the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 17 Jun 2025
How can building representation influence flood hazard and impact modelling: Insights from the 2021 Ahr Valley Flood
Abstract. The increasing flood risk in urban areas, driven by rising urbanization and climate change, underscores the need for accurate representation of buildings and urban features in flood hydrodynamic models. This study investigates the impact of different building representation techniques on flood hydrodynamic and impact modeling, using the 2021 flood event in the Ahr Valley, Germany, as example. Three methods — Building Block (BB), Building Hole (BH), and Building Resistance (BR) —are applied across varying model resolutions to assess their influence on flood extent, water depths, and flow velocities.
Our findings reveal that building representation affects both simulated flood extent and flow dynamics. The Building Block and Building Hole approaches generally lead to larger flooded areas with deeper water and higher velocities, while increased resistance or omitting buildings results in smaller flood extents, shallower water, and slower flow. Additionally, we show a strong link between building representation and model resolution. Our findings show that at coarser resolutions, the choice of building representation is more critical, with larger differences in flood extent across setups. We show that while all methods produce acceptable flood extents, variations in water depths and velocities highlight the importance of choosing the right building representation for accurate flood simulations—particularly in dense urban areas where accurate flood impact assessments rely on realistic flow dynamics. Our results emphasize the importance of selecting appropriate building representation methods based on model resolution to enhance urban flood modeling and impact assessment accuracy, with a general recommendation to include buildings as physical obstacles (BH, BB) in hydraulic models.
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RC1: 'Comment on egusphere-2025-2304', Anonymous Referee #1, 18 Jul 2025
The manuscript provides a comprehensive evaluation of different building representation strategies (Building Block – BB, Building Hole – BH, and Building Resistance – BR) in urban flood modeling, using the 2021 Ahr Valley flood as a case study. The use of multiple spatial resolutions and performance metrics is commendable and offers a rich dataset for comparative analysis. However, several points merit further attention or clarification:
- Evaluation of Calibration Strategy: The paper emphasizes that no calibration was performed for the hydrodynamic model. While this allows for a cleaner intercomparison between building representation methods, the lack of calibration likely contributes significantly to RMSE and bias values, especially for water depths. It would be beneficial for the authors to elaborate more explicitly on how calibration might alter the relative performance of BR, BB, and BH approaches. The brief mention in Section 4.3 (lines 305–310) could be expanded or perhaps discussed earlier in the Methods section to better contextualize the results. Including hypothetical scenarios or past studies where calibration impacted outcomes could provide a more nuanced understanding.
- Human Instability Indicator Use: The adoption of the Jonkman and Penning-Rowsell (2008) human instability threshold is justified but would benefit from additional detail. How sensitive are the results to this 1 m²/s threshold? Have alternative thresholds been tested in the literature or in this study to assess robustness? This is important since conclusions on public safety and hazard reduction hinge on this criterion. If possible, a brief sensitivity analysis or reference to similar studies using different thresholds could strengthen this section.
- Computational Cost vs. Resolution Trade-offs: The manuscript rightly points out the steep increase in computational time for higher-resolution simulations. However, it would be helpful to contextualize this cost-benefit tradeoff more clearly. For example, do the improvements in velocity and depth prediction at 1–2 m justify the 10–50× increase in runtime compared to 5 m? A clearer cost-benefit comparison, possibly in tabular form, would enhance practical guidance for modelers. Including specific examples or case studies where high resolution was critical could also provide practical insights.
- Comparison to Observations – Uncertainty in Depths: While the limitations of the water mark dataset are acknowledged (lines 285–290), these uncertainties are substantial and should be more fully integrated into the discussion of validation results. Could the authors provide uncertainty bounds or sensitivity ranges for RMSE/Bias considering the observational imprecision? Including a graphical representation of these uncertainties might also help in conveying this point more effectively.
- Observed Flood Extent: The use of LfU flood extent data as a validation baseline is valuable. Still, it would be helpful to elaborate on whether the observed extent includes buildings or open surfaces only, and how this influences the comparison across scenarios (e.g., BR might underperform if buildings were ignored in the observed extent). Clarifying this aspect could help in understanding potential discrepancies between observed and simulated extents.
- Generalizability: The conclusions appropriately highlight that the findings are specific to the Ahr Valley’s geomorphology and event type. However, since generalization is mentioned in the final paragraph (lines 350–355), it would be useful to elaborate briefly on where the authors believe the results might not generalize—e.g., in urban deltas or larger floodplains with more heterogeneous flow paths. Including a discussion on potential adaptations or adjustments needed for different settings could make this section more robust.
