Integration of Building Age into Flood Hazard Mapping: A Case Study of Al Ain City, United Arab Emirates
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Flood-Hazard Factors and Mapping
2.3. Score and Weight Assignment
3. Results
Building Age Extraction
4. Discussion
5. Conclusions
6. Recommendations
- There is a need to document flood events spatially and temporally, both from rainfall and from infrastructure failure. Moreover, predictive analysis needs to be improved to assess areas that could be prone to flooding by adding more accurate data coupled with hydrological models.
- Soft solutions: Implementing effective planning rules and enhancing public awareness can play a crucial role in addressing the issue of flooding potential. By establishing comprehensive planning regulations, including zoning and land-management guidelines, Al Ain can ensure sustainable development and minimize the impact of flooding.
- Hard solutions: Al Ain has already constructed several dams to harness rainfall, and further dams can be considered as land use alters the topography. Expansion of green areas and maintenance of efficient drainage systems are also important.
- In assessing building resilience and establishing construction guidelines, it is essential to allow zoning planning and to establish more stringent requirements for issuing building permits. This will ensure that the structures can withstand potential disasters. Educating people about the hazard levels of their structures will provide valuable insights into their structural vulnerabilities and encourage them to implement the necessary improvements to make their buildings more resilient and better prepared for potential hazards [18].
- Assessing the effectiveness of property-level resistance and resilience measures can reduce loss and repair time due to flooding. For example, property owners can retrofit or demolish old buildings and adjust building heights in flood-vulnerable areas [65]. Develop a comprehensive database about buildings, including building age, and use the database in flood hazard and risk mapping.
- Cooperation between various stakeholders, such as central and local governments and the private sector, is needed. Future research can use machine learning models and satellite-derived rainfall data to predict flash floods in Al Ain [66].
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Station | Jan. | Feb. | Mar. | Apr. | May | Jun. | Jul. | Aug. | Sep. | Oct. | Nov. | Dec. | Average |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Al Shuaibah | 33.8 | 7.9 | 40.4 | 4.6 | 6 | 1 | 1 | 0 | 0 | 4 | 4.1 | 11 | 9.5 |
Al Qattara | 8.5 | 2.2 | 10.5 | 3.2 | 1 | 1 | 3 | 2 | 0 | 1 | 2.5 | 5 | 3.3 |
Al Foah | 20.8 | 7.1 | 31.7 | 10.2 | 2 | 2 | 10 | 6 | 4 | 4 | 5.5 | 25 | 10.7 |
Airport | 14.7 | 4.6 | 17.9 | 6.1 | 1 | 1 | 5 | 2 | 1 | 0 | 2.1 | 7.1 | 5.2 |
Average | 19.5 | 5.5 | 25.1 | 6.0 | 2.5 | 1.3 | 4.8 | 2.5 | 1.3 | 2.3 | 3.6 | 12.0 |
Data | Spatial Resolution | Source | Type |
---|---|---|---|
Digital elevation model | 30 m | (EROS, 2023) [36] | Raster |
Landsat image | 30 m | (USGS, 2022) [37] | Raster |
Population | District level | Statistics Centre, Abu Dhabi | Vector |
Geology | - | Ministry of Energy and Infrastructure, Petroleum, Gas and Mineral Resources sector | Vector |
Soil | - | (FAO, 2023) [38] | Raster |
Valleys | - | Digitized from Google Earth and UAE Atlas | Vector |
LULC | Curve Number for Hydrologic Soil Group A | Curve Number for Hydrologic Soil Group B |
---|---|---|
Bare soil | 63 | 77 |
Built-up | 77 | 85 |
Highland | 98 | 98 |
Vegetation and date palms | 39 | 61 |
2022 | Built-Up | Vegetation | Date Palm | Bare Soil | Highland | Total |
---|---|---|---|---|---|---|
Built-up | 55 | 0 | 1 | 0 | 3 | 59 |
Vegetation | 3 | 46 | 0 | 0 | 0 | 49 |
Date Palm | 0 | 0 | 49 | 0 | 0 | 49 |
Bare Soil | 1 | 1 | 2 | 51 | 1 | 56 |
Highland | 0 | 0 | 0 | 0 | 49 | 49 |
Total | 59 | 47 | 52 | 51 | 53 | 262 |
PA | 0.92 | 0.92 | 0.98 | 0.94 | 0.98 | |
UA | 0.9 | 0.88 | 0.94 | 0.93 | 1 | |
Overall | PA = 0.94 and Kappa = 0.91 |
Factor | Feature Category | Score | Weight | Factor | Feature Category | Score | Weight |
---|---|---|---|---|---|---|---|
Elevation (meters) | 0–20 | 5 | 0.10 | TWI | 3.2–5.6 | 1 | 0.19 |
20–40 | 4 | 5.6–6.9 | 2 | ||||
40–60 | 3 | 6.9–8.4 | 3 | ||||
60–80 | 2 | 8.4–10.4 | 4 | ||||
>100 | 1 | 10.4–21.1 | 5 | ||||
Building age | 1972 | 5 | 0.16 | Valley | 300 | 5 | 0.24 |
1993 | 4 | 500 | 4 | ||||
2013 | 3 | 700 | 3 | ||||
2022 | 1 | 900 | 2 | ||||
1100 | 1 | ||||||
TPI | −209 to −45 | 3 | 0.08 | Geology | Silt | 5 | 0.03 |
−45 to −15 | 4 | Mudstone | 4 | ||||
−15 to 13 | 5 | Limestone | 3 | ||||
13 to 55 | 2 | Sand | 2 | ||||
55 to 214 | 1 | Gravel | 1 | ||||
LULC | Built-up | 5 | 0.05 | CN | 61 | 5 | 0.05 |
Vegetation | 3 | 77 | 4 | ||||
Desert | 2 | 85 | 3 | ||||
Highland | 1 | 98 | 1 | ||||
Population density | 0–105 | 1 | 0.10 | ||||
105–494 | 2 | ||||||
494–927 | 3 | ||||||
927–1480 | 4 | ||||||
1480–3813 | 5 |
Buffer Zone (m) | LULC (km2) | Counts | |||||||
---|---|---|---|---|---|---|---|---|---|
Built-Up | Vegetation | Desert | Heritage Sites | Schools | Hospitals | Petrol Station | Mosques | Hotels | |
500 | 37.68 | 10.65 | 43.03 | 1 | 50 | 4 | 4 | 184 | 4 |
1000 | 74.79 | 21.55 | 84.29 | 4 | 70 | 7 | 6 | 374 | 6 |
1500 | 107.26 | 32.16 | 122.77 | 5 | 88 | 11 | 10 | 515 | 7 |
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Alsumaiti, T.; Yagoub, M.M.; Tesfaldet, Y.T.; Alhosani, N.; Pakam, S. Integration of Building Age into Flood Hazard Mapping: A Case Study of Al Ain City, United Arab Emirates. Water 2024, 16, 2408. https://doi.org/10.3390/w16172408
Alsumaiti T, Yagoub MM, Tesfaldet YT, Alhosani N, Pakam S. Integration of Building Age into Flood Hazard Mapping: A Case Study of Al Ain City, United Arab Emirates. Water. 2024; 16(17):2408. https://doi.org/10.3390/w16172408
Chicago/Turabian StyleAlsumaiti, Tareefa, M. M. Yagoub, Yacob T. Tesfaldet, Naeema Alhosani, and Subraelu Pakam. 2024. "Integration of Building Age into Flood Hazard Mapping: A Case Study of Al Ain City, United Arab Emirates" Water 16, no. 17: 2408. https://doi.org/10.3390/w16172408