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Keywords = groundwater funnel areas

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17 pages, 8083 KiB  
Article
Prediction of Ground Subsidence Induced by Groundwater Mining Using Three-Dimensional Variable-Parameter Fully Coupled Simulation
by Jingjing Du, Yan Zhang, Zujiang Luo and Chenghang Zhang
Water 2024, 16(17), 2487; https://doi.org/10.3390/w16172487 - 1 Sep 2024
Viewed by 730
Abstract
In order to predict the ground settlement in a scientific, intuitive, and simple way, based on the theory of Bio-consolidation, a three-dimensional fluid-solid coupled numerical calculation programme FGS-3D for ground settlement was compiled by using the Fortran 95 language, and a front-end operation [...] Read more.
In order to predict the ground settlement in a scientific, intuitive, and simple way, based on the theory of Bio-consolidation, a three-dimensional fluid-solid coupled numerical calculation programme FGS-3D for ground settlement was compiled by using the Fortran 95 language, and a front-end operation platform was developed by using Microsoft VisualBasic, so that a three-dimensional variable-parameter fully coupled viscoelastic-plastic model of ground settlement was constructed using the city of Yancheng as an example, and the development of ground settlement and horizontal displacement changes from 2021 to 2030 were predicted. The results show that the three-dimensional fully coupled finite-element numerical model of building load, groundwater seepage, and soil deformation established by the above computer development program can directly create a hydrogeological conceptual model of groundwater mining and predict ground settlement, so as to achieve the visualisation of the three-dimensional seepage of groundwater and the fully coupled simulation of ground subsidence in the whole process of groundwater mining. Under the joint action of construction load and groundwater mining, the water level of the aquifer in Yancheng City rises by 1.26 m on average in the main groundwater mining area of the group III pressurised aquifer, forming two smaller landing funnels, and the lowest water level of the two landing funnels is −15 m. Full article
(This article belongs to the Special Issue Studies on Water Resource and Environmental Policies)
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<p>Block diagram of the structure of the free surface iteration algorithm.</p>
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<p>Block diagram of the iterative algorithm for viscoplastic-elastic-plastic stress analysis.</p>
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<p>Block diagram of the fully coupled analysis procedure.</p>
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<p>Functions of software modules.</p>
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<p>Grid subdivision stereogram of the study area.</p>
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<p>Parameter zoning diagram of the third confined aquifer.</p>
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<p>Calculated flow field diagram of the third confined aquifer on 31 December 2016.</p>
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<p>Calculated flow field diagram of the third confined aquifer on 31 December 2018.</p>
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<p>Map of cumulative land subsidence from 2016 to 2018.</p>
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<p>Fitted map of ground subsidence at level observation points from 2016 to 2018.</p>
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<p>Isocontour map of forecasted water levels of the third confined aquifer on 31 December 2030 (m).</p>
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<p>Isocontour map of the compressive capacity prediction of the third confined aquifer on 31 December 2030 (mm).</p>
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<p>Projected horizontal displacement of the third confined aquifer on 31 December 2030 (m).</p>
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<p>Contour map of the predicted cumulative land subsidence from 2021 to 2030 (mm).</p>
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15 pages, 5785 KiB  
Article
Mechanical Characteristics of Suspended Buried Pipelines in Coal Mining Areas Affected by Groundwater Loss
by Wen Wang, Fan Wang, Xiaowei Lu, Jiandong Ren and Chuanjiu Zhang
Appl. Sci. 2024, 14(16), 7187; https://doi.org/10.3390/app14167187 - 15 Aug 2024
Viewed by 525
Abstract
Research on the deformation characteristics and failure modes of buried pipelines under local suspension conditions caused by groundwater loss in coal mining subsidence areas is conducive to grasping the failure evolution law of pipelines and providing technical support for the precise maintenance of [...] Read more.
