Quantifying Land Subsidence Probability and Intensity Using Weighted Bayesian Modeling in Shanghai, China

Chengming Jin, Qing Zhan (), Yujin Shi, Chengcheng Wan, Huan Zhang, Luna Zhao, Jianli Liu, Tongfei Tian, Zilong Liu and Jiahong Wen ()
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Chengming Jin: School of Environmental and Geographical Sciences, Shanghai Normal University, Shanghai 200233, China
Qing Zhan: Shanghai Institute of Natural Resources Survey and Utilization, Shanghai 200072, China
Yujin Shi: Shanghai Institute of Natural Resources Survey and Utilization, Shanghai 200072, China
Chengcheng Wan: School of Environmental and Geographical Sciences, Shanghai Normal University, Shanghai 200233, China
Huan Zhang: Shanghai Institute of Natural Resources Survey and Utilization, Shanghai 200072, China
Luna Zhao: School of Environmental and Geographical Sciences, Shanghai Normal University, Shanghai 200233, China
Jianli Liu: School of Science, Technology and Engineering, University of the Sunshine Coast, Sunshine Coast, QLD 4556, Australia
Tongfei Tian: School of Science, Technology and Engineering, University of the Sunshine Coast, Sunshine Coast, QLD 4556, Australia
Zilong Liu: School of Environmental and Geographical Sciences, Shanghai Normal University, Shanghai 200233, China
Jiahong Wen: School of Environmental and Geographical Sciences, Shanghai Normal University, Shanghai 200233, China

Land, 2025, vol. 14, issue 3, 1-20

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.

Keywords: land subsidence; weighted Bayesian model; aquifer distribution; soil layer thickness; construction activity; Shanghai (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2025
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