The authors presented a study on flood relief logistics planning based on Geographic Information System (GIS) analysis and resource allocation optimization models in the Shanghai area. They explored the effectiveness and fairness of resource distribution in managing flood crises under 100-year and 1000-year flood scenarios. They found that the current capacities of emergency flood shelters (EFSs) and emergency reserve warehouses (ERWs) are adequate for a 100-year flood but insufficient for a 1000-year flood scenario, and highlighted the need for greater resource investments to address potential shortages. In general, this study is interesting and has practical significance. Most parts of the manuscript are well structured and expressed. This study would be helpful for the community of disaster management and urban planning. However, the current manuscript needs a major revision before it is published in this journal.

Comments:

1. The paper presents a well-integrated framework for flood relief logistics that combines Geographic Information Systems (GIS) and optimization models. However, the validation of these models is primarily limited to a case study without comparisons to actual event data or established models. Comparing the proposed model outputs with historical flood events or the results from established models would significantly enhance the manuscript's robustness.I suggest the authors to add a discussion in the last part.
2. The manuscript briefly mentions specific details about the optimization methods used, such as the NSGA-II algorithm and parameter settingwithoutin-depth explanations. Providing detailed descriptions of these methods would enhance the reproducibility of the paper and offer a clearer understanding for readers with specialized knowledge.
3. More comprehensive details regarding the data sources used in this study would be beneficial. Clarifying the availability and accessibility of these data for other researchers or planners, as well as disclosing any proprietary or restricted data, would enhance the transparency and applicability of the research.
4. The manuscript mostly cited is relatively old. It is recommended toaddmore recent researches that would update and enhance its relevance to current disaster management and urban planning challenges.
5. The language of this paper needs to be further refined since some language expressions are not accurate, and the expression in some places is too redundant.
6. The captions of figures and table can remove “the”. Data sources and model parameter variables are best represented by tables.
7. The authors selected two scenarios of 100-year and 1000-year for comparison. Does it fully consider the differences in other scenarios ? For example, 500-year, will it affect the results ? It is suggested to add some discussion.

This paper proposes a logistics planning framework for flood relief tailored to coastal cities, with Shanghai serving as a case study. The authors integrate GIS network analysis and resource allocation optimization models to investigate emergency management strategies under different flood scenarios. The framework offers valuable support for decision-making by incorporating geographic and resource allocation data to enhance flood relief efforts. However, the manuscript requires significant revisions. The main concerns are outlined below:

1. The paper does not include a description of the flood models used, referencing only Yin et al. (2020). The referenced study covers various flood scenarios across different years. Why does this paper focus solely on the 2030 scenarios with 100- and 1000-year return periods?

2. The population data utilized in the analysis is from 2010. Given the aging population trend between 2010 and 2030, how might this demographic shift affect the analysis results? Would it significantly impact the findings?

3. In Equation 1, the number of affected individuals is estimated based on the proportion of flooded areas. However, is there a valid linear relationship between the number of people affected and the flooded area? Further justification or discussion of this assumption is needed.

4. Line 90 mentions that previous studies did not consider flood scenarios under climate change. Does the 2030 flood scenario used in this study genuinely reflect a climate change scenario, and if so, how?

5. The application of the bi-objective model in multi-objective optimization is central to this paper. However, the background description of the model is insufficient, particularly regarding the implementation of the NSGA-II algorithm. It is recommended to provide more details on the algorithm's steps and discuss its advantages in practical applications.

6. For extreme flood scenarios, does the model account for time constraints associated with emergency response? How does the model ensure that supplies can be delivered to affected areas in a timely manner?