the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Flood relief logistics planning for coastal cities: a case study in Shanghai, China
Abstract. Coastal cities are becoming more vulnerable to flood risks due to climate change, rising sea levels, intense storm surges, population growth, and land subsidence. Developing emergency preparedness and response strategies can reduce the impact of coastal flooding and enhance a city's resilience. This article presents a flood relief logistics planning aimed at providing decision-makers with a feasible framework. The framework integrates geographic information system (GIS) network analysis and resource allocation optimization models. Considering the fairness of resource allocation, a biobjective allocation model that minimizes the total transportation cost and maximum unsatisfied rate is developed. This flood relief logistics planning approach is applied to Shanghai, China to presents feasible distribution strategies. And, the case study indicates that the current capacity of emergency flood shelters (EFSs) and the supplies stored in emergency reserve warehouses (ERWs) are adequate to meet the demand of the elderly population if affected by a 100-year coastal flood scenario. However, they would not be sufficient to cover the demand in a 1000-year coastal flood scenario and could only serve half of the affected elderly people. The results also suggest that the city-level ERW in Jiading District and the branch warehouse in Minhang District play a crucial role in distribution. Additionally, the study highlights the importance of increasing resource investments to tackle the inherent unfairness caused by resource shortages. This study provides a scientific reference for developing flood relief logistics plans in Shanghai, and it presents a transferable framework that is applicable to other coastal cities.
- Preprint
(972 KB) - Metadata XML
- BibTeX
- EndNote
Status: open (until 29 Sep 2024)
-
CC1: 'Comment on nhess-2024-88', Lu Gao, 22 Aug 2024
reply
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:
- 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.
- 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.
- 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.
- 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.
- 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.
- The captions of figures and table can remove “the”. Data sources and model parameter variables are best represented by tables.
- 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.
Citation: https://doi.org/10.5194/nhess-2024-88-CC1
Viewed
HTML | XML | Total | BibTeX | EndNote | |
---|---|---|---|---|---|
142 | 35 | 52 | 229 | 6 | 5 |
- HTML: 142
- PDF: 35
- XML: 52
- Total: 229
- BibTeX: 6
- EndNote: 5
Viewed (geographical distribution)
Country | # | Views | % |
---|
Total: | 0 |
HTML: | 0 |
PDF: | 0 |
XML: | 0 |
- 1