Research on the deformation characteristics and failure modes of buried pipelines under local suspension conditions caused by groundwater loss in coal mining subsidence areas is conducive to grasping the failure evolution law of pipelines and providing technical support for the precise maintenance of gathering and transportation projects and the coordinated mining of gas and coal resources. First, a test system for monitoring the deformation of pipelines under loading was designed, which mainly includes pipeline load application devices, end fixing and stress monitoring devices, pipeline end brackets, and stress–strain monitoring devices. Then, a typical geological hazard faced by oil and gas pipelines in the gas–coal overlap area—local suspension—was used as the engineering background to simulate the field conditions of a 48 mm diameter gas pipeline with a localized uniform load. At the same time, deformation, top–bottom strain, end forces, and damage patterns of the pipeline were monitored and analyzed. The results show that the strain at the top and bottom of the pipeline increased as the load increased. In this case, the top was under pressure, and the bottom was under tension, and the conditions at the top and bottom were opposite.. For the same load, the strain tended to increase gradually from the end to the middle of the pipeline, and at the top, it increased significantly more than at the bottom. The tensile force carried by the end of the pipeline increased as the applied load increased, and the two were positively correlated by a quadratic function. The overall deformation of the pipeline evolved from a flat-bottom shape to a funnel and then to a triangular shape as the uniform load increased. In addition, plastic damage occurred when the pipeline deformed into a triangular shape. The results of the investigation clarify for the first time the mathematical relationship between local loads and ultimate forces on pipelines and analyze the evolution of pipeline failure, providing a reference for pipeline field maintenance. Based on this, the maximum deformation of and the most vulnerable position in natural gas pipelines passing through a mining subsidence area can be preliminarily judged, and then the corresponding remedial and protection measures can be taken, which has a certain guiding role for the protection of natural gas pipelines. Full article
(This article belongs to the Special Issue Advances in Underground Pipeline Technology, 2nd Edition)
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<p>The schematic diagram of overburden failure and strata movement in the goaf.</p>
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<p>Layout of the experimental device for monitoring the deformation and evolution of natural gas pipelines. (<b>a</b>) Experimental device design diagram. (<b>b</b>) Layout diagram of the experimental device.</p>
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<p>Layout of the experimental device for monitoring the deformation and evolution of natural gas pipelines. (<b>a</b>) Experimental device design diagram. (<b>b</b>) Layout diagram of the experimental device.</p>
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<p>Experimental equipment and foundation pit excavation layout.</p>
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<p>Pipeline strain data acquisition system and layout.</p>
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<p>Distribution diagram of strain evolution at the top of the pipeline. (<b>a</b>) Point A1; (<b>b</b>) point A2; (<b>c</b>) point A3; (<b>d</b>) point A4; (<b>e</b>) overall strain at the top.</p>
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<p>Distribution diagram of strain evolution at the bottom of the pipeline. (<b>a</b>) Point B1; (<b>b</b>) point B2; (<b>c</b>) point B3; (<b>d</b>) point B4; (<b>e</b>) overall strain at the bottom.</p>
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<p>Schematic diagram of the force deformation and strain distribution in the pipeline.</p>
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<p>Scatter diagram of the force at the end of the pipeline.</p>
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<p>Schematic diagram of pipeline deflection change.</p>
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20 pages, 22829 KiB  
Article
Hydrochemical Evolution Process and Mechanism of Groundwater in the Hutuo River Alluvial Fan, North China
by Junbai Gai, Baizhong Yan, Chengbo Fan, Yapeng Tuo and Miaomiao Ma
Water 2024, 16(16), 2229; https://doi.org/10.3390/w16162229 - 7 Aug 2024
Cited by 1 | Viewed by 823
Abstract
Due to extensive groundwater exploitation, a groundwater funnel has persisted in the Hutuo River alluvial fan in Shijiazhuang since the 1980s, lasting nearly 40 years and significantly impacting the groundwater chemical characteristics. In this study, based on the groundwater level and chemistry data, [...] Read more.
