the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 13 Nov 2024
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: final response (author comments only)
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RC1: 'Comment on nhess-2024-183', Anonymous Referee #1, 18 Dec 2024
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 -
RC2: 'Comment on nhess-2024-183', Anonymous Referee #2, 28 Dec 2024
To the Authors
There is already a lot of scientific literature published about the severe flash floods of the summer of 2021 in Germany and Belgium. Without knowing in detail the thematic diversity of this literature, I think that this article brings as more interesting the modeling of alternative scenarios of early warning and evacuation, in view of what actually occurred. There is a great effort to represent the timeline in detail, although it seems to me that the hazard scenario could still be improved (as I discuss later), and there may be some wrong assumptions in terms of the response that occurred and the expected response (because the depths would not be properly modeled, for example).
Overall, it seems to me to be an excellent work that deserves only some further clarification of input data and methods applied, as well as the discussion of some of the uncertainties associated with the model.
Some additional notes:
L18-22: in the abstract, it is unclear if the presented information is the result of the LifeSim model or the historical records as they happened. Please, re-write to become clearer.
L23-24: is it possible to include along the paper (not in the abstract) an estimation of how many people would react accordingly even if they received the “timely warning”? The 80% of life loss reduction accounts for the possibility that people could mis-value/ignore the warning and don’t react by evacuating?
L102-104: the numbering of items (1) and (2) can be deleted.
L215-… Section 2.2.1 Flood Hazard modelling
Although it is a long river section (30 km), a resolution of 5 m in the DTM, seems to me to be a good compromise between the computational requirement and the quality of the flood zone and flood height intended for the application of the methodologies for assessing the degree of preparedness and evacuation. Have higher DTM resolutions (2 m or even 1 m) been tested? It is one thing to evacuate people in a context of a depth of 0.1 m; it is a different thing to do with 0.5 m or more. A less good DTM can translate into more or less significant flood depths differences locally (and velocities), which can condition the assessment of residents' behavior in the face of the need for evacuation. The behavior might have been the correct one given the real flood hazard characteristics, and the wrong one according to the modeled depths and velocities. Could this be relevant?
Roughness coefficients could be more detailed. Buildings can also accommodate flood waters inside but the adopted simplification is acceptable.
Figure 4-b: the two extreme colors are somewhat similar (blue tones). Can the Authors test a different selection of colors for the two classes of lower velocity (for example, using something between the yellow and the green)?
Figure 4-c: the red class (on July 14) seems to be absent from the map. If this is the case, could it be removed?
L242-... In section 2.2.2 Exposure at buildings scale, it would help the reader to have a clear picture of the number of buildings (or households or persons...) that are assessed. 100-m and 1-km grids are mentioned along with a survey of 277 households. It becomes unclear how these several sources were integrated into a values in the LifeSlim model. For example, the reader still doesn’t understand at this point of the paper why an allocation of “population inside buildings” was done (“proportional distribution of the population based on building size”) if the 277 surveys would clearly provide that information. A prior clear explanation of the study area, and of the number of buildings, households, or people considered in the model would greatly help.
When reaching the end of page 11, these totals are presented, but in my opinion the paper would improve its clarity if those figures were presented earlier.
L263: the concept of vulnerability (vulnerable) appears only twice in the paper. An index is mentioned but it seems to be based on age only (at L263-264 a “1km grid resolution” source is referred; so, what was the use of the survey?). I suggest to not refer to the concept of vulnerability or to improve its index with more variables. It is also mentioned on figure 1 but I think it makes no sense to do that reference. On the other hand, would the 277-residents survey allow for a refinement of vulnerability? Would it be valuable for the LifeSlim model?
The description of the collection of the buildings’ characteristics could also be improved. Which ones were obtained from the Census and which ones from the local survey?
Figure 5: the selected buildings footprint (yellow) is creating some noise with the higer two classes of the population grid (light green and yellow). It is also not obvious why the resolution differs from the blue cells (more coarse) to the others (in green and yellow, more fine).
L300-301: RPL appears only here along the paper. Can you explain what it means?
L430, Table 2: my concern still stands on the 5-meter resolution. It surely misses slope rupture breaklines (frequent in urban landscapes) that make a huge difference from standing in a safe place to standing in a dangerous one. Could this apport differences in the loss of life estimations, explaining partly the +28.8 % difference in your results (lines 444-445, in addition to the reasons you present?
L560-562: It is much valuable to assume these limitations. I didn’t notice before that bridges weren’t accounted for in the hydrodynamic simulations. They have a two-sided role in terms of hazard upstream and downstream the element.
L571-572: Yes, this may also introduce some error in the estimations. These models are very complex and it becomes hard to represent accurately the most probable real situation.
Citation: https://doi.org/10.5194/nhess-2024-183-RC2
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