Evaluating the Effectiveness of Soil Profile Rehabilitation for Pluvial Flood Mitigation Through Two-Dimensional Hydrodynamic Modeling

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

The study provides actionable soil-rehabilitation insights that reduce urban flood risk and inform resilient mitigation efforts. After addressing the following comments, it is recommended for publication in the Hydrology Journal. The authors should address all comments closely, but they can also address the most relevant comments.   

 

Abstract

 

1. The Abstract should be written in the simple past tense to reflect what has already been done. For instance, statements describing the methodology and results must appear in the simple past tense (e.g., “This study employed…” rather than “This study employs…”).

 

2. Please state the “study aim” by rewriting the sentence: “This study evaluates …”

 

3. Data specific to the Materials and Methods (such as numeric details of rainfall intensity, LiDAR resolution, etc.) should not appear in the Abstract. Instead, focus on the broader context or main findings. For example, revise the sentence to something more concise and without semicolons. 

 

“The study utilizes June 10, 2021, extreme rainfall events (3.72 in/hour or 94.49 mm/hour); a high resolution (1m) LiDAR Digital Terrain Model (DTM); building footprints; and hydrological soil data.”

 

4. Please clarify the meaning of “The study utilizes June 10, 2021…” by specifying that it analyzed an extreme rainfall event on that date.

 

5. If this is not a case study, considering the paper title, please add one sentence at the end of the abstract to present the study's implications for flood mitigation by applying the soil profile rehabilitation methods.  

 

 

1. Introduction 

 

1.1. Please clarify the sentence. Over 20 million people from where? Do you mean across the world?

 

“Over 20 million people, according to the Intergovernmental Panel on Climate 31 Change (IPCC), have been displaced annually since 2008 caused by extreme weather 32 events such as storms and floods [1]. ”

 

1.2. It is recommended that authors summarize the “literature review” paragraphs to a more focused, deep, and relevant literature review. It is recommended that the Authors apply the following changes:

 

- Lines 42 to 107: the relevant paragraphs should be summarized and focused on previous research gaps and open windows for further study.

 

1.3. The references are old and should be updated to most recent studies in this field. It is recommended that authors focus on researches published in 2024, and 2025.

1.4. Lines 94–102: Authors mentioned that 66% of Baltimore City’s streams are buried, exacerbating flooding. However, these lines offer no detailed mechanism (e.g., surcharged culverts, backwater effects, etc.). It is recommended that authors explain how or why buried streams specifically worsen pluvial flooding.

 

1.5. The previous research gaps should be added as the independent paragraph in line 108 before this paragraph: “Furthermore, the watershed includes …”

 

1.6. In the next paragraph, the authors should add one paragraph and present the three or more primary questions that were answered by this research.

 

1.7. The novelties and contributions should be added in the next paragraph.

 

1.8. The last paragraph seems reasonable and doesn’t need any changes. 

 

2. Material and Method

2.1. Lines 184 to 192:

 

What calibration-validation approach has been done on hydrodynamic model parameters? The optimization test results should be presented as tables or relevant metric diagrams.

 

3. Results and Discussion

 

3.1. Lines 245 to 258:

 

Where is the results of standard quantitative measures (e.g., RMSE, Nash-Sutcliffe, F1-score for flood extent) to confirm the model’s accuracy in a more rigorous way?

 

3.2. Lines 196-200, 221-228:

 

Considering this research focused on the single extreme rainfall event of June 10, 2021, authors should explain the limitations of such study and generalizability issues related to the single-event analysis.

 

3.3.   Lines 266-279:

The authors should explain why the manuscript focuses on public parcels and vacant lots without discussing potential constraints such as contaminated fill, shallow bedrock, or funding limitations. 

While lines 267–279 highlight where soil rehabilitation might occur, the paper does not address whether these site‐specific conditions could prevent actual implementation in certain areas. 

 

3.4. Assessing the bias:

 

Although the authors validate their model with 311 calls, social media, and newspaper reports, they do not address possible biases in these data sources—such as underreporting from specific neighborhoods, discrepancies in social media usage, or inconsistent media coverage. 

