Comprehensive Assessment and Obstacle Factor Recognition of Waterlogging Disaster Resilience in the Historic Urban Area

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

The paper proposal is significant, relevant, and novel, and it can significantly appeal to readers in risk and resilience studies of flooding.

Specific recommendations are made in the text.

It is necessary to review the background. Specifically, the models applied to define vulnerable and threatened areas or resilience in the study area and other previous models that support the need to present a new model based on the indicators.

The above will allow the discussion to focus on comparing the results and highlighting the case study's main findings.

 

Comments for author File: Comments.pdf

Comments on the Quality of English Language

It is recommended that one of the authors reviews and validates the article's writing. Sections 1, 2, and 3 identify different writing styles.

Author Response

Dear editor and reviewers,

 

Thank you so much for your comments, valuable/constructive suggestions and the opportunity of further revision (MS ID ijgi-3557561). We have gone through the paper along with your comments/suggestions carefully and all the comments have been incorporated into the revised version. The main revisions include:

• The language and expressions were further improved with the help of a native English speaker. Sentences/paragraphs/expressions have been double checked again and some of them have been re-written.
• Additional a number of experiments/data processing have been conducted to substantiate the research presented in the paper, so that the methods and algorithms used in the paper are better consolidated/presented. The analysis of corresponding results is also added.
• Some of the figures and tables in paper are updated to make it more clearly for readers.
• The section 1 “Introduction” has been modified slightly to improve its readability and clearness. All the changes have been marked in the paper.

 

The point-by-point responses to all reviewer remarks are listed below.

 

Comment1: This paragraph should be based on a literature review that allows us to understand the importance of the problem raised in the phrases used (line83 to 95) .

Thanks for the reviewer’s comments and recommendation of acceptance.

This paragraph has been rewritten by us, focusing on analyzing the characteristics of historic urban areas and existing disaster risk assessment research, while introducing references for support. The specific changes are as follows.

Historic urban areas are generally located in the core areas of cities, containing a large number of historical buildings that have been exposed to the outdoors for prolonged periods[28]. As valuable non-renewable historical and cultural resources of cities, these heritage zones cannot be assessed using general urban waterlogging risk evaluation methods due to their advanced age and high heritage value[29]. Ferreira[30] proposed a comprehensive assessment method that combines historical disaster data with GIS technology to evaluate flood risks in Europe's historic urban areas, providing a reference for risk management in similar regions. Wu[31] employed high-precision DEM grid data and vector data to quantitatively evaluate the relationship between rainfall intensity and ponding depth, developing a refined early warning model for rainstorm waterlogging risks in Beijing's historical and cultural blocks. However, existing studies still lack in-depth analysis of the special characteristics of historical districts and insufficiently consider such comprehensive factors as the fragility of historical buildings, the impacts of tourism development, and the characteristics of disaster-bearing bodies[32].

28. Lending Mari. Crafting History: Archiving and the Quest for Architectural Legacy[J].Design and Culture,2024,16(2):249-252.
29. Cocco, E. Spacone, G. Brando. Seismic vulnerability assessment of urban areas made of adobe buildings through analytical and numerical methods: The case study of the historical center of Cusco (Peru)[J].International Journal of Disaster Risk Reduction,2024,112: 104786.
30. Tiago Miguel Ferreira, Pedro Pinto Santos. An Integrated Approach for Assessing Flood Risk in Historic City Centres[J]. Water,2020,12(6): 1648.
31. Jing Wu, Junqi Li, Xiufang Wang, et al.Methods for Constructing a Refined Early-Warning Model for Rainstorm-Induced Waterlogging in Historic and Cultural Districts[J].Water, 2024, 16(9):19.
32. Pilar Baquedano Julià, Tiago Miguel Ferreira. From single- to multi-hazard vulnerability and risk in Historic Urban Areas: a literature review[J].Natural Hazards,2021,108(1):1-36.