Due to extensive groundwater exploitation, a groundwater funnel has persisted in the Hutuo River alluvial fan in Shijiazhuang since the 1980s, lasting nearly 40 years and significantly impacting the groundwater chemical characteristics. In this study, based on the groundwater level and chemistry data, the hydrochemical evolution processes and mechanisms of the groundwater during the 1980 groundwater funnel period and the post-2015 artificial governance period were investigated using traditional hydrogeochemical methods and inverse hydrogeochemical simulations. The results show the following: (1) The ion concentrations gradually increased along the groundwater flow path, where they displayed a pattern of lower levels in the northwest and higher levels in the southeast. From 1980 to 2021, the concentrations of major ions were increased. (2) In 1980s, the groundwater hydrochemical type predominantly exhibited HCO3—Ca. From 1980 to 2015, the hydrochemical types diversified into HCO3·Cl—Ca, HCO3—Ca·Mg, and HCO3·SO4—Ca types. Following the artificial governance, the groundwater level rise led to an increase in the concentrations of SO42− and Mg2+. Post-2015, the prevailing hydrochemical type changed to HCO3·SO4—Ca·Mg. (3) The changes in the groundwater level and ion concentrations were quantitatively strongly correlated and exhibited spatial similarity. (4) In the 1980s, the groundwater hydrochemical composition was primarily controlled by the dissolution of albite, dolomite, halite, and quartz; reverse cation exchange; and groundwater exploitation. Since 2015, the hydrochemical composition has mainly been influenced by the dissolution of albite, calcite, and quartz; positive cation exchange; river–groundwater mixing; and industrial activities, with increasing intensities of both water–rock interactions and human activities. Full article
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<p>(<b>a</b>) Distribution of the groundwater and precipitation monitoring points in the study area; (<b>b</b>) hydrogeologic profile along the I–I’ line.</p>
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<p>Groundwater flow fields in 1980, 2015, and 2021.</p>
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<p>(<b>a</b>) Duration curve of groundwater hydrochemical composition; (<b>b</b>) hydrochemical Piper plots of groundwater from 1980 to 2021.</p>
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<p>Spatial distributions of major ions.</p>
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<p>(<b>a</b>,<b>b</b>) Gibbs diagram of groundwater; (<b>c</b>,<b>d</b>) Diagram of relative contribution rate of rock weathering and dissolution.</p>
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<p>Relationship between chloro–alkali index and TDSs.</p>
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<p>(<b>a</b>) Ratio of Na<sup>+</sup> to Cl<sup>−</sup> in groundwater; (<b>b</b>) Ratio of Ca<sup>2+</sup> to HCO<sub>3</sub><sup>−</sup> in groundwater; (<b>c</b>) Ratio of Ca<sup>2+</sup> + Mg<sup>2+</sup> to HCO<sub>3</sub><sup>−</sup> in groundwater; (<b>d</b>) Ratio of Ca<sup>2+</sup> + Mg<sup>2+</sup> to HCO<sub>3</sub><sup>−</sup> + SO<sub>4</sub><sup>2−</sup> in groundwater.</p>
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<p>Comparison between the variation of groundwater level and ion concentration.</p>
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11 pages, 3383 KiB  
Article
A Pathway Analysis of Evapotranspiration Variation Characteristics and Influencing Factors of Summer Maize in the Haihe Plain
by Wenzhe Guo, Jundong Xu, Xuetong Liu, Hongkai Dang, Shibo Fang and Yueying Li
Water 2024, 16(13), 1819; https://doi.org/10.3390/w16131819 - 26 Jun 2024
Viewed by 909
Abstract
The Haihe Plain in China is situated in the world’s largest groundwater funnel area, with per capita water resources far below the internationally recognized “extremely water-scarce” standard. To address the issue of water shortage in summer maize-planting areas of the Haihe Plain, we [...] Read more.
