scholarly journals Time-varying land subsidence detected by radar altimetry: California, Taiwan and north China

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Cheinway Hwang ◽  
Yuande Yang ◽  
Ricky Kao ◽  
Jiancheng Han ◽  
C. K. Shum ◽  
...  
2020 ◽  
Vol 12 (21) ◽  
pp. 3579
Author(s):  
Min Shi ◽  
Huili Gong ◽  
Mingliang Gao ◽  
Beibei Chen ◽  
Shunkang Zhang ◽  
...  

Groundwater resources have been exploited and utilized on a large scale in the North China Plain (NCP) since the 1970s. As a result of extensive groundwater depletion, the NCP has experienced significant land subsidence, which threatens geological stability and infrastructure health and exacerbates the risks of other geohazards. In this study, we employed multi-track Synthetic Aperture Radar (SAR) datasets acquired by the Sentinel-1A (S1A) satellite to detect spatial and temporal distributions of surface deformation in the NCP from 2016 to 2018 based on multi-temporal interferometric synthetic aperture radar (MT-InSAR). The results show that the overall ground displacement ranged from −165.4 mm/yr (subsidence) to 9.9 mm/yr (uplift) with a standard variance of 28.8 mm/yr. During the InSAR monitoring period, the temporal pattern of land subsidence was dominated by a decreasing tendency and the spatial pattern of land subsidence in the coastal plain exhibited an expansion trend. Validation results show that the S1A datasets agree well with levelling data, indicating the reliability of the InSAR results. With groundwater level data, we found that the distribution of subsidence in the NCP is spatially consistent with that of deep groundwater depression cones. A comparison with land use data shows that the agricultural usage of groundwater is the dominant mechanism responsible for land subsidence in the whole study area. Through an integrated analysis of land subsidence distribution characteristics, geological data, and previous research results, we found that other triggering factors, such as active faults, precipitation recharge, urbanization, and oil/gas extraction, have also impacted land subsidence in the NCP to different degrees.


2015 ◽  
Vol 12 (6) ◽  
pp. 6043-6075 ◽  
Author(s):  
J. P. Moiwo ◽  
F. Tao

Abstract. Worsening water storage depletion (WSD) contributes to environmental degradation, land subsidence and earthquake and could disrupt food production/security and social stability. There is need for efficient water use strategies in North China, a pivotal agrarian, industrial and political base in China with a widespread WSD. This study integrates satellite, model and field data products to investigate WSD and land subsidence in North China. In the first step, GRACE (Gravity Recovery and Climate Experiment) mass rates are used to show WSD in the region. Next, GRACE total water storage (TWS) is corrected for soil water storage (SWS) to derive groundwater storage (GWS) using GLDAS (Global Land Data Assimilation System) data products. The derived GWS is compared with GWS obtained from field-measured groundwater level to show land subsidence in the study area. Then GPS (Global Positioning System) data of relative land surface change (LSC) are used to confirm the subsidence due to WSD. A total of ~ 96 near-consecutive months (January 2002 through December 2009) of datasets are used in the study. Based on GRACE mass rates, TWS depletion is 23.76 ± 1.74 mm yr−1 or 13.73 ± 1.01 km3 yr−1 in the 578 000 km2 study area. This is ~ 31 % of the slated 45 km3 yr−1 water delivery in 2050 via the South–North Water Diversion Project. Analysis of relative LSC shows subsidence of 7.29 ± 0.35 mm yr−1 in Beijing and 2.74 ± 0.16 mm yr−1 in North China. About 11.53 % (2.74 ± 0.18 mm or 1.58 ± 0.12 km3) of the TWS and 8.37 % (1.52 ± 0.70 mm or 0.88 ± 0.03 km3) of the GWS are attributed to storage reductions accompanying subsidence in the region. Although interpretations of the findings require caution due to the short temporal and large spatial coverage, the concurrence of WSD and land subsidence could have adverse implications for the study area. It is critical that the relevant stakeholders embark on resource-efficient measures to ensure water availability, food security, ecological sustainability and social stability in this pivotal region.