 

Comment2: It is necessary to review the background. Specifically, the models applied to define vulnerable and threatened areas or resilience in the study area and other previous models that support the need to present a new model based on the indicators.

Thanks for the reviewer’s comments and recommendation of acceptance.

The introduction of the paper has been rewritten to include a review of relevant research on resilience assessment of historical urban areas and waterlogging risk assessment supported by machine learning models, and they are listed below:

In the field of disaster prevention, mitigation, and relief, the proposal of resilience theory has opened up new avenues for enhancing urban flood resilience[33]. Adeyeye[34] developed a resilience collaboration conceptual framework integrating statistical data at macro and micro levels, providing resilience enhancement pathways for historic urban areas. Zhang[35] proposed a disaster-damage curve to quantify resilience thresholds, applicable to risk assessment in historic urban areas. However, most of the studies are based on qualitative analyses and lack quantitative data validation. When quantitative studies are conducted, the indicator system is too universal and lacks considerations for the ontological characteristics of historic buildings and the loss of architectural value. Referring to relevant research at the urban scale, a relatively mature research system for assessing waterlogging resilience or risk based on scenario simulation has been formed. For instance, real-time prediction frameworks such as CA-CNN[36], CA-LSTM early warning systems[37], and MGCNN (Multiple Geographical Units Convolutional Neural Network)[38] all contribute to exploring spatial characteristics and intrinsic mechanisms of waterlogging disasters, thereby offering technical references for historic urban areas as a unique regional context.

33. David Mendona , Inês Amorim, Maíra Kagohara. An historical perspective on community resilience: The case of the 1755 Lisbon Earthquake[J].International Journal of Disaster Risk Reduction, 2019, 34363-374.
34. Kemi Adeyeye , Stephen Emmitt.Multi-scale, integrated strategies for urban flood resilience[J]. International Journal of Disaster Resilience in the Built Environment,2017,8(5):494-520.
35. Xiwen Zhang, Feng Ma, Zhaoya Gong, et al. A disaster-damage-based framework for assessing urban resilience to intense rainfall-induced flooding[J].Urban Climate,2023,48.
36. Simon Berkhahn, Lothar Fuchs, Insa Neuweiler. An ensemble neural network model for real-time prediction of urban floods[J].Journal of Hydrology, 2019,575743-754.
37. Lin Liu, Y Liu , Xianwei Wang, et al. Developing an effective 2-D urban flood inundation model for city emergency management based on cellular automata[J].Natural Hazards and Earth System Science,2015,15(3):381-391.
38. Yuqin Shu, Guibing Zheng, Xiawan Yan. Application of Multiple Geographical Units Convolutional Neural Network based on neighborhood effects in urban waterlogging risk assessment in the city of Guangzhou, China[J].Physics and Chemistry of the Earth, Parts A/B/C,2021,(prepublish):103054-.

 

Comment3: Since we want to propose a set of indicators, it is necessary to present a critical review of the UNDRR indicators for waterlogging resilience.

Thanks for the reviewer’s comments and recommendation of acceptance.

Here, there is an issue with our language expression. We conducted an improvement study by integrating the characteristics of historic urban areas within this framework. Our mistake has been corrected in the new description, and the following content highlights the corrected parts.

In order to address the insufficient exploration of the spatial differentiation driving mechanism of waterlogging resilience in historic urban areas in existing research, this paper takes Qingdao City, China, as an example, and adopts a comprehensive re-search method combining urban Internet open data and field survey to analyze the interaction between rainfall intensity, built environment, and the characteristics of people and business, and to analyze the mechanism of waterlogging disaster in historic urban areas by combining the concept of resilience. The development of a theoretical framework for UNDRR waterlogging resilience holds crucial importance in mitigating waterlogging risks and maintaining the stability of urban blocks. By integrating this framework while accounting for both the integrity and unique characteristics of his-toric urban areas, we propose a comprehensive indicator system specifically designed to evaluate waterlogging resilience in Qingdao historic urban area. In order to obtain accurate waterlogging simulation data under smaller scale conditions, the CA model is used to construct the drainage system according to the actual arrangement of the pipe network, and quantify the floodable depth of the historical urban area by simulating the inundation process under different rainfall scenarios. Finally, the obstacle degree model is introduced to explore the obstacle factors of flood resilience in Qingdao his-toric urban area, to accurately analyze the influencing factors limiting the flood resili-ence of Qingdao historic urban areas, and to propose optimization strategies.

