the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 12 Mar 2025
How are public compensation efforts implemented in multi-hazard events? Insights from the 2020 Gloria storm in Catalonia
Abstract. Natural disasters result in increasing economic losses worldwide. Existing loss databases primarily capture insured damages and therefore often overlook uninsured assets and public compensation efforts. This study examines the role of public-sector compensation in disaster recovery, using the multi-hazard 2020 Storm Gloria in Catalonia as a case study. By systematically collecting, classifying and analyzing public compensation data related to rebuilding and restoring the direct tangible damages, we provide new insights into financial aid distribution for disaster recovery. In addition, an analysis of single major hazards is performed to understand the event's frequency, as well as its temporal and spatial distribution. Finally, the damages caused by the storm are used to estimate losses based on the probability of the triggering hazard's occurrence. The findings reveal that fluvial and coastal hazards caused over 80 % of recorded damages, while meteorological and slope hazards contributed the remainder. Concerning the affected elements, infrastructure sustained the highest losses, followed by economic and social sectors. Rebuilding and reconstruction costs for Storm Gloria were split evenly between fully public and public-private partnerships efforts. Public funding prioritized community assets and critical infrastructure, using hazard-dependent cost assessments and standardized government procedures. Additionally, the study identifies potential multi-hazard municipalities where overlapping hazards intensified damages, highlighting the need for comprehensive disaster documentation. Results also indicate that fully public compensations lack a direct correlation with hazard probability, reflecting prioritization based on recovery needs rather than hazard frequency. The research underscores the critical role of public intervention in disaster risk management and calls for enhanced data standardization to improve loss estimation methodologies in multi-hazard scenarios. Finally, this study contributes to the improve our understanding on disaster loss assessment and provides a framework for future evaluations of government interventions in post-disaster recovery.
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RC1: 'Comment on egusphere-2025-1009', Anonymous Referee #1, 26 Apr 2025
The paper examines public-sector compensation in disaster recovery, focusing on the 2020 Gloria storm in Catalonia. It highlights the importance of public compensation for uninsured assets and provides insights into financial aid distribution for disaster recovery. The study reveals that fluvial and coastal hazards caused over 80% of recorded damages, with infrastructure sustaining the highest losses. Public funding prioritized community assets and critical infrastructure.
The paper details the data used and the methodology with great precision and presents clear and concise results. The case study seems very appropriate to me. In addition to good conclusions. For all these reasons, I recommend its publication. I simply add some personal recommendations that the authors may or may not consider:
- In line 226, Gumbel is mentioned but its use is not justified as it is done with GEV previously.
- In figure 6, it would be advisable to add the letter labels to know what the caption refers to (a, b, c…).
- In the conclusions, add future contributions following the line of research.
- If possible, a graphical diagram of the methodology used in the paper.
Congratulations to the authors for their work, I found it very interesting.
Citation: https://doi.org/10.5194/egusphere-2025-1009-RC1 - AC1: 'Reply on RC1', Nuria Pantaleoni Reluy, 03 Jun 2025
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CC1: 'Comment on egusphere-2025-1009', Samuele Segoni, 06 May 2025
Dear Authors,
I was at EGU2025 and I had the opportunity of reading a poster based on this publication. I also got the chance to have an interesting discussion with the first author. Since I liked the work described in the poster very much, I was also very curious to check the submitted manuscript.
For what my opinion is worth (I'm not an official reviewer), I highly recommend the publication of this work, as I found it very interesting, based on a sound methodology, and original.
I just put forward a couple of comments that you are free to address or discard.
- I don't know how frequent similar events in Catalonia are, but one thing that maybe could be stated more clearly is that it is safe to assume that this disaster didn't come on top of another precedent disaster fro which the study area hadn't fully recovered yet. This is to avoid complex compound effects among repeated shocks that stack each other in a non-linear way, complicating any mathematics beyond the analysis.
- While readying the paper I was very curious of finding some explanations or speculations about the spatial pattern of damages: why did some areas receive more direct damage than others? This issue is partially addressed in the manuscript, and I think you correctly pointed out that the impacts of some hazard are widespread, while others are clustered around the spots where the most severe phenomena occurred. Here I would suggest a rapid search for possible correlations with soil sealing (or soil consumption or imperviousness). Indeed, in recent research of my group (DOI 10.1088/1748-9326/ad5fa1), we discovered that such impacts do not occur at random places, are not driven only by the severity of the driving hazardous process (e.g. rainfall or discharge return time), but depend (a lot!) on how much each municipality built buildings and infrastructure, and, more importantly, where the urbanization occurred (specifically, to what extent high hazard and medium hazard areas were spared or aggressed by urbanization). I see that this is partially beyond the scopes of the work, but I think it is relevant for discussion and conclusion, as it could be useful information to better address future intervention by both the public and private sectors.