Author(s):  
H. Guo ◽  
L. Wang ◽  
G. Cheng ◽  
Z. Zhang

Abstract. Land subsidence can be induced when various factors such as geological, and hydrogeological conditions and intensive groundwater abstraction combine. The development and utilization of groundwater in the North China Plain (NCP) bring great benefits, and at the same time have led to a series of environmental and geological problems accompanying groundwater-level declines and land subsidence. Subsidence occurs commonly in the NCP and analyses show that multi-layer aquifer systems with deep confined aquifers and thick compressible clay layers are the key geological and hydrogeological conditions responsible for its development in this region. Groundwater overdraft results in aquifer-system compaction, resulting in subsidence. A calibrated, transient groundwater-flow numerical model of the Beijing plain portion of the NCP was developed using MODFLOW. According to available water supply and demand in Beijing plain, several groundwater regulation scenarios were designed. These different regulation scenarios were simulated with the groundwater model, and assessed using a multi-criteria fuzzy pattern recognition model. This approach is proven to be very useful for scientific analysis of sustainable development and utilization of groundwater resources. The evaluation results show that sustainable development of groundwater resources may be achieved in Beijing plain when various measures such as control of groundwater abstraction and increase of artificial recharge combine favourably.


2013 ◽  
Vol 73 (3) ◽  
pp. 723-731 ◽  
Author(s):  
Xiu-yan Wang ◽  
Lin Sun ◽  
Zhi-liang Wang ◽  
Chang-li Liu ◽  
Yun Zhang

2015 ◽  
Vol 74 (2) ◽  
pp. 1415-1427 ◽  
Author(s):  
Haipeng Guo ◽  
Zuochen Zhang ◽  
Guoming Cheng ◽  
Wenpeng Li ◽  
Tiefeng Li ◽  
...  

Author(s):  
Weilai Wang ◽  
Guangyao Cai ◽  
Guijuan Lai ◽  
Mingfei Chen ◽  
Long Zhang

Abstract High-frequency (>1  Hz) ambient noise is usually closely related to anthropogenic activities. During the outbreak and spread of the COVID-19, as various anthropogenic activities are restricted, high-frequency ambient noise level has been observed to be reduced on a worldwide scale. The continuous waveform data at dense broadband seismic stations from ChinArray in eastern North China provides a good opportunity to study the temporal and spatial patterns of the ambient noise level in the region, and to further study the influencing factors, such as the topography and the population density. In this study, we calculated the average power spectral density of ambient noise at each station ±90 days around the Spring Festival in 2019 and in 2020, analyzed the noise level at different stations through normal times, Spring Festivals, epidemic control period, and recovery period, and studied the influencing factors of the noise level. We found that normally high-frequency (1–10 Hz) ambient noise correlates well with the surrounding sedimentary thickness: The noise level is higher when the surrounding sedimentary layer is thicker and vice versa. It correlates moderately with local population density and is time-varying due to anthropogenic activities. During the Spring Festival in 2019 and in 2020, and the epidemic control period after the Spring Festival in 2020, the reduction extent of the noise level correlates moderately with both the sedimentary thickness and population density; the ambient noise level reduces more significantly to the south of 40° N than to the north of it in the study region. Considering that the sedimentary thickness beneath each station is not time-varying, the variation in ambient noise level due to anthropogenic activities is clearly amplified by the sedimentary layer.


2021 ◽  
Author(s):  
Xiaojun Qiao ◽  
Tianxing Chu ◽  
Philippe Tissot ◽  
Jason Louis

2018 ◽  
Vol 26 (5) ◽  
pp. 1417-1427 ◽  
Author(s):  
Huili Gong ◽  
Yun Pan ◽  
Longqun Zheng ◽  
Xiaojuan Li ◽  
Lin Zhu ◽  
...  

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