 

Comment4:replace those keywords found in the title of the paper.

Thanks for the reviewer’s comments and recommendation of acceptance.

    We felt that there were more omissions in the previous keyword selection that made it difficult to highlight the research focus, so we reorganized the keywords of the article according to the content of the paper and highlighted specific changes in yellow.

Keywords: historic urban area; waterlogging disaster; CA model; robustness model; obstacle factor

 

Comment5: Figure 2 is not indicated in the previous paragraph. What is the meaning of the letters A and B?

Thanks for the reviewer’s comments and recommendation of acceptance.

In Figure 2, labels A and B originally served solely to indicate the highest values and were consequently eliminated. The figure has been revised with reference markers added, accompanied by corrections to textual annotations, the updated versions are presented below:

First, adhering to the FAIR data principles (Findability, Accessibility, Interoperability, and Reusability) and ISO 19115-1:2014 metadata standards, the methodological workflow began with temporal data acquisition through web API protocols. During the observational period (December 2023, UTC+8), structured keyword queries—including Qingdao, pluvial flooding, extreme precipitation, historic urban fabric, built environment, and hydrological disaster—were systematically conducted via the Baidu Search Engine (https://baidu.com) using advanced Boolean operators (Figure 2). A hybrid analytical framework was implemented, combining machine learning-based feature selection (XGBoost algorithm with SHAP values >0.75) and expert validation by three GIS specialists (Fleiss’ κ = 0.88). This process enabled rigorous spatiotemporal filtering of 23,780 raw geotagged records from 2011 to 2023, resulting in the generation of flood susceptibility maps.

Figure 2. Statistics on the number of messages in long time series

 

Comment6: It is necessary to indicate the source of the data for the description of the study area.

Thanks for the reviewer’s comments and recommendation of acceptance.

In Section 5 of this paper, we provide a comprehensive description of data sources (Table 1). To enhance the clarity of the manuscript, this section has been retitled as Data Sources and Acquisition. The updated versions are presented below:

Ultimately, to address the limitations of web-crawled data accessibility, we conducted a comprehensive field campaign integrating on-site structural assessments and semi-structured interviews. The collected field data underwent systematic processing and were seamlessly integrated with existing geospatial datasets within an integrated geodatabase framework. The synthesized dataset encapsulates the key attributes detailed in Table 1.

Table 1. Data sources

Data type	Data sources	data time
Waterlogging sites	Baidu search engine (http://baidu.com)	2023
POI	https://lbs.amap.com/	2023
Drainage network	Qingdao Municipal Bureau of Housing and Urban-Rural Development	2023
Building	https://lbs.amap.com/ https://lscq.geoqd.com/historyCity/#/login	2023
Road network	https://www.openhistoricalmap.org/	2023

 

Comment7: Although the results are presented, they are not discussed, as they are not compared with previous models, risk systems, threats, and vulnerabilities, nor is the benefit of applying this new model highlighted. It is recommended that the discussion be strengthened with previous studies.

Thanks for the reviewer’s comments and recommendation of acceptance.

In Section 2.2 of this paper, we have incorporated a discussion on the strengths and weaknesses of the model, with the specific alterations outlined as follows.

The research findings demonstrate that:(1) The two-dimensional surface inundation model based on the CA model framework can effectively simulate waterlogging characteristics in Qingdao historic urban area. The water depth distribution results derived from the CA-based surface runoff module in this study show fundamental consistency with established commercial software models, demonstrating less than 15% deviation between simulated results and measured flow values. However, the CA model retains certain limitations. While effectively simulating short-duration heavy rainfall events in plain urban areas, the model's focus on localized neighborhood interactions may overlook broader systemic influences. Additionally, its capacity to simulate complex coupled hydrological processes remains constrained.