Lastly, I would like to remark that my comments should not be intended as critics, and that I appreciate this work very much.
Citation: https://doi.org/10.5194/egusphere-2025-1009-CC1 - AC2: 'Reply on CC1', Nuria Pantaleoni Reluy, 03 Jun 2025
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RC2: 'Comment on egusphere-2025-1009', Anonymous Referee #2, 17 Jul 2025
This paper presents an analysis of public-sector compensation following Storm Gloria in Catalonia, Spain. The manuscript consists of three analyses focusing on: (1) losses, (2) hazards, and (3) the intersection of losses and hazards. A direct economic loss database was derived by categorizing public funds that were allocated towards recovery. The hazard analysis focused on three hazards (meteorological, coastal, and fluvial) and consisted of evaluating the spatial extent of hazards and determining hazard return periods. The intersection of losses and hazards evaluated the relationship between losses and hazard return period. The results shows that fluvial and coastal hazards are the major driver of losses for Storm Gloria, and that infrastructure sustained the largest losses. The manuscript shows no relationship between losses and hazard return period.
This manuscript is organized, well written, and easy to read. The reviewer recommends revisions prior to acceptance in Natural Hazards and Earth System Sciences. The reasons for this recommendation, and suggestions for improvement of the manuscript, are outlined below.
Major comments:
- Line 148: The introduction to the methods section outlines a three-step methodology; however, the methods section only consists of two subsections (3.1 Direction Loss Analysis and 3.2 Hazard Analysis). Is there anything that can be said in the methods about the third step in the methodology? This doesn’t need to be long. It would help readers know what to expect further in the manuscript. As it stands now, line 151 (the one-sentence description of the third step) isn’t clear to me.
- Line 229: Why is the return period for meteorological hazards of each pixel assigned to the nearest return period available from the Catalan meteorological service (2, 5, 10, … 500-yr)? That is, in the example given, why is the interpolated return period of 29-year event re-assigned a return period of 20-years? This does not appear to be consistently done across hazards (see line 255 where the return period for fluvial hazards is estimated as 12-years and not re-assigned).
- Line 269: Can the authors provide a justification for using Block Maxima method as opposed to something such as peaks over threshold?
- Fig. 6f: Am I viewing this figure correctly in that the largest possible number of effected elements in each municipality is only 9? I would assume there are more elements/assets in a municipality. It would help readers if the total number of entries in the compiled database is clearly presented throughout the manuscript (maybe a new column Table 1 with "number of elements" or shown somewhere in Fig. 4?).
- Fig. 7: Are the return period maps shown in Fig. 7 all produced using the same linear distribution that is shown in Fig. 3 (e.g., the 48-hr duration)? If so, why are the 1-hr and 12-hr maps using the 48-hr duration return period estimate? If the maps are produced using their own duration return period estimates, why are these so drastically different? I would assume that there should be more similarity in return periods across for different durations.
- Line 488: The authors suggest that there are “potential geographical variations in historical records across these [north-south] regions”. Is this not something that can be directly determined rather than using terms “suggesting” and “potential”? It appears the authors have hourly wave data at 52 nodes (Table 2) and could perform an extreme value analysis at each node to determine if there are indeed north-south variations in the historical records. This isn’t something that needs to be shown per se, rather something that could be definitively stated. It’s additionally not clear why the return periods of significant wave heights (I’m assuming this is referring to the observed significant wave heights for Gloria) are used to justify this statement. That is, significant wave height can vary along the coast for a single storm.
Minor comments:
- Line 12: Could this sentence be rephrased? I was expecting “losses” to be computed from “damages”; however, I did not see this in the manuscript.
- Line 29: The authors use USD here, but EUR elsewhere (e.g., line 289). If possible, I’d suggest staying consistent across this manuscript.
- Fig. 1: I’d suggest the authors consider adding a small inset map showing where Catalonia is located in Spain / Western Europe. This is not a requirement, but something to consider.
