the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Assessing the impact of early warning and evacuation on human losses during the 2021 Ahr Valley flood in Germany using agent-based modelling
Abstract. Between 12 and 19 July 2021, a quasi-stationary atmospheric low-pressure system named "Bernd" caused intense precipitation on already saturated soil, resulting in severe flooding in Germany, Belgium, and the Netherlands. The Ahr River Valley in Rhineland-Palatinate was particularly affected, with approximately 42,000 residents impacted, around 8,800 buildings damaged, and 134 fatalities recorded. The flood in the Ahr River Valley significantly exceeded the scenarios outlined in official hazard maps, leaving decision-makers and the public unprepared. Substantial issues occurred with the content, issuance, and dissemination of warnings, thereby reducing the effectiveness of emergency response. We evaluate how human losses in the Ahr Valley might have differed under alternative flood early warning and evacuation (FEWE) scenarios, using the agent-based model (ABM) LifeSim. To run the model for the 2021 Ahr flood, we utilised a reconstructed modelled time series of water depth and flow velocities and estimated the FEWE timeline based on a post-event survey of the affected population. For the reconstructed FEWE timeline, we identified the first flood warning approximately 13 hours before the peak of the flood upstream of the simulated domain. Only 17.5 % of those affected received a warning with evacuation instructions, with most becoming aware of evacuation necessities only after flooding had already reached them. Consequently, only about 34 % of the population evacuated their homes or were rescued. The median life loss estimation of the reconstructed flood overestimates the actual life loss by 28.8 %. Simulations of alternative FEWE scenarios indicate a potential life loss reduction of up to 80 % with timely warning dissemination and increased population evacuation. However, scenarios in which the FEWE prompted the population to evacuate at the moment of the imminent hazard at their buildings result in higher human losses. In these cases, vertical evacuation within buildings is more effective. Using a life loss ABM, such as LifeSim, can support decisions on FEWEs and improve emergency response planning.
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Status: open (until 25 Dec 2024)
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RC1: 'Comment on nhess-2024-183', Anonymous Referee #1, 18 Dec 2024
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General comment
The manuscript presents the application of the agent-based model LifeSim to the 2021 flood in the Ahr Valley (Germany), with the aim of better understanding the dynamics and factors that contributed to the exceptionally high number of fatalities during the event. After simulating the flood event, significant effort was dedicated to examining the exposure and vulnerability of the study area, as well as reconstructing the flood early warning and evacuation procedures that took place. Furthermore, the manuscript aims to infer the potential impacts of alternative warning and evacuation scenarios in reducing the number of fatalities.
The work aligns well with the aims and scope of the journal. The document is well-structured, clearly written, and effectively communicates its findings. The investigations carried out provide strong support for the discussion and conclusions of the study. Therefore, I believe the manuscript is suitable for publication, pending consideration of some (non-critical) comments.
Specific comments:
- Abstract: in general, I’d suggest not using acronyms in the abstract. Although FEWE is frequently used, I do not think the use of ABM here is particularly needed.
- L51: better “how to protect”?
- L90: “extensive variety of flood types” - please, be more precise and clearer on explaining what you mean here.
- L107-109: very minor comment: I think it is more logic to invert the order of these two approaches. First the issuing of the flood warning and then the efficiency of the communication solutions and strategies.
- Figure 3: I suggest adding the hydraulic networks in panel c)
- L228: what is the sensitivity of the LifeSim model to such error threshold? In other words, does a difference of nearly 0.5 m on water depth affect the way the model consider the level of people safety?
- L269-274: this part is not totally clear to me. Please, try to improve the explanation.
- L279: Here, you refer to the safe evacuation places defined within the model. I have the following questions: Were these locations known to people prior to the event (i.e., before the flood occurred)?
Were their locations communicated in any way during the event?
How were these evacuation places defined? Were they identified based on official reports or risk management plans, or were they determined based on your own expertise?
- Figure 5: some of the safe places seem to be very close to the river. It might be useful to add the maximum flood extent, in order to see if some of them were affected.
- L293: please check the brackets.
- L300: RPL acronym is not introduced in the text.
- L303: you always refer to CEST when reporting the timing. Is it really necessary?
- L300-305: I believe adding a kind of temporal timeline in which you summarize the different steps and phases that took place during the event would clarify the sequence of what was done during the event.
- Figure 6 and explanation: this figure was not totally clear to me at first read. I guess both panels show a range (the area within the two curves) of the people warned or active in time. However, in both text and caption you always refer to single curve. I think I got the current interpretation of the figure after having look at figure 7, where the representation of curves, plus range of variability, is clearer. I suggest revising this part.
- Scenarios: are scenarios A1.4 and A1.5 related to the curves (or range of variability) reported in figure 6. If this is the case, I suggest to better stress this link in the text.
- Figure 8: results in the figure show a significant equifinality of the model outcomes, although moving from 0 to 1 in the x-axis express a significant difference on the efficiency of the considered measure. Summing those two aspects make this problem even worse (panel c). Any comments on this and regarding how this could affect the considerations emerged from the study?
- Figure 9: figure shows the outcomes of the model, but, how do they correlate with the spatial and quantitative distribution of the occurred fatalities? The comparison is always done considering the whole number, while their spatial allocation is not analyzed. This comparison could provide useful insight regarding the approach adopted ty LifeSim model and the representativeness of the proxy variables adopted to simulate exposure and vulnerability.
- L598 (conclusions, first line): I suggest revising this sentence since I do not think the applicability itself of the ABM to the case study was the purpose of the study, right? Actually, you chose this model based on literature evidences, looking at previous experiences and applications of the model to similar events.
- 603: where can I see evidences of this? It seems to me that this result is not supported by the study outcomes. See also my previous comment regarding Figure 9.
- L619: When you mention the potential fatalities reduction (24.3%), I suggest explicitly referencing the figures or the specific scenario associated with this number for clarity and context.
Citation: https://doi.org/10.5194/nhess-2024-183-RC1
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