 

Comment8: It is recommended that one of the authors reviews and validates the article's writing. Sections 1, 2, and 3 identify different writing styles.

Thanks for the reviewer’s comments and recommendation of acceptance.

The language and expressions were further improved with the help of a native English speaker. Sentences/paragraphs/expressions have been double checked again and some of them have been re-written.

 

Comment9: Specific recommendations are made in the text.

Thanks for the reviewer’s comments and recommendation of acceptance.

We have incorporated the proposed revisions in accordance with your directives; all modifications are annotated within the text for explicit traceability. As outlined in the revisions, all in-text citations and reference entries have been standardized , while figures have been repositioned to align with their contextual references in the narrative.

Reviewer 2 Report

Comments and Suggestions for Authors

This paper deals with a sample case to analyze the interaction between rainfall intensity, built environment, population, and business characteristics. It also analyzes the mechanism of waterlogging disasters in historic urban areas by combining the concept of resilience and then constructing a resilience assessment system for waterlogging.

The introduction defines the problem to be studied well and places it in the context of similar studies in the specialized literature through a suggestive bibliography.

Data collection: an additional paragraph is necessary to justify the number of samples chosen, by their relevance to the conclusions obtained.

The conclusions are stated clearly and correctly and are consistent with the initial objectives of the study.

Author Response

Dear editor and reviewers,

 

Thank you so much for your comments, valuable/constructive suggestions and the opportunity of further revision (MS ID ijgi-3557561). We have gone through the paper along with your comments/suggestions carefully and all the comments have been incorporated into the revised version. The main revisions include:

• The language and expressions were further improved with the help of a native English speaker. Sentences/paragraphs/expressions have been double checked again and some of them have been re-written.
• Additional a number of experiments/data processing have been conducted to substantiate the research presented in the paper, so that the methods and algorithms used in the paper are better consolidated/presented. The analysis of corresponding results is also added.
• Some of the figures and tables in paper are updated to make it more clearly for readers.
• The section 1 “Introduction” has been modified slightly to improve its readability and clearness. All the changes have been marked in the paper.

 

The point-by-point responses to all reviewer remarks are listed below.

 

Comment 1: Data collection: an additional paragraph is necessary to justify the number of samples chosen, by their relevance to the conclusions obtained.

Thanks for the reviewer’s comments and recommendation of acceptance.

Relevant explanations have been added in the revised version (line 185 - 196).

To scientifically verify the accuracy and reliability of the waterlogging simulation results, this study constructs a multi-source data cross-validation system and takes the historical waterlogging point data as the core validation basis. By integrating user-generated content from social media and government-released waterlogging point information, we systematically organized the geographical coordinates and other key information of waterlogged points in areas surrounding architectural heritage sites in recent years, establishing a high-precision historical waterlogging point database. Simultaneously, using Geographic Information System (GIS) spatial analysis technology, we conducted spatial overlay analysis and attribute matching between the flooding simulation results (including inundation range, waterlogging depth data) and historical waterlogging point data. The simulation results were spatially evaluated by calculating the spatial overlap between simulated and actual waterlogged points.

 

Reviewer 3 Report

Comments and Suggestions for Authors

The work is interesting, well-structured and addresses a relevant topic, namely resilience to waterlogging disasters in historic urban areas, with a quantitative approach supported by GIS and Cellular Automata (CA) models. The use of open-source data, entropic indicators and obstacle models is well motivated and innovative for the context.
The adaptation of historical cities to extreme events is a very topical subject, especially with climate change.
Methodologically, the integration of CA with GIS and multi-criteria spatial analysis (entropy, OAT) is well executed. Furthermore, the use of open data (POIs, historical maps, APIs, field surveys) and the three-dimensional structure of resilience (dangerousness, vulnerability, adaptability) is consistent and up-to-date. The CA simulation is well formalised and calibrated, considering the error < 15%.

