Land subsidence and its relation with groundwater aquifers in Beijing Plain of China

Chen, Beibei; Gong, Huili; Chen, Yun; Li, Xiaojuan; Zhou, Chaofan; Lei, Kunchao; Zhu, Lin; Duan, Li; Zhao, Xiaoxiao

doi:10.1016/j.scitotenv.2020.139111

Cited by 94 publications

(63 citation statements)

References 41 publications

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“…Flood vulnerability refers to the human and socio-economic system's endurance and response to the spatial variable forces produced by hazardous events [41]. Here, the conceptual evaluation model of flood risk in the subway system can be expressed as Equation (1).…”

Section: Risk Assessment Modelmentioning

confidence: 99%

“…Flood risk = f (H, E, V) = Hazard × Exposure × Vulnerability (1) Considering that the different risk evaluation indicators play different importance in flood risk results, we modified the conceptual model of flood risk assessment to Equation (2).…”

Section: Risk Assessment Modelmentioning

confidence: 99%

“…Flooding is one of the most devastating natural disasters that can cause tremendous loss of life and economic damages [1][2][3]. According to a preliminary investigation, floods resulted in approximately 7 million deaths and more than USD 700 billion losses worldwide from 1900 to 2016 [4].…”

Section: Introductionmentioning

confidence: 99%

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Flood Risk Assessment of Subway Systems in Metropolitan Areas under Land Subsidence Scenario: A Case Study of Beijing

Wang

Liu

et al. 2021

Remote Sensing

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Flooding is one of the most destructive natural events that severely damage the ground and inundate underground infrastructure. Subway systems in metropolitan areas are susceptible to flooding, which may be exacerbated when land subsidence occurs. However, previous studies have focused on flood risk evaluation on regional/watershed-scales and land subsidence monitoring in plains, instead of on subway flood risk evaluation and how land subsidence aggravates the flood risk in subway systems. Using the proposed risk indicators and field survey data, we present a method assessing the flood risk of metropolitan subway systems under a subsidence condition based on the fuzzy analytic hierarchy process (FAHP) combined with a geographic information system (GIS). We use the regional risk level within the 500 m buffer zone of the subway line to depict the flood risk of the subway system. The proposed method was used to evaluate the flood risk of the Beijing subway system. The results show that the flood risks of the Beijing subway show a ring-like distribution pattern—risk levels decreasing from the central urban area to the suburbs. Very high and high risks are mainly located within third and fourth ring roads, accounting for 63.58% (29.40 km2) and 63.83% (81.19 km2) of the total area. Land subsidence exacerbated the Beijing subway system’s flood risk level—the moderate to very high risk increased by 46.88 km2 (16.33%), indicating that land subsidence is an essential factor affecting the flood risk level of subway systems. In addition to enhancing flood warnings, future subway flooding could be reduced by elevating the height of the stations’ exit (entrance) and installing water stop plates and watertight doors. This study is of great significance for flood warning and prevention in the Beijing subway system; it provides a theoretical basis for flood risk evaluation in other metropolitan areas.

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Section: Risk Assessment Modelmentioning

confidence: 99%

Section: Risk Assessment Modelmentioning

confidence: 99%

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Flood Risk Assessment of Subway Systems in Metropolitan Areas under Land Subsidence Scenario: A Case Study of Beijing

Wang

Liu

et al. 2021

Remote Sensing

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“…Groundwater supplies more than two-thirds of the water consumption in Beijing [9]. Underground water overexploitation has led to a dramatic decrease in the level of groundwater, resulting in the consolidation of aquifer systems and obvious land subsidence [3,11]. Beijing is prone to land subsidence owing to its geological environment, especially in the Eastern Beijing Plain, where the largest land subsidence funnel has been formed [12].…”

Section: Introductionmentioning

confidence: 99%

“…Early studies suggested that the main driving factor for land subsidence is groundwater overdraft in the Beijing Plain (BP) [6,17]. In 2020, Chen et al [11] used the random forest and geographical detectors methods to quantitatively establish the contribution of the groundwater level in different aquifers to cumulative land subsidence. Yu et al [10] performed the same analysis by using the geographical weighted regression method.…”

Section: Introductionmentioning

confidence: 99%

Analysis of the Contribution Rate of the Influencing Factors to Land Subsidence in the Eastern Beijing Plain, China Based on Extremely Randomized Trees (ERT) Method

Gong

Chen

et al. 2020

Remote Sensing

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As a common geological hazard, land subsidence is widely distributed in the Eastern Beijing Plain. The pattern of evolution of this geological phenomenon is controlled by many factors, including groundwater level change in different aquifers, compressible layers of different thicknesses, and static and dynamic loads. First, based on the small baseline subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technique, we employed 47 ENVISAT ASAR images and 48 RADARSAT-2 images to acquire the ground deformation of the Beijing Plain from June 2003 to November 2015 and then validated the results using leveling benchmark monitoring. Second, we innovatively calculated additional stress to obtain static and dynamic load information. Finally, we evaluated the contribution rate of the influencing factors to land subsidence by using the Spearman’s rank correlation coefficient (SRCC) and extremely randomized trees (ERT) machine learning methods. The SBAS-InSAR outcomes revealed that the maximum deformation rate was 110.7 mm/year from 2003 to 2010 and 144.4 mm/year from 2010 to 2015. The SBAS-InSAR results agreed well with the leveling benchmark monitoring results; the correlation coefficients were 0.97 and 0.96 during the 2003–2010 and 2013–2015 periods, respectively. The contribution rate of the second confined aquifer to the cumulative land subsidence was 49.3% from 2003 to 2010, accounting for the largest proportion; however, its contribution rate decreased to 23.4% from 2010 to 2015. The contribution rate of the third confined aquifer to the cumulative land subsidence increased from 2003 to 2015. Although the contribution of additional stress engendered from static and dynamic loads to the cumulative land subsidence was slight, it had a significant effect on the uneven land subsidence, with a contribution rate of 33.8% from 2003 to 2010 and 23.1% from 2010 to 2015. These findings provide scientific support for mitigating hazards associated with land subsidence.

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Land Subsidence and Groundwater Seepage

Li,

Wu,

Zhou

et al. 2023

Environmental Earth Sciences

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Land subsidence and its relation with groundwater aquifers in Beijing Plain of China

Cited by 94 publications

References 41 publications

Flood Risk Assessment of Subway Systems in Metropolitan Areas under Land Subsidence Scenario: A Case Study of Beijing

Flood Risk Assessment of Subway Systems in Metropolitan Areas under Land Subsidence Scenario: A Case Study of Beijing

Analysis of the Contribution Rate of the Influencing Factors to Land Subsidence in the Eastern Beijing Plain, China Based on Extremely Randomized Trees (ERT) Method

Land Subsidence and Groundwater Seepage

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