The Haihe Plain in China is situated in the world’s largest groundwater funnel area, with per capita water resources far below the internationally recognized “extremely water-scarce” standard. To address the issue of water shortage in summer maize-planting areas of the Haihe Plain, we conducted research on the variation of summer maize evapotranspiration using a medium-sized lysimeter. This study aims to provide technical support for water-saving irrigation in summer maize fields. Through path analysis, direct and indirect influencing factors affecting the evapotranspiration of summer maize fields were determined. The results showed that the cumulative evapotranspiration of bare ground and farmland during the entire growth period of summer maize was 173.57 mm and 382.97 mm, respectively, with evapotranspiration intensities of 1.52 mm/d and 3.36 mm/d, respectively. Evapotranspiration during the maturity stage of summer maize was the least, accounting for only 1.82% of total evapotranspiration during the entire growth period. The period from the jointing to milk-ripening stage is when evapotranspiration in maize fields is at its highest. During this period, evapotranspiration in maize fields amounted to 265.58 mm, accounting for 69.35% of total evapotranspiration. The evapotranspiration intensity was 3.59 mm/day, which is 1.34 times higher than that of bare soil. The evapotranspiration intensities during each growth stage were ranked as jointing stage > tasseling-silking stage > seedling stage > milk maturity stage > maturity stage. The daily evapotranspiration of summer maize fields showed a “unimodal” curve with low values in the morning and evening, and high values at noon. Path analysis indicated that daily radiation and maximum daily temperature had the greatest impact on the evapotranspiration of maize fields, with the direct effect of maximum daily temperature being restrictive and the indirect effect being promotive, resulting in an overall promotive effect. Full article
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<p>Meteorological chart of the 2021 summer maize-growing season.</p>
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<p>Meteorological chart of the 2023 summer maize-growing season.</p>
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<p>Daily variation of hourly evapotranspiration in maize fields on typical sunny and cloudy days in 2021.</p>
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<p>Daily variation of hourly evapotranspiration in maize fields on typical sunny and cloudy days in 2023.</p>
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<p>Daily variation of hourly evapotranspiration in maize fields and bare soil on typical days in 2021.</p>
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<p>Hourly variation of evapotranspiration in maize fields on 13–16 August 2021 and 2023.</p>
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<p>Hourly variation of meteorological values at the experimental site on 13–16 August 2021 and 2023.</p>
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<p>Daily changes in evapotranspiration during the entire growth period of maize fields and bare land in 2021.</p>
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<p>Changes in evapotranspiration intensity at different growth stages of summer maize in 2021.</p>
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19 pages, 20244 KiB  
Article
Estimation of Land Deformation and Groundwater Storage Dynamics in Shijiazhuang–Baoding–Cangzhou–Hengshui Using Multi-Temporal Interferometric Synthetic Aperture Radar
by Qiuhong Yang, Xing Zhang, Jun Hu, Rong Gui and Liuming Yang
Remote Sens. 2024, 16(10), 1724; https://doi.org/10.3390/rs16101724 - 13 May 2024
Viewed by 895
Abstract
Groundwater resources are crucial to socio-economic development and the ecosystem, and over-extraction can cause the groundwater level to drop, deplete reserves, and trigger geological hazards like land subsidence. The North China Plain (NCP) has experienced both subsidence and groundwater depletion due to over-extraction [...] Read more.