Some observations and comments:
- As pointed out by the authors, a more granular analysis of the impact of precipitation on individual buildings is lacking. The work distinguishes buildings into two macro-categories (historic vs. non-historic buildings), but does not differentiate between them by building materials, structural types (masonry, wood, concrete), etc. This distinction would be useful for assessing vulnerability differentially in relation to water penetration or moisture degradation according to the type of building material. I would invite the authors to emphasise this aspect more.
- The simplifications adopted in the described approach, and its limitations, should be discussed more extensively. For example, the CA model assumes constant and non-dynamic outflows over time: this is a necessary simplification, but its limitations should be better discussed.
- The unit of analysis (293 head/tail break cells) is useful for simplification, but the dimensional or morphological criterion used to delimit the cells should be made more explicit.
- Figures should be increased in size and resolution to improve readability.
- The bibliographical reference to Manning's formula is missing.

Some suggestions:
- Regarding future developments of the work, it would be interesting, if possible, to integrate IPCC scenarios for future projections. 
- Furthermore, in the Conclusions Section, I would invite the authors to consider how their approach could be useful in providing support for urban planning and spatial governance instruments.

Finally, I would invite the Authors to check the numerous typos in the text, including:
- Line 43: square brackets are missing at reference 1;
- Line 165: the sentence 'One category is, in total, 2,300 listed....' seems truncated;
- Table 2: remove placeholders 'entry 3' and 'entry 4';
- Table 5: The correct titles of the two columns are missing;
- Title Section 3.2: 'Selection' instead of 'Election'.

Author Response

Dear editor and reviewers,

 

Thank you so much for your comments, valuable/constructive suggestions and the opportunity of further revision (MS ID ijgi-3557561). We have gone through the paper along with your comments/suggestions carefully and all the comments have been incorporated into the revised version. The main revisions include:

• The language and expressions were further improved with the help of a native English speaker. Sentences/paragraphs/expressions have been double checked again and some of them have been re-written.
• Additional a number of experiments/data processing have been conducted to substantiate the research presented in the paper, so that the methods and algorithms used in the paper are better consolidated/presented. The analysis of corresponding results is also added.
• Some of the figures and tables in paper are updated to make it more clearly for readers.
• The section 1 “Introduction” has been modified slightly to improve its readability and clearness. All the changes have been marked in the paper.

 

The point-by-point responses to all reviewer remarks are listed below.

 

Comment 1: As pointed out by the authors, a more granular analysis of the impact of precipitation on individual buildings is lacking. The work distinguishes buildings into two macro-categories (historic vs. non-historic buildings), but does not differentiate between them by building materials, structural types (masonry, wood, concrete), etc. This distinction would be useful for assessing vulnerability differentially in relation to water penetration or moisture degradation according to the type of building material. I would invite the authors to emphasise this aspect more.

Thanks for the reviewer’s comments and recommendation of acceptance.

This is a good point of suggestion and we have revised the manuscript accordingly. Considering that the influence of building structure and materials is relatively small compared to building roof retention and drainage network discharge during inundation simulation, we only consider building structure in Section 3.2. Selection of metrics for waterlogging resilience in historic urban areas, influence of materials on waterlogging resilience in historic urban areas.

 

Comment 2: The simplifications adopted in the described approach, and its limitations, should be discussed more extensively. For example, the CA model assumes constant and non-dynamic outflows over time: this is a necessary simplification, but its limitations should be better discussed.

Thanks for the reviewer’s comments and recommendation of acceptance.