Citation: https://doi.org/10.5194/egusphere-2025-2304-RC1 -
RC2: 'Comment on egusphere-2025-2304', Anonymous Referee #2, 26 Jul 2025
This study investigated the impacts of different building representation techniques and DEM resolutions on flood model outputs based on a case study of 2021 Ahr Valley flood in Germany. The performances of different model configurations were compared in terms of flood inundation extents, water depths, and flow velocities. Overall, the study is interesting, and the findings seem to be meaningful. However, I still have several comments and suggestions as follows.
1) More details regarding the hydrodynamic model, RIM2D, should be presented: What is the size of the structured grid, and would it affect the simulation accuracy and efficiency? What is the downstream boundary condition of the model? What is the time step used in the numerical simulation, and is it sensitive to the model stability?
2) Eqns. (1)-(3): Please explain more about the terms q_x and q_y. It seems that the dimensions of some terms in Eqn. (2) are not the same on both sides of the equation.
3) Figures 2, 7, and 8: To avoid confusion, please change the legend from “lines” to “points”.
4) Figures 3 and 4: It may be unfair to only employ “BR10x” instead of “BR2x” and other configurations within the category of “BR” for comparison purposes.
5) Figure 6: Why was “BB” not included in the instability comparison?
6) Line 290: The Bias may not be a good metric, because over-estimations and under-estimations would be canceled out. Also, given the sampling uncertainty and measurement errors in both temporal and spatial data, it is suggested to present the values of metrics through a statistical distribution instead of a fixed number. The authors can refer to the article below for more information about the limitations of some commonly used evaluation metrics in flood modeling.
Reference: “Beyond a fixed number: Investigating uncertainty in popular evaluation metrics of ensemble flood modeling using bootstrapping analysis” (https://doi.org/10.1111/jfr3.12982)
7) Figures 7 and 8: Why did the models with finer resolutions not necessarily yield a better performance in terms of the evaluation metrics? The results also indicated that “BR2x” was more consistent and robust with the change in DEM resolutions.
8) Any guidance or suggestions on how to make a choice between BB and BH methods?
Minor Issues
9) Line 5: Change “extent” to the plural form “extents”.
10) Line 117: It is suggested to provide link access to RIM2D if it is open source.
11) Line 255: Did the different building representation methods significantly affect the computational costs? Can the model RIM2D run using CPU? If yes, how about the CPU computational cost for this case?
Citation: https://doi.org/10.5194/egusphere-2025-2304-RC2 -
RC3: 'Comment on egusphere-2025-2304', Anonymous Referee #3, 30 Jul 2025
Overview
This manuscript presents a detailed investigation into how different building representation approaches (Building Block, Building Hole, Building Resistance) influence flood hazard and impact modelling, using the catastrophic 2021 Ahr Valley flood as a case study and the RIM2D model as a hydrodynamic simulator.
General Comment
The work is relevant and addresses a question of high practical importance for flood modellers and planners. The use of multiple model resolutions and a consistent comparison framework is commendable. The study is methodologically sound and features a well-structured comparison of seven building representation scenarios across four spatial resolutions. The use of consistent model setups allows for a clear isolation of the effects of different building representation techniques.
However, while I appreciate the effort made by the authors, I cannot recommend the paper for publication in its current form due to several major concerns outlined below:
1- Although the paper is methodologically rigorous and well-executed, its contribution to the field appears somewhat incremental. Building representation approaches and their effects on flood modelling have already been extensively explored in previous studies, many of which are either overlooked or only partially addressed in the current manuscript. The literature review needs to be significantly strengthened to adequately represent the state of the art in this specific area, clearly identify the knowledge gaps, and justify the novelty and motivation of the present work. As it stands, the manuscript does not convey which specific gap in the literature the study aims to address.
2- The contribution also appears incremental concerning the authors' prior works, which are cited in the manuscript. It seems that this paper merely expands on a specific aspect of their earlier studies. However, it is unclear whether this extension is sufficient to constitute a significant and novel contribution. Greater emphasis should be placed on clarifying the unique aspects and added value of this study compared to the authors' previous publications.
3- The comparison with observation is, in part, misleading. The results of the simulations are not only dependent on the different techniques used for modelling the buildings, but also on several sources of uncertainty that you have not quantified at all. Among these, the roughness values assumed in the domain (which in turn is also influenced by the grid resolution). This kind of comparison confused me a lot.
4- I appreciated the attempt to interpret the results in terms of flood impact, which is a key aspect from an emergency management perspective. The authors introduced a threshold (1 m²/s) to define the impact. While I acknowledge the practical motivation behind this choice, I believe it is an overly simplified approach that may obscure important differences among the simulation results. A possible direction to enhance the novelty of the work would be to evaluate all simulations using multiple impact criteria. I suggest referring to the study DOI: 10.1007/s11269-024-03988-5 as a practical example to support a discussion of the variability in your results, to the corresponding variability in flood impact assessment when applying different criteria. This would allow the authors to offer new insights, not only to their previous work, but also in comparison to similar studies in the literature. Please consider revisiting your work with the following guiding question in mind: What is the variability introduced by the building representation approaches compared to the uncertainty inherent in flood impact indicators?