- Line 286: I’d suggest the authors consider removing “on one side” (e.g., “…we present the density distribution (Fig. 4) and the ___ (Fig. 5)”.) This currently reads as if the two plots shown are on the same figure.
- Line 318: “first-level” not “firs-level”.
- Fig. 6: Each subplot is missing a, b, c, etc.
- Fig. 8: Is it possible to label the other rivers that the authors discuss in the text? Namely the Tordera, Fluvià, and Besòs. This would make it easier for the readers to identify which rivers the authors are discussing without having to refer to Fig. 1. Otherwise, this is a great figure.
- Fig. 10 and line 504: Can the authors provide more information on what is meant by “standardized return period”?
- Line 561: “have” should be “has”.
- Line 618: Check on the word “fist”. Could this be removed, or should this be “first”?
Citation: https://doi.org/10.5194/egusphere-2025-1009-RC2 - AC3: 'Reply on RC2', Nuria Pantaleoni Reluy, 24 Jul 2025
Status: closed
-
RC1: 'Comment on egusphere-2025-1009', Anonymous Referee #1, 26 Apr 2025
The paper examines public-sector compensation in disaster recovery, focusing on the 2020 Gloria storm in Catalonia. It highlights the importance of public compensation for uninsured assets and provides insights into financial aid distribution for disaster recovery. The study reveals that fluvial and coastal hazards caused over 80% of recorded damages, with infrastructure sustaining the highest losses. Public funding prioritized community assets and critical infrastructure.
The paper details the data used and the methodology with great precision and presents clear and concise results. The case study seems very appropriate to me. In addition to good conclusions. For all these reasons, I recommend its publication. I simply add some personal recommendations that the authors may or may not consider:
- In line 226, Gumbel is mentioned but its use is not justified as it is done with GEV previously.
- In figure 6, it would be advisable to add the letter labels to know what the caption refers to (a, b, c…).
- In the conclusions, add future contributions following the line of research.
- If possible, a graphical diagram of the methodology used in the paper.
Congratulations to the authors for their work, I found it very interesting.
Citation: https://doi.org/10.5194/egusphere-2025-1009-RC1 - AC1: 'Reply on RC1', Nuria Pantaleoni Reluy, 03 Jun 2025
-
CC1: 'Comment on egusphere-2025-1009', Samuele Segoni, 06 May 2025
Dear Authors,
I was at EGU2025 and I had the opportunity of reading a poster based on this publication. I also got the chance to have an interesting discussion with the first author. Since I liked the work described in the poster very much, I was also very curious to check the submitted manuscript.
For what my opinion is worth (I'm not an official reviewer), I highly recommend the publication of this work, as I found it very interesting, based on a sound methodology, and original.
I just put forward a couple of comments that you are free to address or discard.
- I don't know how frequent similar events in Catalonia are, but one thing that maybe could be stated more clearly is that it is safe to assume that this disaster didn't come on top of another precedent disaster fro which the study area hadn't fully recovered yet. This is to avoid complex compound effects among repeated shocks that stack each other in a non-linear way, complicating any mathematics beyond the analysis.
- While readying the paper I was very curious of finding some explanations or speculations about the spatial pattern of damages: why did some areas receive more direct damage than others? This issue is partially addressed in the manuscript, and I think you correctly pointed out that the impacts of some hazard are widespread, while others are clustered around the spots where the most severe phenomena occurred. Here I would suggest a rapid search for possible correlations with soil sealing (or soil consumption or imperviousness). Indeed, in recent research of my group (DOI 10.1088/1748-9326/ad5fa1), we discovered that such impacts do not occur at random places, are not driven only by the severity of the driving hazardous process (e.g. rainfall or discharge return time), but depend (a lot!) on how much each municipality built buildings and infrastructure, and, more importantly, where the urbanization occurred (specifically, to what extent high hazard and medium hazard areas were spared or aggressed by urbanization). I see that this is partially beyond the scopes of the work, but I think it is relevant for discussion and conclusion, as it could be useful information to better address future intervention by both the public and private sectors.
Lastly, I would like to remark that my comments should not be intended as critics, and that I appreciate this work very much.