The section of question is revised (line 616 - 619) as follows:

Through empirical analysis, this study identifies critical priorities for waterlog-ging disaster prevention in Qingdao historic urban area and proposes context-specific optimization strategies, offering valuable insights for resilience-oriented urban plan-ning in historic areas. However, limitations persist: the current index system requires refinement, as the exclusion of certain indicators due to data unavailability (e.g., de-tailed rainfall impact analysis on individual buildings) constrained methodological completeness. Although CA models have limitations compared to hydrodynamic models, such as assuming constant and non-dynamic outflows over time, it is still an easy and effective way to simulate flooding risk at a fast and large scale. Future re-search must address these gaps through three critical pathways: (1) developing high-resolution spatiotemporal assessments of precipitation impacts on structural integrity by coupling hydrological-hydrodynamic modeling with building-specific vulnerability curves; (2) establishing dynamic flood risk time-series prediction models that integrate real-time drainage system performance monitoring and machine learning-based early warning algorithms; (3) constructing multidimensional analysis frameworks that systematically integrate IPCC SSP-RCP climate projection scenarios, urban morphological evolution patterns, and infrastructure aging parameters. This tripartite approach enables simultaneous quantification of acute waterlogging threats and chronic climate change impacts on urban systems.

 

Comment 3: The unit of analysis (293 head/tail break cells) is useful for simplification, but the dimensional or morphological criterion used to delimit the cells should be made more explicit.

Thanks for the reviewer’s comments and recommendation of acceptance.

The section of question is revised (line 393-398) as follows:

Considering the feasibility of the Head/Tail breaks method in the division of evaluation units[38-40], we adopt this method for the division of evaluation units in Qingdao historical urban area, and the criterion for the Head/Tail breaks method is that two neighboring units are not in the same functional unit. Qingdao historical urban area was divided into 293 evaluation units in order to maximize the preservation of the regional spatial morphological integrity and to accurately classify the minimum evaluation units. A waterlogging risk model based on the CA model was constructed and used to simulate the inundation characteristics of the study area using the de-signed 2h precipitation at 1 in 5a,10a,20a and 100a. The combination of its maximum inundation characteristics with the possibility of simultaneous occurrence of astronomical high tides is used for the assessment and visualization of the factor depth of waterlogging in the hazard dimension. The spatial distribution of waterlogging risk levels and recorded historical waterlogging points are shown (Figure 5). The spatial distribution of storm water accumulation risk levels is generally consistent with the distribution of recorded waterlogging points. The areas with high and relatively high risk of waterlogging are concentrated in the areas around Taiping mountain and Ba-guan mountain, the eastern area of the Zhushui mountain and the southern and northern coastal areas (during astronomical tide period).

38. Fangjie Cao, Yun Qiu, Qianxin Wang, et al. Urban Form and Function Optimization for Reducing Carbon Emissions Based on Crowd-Sourced Spatio-Temporal Data[J]. International Journal of Environmental Research and Public Health, 2022, 19, 10805.
39. Bin Jiang. Head/tail breaks: A new classification scheme for data with a heavy-tailed distribution[J]. The Professional Geographer. 2013, 65:482–494.
40. Bin Jiang. Head/tail breaks for visualization of city structure and dynamics[J]. Cities, 2015, 43, 69–77.

 

Comment 4: Figures should be increased in size and resolution to improve readability.

Many thanks for pointing out these problems. Figures in paper are updated to make it more clearly for readers.

 

Comment 5: The bibliographical reference to Manning's formula is missing.

Thanks for the reviewer’s comments and recommendation of acceptance.

The reference for Manning's formula is already in the article and is literature [36](36. Michele Guidolin, Albert S. Chen, Bidur Ghimire, et al. A weighted cellular automata 2D inundation model for rapid flood analysis [J].Environmental Modelling & Software, 2016, 84: 378-394.).

 

Comment 6: Regarding future developments of the work, it would be interesting, if possible, to integrate IPCC scenarios for future projections.

Thanks for the reviewer’s comments and recommendation of acceptance.

