “s” as a sub-index in “βs”.
- L497-L500. Reconsider the phrasing of the conclusion regarding what-if scenarios percentages. This is not very clear in the conclusions as it is in Section 5.2.
Citation: https://doi.org/10.5194/egusphere-2023-3039-RC1 - AC1: 'Reply on RC1', Mathieu Boudreault, 25 Mar 2024
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RC2: 'Comment on egusphere-2023-3039', Anonymous Referee #2, 06 Mar 2024
The manuscript presents a set of flood occurrence and impact models that may be applied to a large-scale spatial extents at low cost. The impact model focuses on population displacement, a key metric of concern for future climate change scenarios. While the structure and methodology is presented in a clear manner, improvements in framing and clarification of some concepts and methodological choices would enhance the manuscript.
- Definition: It would be good to clarify what is captured by the outcome variable “population displacement”. Does it include both temporary and permanent displacement? Does it include evacuations from areas that were not necessarily directly affected by floods? What are the implications of these different definitions for the manuscript’s consideration of displacement as a proxy for socioeconomic impacts?
- DFO polygons: The motivation for using DFO polygons to identify flood occurrence is unclear. Others in the literature have utilized satellite-based flood extents that are much higher resolution. (e.g. Vestby et al 2024)
- Exposure vs Vulnerability: The authors assign population displaced per watershed based on the total population exposed, which does not address factors that make certain groups more vulnerable to displacement. How might considerations of differential vulnerability impact the models’ predictions?
- Model variables: The motivation for adding 32 interactions across the climatic variables is unclear (260-263). Please describe in more detail the key interactions of interest that are being tested.
- (301-304) The validation against DFO polygon boundaries is confusing, given the polygons were used to provide flood occurrence data in the first place. Please clarify?
- (171) “Given how floods are reported in the DFO database, we include socioeconomic variables such as population and wealth in the proposed flood model." What does this mean?
Vestby, J., Schutte, S., Tollefsen, A. F., & Buhaug, H. (2024). Societal determinants of flood-induced displacement. Proceedings of the National Academy of Sciences, 121(3), e2206188120. https://doi.org/10.1073/pnas.2206188120
Citation: https://doi.org/10.5194/egusphere-2023-3039-RC2 - AC2: 'Reply on RC2', Mathieu Boudreault, 25 Mar 2024
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2023-3039', Anonymous Referee #1, 26 Feb 2024
Revision of the manuscript number “egusphere-2023-3039” entitled “Flood Occurrence and Impact Models for Socioeconomic Applications over Canada and the United States”.
This manuscript contributes by modelling flood occurrences and their impacts using statistical and machine learning methods. The paper demonstrates high-quality research based on the methodology's effectiveness in modeling floods and predicting not only affected areas but also the displacement of populations. It presents important characteristics related to flood occurrence and impact that will inspire future research, especially when extrapolating the methodology to other regions. The paper is well-presented overall, containing crucial information that is carefully provided.
Additional comments:
- Check for spacing between numbers and units throughout the paper to correct numerous typos in this regard.
- Provide a more detailed explanation of what is meant by "Modelling flood occurrence is akin to a classification problem." Be specific.
- Represent “s” as a sub-index in “βs”.
- L497-L500. Reconsider the phrasing of the conclusion regarding what-if scenarios percentages. This is not very clear in the conclusions as it is in Section 5.2.
Citation: https://doi.org/10.5194/egusphere-2023-3039-RC1 - AC1: 'Reply on RC1', Mathieu Boudreault, 25 Mar 2024
-
RC2: 'Comment on egusphere-2023-3039', Anonymous Referee #2, 06 Mar 2024
The manuscript presents a set of flood occurrence and impact models that may be applied to a large-scale spatial extents at low cost. The impact model focuses on population displacement, a key metric of concern for future climate change scenarios. While the structure and methodology is presented in a clear manner, improvements in framing and clarification of some concepts and methodological choices would enhance the manuscript.
- Definition: It would be good to clarify what is captured by the outcome variable “population displacement”. Does it include both temporary and permanent displacement? Does it include evacuations from areas that were not necessarily directly affected by floods? What are the implications of these different definitions for the manuscript’s consideration of displacement as a proxy for socioeconomic impacts?
- DFO polygons: The motivation for using DFO polygons to identify flood occurrence is unclear. Others in the literature have utilized satellite-based flood extents that are much higher resolution. (e.g. Vestby et al 2024)
- Exposure vs Vulnerability: The authors assign population displaced per watershed based on the total population exposed, which does not address factors that make certain groups more vulnerable to displacement. How might considerations of differential vulnerability impact the models’ predictions?
- Model variables: The motivation for adding 32 interactions across the climatic variables is unclear (260-263). Please describe in more detail the key interactions of interest that are being tested.
- (301-304) The validation against DFO polygon boundaries is confusing, given the polygons were used to provide flood occurrence data in the first place. Please clarify?
- (171) “Given how floods are reported in the DFO database, we include socioeconomic variables such as population and wealth in the proposed flood model." What does this mean?
Vestby, J., Schutte, S., Tollefsen, A. F., & Buhaug, H. (2024). Societal determinants of flood-induced displacement. Proceedings of the National Academy of Sciences, 121(3), e2206188120. https://doi.org/10.1073/pnas.2206188120
Citation: https://doi.org/10.5194/egusphere-2023-3039-RC2 - AC2: 'Reply on RC2', Mathieu Boudreault, 25 Mar 2024
Peer review completion
Journal article(s) based on this preprint
Data sets
Flood Occurrence and Impact Models for Socioeconomic Applications over Canada and the United States (Supplementary Material) Manuel Grenier, Mathieu Boudreault, David A. Carozza, Jérémie Boudreault, and Sébastien Raymond https://doi.org/10.5281/zenodo.10201817
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Manuel Grenier
Mathieu Boudreault
David A. Carozza
Jérémie Boudreault
Sébastien Raymond
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.