Citation: https://doi.org/10.5194/egusphere-2025-1009-CC1 - AC2: 'Reply on CC1', Nuria Pantaleoni Reluy, 03 Jun 2025
-
RC2: 'Comment on egusphere-2025-1009', Anonymous Referee #2, 17 Jul 2025
This paper presents an analysis of public-sector compensation following Storm Gloria in Catalonia, Spain. The manuscript consists of three analyses focusing on: (1) losses, (2) hazards, and (3) the intersection of losses and hazards. A direct economic loss database was derived by categorizing public funds that were allocated towards recovery. The hazard analysis focused on three hazards (meteorological, coastal, and fluvial) and consisted of evaluating the spatial extent of hazards and determining hazard return periods. The intersection of losses and hazards evaluated the relationship between losses and hazard return period. The results shows that fluvial and coastal hazards are the major driver of losses for Storm Gloria, and that infrastructure sustained the largest losses. The manuscript shows no relationship between losses and hazard return period.
This manuscript is organized, well written, and easy to read. The reviewer recommends revisions prior to acceptance in Natural Hazards and Earth System Sciences. The reasons for this recommendation, and suggestions for improvement of the manuscript, are outlined below.
Major comments:
- Line 148: The introduction to the methods section outlines a three-step methodology; however, the methods section only consists of two subsections (3.1 Direction Loss Analysis and 3.2 Hazard Analysis). Is there anything that can be said in the methods about the third step in the methodology? This doesn’t need to be long. It would help readers know what to expect further in the manuscript. As it stands now, line 151 (the one-sentence description of the third step) isn’t clear to me.
- Line 229: Why is the return period for meteorological hazards of each pixel assigned to the nearest return period available from the Catalan meteorological service (2, 5, 10, … 500-yr)? That is, in the example given, why is the interpolated return period of 29-year event re-assigned a return period of 20-years? This does not appear to be consistently done across hazards (see line 255 where the return period for fluvial hazards is estimated as 12-years and not re-assigned).
- Line 269: Can the authors provide a justification for using Block Maxima method as opposed to something such as peaks over threshold?
- Fig. 6f: Am I viewing this figure correctly in that the largest possible number of effected elements in each municipality is only 9? I would assume there are more elements/assets in a municipality. It would help readers if the total number of entries in the compiled database is clearly presented throughout the manuscript (maybe a new column Table 1 with "number of elements" or shown somewhere in Fig. 4?).
- Fig. 7: Are the return period maps shown in Fig. 7 all produced using the same linear distribution that is shown in Fig. 3 (e.g., the 48-hr duration)? If so, why are the 1-hr and 12-hr maps using the 48-hr duration return period estimate? If the maps are produced using their own duration return period estimates, why are these so drastically different? I would assume that there should be more similarity in return periods across for different durations.
- Line 488: The authors suggest that there are “potential geographical variations in historical records across these [north-south] regions”. Is this not something that can be directly determined rather than using terms “suggesting” and “potential”? It appears the authors have hourly wave data at 52 nodes (Table 2) and could perform an extreme value analysis at each node to determine if there are indeed north-south variations in the historical records. This isn’t something that needs to be shown per se, rather something that could be definitively stated. It’s additionally not clear why the return periods of significant wave heights (I’m assuming this is referring to the observed significant wave heights for Gloria) are used to justify this statement. That is, significant wave height can vary along the coast for a single storm.
Minor comments:
- Line 12: Could this sentence be rephrased? I was expecting “losses” to be computed from “damages”; however, I did not see this in the manuscript.
- Line 29: The authors use USD here, but EUR elsewhere (e.g., line 289). If possible, I’d suggest staying consistent across this manuscript.
- Fig. 1: I’d suggest the authors consider adding a small inset map showing where Catalonia is located in Spain / Western Europe. This is not a requirement, but something to consider.
- Line 286: I’d suggest the authors consider removing “on one side” (e.g., “…we present the density distribution (Fig. 4) and the ___ (Fig. 5)”.) This currently reads as if the two plots shown are on the same figure.
- Line 318: “first-level” not “firs-level”.
- Fig. 6: Each subplot is missing a, b, c, etc.
- Fig. 8: Is it possible to label the other rivers that the authors discuss in the text? Namely the Tordera, Fluvià, and Besòs. This would make it easier for the readers to identify which rivers the authors are discussing without having to refer to Fig. 1. Otherwise, this is a great figure.
- Fig. 10 and line 504: Can the authors provide more information on what is meant by “standardized return period”?
- Line 561: “have” should be “has”.
- Line 618: Check on the word “fist”. Could this be removed, or should this be “first”?
Citation: https://doi.org/10.5194/egusphere-2025-1009-RC2 - AC3: 'Reply on RC2', Nuria Pantaleoni Reluy, 24 Jul 2025
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