Groundwater resources are crucial to socio-economic development and the ecosystem, and over-extraction can cause the groundwater level to drop, deplete reserves, and trigger geological hazards like land subsidence. The North China Plain (NCP) has experienced both subsidence and groundwater depletion due to over-extraction in the past 70 years. In this study, we used MT-InSAR technology and ascending C-band Sentinel-1 SAR data from 2017 to 2023 to study land deformation in the junction area of Shijiazhuang–Baoding–Cangzhou–Hengshui. We identified multiple subsidence funnels with a maximum rate exceeding −150 mm/year and a total deformation surpassing 600 mm. Seasonal decomposition methods accurately separated seasonal signals in the time-series deformation and groundwater level data. An exponential function model applied to long-term deformation showed no significant decrease in subsidence in severely affected areas. By modeling seasonal deformation and seasonal groundwater levels, we determined the elastic skeletal storage coefficients (Ske) to be in the range of 1.02 × 10−3~6.53 × 10−3 in subsidence areas. We obtained the spatiotemporal evolution of the total groundwater storage (TGWS), irreversible ground storage (IGWS), and recoverable ground storage (RGWS). The TGWS and IGWS decreased annually while the RGWS increased, which is attributable to the implementation of the South-to-North Water Diversion Project (SNWDP) and the issuance of groundwater withdrawal policies in the NCP. Full article
(This article belongs to the Special Issue Monitoring Geohazard from Synthetic Aperture Radar Interferometry)
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<p>Study region (AOI) and the SAR data coverage (pink: Piedmont plain (PP), yellow: flood plain (FP), orange: coast plain (CP)).</p>
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<p>Flowchart of the data-processing procedure.</p>
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<p>Temporal/perpendicular baseline for the Sentinel-1A interferometric pairs.</p>
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<p>(<b>a</b>) The average vertical deformation rate in the AOI from 2017 to 2023, (<b>b</b>) The A-B profile results from 2017 to 2023.</p>
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<p>Groundwater level time series vs. time-series deformation for the nearby well station and detrend groundwater level time series vs. detrend time-series deformation for the nearby well station: (<b>a</b>,<b>b</b>) Q3, (<b>c</b>,<b>d</b>) Q7, (<b>e</b>,<b>f</b>) Q8, (<b>g</b>,<b>h</b>) Q10, (<b>i</b>,<b>j</b>) Q18 and (<b>k</b>,<b>l</b>) Q20.</p>
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<p>The map of the (k) decay coefficient (<b>a</b>) and the (M) magnitude coefficient (<b>b</b>) from long-time deformation, the (A) amplitude (<b>c</b>), and the peak time (<b>d</b>) from seasonal deformation.</p>
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<p>Interpolated <span class="html-italic">S<sub>ke</sub></span> coefficient.</p>
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<p>Annual TGWS: (<b>a</b>) 2018, (<b>b</b>) 2019, (<b>c</b>) 2020, (<b>d</b>) 2021 and (<b>e</b>) 2022.</p>
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<p>Annual RGWS and IGWS: (<b>a</b>,<b>b</b>) 2018, (<b>c</b>,<b>d</b>) 2019, (<b>e</b>,<b>f</b>) 2020 and (<b>g</b>,<b>h</b>) 2021.</p>
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<p>Flood and ground subsidence analysis.</p>
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<p>Surface rebound deformation map.</p>
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<p>Correlation analysis between the times series of vertical deformation and rainfall.</p>
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22 pages, 10018 KiB  
Article
Monitoring and Analysis of Land Subsidence in Cangzhou Based on Small Baseline Subsets Interferometric Point Target Analysis Technology
by Xinyue Xu, Chaofan Zhou, Huili Gong, Beibei Chen and Lin Wang
Land 2023, 12(12), 2114; https://doi.org/10.3390/land12122114 - 28 Nov 2023
Cited by 3 | Viewed by 1385
Abstract
Cangzhou is located in the northeast part of the North China Plain; here, groundwater is the main water source for production and living. Due to the serious regional land subsidence caused by long-term overexploitation of groundwater, the monitoring of land subsidence in this [...] Read more.
