| Issue |
E3S Web Conf.
Volume 513, 2024
International Conference on SDGs for Sustainable Future (ICSSF 2024)
|
|
|---|---|---|
| Article Number | 01001 | |
| Number of page(s) | 13 | |
| Section | Earth and Environmental Sciences | |
| DOI | https://doi.org/10.1051/e3sconf/202451301001 | |
| Published online | 24 April 2024 | |
An overview of flood risk components: The Land subsidence case of Pekalongan City
1 Magister Study Program of Water Resources Management, Faculty of Civil and Environmental Engineering, Bandung Institute of Technology, Jl. Ganesha 10, 40132 Bandung, Indonesia
2 Water Resources Engineering Research Group, Faculty of Civil and Environmental Engineering, Bandung Institute of Technology, Jl. Ganesha 10, Bandung, 40132, Indonesia
3 Geodesy Research Group, Faculty of Earth Sciences and Technology, Bandung Institute of Technology, Jl. Ganesha 10, 40132 Bandung, Indonesia
4 Regional Development Planning Board of Pekalongan City, 51111 Pekalongan, Indonesia
5 Nagoya Institute of Technology, 466-8555 Nagoya, Japan
* Corresponding author: nugrohoeka@itb.ac.id
Pekalongan City is located on the northern coast of Java Island, where climate crises, floods, and tidal waves pose significant threats exacerbated by rising sea levels and land subsidence. This study aims to mitigate of flood future losses and emphasizes its potential as input for Indonesian regulators to enhance current flood hazard classifications. This study conducts a comprehensive flood risk analysis, emphasizing a detailed review of flood hazard classification quantitatively with flood hazard mapping. The results showed flood hazard mapping, comparing NDMA, MPW, and proposed classifications, indicates a tendency for MPW to overestimate hazards, particularly categorizing depths of 0.5 m and above as “High”. The flood depth according to MPW with land subsidence produces a flood inundation area of 14,145 Ha. This shows that land subsidence recorded in risk mapping can increase the overall hazard level. This study proposed flood depth and velocity classification, balancing NDMA’s underestimation and MPW’s overestimation, offering a more comprehensive approach.
© The Authors, published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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