Data-driven approach to spatiotemporal dynamic risk assessment of urban flooding based on shared socio-economic pathways
Driven by the change in intense land use and land cover (LULC) due to fast urbanization, urban flooding events have become the most frequent and influential hazards over the last few decades. Accurately predicting possible flood-prone locations under the dynamic fluctuations of LULC is crucial for s...
| Main Authors: | , , , , , , , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2024
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| Online Access: | https://hdl.handle.net/10356/174164 |
| _version_ | 1824454994578898944 |
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| author | Wang, Mo Chen, Furong Zhang, Dongqing Chen, Zijing Su, Jin Zhou, Shiqi Li, Jianjun Chen, Jintang Li, Jiaying Tan, Soon Keat |
| author2 | School of Civil and Environmental Engineering |
| author_facet | School of Civil and Environmental Engineering Wang, Mo Chen, Furong Zhang, Dongqing Chen, Zijing Su, Jin Zhou, Shiqi Li, Jianjun Chen, Jintang Li, Jiaying Tan, Soon Keat |
| author_sort | Wang, Mo |
| collection | NTU |
| description | Driven by the change in intense land use and land cover (LULC) due to fast urbanization, urban flooding events have become the most frequent and influential hazards over the last few decades. Accurately predicting possible flood-prone locations under the dynamic fluctuations of LULC is crucial for sustainable urban development. However, there has been sparse studies on systematic integration of LULC changes into anticipate urban development scenarios coupled with flooding vulnerability assessment. Therefore, this study proposed a robust and powerful cascade modeling chain consisting of Maximum Entropy, System Dynamics and Patch-generating Land Use Simulation in combination with shared socio-economic pathways for projecting temporal and spatial dynamic changes associated with urban flooding vulnerability. Taking Guangdong Hong Kong Macao Greater Bay Area (GBA) as case study, the results showed that the increase in urban flooding was largely caused by the expansion of construction land. Overall, a substantial distinction within the scenarios was observed and the flooding vulnerability was ranked in the order of SSP126 < SSP245 < SSP585. Under SSP585, the areas with high flooding risk will be expected to increase significantly, accounting for 26% of the total areas, and nearly half of the built-up areas are exposed to flooding risk by 2050. Under SSP245, the built-up areas exposed to medium and high flooding risk were anticipated to cover nearly a fifth of the total areas. Under SSP126 scenario, nearly no change was predicted in the area with high flooding risk, with only increase of 1%, future urbanization hotspots associated with serious flooding risks will likely to be found on the fringe of the GBA's construction areas, in line with the extent of construction land expansion. The finding of this study will shed a comprehensive insight into the identification of spatial and temporal distribution of urban flooding vulnerability to facilitate the exploration of flood mitigation measures associated with dynamic changes in urban land use. |
| first_indexed | 2025-02-19T03:31:09Z |
| format | Journal Article |
| id | ntu-10356/174164 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2025-02-19T03:31:09Z |
| publishDate | 2024 |
| record_format | dspace |
| spelling | ntu-10356/1741642024-03-22T15:33:42Z Data-driven approach to spatiotemporal dynamic risk assessment of urban flooding based on shared socio-economic pathways Wang, Mo Chen, Furong Zhang, Dongqing Chen, Zijing Su, Jin Zhou, Shiqi Li, Jianjun Chen, Jintang Li, Jiaying Tan, Soon Keat School of Civil and Environmental Engineering Engineering Shared socio-economic pathway Patch-generating land use simulation Driven by the change in intense land use and land cover (LULC) due to fast urbanization, urban flooding events have become the most frequent and influential hazards over the last few decades. Accurately predicting possible flood-prone locations under the dynamic fluctuations of LULC is crucial for sustainable urban development. However, there has been sparse studies on systematic integration of LULC changes into anticipate urban development scenarios coupled with flooding vulnerability assessment. Therefore, this study proposed a robust and powerful cascade modeling chain consisting of Maximum Entropy, System Dynamics and Patch-generating Land Use Simulation in combination with shared socio-economic pathways for projecting temporal and spatial dynamic changes associated with urban flooding vulnerability. Taking Guangdong Hong Kong Macao Greater Bay Area (GBA) as case study, the results showed that the increase in urban flooding was largely caused by the expansion of construction land. Overall, a substantial distinction within the scenarios was observed and the flooding vulnerability was ranked in the order of SSP126 < SSP245 < SSP585. Under SSP585, the areas with high flooding risk will be expected to increase significantly, accounting for 26% of the total areas, and nearly half of the built-up areas are exposed to flooding risk by 2050. Under SSP245, the built-up areas exposed to medium and high flooding risk were anticipated to cover nearly a fifth of the total areas. Under SSP126 scenario, nearly no change was predicted in the area with high flooding risk, with only increase of 1%, future urbanization hotspots associated with serious flooding risks will likely to be found on the fringe of the GBA's construction areas, in line with the extent of construction land expansion. The finding of this study will shed a comprehensive insight into the identification of spatial and temporal distribution of urban flooding vulnerability to facilitate the exploration of flood mitigation measures associated with dynamic changes in urban land use. Published version This work was supported by Guangdong Basic and Applied Basic Research Foundation, China [grant number 2023A1515030158, 2023A1515012130], Guangzhou Science and Technology Programme, China [grant number 202201010431], Maoming Science and Technology Programme, China [grant number 2021S0054], and Guangzhou University, China [grant number RC2023008]. 2024-03-18T06:18:25Z 2024-03-18T06:18:25Z 2023 Journal Article Wang, M., Chen, F., Zhang, D., Chen, Z., Su, J., Zhou, S., Li, J., Chen, J., Li, J. & Tan, S. K. (2023). Data-driven approach to spatiotemporal dynamic risk assessment of urban flooding based on shared socio-economic pathways. Ecological Indicators, 154, 110764-. https://dx.doi.org/10.1016/j.ecolind.2023.110764 1470-160X https://hdl.handle.net/10356/174164 10.1016/j.ecolind.2023.110764 2-s2.0-85166627686 154 110764 en Ecological Indicators © 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/bync-nd/4.0/). application/pdf |
| spellingShingle | Engineering Shared socio-economic pathway Patch-generating land use simulation Wang, Mo Chen, Furong Zhang, Dongqing Chen, Zijing Su, Jin Zhou, Shiqi Li, Jianjun Chen, Jintang Li, Jiaying Tan, Soon Keat Data-driven approach to spatiotemporal dynamic risk assessment of urban flooding based on shared socio-economic pathways |
| title | Data-driven approach to spatiotemporal dynamic risk assessment of urban flooding based on shared socio-economic pathways |
| title_full | Data-driven approach to spatiotemporal dynamic risk assessment of urban flooding based on shared socio-economic pathways |
| title_fullStr | Data-driven approach to spatiotemporal dynamic risk assessment of urban flooding based on shared socio-economic pathways |
| title_full_unstemmed | Data-driven approach to spatiotemporal dynamic risk assessment of urban flooding based on shared socio-economic pathways |
| title_short | Data-driven approach to spatiotemporal dynamic risk assessment of urban flooding based on shared socio-economic pathways |
| title_sort | data driven approach to spatiotemporal dynamic risk assessment of urban flooding based on shared socio economic pathways |
| topic | Engineering Shared socio-economic pathway Patch-generating land use simulation |
| url | https://hdl.handle.net/10356/174164 |
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