Coastal Flooding Hazard, Exposure, and Readiness of Buildings in Hong Kong in 2080–2100, and the Implications for Real Estate Management
Abstract
:1. Introduction
1.1. Literature Review
Research Gap
1.2. Conceptual Models for Assessing Real Estate Flooding Risks
1.2.1. Components of Risk
1.2.2. Review of Assessment Method
1.3. Tools for Flooding Risk Quantification
1.3.1. Methods for Predicting Future Coastal Floods
1.3.2. GIS and ML as Tools for Risk Analysis
1.4. Loss Mitigation and Stakeholders
1.5. Research Aim and Objectives
- (a)
- What will be the exposure and hazard level of coastal flooding in Hong Kong at the end of the 21st century under different scenarios?
- (b)
- What are the characteristics of coastal flooding risks and preparedness of the buildings in Hong Kong?
- (c)
- How to reduce the potential loss through improving the current situation through real estate management measures regarding the future potential flooding risks?
- Understanding the spatial distribution of exposed area and hazard of coastal flooding in Hong Kong in 2080–2100 under SSP 4.5 and SSP 8.5.
- Assessing the flooding hazard, exposure, and readiness of buildings under the two SSPs and categorizing the buildings into several types by considering their risk profiles.
- Analyzing the representative risk profiles of building from the results, and hence, discussing implications for real estate management and adaption to 2100.
2. Materials and Methods
2.1. Assessment Framework Design
2.2. Data Collection and Preprocessing
2.3. Data Analysis
2.3.1. ESL and Return Period Estimation
2.3.2. Spatial Analysis: Magnitude and Readiness
2.3.3. Clustering Analysis
2.4. Research Approach
3. Results
3.1. Spatial Distribution of Flooding Risks
3.1.1. ESL Events
3.1.2. Coastal Flooding Exposure
3.1.3. Coastal Flooding Hazard Index
3.2. Analysis of Risk Characteristics
3.2.1. Result of Clustering
3.2.2. Quality of Clustering
4. Discussion
4.1. Characteristics and Sources of Coastal Flooding Risks
4.2. Recommendations for Stakeholders
4.3. Limitations and Future Research
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Appendix A
Method | Probability of Exceedence, P | Average Recurrence Interval, T |
---|---|---|
California | m/n | n/m |
Hazen | (m − 0.5)/n | n/(m − 0.5) |
Weibull | m/(n + 1) | (n + 1)/m |
Chegodayev | (m − 0.3)/(n + 0.4) | (n + 0.4)/(m − 0.3) |
Blom | (m − 3/8)/(n + 1/4) | (n + 1/4)/(m − 3/8) |
Gringorten | (m − 0.44)/(n + 0.12) | (n + 0.12)/(m − 0.44) |
Appendix B
Appendix C
Category | Data | Source | Type | |
---|---|---|---|---|
Flooding prediction | Future climate data under SSP4.5 and 8.5 | Sea level rise | (Fox-Kemper et al., 2021) | Numerical value |
Historical climate data | Sea level record | (HKO, 2022b) | Numerical record | |
Storm surge database | ||||
Tide amplification | (Dominicis et al., 2020) | Numerical value | ||
Risk assessment | Local data | 5 m digital elevation model | (Lands Department, 2021) | Shapefile |
Location of tide observatory station | Manually recorded | - | ||
Building | (Lands Department, 2021) | Shapefile | ||
Dune restoration areas | Shapefile | |||
Flooding control infrastructure | Shapefile | |||
Mangrove | Shapefile | |||
Sea grass bed | (AFCD, 2021) | Map | ||
Green building | (HKGBC, 2021) | Building address | ||
Landcover | (Lands Department, 2021) | Shapefile | ||