Cangzhou is located in the northeast part of the North China Plain; here, groundwater is the main water source for production and living. Due to the serious regional land subsidence caused by long-term overexploitation of groundwater, the monitoring of land subsidence in this area is significant. In this paper, we used the Small Baseline Subsets Interferometric Point Target Analysis (SBAS-IPTA) technique to process the Envisat-ASAR, Radarsat-2, and Sentinel-1A data and obtained the land subsidence of Cangzhou from 2004 to 2020. Additionally, we obtained winter wheat distribution information in Cangzhou using the Pixel Information Expert Engine (PIE-Engine) remote sensing cloud platform. On this basis, we analyzed the relationship between ground water level, winter wheat planting area, and the response of land subsidence according to the land use type and groundwater level monitoring data near the winter wheat growing area. The results show that during 2004–2020, the average annual subsidence rate of many places in Cangzhou was higher than 30 mm/year, and the maximum subsidence rate was 115 mm/year in 2012. From 2004 to 2020, the area of the subsidence funnel showed a trend of first increasing and then decreasing. In 2020, the subsidence funnel area reached 6.9 × 103 km2. The winter wheat planting area in the urban area showed a trend of first decreasing, then increasing and then decreasing, and it accounted for a large proportion in the funnel area. At the same time, we studied the relationship between the land subsidence rate and the water level at different burial depths and the response of winter wheat planting area. The results showed that the change of confined water level had a stronger response with the other two variables. Full article
(This article belongs to the Special Issue Ground Deformation Monitoring via Remote Sensing Time Series Data)
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<p>(<b>a</b>) The coverage of radar data. (<b>b</b>) The coverage of optical remote sensing data. (<b>c</b>) The overview of the study area.</p>
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<p>Flowchart of SBAS-IPTA technology.</p>
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<p>Flowcharts of how winter wheat planting distribution was extracted using the PIE-Engine.</p>
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<p>Distribution of the average subsidence rate in Cangzhou from 2004 to 2020 ((<b>a</b>) from 2004 to 2010; (<b>b</b>) 2012–2016; (<b>c</b>) 2016–2020).</p>
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<p>The moving trajectory of ground subsidence funnel center in Cangzhou from 2004 to 2020.</p>
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<p>Temporal distribution map of interannual variation in land subsidence funnels in Cangzhou from 2004 to 2020.</p>
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<p>Superposition diagram illustrating the interannual variation in subsidence funnel and the sowing distribution of winter wheat.</p>
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<p>Area proportion of winter wheat in subsidence funnel area.</p>
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<p>Remote sensing images of three well points (the three images in (<b>a</b>,<b>c</b>,<b>e</b>) are remote sensing images of winter wheat before planting. The three images in (<b>b</b>,<b>d</b>,<b>f</b>) are remote sensing images during the growth of winter wheat.)</p>
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<p>Overlay of results of confined wells and phreatic wells with winter wheat planting area and subsidence rate. (Well 1 includes monitoring of confined water and phreatic water levels. Well 2 is a phreatic well, and Well 3 is a confined water well). ((<b>a</b>) is well 1 in <a href="#land-12-02114-f009" class="html-fig">Figure 9</a>, (<b>b</b>) is well 3, (<b>c</b>) is well 1, (<b>d</b>) is well 2).</p>
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<p>Comparison of winter wheat planting area obtained by PIE-Engine with the results from the <span class="html-italic">Cangzhou Statistical Yearbook</span>.</p>
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<p>The spatial distribution of winter wheat obtained by PIE.</p>
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15 pages, 3830 KiB  
Article
Rapid Urbanization Has Changed the Driving Factors of Groundwater Chemical Evolution in the Large Groundwater Depression Funnel Area of Northern China
by Long Wang, Qianqian Zhang and Huiwei Wang
Water 2023, 15(16), 2917; https://doi.org/10.3390/w15162917 - 12 Aug 2023
Cited by 4 | Viewed by 1302
Abstract
With the rapid development of urbanization, the chemical evolution of groundwater has been significantly affected by human activities. However, the driving mechanisms of groundwater chemical evolution at different stages of urbanization are still unclear, which severely affects the implementation of groundwater protection. This [...] Read more.