Similarly, potential selection or parameter biases are not explicitly mentioned when targeting vacant lots and public parcels for soil rehabilitation scenarios. 

Thus, biases in data collection or methodological choices are not formally acknowledged or assessed.

 

3.5. Study limitations and future contributions

 

It is recommended that authors add a subsection, namely “Study limitations and future contributions,” before the Conclusion to address the following issues:

 

- Explaining the study limitations in data, methodology, and results (generalizability)

- Discussing the uncertainties (in infiltration, LiDAR accuracy, etc.) might shift the results

- Presenting recommendations for future studies based on the results of and methodology developed in this study

- Addressing how findings might transfer to other watersheds, nor does it discuss cost, maintenance, or scaling limitations—thereby missing a clear statement on broader applicability.

 

Comments on the Quality of English Language

It can be improved in Abstract and Introduction. 

Author Response

Editor Comment 1: The Abstract should be written in the simple past tense to reflect what has already been done. For instance, statements describing the methodology and results must appear in the simple past tense (e.g., “This study employed…” rather than “This study employs…”).

Authors’ Response 1: Thank you for the comment. The abstract has been revised to suit the comments made.

Pluvial flooding, driven by increasing impervious surfaces and intense storm events, presents a growing challenge for urban areas worldwide. In Baltimore City, MD, USA, climate change, rapid urbanization, and aging stormwater infrastructure exacerbate flooding impacts, resulting in significant socio-economic consequences. This study evaluated the effectiveness of the soil profile rehabilitation scenario using a 2D hydrodynamic modeling approach in the Tiffany Run watershed, Baltimore City. The study utilized different extreme storm events; a high-resolution (1m) LiDAR Digital Terrain Model (DTM); building footprints; and hydrological soil data. These datasets were integrated into a fully coupled 2D hydrodynamic model, City Catchment Analysis Tool (CityCAT) to simulate urban flood dynamics. Pre-soil rehabilitation simulation revealed a maximum water depth of 3.00 meters in most areas with hydrologic soil groups C and D, especially downstream of the study area. Post-soil rehabilitation was targeted at vacant lots and public parcels, accounting for 33.20% of the total area of the watershed. This resulted in a reduced water depth of 2.50 meters. Additionally, the baseline runoff coefficient of 0.49 decreased to 0.47 following rehabilitation, and the model consistently recorded a peak runoff reduction rate of 4.10 across varying rainfall intensities. Validation using a contingency matrix demonstrated true positive rates of 0.75, 0.50, 0.64, and 0 for selected events, confirming the model’s capability in capturing real-world flood occurrences.

Editor Comment 2: Please state the “study aim” by rewriting the sentence: “This study evaluates …”

Authors’ Response 2: Thank you for the response. This has been addressed in line 112 to line 114.

By leveraging CityCAT’s capabilities, the study aims to contribute to actionable recommendations for reducing pluvial flood risk in urban areas.

Editor Comment 3: Data specific to the Materials and Methods (such as numeric details of rainfall intensity, LiDAR resolution, etc.) should not appear in the Abstract. Instead, focus on the broader context or main findings. For example, revise the sentence to something more concise and without semicolons.

Authors’ Response 3: Thank you for the response. This has been addressed, and the abstract has been revised.

Pluvial flooding, driven by increasing impervious surfaces and intense storm events, presents a growing challenge for urban areas worldwide. In Baltimore City, MD, USA, climate change, rapid urbanization, and aging stormwater infrastructure exacerbate flooding impacts, resulting in significant socio-economic consequences. This study evaluated the effectiveness of the soil profile rehabilitation scenario using a 2D hydrodynamic modeling approach in the Tiffany Run watershed, Baltimore City. The study utilized different extreme storm events; a high-resolution (1m) LiDAR Digital Terrain Model (DTM); building footprints; and hydrological soil data. These datasets were integrated into a fully coupled 2D hydrodynamic model, City Catchment Analysis Tool (CityCAT) to simulate urban flood dynamics. Pre-soil rehabilitation simulation revealed a maximum water depth of 3.00 meters in most areas with hydrologic soil groups C and D, especially downstream of the study area. Post-soil rehabilitation was targeted at vacant lots and public parcels, accounting for 33.20% of the total area of the watershed. This resulted in a reduced water depth of 2.50 meters. Additionally, the baseline runoff coefficient of 0.49 decreased to 0.47 following rehabilitation, and the model consistently recorded a peak runoff reduction rate of 4.10 across varying rainfall intensities. Validation using a contingency matrix demonstrated true positive rates of 0.75, 0.50, 0.64, and 0 for selected events, confirming the model’s capability in capturing real-world flood occurrences.

Editor Comment 4: Please clarify the meaning of “The study utilizes June 10, 2021…” by specifying that it analyzed an extreme rainfall event on that date.

Authors’ Response 4: Thank you for the response. This has been addressed and revised in the abstract.

Pluvial flooding, driven by increasing impervious surfaces and intense storm events, presents a growing challenge for urban areas worldwide. In Baltimore City, MD, USA, climate change, rapid urbanization, and aging stormwater infrastructure exacerbate flooding impacts, resulting in significant socio-economic consequences. This study evaluated the effectiveness of the soil profile rehabilitation scenario using a 2D hydrodynamic modeling approach in the Tiffany Run watershed, Baltimore City. The study utilized different extreme storm events; a high-resolution (1m) LiDAR Digital Terrain Model (DTM); building footprints; and hydrological soil data. These datasets were integrated into a fully coupled 2D hydrodynamic model, City Catchment Analysis Tool (CityCAT) to simulate urban flood dynamics. Pre-soil rehabilitation simulation revealed a maximum water depth of 3.00 meters in most areas with hydrologic soil groups C and D, especially downstream of the study area. Post-soil rehabilitation was targeted at vacant lots and public parcels, accounting for 33.20% of the total area of the watershed. This resulted in a reduced water depth of 2.50 meters. Additionally, the baseline runoff coefficient of 0.49 decreased to 0.47 following rehabilitation, and the model consistently recorded a peak runoff reduction rate of 4.10 across varying rainfall intensities. Validation using a contingency matrix demonstrated true positive rates of 0.75, 0.50, 0.64, and 0 for selected events, confirming the model’s capability in capturing real-world flood occurrences.

Editor Comment 5: Please clarify the sentence. Over 20 million people from where? Do you mean across the world?

Authors’ Response 5: Thank you for the comment. This has been addressed in line 33 to line 40.

Over 20 million people, according to the Intergovernmental Panel on Climate Change (IPCC), have been displaced annually since 2008 caused by extreme weather events such as storms and floods [1]. The frequency and duration of flood events have increased globally, and this trend is likely to continue [2] [3]. Pluvial flooding, a form of urban flooding, occurs when intense rainfall exceeds the capacity of urban drainage systems. This issue is becoming more prevalent in both developed and developing nations due to climate change and rapid urbanization [3].

Editor Comment 6: It is recommended that authors summarize the “literature review” paragraphs to a more focused, deep, and relevant literature review. It is recommended that the Authors apply the following changes:

- Lines 42 to 107: the relevant paragraphs should be summarized and focused on previous research gaps and open windows for further study.

Authors’ Comment 6: Thank you for the comment. This has been addressed in line 42 to line 65.

Unlike fluvial flooding, which occurs from rivers overtopping their banks, pluvial flooding results from local rainfall events, surpassing soil infiltration capacity and drainage infrastructure. One significant factor exacerbating the risk of pluvial flooding is urbanization, which has dramatically altered the natural hydrological cycle, primarily through the proliferation of impervious surfaces, such as roads, rooftops, and parking lots. This has significantly altered soil profiles, reducing their infiltration and retention capacity. This exacerbates surface runoff, leading to urban/pluvial flooding, reduced groundwater recharge, water quality degradation, altering the hydrology and geomorphology of streams [4] [5]. Studies highlight that compacted soils at the interface of impervious and pervious surfaces lose their natural infiltration capacity, worsening flood risk in expanding urban areas [6].

The United Nations projects that by 2050, 68% of the world’s population will reside in urban areas, contributing to an overall global population growth of about 2.5 million people, with urban land cover potentially expanding to 1.2 million km2 by 2030 [7] [8]. In the U.S., urban soils with an area of 106,000 square miles, known as “anthrosols”, occupy over the land area, are expected to double by 2060 [9]. Moreover, pavement and other impervious surfaces cover over 43,000 square miles, contributing to severe pluvial flooding [10]. In 2021, pluvial floods/flash floods in Germany resulted in damages exceeding €30 billion, while in the U.S., flood-related property damage costs range between $179.8 billion and 496 billion annually [11] [12]. The flood-related damage by NOAA is estimated to be between $7 to $9 billion annually, and this financial burden is expected to increase by 41.4% in 2050 [12]. Beyond economic losses, urban flooding disrupts transportation networks, damages infrastructure, and heightens public health risks due to water contamination and exposure to flood-borne pathogens/molds. This makes marginalized communities particularly vulnerable, facing disproportionate recovery challenges and deepening social inequities. 

Editor Comment 7: The references are old and should be updated to most recent studies in this field. It is recommended that authors focus on research published in 2024, and 2025.

Authors’ Comment 7: Thank you for the comment. Most of these references have been addressed, however, those that have not been addressed are important to the article.

The Green-Ampt model, widely used since 1983 for representing infiltration dynamics under various soil conditions, was employed to simulate how modifying soil profiles enhances infiltration capacity and reduces surface runoff [43] [44].

Editor Comment 8: Lines 94–102: Authors mentioned that 66% of Baltimore City’s streams are buried, exacerbating flooding. However, these lines offer no detailed mechanism (e.g., surcharged culverts, backwater effects, etc.). It is recommended that authors explain how or why buried streams specifically worsen pluvial flooding.

Authors’ Comment 8: Thank you for the comment. This has been rephrased and indicated in line 74.

A key factor of its flood dynamics is the presence of buried streams, which, when combined with undersized stormwater infrastructure, frequently cause surcharged culverts during high-intensity rainfall.

Editor Comment 9:  The previous research gaps should be added as the independent paragraph in line 108 before this paragraph: “Furthermore, the watershed includes …”

Authors’ Comment 9: Thank you for the comment. This has been addressed in line 83 to line 114.

frequent and severe localized flooding [19]. Furthermore, the watershed includes a diverse mix of residential, commercial, and industrial areas, all contributing to extensive impervious surfaces that hinder the natural infiltration of rainwater. As a result, even moderate rainfall generates significant surface runoff, overwhelming drainage systems, and causing localized flooding.

Also, based on the intensity of pluvial flooding in the study area, there is an urgent need for innovative stormwater management strategies, such as soil profile rehabilitation, which has emerged as a promising approach. Unlike conventional stormwater management solutions, this approach restores urban soils, which are often degraded or impervious to a natural water-absorbing ability through techniques like adding soil amendments and planting trees, enhancing soil permeability and infiltration [20]. Recent studies have demonstrated that nature-based solutions (NBSs) can significantly mitigate pluvial flooding by enhancing local infiltration capacities and reducing surface runoff. For example, researchers have evaluated various decentralized measures such as green roofs, infiltration systems, and Swales using 1D/2D hydrodynamic models to simulate urban flooding [21] [22] [23] [24]. While many existing studies have focused on structural interventions or on coupling different NBSs in a 1D/2D framework [21] [25], our research uniquely evaluates the impact of soil profile rehabilitation as a standalone mitigation strategy. By employing the Green-Ampt method in a 2D hydrodynamic model, the effectiveness of soil profile rehabilitation, a key driver in reducing surface runoff, is assessed. Therefore, to assess the effectiveness of this intervention, the study employs a 2D hydrodynamic approach, integrating detailed soil profile data and urban hydrology parameters to provide practical insights into mitigating pluvial flooding and its associated impacts. A key component of this research is the use of CityCAT, a 2D hydrodynamic model developed by Glenis et al. [26] at Newcastle University, UK, to simulate urban floods, especially in complex urban settings. CityCAT simulates surface water flow dynamics under various rainfall scenarios, considering urban topography, land cover, and building footprint. Its detailed spatial and temporal resolution makes it well-suited for evaluating flood extent, depth, and assessing the performance of flood mitigation strategies, such as soil profile rehabilitation. By leveraging CityCAT’s capabilities, the study aims to contribute to actionable recommendations for reducing pluvial flood risk in urban areas.

Editor Comment 10: In the next paragraph, the authors should add one paragraph and present the three or more primary questions that were answered by this research.

Authors’ Comment 10: Thank you for the comment. This has been addressed in line 117 to line 122.

The study addressed the following key questions: 1) How do buried streams influence pluvial flooding patterns in the Tiffany Run watershed? 2) How effective is the 2D hydrodynamic modeling approach, such as the City Catchment Analysis Tool (CityCAT), in simulating urban flood dynamics and validating against real-world flood occurrences? 3) How do different storm intensities influence pluvial flood patterns? 4) To what extent can soil rehabilitation strategies enhance infiltration capacity and reduce flood depth?

Editor Comment 11: The novelties and contributions should be added in the next paragraph.

Authors’ Comment 11: Thank you for the comment. This has been addressed in line 122 to line 128.

Validation of flood simulations using 311 call logs, newspaper reports, and social media reports represents another advancement, as this bridges the gap of validating model outputs using real-world flood incidences reported by affected communities. Furthermore, this research provides actionable recommendations for flood mitigation, offering insights that can inform urban planning and policymaking, particularly in cities facing similar challenges of aging infrastructure and increasing pluvial flood risks.

Editor Comment 12:   The last paragraph seems reasonable and doesn’t need any changes.

Authors’ Comment 12: Thank you for the comment and the feedback. This is well noted.

Editor Comment 13:   Lines 184 to 192: What calibration-validation approach has been done on hydrodynamic model parameters? The optimization test results should be presented as tables or relevant metric diagrams.

Authors’ Comment 13: Thank you for the comment. This has been addressed as the contingency matrix in line 203 to line 222.

The model results were validated against the observed data using a contingency table matrix adapted from [35] [36]. This matrix comprises four classes, which help to compare the observed and predicted flood occurrences. These are True Positives (TP), False Positives (FP), False Negatives (FN), and True Negatives (TN). TP are areas where both the model and the observed flood incidences indicate flooding; FP are areas where the model predicts flooding, but observations do not confirm it; FN are areas where observations show flooding, but the model fails to predict it; and TN are areas where both the model and observations indicate no flooding.

                                                                       

                                                                                       

Figure 2. A contingency table was applied to validate modeled flood results (Source: [37])

Where tp rate (0-1) or hit rate is referred to as the rate of observed floods correctly predicted; fp rate (0-1) or false alarm rate, is the rate of non-flooded areas wrongly predicted as flooded; accuracy (0-1) indicates the fraction of correctly identifying classifiers; CSI (critical success index) (0-1) measures how well the model captures flood events; and bias informs whether the model is under or overestimating areas been flooded. Thus, bias > 1 = Over prediction; bias < 1 = Under prediction; and bias = 1 = Perfect prediction.

Editor Comment 14:   3.1.  Lines 245 to 258: Where is the results of standard quantitative measures (e.g., RMSE, Nash-Sutcliffe, F1-score for flood extent) to confirm the model’s accuracy in a more rigorous way?

Authors’ Comment 14: Thank you for the comment. This has been addressed in line 362 to line 384.

The contingency matrix provided key performance metrics, including the true positive (TP) rate, Critical Success Index (CSI), and bias, which offer insights into the reliability of the model's predictions. For example, the analysis revealed that on July 22, 2020, the TP rate was 0.75, indicating that 75% of the flood events predicted by the model were confirmed by the 311 reports. On September 12, 2023, the TP rate dropped to 0.50, while on June 10, 2021, it was 0.64. Another variability observed was the TP rate for July 24, 2020, storm event recording 0.00. This means two things; either the model results did not correspond with the reports received, or the impact of this water depth recorded was not severe for one to call the emergency response team. This, however, calls for further field investigation to assess the performance of the model. These variations in performance metrics suggest that, while the model accurately captured flood occurrences during some events, there were instances where its predictions did not align well with the real-world data. The corresponding CSI and bias values mirrored these trends, underscoring that the model's predictive capabilities are influenced by factors such as storm intensity and local drainage infrastructure conditions. Overall, the contingency matrix not only validates the model's performance but also highlights areas where improvements are needed. Future research should consider integrating additional data sources such as field surveys, high-resolution remote sensing, and enhanced hydrological monitoring to further refine the validation process and improve the model’s accuracy in predicting urban pluvial flooding.

 

Editor Comment 15: Lines 196-200, 221-228: Considering this research focused on the single extreme rainfall event of June 10, 2021, authors should explain the limitations of such study and generalizability issues related to the single-event analysis.

Authors’ Comment 15: Thank you for the comment. This has been addressed by including multiple extreme storms. This has been indicated in line 305 in Table 1.

Dates, Duration	Rainfall Intensity (mm/hr)
July 22, 2020; 16:40 – 19:35	158.50
September 12, 2023; 22:30 -23:50	121.92
June 10, 2021; 11:30 – 16:25	85.34
July 24, 2020; 16:40 - 19:35	36.58

Editor Comment 16: 3.3.   Lines 266-279: The authors should explain why the manuscript focuses on public parcels and vacant lots without discussing potential constraints such as contaminated fill, shallow bedrock, or funding limitations. While lines 267–279 highlight where soil rehabilitation might occur, the paper does not address whether these site‐specific conditions could prevent actual implementation in certain areas.

Authors’ Comment 16: Thank you for the comment. This has been addressed in line 395 to line 405

This soil modification scenario was prioritized in public parcels and vacant lots because they represent underutilized urban spaces that can be readily repurposed for pluvial flood mitigation interventions. These spaces are often managed by local governments or are earmarked for redevelopment, which facilitates coordinated planning and implementation. This allows for easier access to interventions such as soil aeration, deep tillage, and the addition of organic amendments, offering tangible flood mitigation benefits without disrupting fully developed or privately owned properties. Although the study does not address specific constraints in detail, such as different purposes assigned by the government in these areas, this may vary from one location to the other. This underscores the importance of conducting detailed site-specific feasibility assessments prior to deployment of these types of projects. These evaluations would help ensure that the selected interventions are both practically and technically viable for the local context.

Editor Comment 17: 3.4.  Assessing the bias: Although the authors validate their model with 311 calls, social media, and newspaper reports, they do not address possible biases in these data sources—such as underreporting from specific neighborhoods, discrepancies in social media usage, or inconsistent media coverage. Similarly, potential selection or parameter biases are not explicitly mentioned when targeting vacant lots and public parcels for soil rehabilitation scenarios. Thus, biases in data collection or methodological choices are not formally acknowledged or assessed.

Authors’ Comment 17: Thank you for the comment. This has been addressed in line 362 to line 376. This has also been addressed in line 522 to line 528.

The contingency matrix provided key performance metrics, including the true positive (TP) rate, Critical Success Index (CSI), and bias, which offer insights into the reliability of the model's predictions. For example, the analysis revealed that on July 22, 2020, the TP rate was 0.75, indicating that 75% of the flood events predicted by the model were confirmed by the 311 reports. On September 12, 2023, the TP rate dropped to 0.50, while on June 10, 2021, it was 0.64. Another variability observed was the TP rate for July 24, 2020, storm event recording 0.00. This means two things; either the model results did not correspond with the reports received, or the impact of this water depth recorded was not severe for one to call the emergency response team. This, however, calls for further field investigation to assess the performance of the model. These variations in performance metrics suggest that, while the model accurately captured flood occurrences during some events, there were instances where its predictions did not align well with the real-world data. The corresponding CSI and bias values mirrored these trends, underscoring that the model's predictive capabilities are influenced by factors such as storm intensity and local drainage infrastructure conditions. Overall, the contingency matrix not only validates the model's performance but also highlights areas where improvements are needed.

This matrix allowed the calculation of key performance metrics, including the true positive rate, the Critical Success Index (CSI), and bias, for each storm event. These results indicated that, for instance, on July 22, 2020, 75% of the flood events predicted by the model were confirmed by real-world reports, whereas the model performed less reliably during another July 24, 2020, event when all metrics dropped to zero. Such variability highlights both the strengths and limitations of the model under different storm conditions.

Editor Comment 18: 3.5.  Study limitations and future contributions. It is recommended that authors add a subsection, namely “Study limitations and future contributions,” before the Conclusion to address the following issues:- Explaining the study limitations in data, methodology, and results (generalizability); - Discussing the uncertainties (in infiltration, LiDAR accuracy, etc.) might shift the results; - Presenting recommendations for future studies based on the results of and methodology developed in this study;- Addressing how findings might transfer to other watersheds, nor does it discuss cost, maintenance, or scaling limitations—thereby missing a clear statement on broader applicability.

Authors’ Comment 18: Thank you for this comment. This has been addressed in line 578 to line 597.

First, the analysis primarily relied on 311 call logs, social media posts, and news articles to validate the model outputs. Although these data sources provided valuable insights into flood occurrences, they may not capture every incident, especially in neighborhoods with limited access to technology or where reporting practices are inconsistent. Additionally, the study was confined to a specific period during the summer season storm events. This narrow temporal focus limits the generalizability of the findings to other seasons or longer-term flood behavior, and the spatial resolution of the model may not fully account for localized variations in urban infrastructure and land use. Moreover, the hydrodynamic model, while robust, operates under several simplifying assumptions regarding rainfall intensity, runoff coefficients, and soil permeability. Uncertainties in key parameters, such as infiltration rates and LiDAR-derived terrain accuracy, could shift the results, particularly under prolonged or consecutive storm events where antecedent moisture conditions play a critical role.

Future contributions should focus on enhancing the accuracy and comprehensiveness of flood risk assessments by integrating additional data sources such as real-time hydrological monitoring, field surveys, and high-resolution remote sensing imagery, to provide a complete and more nuanced picture of urban flood dynamics. Studies should cover multiple seasons and long-storm patterns, thereby capturing the effects of continuous or successive storm events on both flooding behavior and the long-term efficacy of soil rehabilitation strategies.

Reviewer 2 Report

Comments and Suggestions for Authors

The work presents a timely subject regarding evaluating the effectiveness of the soil profile rehabilitation scenario, utilizing hydrodynamic modelling, with remarkable results concerning the study area.

Introduction:

I think it's recommended that some sentences be added about the method applied. Specify if it has been used in other areas and what results were obtained. In addition to the aim, the objectives pursued should be mentioned. Also, the study's novelty should be highlighted, specifying how it differs from existing methods.

In Figure 1: The colour representing the altitude of 255 meters is too dark (at first glance, the map may give the impression of altitudes exceeding 1000 meters in the northern area). Adjusting the colour palette for the Digital Elevation Model (DEM) to align with the actual altitudes is recommended.

I'd suggest that you separate sections for results and discussions to help you understand the methodology and results and their interpretation.

The bibliography should include recent works related to the studied topic.

Author Response

Editor Comment 1: I think it's recommended that some sentences be added about the method applied. Specify if it has been used in other areas and what results were obtained. In addition to the aim, the objectives pursued should be mentioned. Also, the study's novelty should be highlighted, specifying how it differs from existing methods.

Authors’ response 1: Thank you for your comment. This has been addressed from line 88 to line 114. Thus, there are several studies carried out in this area. However, this type of analysis is not carried out alone. It is usually combined with two or more nature-based solutions. The novelty of this study is integrating the Green Ampt method and assessing the performance of soil profile rehabilitation as a standalone nature-based solution in mitigating pluvial flooding.

Editor Comment 2: The colour representing the altitude of 255 meters is too dark (at first glance, the map may give the impression of altitudes exceeding 1000 meters in the northern area). Adjusting the colour palette for the Digital Elevation Model (DEM) to align with the actual altitudes is recommended.

Authors’ response 2: Thank you for the comment and for bringing this to our notice. This has been addressed in line 147 to line 148.

Editor Comment 3: I'd suggest that you separate sections for results and discussions to help you understand the methodology and results and their interpretation.

Authors’ response 3: Thank you for the comment. This has been separated and is indicated in line 278 to line 486.

Editor Comment 4: The bibliography should include recent works related to the studied topic.

Authors’ response 4: Thank you for the comment. Most of the bibliographies have been updated, but the relevant ones were maintained. For example, line 544 cannot be changed because there we needed to reference the period the Green-Ampt method was developed.