Qualitative analysis | Qualitative data | Reports | - | - |
Open websites | - | - | ||
Policy documents | - | - |
Appendix D
Appendix E
Appendix F
Appendix G
SSP4.5 | SSP8.5 | ||||
---|---|---|---|---|---|
Extreme Sea Level (m) | Frequency of Exceedance (time) | Average Recurrence Interval (years) | Frequency of Exceedance (time) | Average Recurrence Interval (years) | Historical Frequency |
3–3.25 | 91.75 | 0.22 | No data | No data | 15.37 |
3.25–3.5 | 90.25 | 0.22 | No data | No data | 6.13 |
3.5–3.75 | 83.14 | 0.24 | 91.75 | 0.22 | 3.14 |
3.75–4 | 66.04 | 0.31 | 87.88 | 0.23 | 1.51 |
4–4.25 | 43.95 | 0.46 | 75.90 | 0.26 | 1.14 |
4.25–4.5 | 23.85 | 0.88 | 54.68 | 0.37 | 0.89 |
4.5–4.75 | 10.50 | 2.02 | 31.84 | 0.65 | 0.64 |
4.75–5 | 4.51 | 4.68 | 15.12 | 1.42 | 0.39 |
5–5.25 | 2.01 | 10.42 | 6.51 | 3.18 | 0.14 |
5.25–5.5 | 1.14 | 17.57 | 3.14 | 6.84 | - |
5.5–5.75 | 0.89 | 22.51 | 1.51 | 13.31 | - |
5.75–6 | 0.64 | 31.30 | 1.14 | 17.57 | - |
6–6.25 | 0.39 | 51.36 | 0.89 | 22.51 | - |
6.25–6.5 | 0.14 | 143.07 | 0.64 | 31.30 | - |
6.5–6.75 | 0 | - | 0.39 | 51.36 | - |
6.75–7 | 0 | - | 0.14 | 143.07 | - |
Appendix H
Centroids | |||||
Green_Inf_Score | Constructed_Inf_Score | ||||
Mean | Std. Deviation | Mean | Std. Deviation | ||
Cluster | 1 | 0.4645176583 | 0.2512619733 | 0.0605948198 | 0.1339347757 |
2 | 0.4350815964 | 0.2505961258 | 0.0463057050 | 0.1214751176 | |
3 | 0.5632801059 | 0.0669191738 | 0.0042013062 | 0.0207303980 | |
4 | 0.5772796665 | 0.0853719109 | 0.3108347831 | 0.1164582531 | |
5 | 0.0589767462 | 0.0999676423 | 0.0079357425 | 0.0274109394 | |
6 | 0.0118858385 | 0.0508670896 | 0.3215528210 | 0.1112618549 | |
7 | 0.5473243087 | 0.0502970432 | 0.0050557840 | 0.0204797062 | |
8 | 0.0493772322 | 0.0939848181 | 0.0063556892 | 0.0244509358 | |
Combined | 0.2797722611 | 0.2695964133 | 0.0544336122 | 0.1240818137 | |
Centroids | |||||
Small Event SSP 4.5 | Large Event SSP 4.5 | ||||
Mean | Std. Deviation | Mean | Std. Deviation | ||
Cluster | 1 | 0.9755303378 | 0.1235623460 | 1.0000000000 | 0.0000000000 |
2 | 0.3524950927 | 0.0000000000 | 1.0000000000 | 0.0000000000 | |
3 | 0.0000000000 | 0.0000000000 | 1.0000000000 | 0.0000000000 | |
4 | 0.0000000000 | 0.0000000000 | 0.3586836831 | 0.4419920241 | |
5 | 0.0000000000 | 0.0000000000 | 1.0000000000 | 0.0000000000 | |
6 | 0.0000000000 | 0.0000000000 | 0.4259523381 | 0.4688937406 | |
7 | 0.0000000000 | 0.0000000000 | 0.0478942517 | 0.0588723157 | |
8 | 0.0000000000 | 0.0000000000 | 0.0487362778 | 0.0590389025 | |
Combined | 0.0499900800 | 0.1744467136 | 0.4757952963 | 0.4751547113 | |
Centroids | |||||
Small Event SSP 8.5 | Large Event SSP 8.5 | ||||
Mean | Std. Deviation | Mean | Std. Deviation | ||
Cluster | 1 | 0.9801540233 | 0.1002145194 | 1.0000000000 | 0.0000000000 |
2 | 0.3524950927 | 0.0000000000 | 1.0000000000 | 0.0000000000 | |
3 | 0.4748449245 | 0.0000000000 | 0.0230994891 | 0.0000000000 | |
4 | 0.0000000000 | 0.0000000000 | 0.0120117396 | 0.0095643814 | |
5 | 0.0000000000 | 0.0000000000 | 0.0230994891 | 0.0000000000 | |
6 | 0.0000000000 | 0.0000000000 | 0.0124863016 | 0.0101430520 | |
7 | 0.0000000000 | 0.0000000000 | 0.0056149324 | 0.0069019571 | |
8 | 0.0000000000 | 0.0000000000 | 0.0057136482 | 0.0069214871 | |
Combined | 0.0591354033 | 0.1923543464 | 0.1096378780 | 0.2942846165 | |