With the rapid development of urbanization, the chemical evolution of groundwater has been significantly affected by human activities. However, the driving mechanisms of groundwater chemical evolution at different stages of urbanization are still unclear, which severely affects the implementation of groundwater protection. This study investigated the driving mechanisms of groundwater chemical evolution based on the long-term series (from 1985 to 2015) of hydrochemical data from 19 groundwater monitoring sites in rapidly urbanizing areas (Shijiazhuang, Hebei Province, China). The results show that the concentrations of various chemical components in groundwater gradually increase with the acceleration of the urbanization process, especially NO3, which has increased from 13.7 mg/L in the primary stage of urbanization (PSU) to 65.1 mg/Lin the advanced stage of urbanization (ASU), exceeding the World Health Organization (WHO) drinking water standard (50 mg/L), indicating that the groundwater chemistry has been significantly affected by human activities. The main hydrochemical types have changed from the HCO3•SO4-Ca•Mg-type water in the primary stage of urbanization (PSU) to the SO4•HCO3-Ca•Mg-type water in the advanced stage of urbanization (ASU). It is worth noting that there are obvious differences in driving factors of groundwater chemical evolution at different urbanization stages. In the primary stage of urbanization (PSU), the driving factors were carbonate and rock salt dissolution, cation exchange, and industrial activities. However, in the intermediate stage and advanced stage, the driving factors were changed to carbonate and gypsum dissolution, groundwater over-exploitation, agricultural fertilization, and domestic sewage. Based on the above conclusions, it is suggested that future groundwater management should control the amount of agricultural fertilizers, apply scientific fertilization, and prohibit the discharge of various types of non-compliant sewage, while strengthening the supervision of groundwater extraction to reduce the impact of urbanization development on the groundwater chemical evolution process. Full article
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<p>Distribution map of groundwater sample sites.</p>
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<p>Relationship between pH and the total cations in groundwater.</p>
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<p>Characteristics of changes in the major chemical components of groundwater.</p>
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<p>Piper trilinear diagram of groundwater in different stages of urbanization.</p>
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<p>Gibbs diagrams for groundwater in different stages of urbanization ((<b>a</b>) Cl<sup>−</sup>/[Cl<sup>−</sup> + HCO<sub>3</sub><sup>−</sup>] vs. TDS; (<b>b</b>) Na<sup>+</sup>/[Na<sup>+</sup> + Ca<sup>2+</sup>] vs. TDS).</p>
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<p>Gibbs diagrams for groundwater in different stages of urbanization ((<b>a</b>) Cl<sup>−</sup>/[Cl<sup>−</sup> + HCO<sub>3</sub><sup>−</sup>] vs. TDS; (<b>b</b>) Na<sup>+</sup>/[Na<sup>+</sup> + Ca<sup>2+</sup>] vs. TDS).</p>
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<p>Relationships between major ions for groundwater samples in different stages of urbanization (<b>a</b>) (Ca<sup>2+</sup>/Na<sup>+</sup>) vs. (Mg<sup>2+</sup>/Na<sup>+</sup>); (<b>b</b>) Cl<sup>−</sup> vs. Na<sup>+</sup>; (<b>c</b>) [Ca<sup>2+</sup> + Mg<sup>2+</sup>] vs. [HCO<sub>3</sub><sup>−</sup> + SO<sub>4</sub><sup>2−</sup>]; (<b>d</b>) SO<sub>4</sub><sup>2−</sup> vs. Ca<sup>2+</sup>; (<b>e</b>) [Na<sup>+</sup> + K<sup>+</sup>-Cl<sup>−</sup>] vs. [Ca<sup>2+</sup> + Mg<sup>2+</sup>-HCO<sub>3</sub><sup>−</sup>-SO<sub>4</sub><sup>2−</sup>].</p>
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<p>Variation relationship of SO<sub>4</sub><sup>2−</sup>/Na<sup>+</sup> and NO<sub>3</sub><sup>−</sup>/Na<sup>+</sup> in different stages of urbanization.</p>
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16 pages, 7121 KiB  
Article
Surface Subsidence of Nanchang, China 2015–2021 Retrieved via Multi-Temporal InSAR Based on Long- and Short-Time Baseline Net
by Hua Gao, Luyun Xiong, