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Published VersionCC BY V4.0, Open Access
Journal article
Quantifying Land Subsidence Probability and Intensity Using Weighted Bayesian Modeling in Shanghai, China
Land , Vol.14(3), pp.1-20
2025
Abstract
Land subsidence, a slow-onset geohazard, poses a severe threat to cities worldwide. However, the lack of quantification in terms of intensity, probability, and hazard zoning complicates the assessment and understanding of the land subsidence risk. In this study, we employ a weighted Bayesian model to explicitly present the spatial distribution of land subsidence probability and map hazard zoning in Shanghai. Two scenarios based on distinct aquifers are analyzed. Our findings reveal the following: (1) The cumulative land subsidence probability density functions in Shanghai follow a skewed distribution, primarily ranging between 0 and 50 mm, with a peak probability at 25 mm for the period 2017-2021. The proportions of cumulative subsidence above 100 mm and between 50 and 100 mm are significantly lower for 2017-2021 compared to those for 2012-2016, indicating a continuous slowdown in land subsidence in Shanghai. (2) Using the cumulative subsidence from 2017-2021 as a measure of posterior probability, the probability distribution of land subsidence under the first scenario ranges from 0.02 to 0.97. The very high probability areas are mainly located in the eastern peripheral regions of Shanghai and the peripheral areas of Chongming District. Under the second scenario, the probability ranges from 0.04 to 0.98, with high probability areas concentrated in the eastern coastal area of Pudong District and regions with intensive construction activity. (3) The Fit statistics for Scenario I and Scenario II are 67% and 70%, respectively, indicating a better fit for Scenario II. (4) High-, medium-, low-, and very low-hazard zones in Shanghai account for 14.2%, 48.7%, 23.6%, and 13.5% of the city, respectively. This work develops a method based on the weighted Bayesian model for assessing and zoning land subsidence hazards, providing a basis for land subsidence risk assessment in Shanghai.
Details
- Title
- Quantifying Land Subsidence Probability and Intensity Using Weighted Bayesian Modeling in Shanghai, China
- Authors
- Chengming Jin - Shanghai Normal UniversityQing Zhan (Corresponding Author) - Shanghai Institute of Geological SurveyYujin Shi - Shanghai Institute of Geological SurveyChengcheng Wan - Shanghai Normal UniversityHuan Zhang - Shanghai Institute of Geological SurveyLuna Zhao - Shanghai Normal UniversityJianli Liu - University of the Sunshine Coast, Queensland, School of Science, Technology and EngineeringTongfei Tian - University of the Sunshine Coast, Queensland, School of Science, Technology and EngineeringZilong Liu - Shanghai Normal UniversityJiahong Wen - Shanghai Normal University
- Publication details
- Land , Vol.14(3), pp.1-20
- Publisher
- MDPI AG
- Date published
- 2025
- DOI
- 10.3390/land14030470
- ISSN
- 2073-445X; 2073-445X
- Copyright note
- Copyright: © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/ licenses/by/4.0/).
- Data Availability
- Data will be made available on request.
- Grant note
- This research was supported by the Key Laboratory of Monitoring and Prevention of Land Subsidence, the Ministry of Natural Resources’ Open Fund Projects (KLLSMP20230X), and the National Natural Science Foundation of China (42171080).
- Organisation Unit
- School of Science, Technology and Engineering
- Language
- English
- Record Identifier
- 991127099902621
- Output Type
- Journal article
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web Of Science research areas
- Environmental Studies