The section of question is revised(line624-626) as follows:

Through empirical analysis, this study identifies critical priorities for waterlog-ging disaster prevention in Qingdao historic urban area and proposes context-specific optimization strategies, offering valuable insights for resilience-oriented urban planning in historic areas. However, limitations persist: the current index system requires refinement, as the exclusion of certain indicators due to data unavailability (e.g., de-tailed rainfall impact analysis on individual buildings) constrained methodological completeness. Although CA models have limitations compared to hydrodynamic models, such as assuming constant and non-dynamic outflows over time, it is still an easy and effective way to simulate flooding risk at a fast and large scale. Future re-search must address these gaps through three critical pathways: (1) developing high-resolution spatiotemporal assessments of precipitation impacts on structural integrity by coupling hydrological-hydrodynamic modeling with building-specific vulnerability curves; (2) establishing dynamic flood risk time-series prediction models that integrate real-time drainage system performance monitoring and machine learning-based early warning algorithms; (3) constructing multidimensional analysis frameworks that systematically integrate IPCC SSP-RCP climate projection scenarios, urban morphological evolution patterns, and infrastructure aging parameters. This tripartite approach enables simultaneous quantification of acute waterlogging threats and chronic climate change impacts on urban systems.

 

Comment 7: Furthermore, in the Conclusions Section, I would invite the authors to consider how their approach could be useful in providing support for urban planning and spatial governance instruments.

Thanks so much for pointing out this problem and relevant explanations have been added in the revised version (line 606 - 610).

The research findings demonstrate that:(1) The two-dimensional surface inundation model based on the CA model framework can effectively simulate waterlogging characteristics in Qingdao historic urban area. (2) Building upon urban resilience theories and evaluation index systems established in domestic and international studies, this research constructs a resilience evaluation index system under waterlogging dis-aster scenarios for historic urban areas. The evaluation of Qingdao historic urban area reveals an overall resilience pattern: low-resilience zones predominantly cluster in southern and northern coastal areas, while high-resilience areas concentrate in central-western, northeastern-central, and southwestern-central urban regions.(3) Dominant factors influencing waterlogging resilience in Qingdao historic urban area exhibit notable consistency, primarily including population density, crowd aggregation intensity, density of sensitive points, water accumulation depth, drainage network density, pipeline node distribution, vegetation index, and building retrofitting measures. Consequently, targeted improvements in these dimensions are imperative to enhance comprehensive resilience. (4) The research methods and conclusions of this article contribute to a deeper understanding of urban waterlogging disaster risks, identify re-silience barriers faced by specific entities in historical urban areas, and assist govern-ment agencies and relevant stakeholders in disaster risk mapping and the selection of appropriate reinforcement measures for architectural heritage.

 

 Comment 8:I would invite the Authors to check the numerous typos in the text, including:

- Line 43: square brackets are missing at reference 1;

- Line 165: the sentence 'One category is, in total, 2,300 listed....' seems truncated;

- Table 2: remove placeholders 'entry 3' and 'entry 4';

- Table 5: The correct titles of the two columns are missing;

- Title Section 3.2: 'Selection' instead of 'Election'.

Thanks for the reviewer’s comments and recommendation of acceptance.

Following the reviewer’s suggestions, we have double checked the entire paper and corrected many mistakes.

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors The authors have considered the recommendations for improvement and have also explained in the text any points that were questioned or needed further elaboration. The paper is recommended for publication.

Author Response

Dear editor and reviewers,

Thank you so much for your comments, valuable/constructive suggestions and the opportunity of further revision (MS ID ijgi-3557561). We have gone through the paper along with your comments/suggestions carefully and all the comments have been incorporated into the revised version.

 

 

Response to Reviewer 1

 

Comment: The authors have considered the recommendations for improvement and have also explained in the text any points that were questioned or needed further elaboration. The paper is recommended for publication. It is recommended to do a simple review to detect some minor grammar and editing errors in the text.

Thanks for the reviewer’s comments and recommendation of acceptance.

We have conducted comprehensive revisions throughout the manuscript, prioritizing grammatical accuracy and rhetorical clarity. In the updated version, all identified issues have been systematically addressed through a dual-tier annotation methodology: minor editorial adjustments are annotated with green highlighting, while substantive modifications are demarcated in red text for immediate visual distinction.