Centroids | |||||
Hazard Index SSP 8.5 | Hazard Index SSP 8.5 | ||||
Mean | Std. Deviation | Mean | Std. Deviation | ||
Cluster | 1 | 443.4320640 | 383.0951058 | 353.5613576 | 385.8927786 |
2 | 97.43114152 | 97.34005910 | 48.92406200 | 67.12296271 | |
3 | 1.121440697 | 0.5442923250 | 4.651526525 | 5.816017925 | |
4 | 0.4776370161 | 0.5099163045 | 1.200902100 | 2.835063784 | |
5 | 1.050332457 | 0.7249202712 | 4.441316414 | 7.541842340 | |
6 | 0.4987326510 | 0.5118573014 | 1.312858942 | 2.516747499 | |
7 | 02100954671 | 0.3548501279 | 0.1665549074 | 0.5277449115 | |
8 | 0.2215145317 | 0.3907466925 | 0.1797211840 | 0.6213871392 | |
Combined | 18.58141780 | 97.40475880 | 13.91542262 | 84.03370024 | |
Green_Building | |||||
0 | 1 | ||||
Frequency | Percent | Frequency | Percent | ||
Cluster | 1 | 687 | 2.5% | 1 | 2.3% |
2 | 2066 | 7.4% | 1 | 2.3% | |
3 | 4342 | 15.5% | 0 | 0% | |
4 | 1288 | 4.6% | 0 | 0% | |
5 | 3997 | 14.3% | 0 | 0% | |
6 | 2635 | 9.4% | 42 | 95.5% | |
7 | 5059 | 18.1% | 0 | 0% | |
8 | 7883 | 28.2% | 0 | 0% | |
Combined | 27,957 | 100.0% | 44 | 100% |
Appendix I
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Criteria | Sub-Criteria | Weighting | |
---|---|---|---|
Exposure | Frequency of exceedance | - | - |
Hazard level | Water depth | - | - |
Readiness | Flooding control facility | Stormwater path | 1.5 |
Stormwater pump station | 1.5 | ||
Green infrastructure | Mangrove | 1.75 | |
Wood | 1.25 | ||
Marsh and wetland | 1 | ||
Seagrass bed | 0.75 | ||
Dune restoration area | 0.75 | ||
Green building | - | 1.5 |
Method | Analysis | Software | Data Type | |
---|---|---|---|---|
Quantitative | Statistics | Modelling hazard data | Excel | Climate data |
GIS analysis | Spatial analysis | ArcGIS | Local geographic data | |
ML | Cluster analysis | SPSS | Risk data | |
Qualitative | Results and desktop research for discussion | - | - |
N | % of Combined | % of Total | ||
---|---|---|---|---|
Cluster | 1 | 688 | 2.5% | 2.5% |
2 | 2067 | 7.4% | 7.4% | |
3 | 4342 | 15.5% | 15.5% | |
4 | 1288 | 4.6% | 4.6% | |
5 | 3997 | 14.3% | 14.3% | |
6 | 2677 | 9.6% | 9.6% | |
7 | 5059 | 18.1% | 18.1% | |
8 | 7883 | 28.2% | 28.2% | |
Combined | 28,001 | 100% | 100% | |
Total | 28,001 | 100% |
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Singh, M.; Cai, X. Coastal Flooding Hazard, Exposure, and Readiness of Buildings in Hong Kong in 2080–2100, and the Implications for Real Estate Management. ISPRS Int. J. Geo-Inf. 2023, 12, 86. https://doi.org/10.3390/ijgi12030086
Singh M, Cai X. Coastal Flooding Hazard, Exposure, and Readiness of Buildings in Hong Kong in 2080–2100, and the Implications for Real Estate Management. ISPRS International Journal of Geo-Information. 2023; 12(3):86. https://doi.org/10.3390/ijgi12030086
Chicago/Turabian StyleSingh, Minerva, and Xin Cai. 2023. "Coastal Flooding Hazard, Exposure, and Readiness of Buildings in Hong Kong in 2080–2100, and the Implications for Real Estate Management" ISPRS International Journal of Geo-Information 12, no. 3: 86. https://doi.org/10.3390/ijgi12030086
APA StyleSingh, M., & Cai, X. (2023). Coastal Flooding Hazard, Exposure, and Readiness of Buildings in Hong Kong in 2080–2100, and the Implications for Real Estate Management. ISPRS International Journal of Geo-Information, 12(3), 86. https://doi.org/10.3390/ijgi12030086