Supply-Demand risk assessment of urban flood resilience from the perspective of the ecosystem services: A case study in Nanjing, China

Urban flooding has become a pressing challenge for many countries and regions. Meanwhile, ecosystem-based disaster risk reduction approaches have been recognized as a sustainable and effective strategy for managing flood risks. This study designed a conceptual framework for assessing the supply–dema...

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Main Authors: Peng Zhang, Xukan Xu, Wentong Yang, Yiming Li, Shengqi Yao
Format: Article
Language:English
Published: Elsevier 2025-04-01
Series:Ecological Indicators
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Online Access:http://www.sciencedirect.com/science/article/pii/S1470160X25003279
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author Peng Zhang
Xukan Xu
Wentong Yang
Yiming Li
Shengqi Yao
author_facet Peng Zhang
Xukan Xu
Wentong Yang
Yiming Li
Shengqi Yao
author_sort Peng Zhang
collection DOAJ
description Urban flooding has become a pressing challenge for many countries and regions. Meanwhile, ecosystem-based disaster risk reduction approaches have been recognized as a sustainable and effective strategy for managing flood risks. This study designed a conceptual framework for assessing the supply–demand risk of urban flood resilience (UFR) from the perspective of ecosystem services (ESs). Taking the city of Nanjing, China, as an example, the InVEST model and the multi-criteria comprehensive evaluation method were employed to quantify the supply of UFR provided by natural ecosystems and the demand for UFR from socio-economic systems. Additionally, based on UFR supply–demand evaluation indicators calculated for each subdistrict, the Self-Organizing Map (SOM) was used to cluster the subdistricts. Finally, UFR supply–demand matching was conducted on the subdistrict clusters, and different flood-risk levels were identified based on the supply–demand ratio. The results showed that high flood-risk subdistricts are mainly concentrated in central urban area, low flood-risk subdistricts are primarily in urban periphery, and subdistricts in urban–rural transitional zones exhibit medium flood risk. Statistical analysis revealed that this zonal pattern is closely related to land use types and the distribution of social resources. Therefore, this study provides a scientific basis for developing management strategies of urban flood prevention from the perspective of ESs.
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issn 1470-160X
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spelling doaj-art-79bd14e80ab94c2d84124a81619b90ff2025-08-20T02:17:26ZengElsevierEcological Indicators1470-160X2025-04-0117311339710.1016/j.ecolind.2025.113397Supply-Demand risk assessment of urban flood resilience from the perspective of the ecosystem services: A case study in Nanjing, ChinaPeng Zhang0Xukan Xu1Wentong Yang2Yiming Li3Shengqi Yao4Business School, Hohai University, Nanjing 211100, ChinaBusiness School, Hohai University, Nanjing 211100, China; Institute of Statistics and Data Science, Hohai University, Nanjing 211100, China; Key Laboratory of Industrial Big Data Mining and Knowledge Management, Changzhou 213000, ChinaBusiness School, Hohai University, Nanjing 211100, ChinaBusiness School, Hohai University, Nanjing 211100, ChinaBusiness School, Hohai University, Nanjing 211100, ChinaUrban flooding has become a pressing challenge for many countries and regions. Meanwhile, ecosystem-based disaster risk reduction approaches have been recognized as a sustainable and effective strategy for managing flood risks. This study designed a conceptual framework for assessing the supply–demand risk of urban flood resilience (UFR) from the perspective of ecosystem services (ESs). Taking the city of Nanjing, China, as an example, the InVEST model and the multi-criteria comprehensive evaluation method were employed to quantify the supply of UFR provided by natural ecosystems and the demand for UFR from socio-economic systems. Additionally, based on UFR supply–demand evaluation indicators calculated for each subdistrict, the Self-Organizing Map (SOM) was used to cluster the subdistricts. Finally, UFR supply–demand matching was conducted on the subdistrict clusters, and different flood-risk levels were identified based on the supply–demand ratio. The results showed that high flood-risk subdistricts are mainly concentrated in central urban area, low flood-risk subdistricts are primarily in urban periphery, and subdistricts in urban–rural transitional zones exhibit medium flood risk. Statistical analysis revealed that this zonal pattern is closely related to land use types and the distribution of social resources. Therefore, this study provides a scientific basis for developing management strategies of urban flood prevention from the perspective of ESs.http://www.sciencedirect.com/science/article/pii/S1470160X25003279Urban flood resilienceEcosystem servicesSelf-Organizing MapSupply-demand matchingRisk assessment
spellingShingle Peng Zhang
Xukan Xu
Wentong Yang
Yiming Li
Shengqi Yao
Supply-Demand risk assessment of urban flood resilience from the perspective of the ecosystem services: A case study in Nanjing, China
Ecological Indicators
Urban flood resilience
Ecosystem services
Self-Organizing Map
Supply-demand matching
Risk assessment
title Supply-Demand risk assessment of urban flood resilience from the perspective of the ecosystem services: A case study in Nanjing, China
title_full Supply-Demand risk assessment of urban flood resilience from the perspective of the ecosystem services: A case study in Nanjing, China
title_fullStr Supply-Demand risk assessment of urban flood resilience from the perspective of the ecosystem services: A case study in Nanjing, China
title_full_unstemmed Supply-Demand risk assessment of urban flood resilience from the perspective of the ecosystem services: A case study in Nanjing, China
title_short Supply-Demand risk assessment of urban flood resilience from the perspective of the ecosystem services: A case study in Nanjing, China
title_sort supply demand risk assessment of urban flood resilience from the perspective of the ecosystem services a case study in nanjing china
topic Urban flood resilience
Ecosystem services
Self-Organizing Map
Supply-demand matching
Risk assessment
url http://www.sciencedirect.com/science/article/pii/S1470160X25003279
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