5- The manuscript currently lacks a dedicated discussion section, as the discussion is merged with the presentation of results. I strongly recommend separating these two sections. A standalone discussion is essential to emphasise the novelty of the work in the context of existing literature. This section should clearly articulate what new insights the reader gains from this study that are not already available in previous works. Which aspects of the findings confirm or contradict earlier studies? How do the results expand the current understanding of building representation in urban flood modelling?
6- The conclusions are difficult to generalise, as they are drawn from a single case study with highly specific geomorphological and hydrodynamic characteristics that may not be representative of other urban flood contexts. Could you please discuss more on this?
7- The conclusions are tightly linked to the use of RIM2D; more sophisticated models based on fully dynamic shallow water equations could lead to different outcomes, as the velocity field around buildings may differ significantly, especially when using the BH approach. There are several papers in the literature which show comparisons among different complexity models in urban areas with different treatments of buildings. I recommend discussing the soundness of your conclusions in light of these works.
Overall, the paper has practical merit, but in its current form, it is better suited as a technical note rather than a full research article. I hope these comments will help the authors enhance the research novelty of the manuscript by incorporating additional analyses and broader considerations.
Citation: https://doi.org/10.5194/egusphere-2025-2304-RC3 -
RC4: 'Comment on egusphere-2025-2304', Anonymous Referee #4, 30 Jul 2025
Thank you for submitting this article. The research is well described and clear. The methods are valid. A few points of enhancement can be performed, especially in the introduction and in the description of results/discussion. Please see my comments below:
More specifically, into 95 to 115, the local-inertial approximation neglecting convective acceleration substantially improves diffusive-wave or kinematic-wave models by considering local acceleration. However, it presumes sub-critical flows. Since the event simulated is unprecedented and the channels are steep, I wonder if the approximation is valid. Supporting information for this would be the computed Froude numbers from a full momentum model. Local-inertial models when faced super-critical flows, tend to produce mass balance errors. I encourage the authors to provide both: (1) mass balance computational errors and (2) a map of computed maximum Froude numbers in the domain. If possible, it will certainly enhance the confidence in the results. As shown by de Almeida (2012), the local-inertial model has a good performance under sub-critical flows but its performance for super-critical flows is still a topic of further research.
Paragraph 130: What specific modifications were made in the DEM for NoB?
In Fig A1, around 2000 min, a sudden fall of the stage-hydrograph is shown. Any reason for that? In addition, the authors mentioned that the reconstruction of inflow boundary condition was made upon the observed stage-hydrograph. I wonder how much volume it yielded and how much that would represent in mm from the upstream catchment, so a comparison can be made to validate the approach since rainfall most likely is available for the upstream catchment of this gauge. The authors already mentioned that calibrating roughness for all scenarios is not the purpose of the study. But simulating an event without checking the appropriateness of it nor estimating proper manning roughness coefficients for the scenarios might look like an extensive theoretical exercise rather than an application focused on explaining the observed reality.
Increasing resistance scenarios resulting in lower maximum depths seems a little counteractive to me, since larger roughness coefficients typically lead to higher flood depths, for a same flow discharge. I’d like the authors to include more physical explanations on why this opposing effect occurred for scenarios like BR10x. Also, please cite relevant literature to compare your results with previous results when discussing (220-225)
In line 230, please justify the reason or at least contextualize on why not to use a human instability risk metric based on human body physics flood momentum calculations. Recent examples of these approaches can be seemed in:
Gomes, Marcus N., Vijay Jalihal, Maria Castro, and Eduardo M. Mendiondo. "Exploring the impact of rainfall temporal distribution and critical durations on flood hazard modeling." Natural Hazards 121, no. 9 (2025): 10989-11012.
Postacchini, Matteo, Gabriele Bernardini, Marco D’Orazio, and Enrico Quagliarini. "Human stability during floods: Experimental tests on a physical model simulating human body." Safety science 137 (2021): 105153.
In Fig. 6 the authors mentioned a critical velocity of “1m2/s”?, which I believe it is 1 m/s. The authors should clearly provide a more extensive literature review on human instability models on their introduction.
Fig 1: please fix the name Atenahr to fit in the box, not crossing it. Also, add country boundaries in the left inset chart, not just state the coordinates. For the DEM image, is this a DEM or a DTM? I presume the white parts are human developments, but this is not clear from the figure. Please make the figure more descriptive. Finally, no need to use scientific notation for 5e1 to 5e2.
Citation: https://doi.org/10.5194/egusphere-2025-2304-RC4
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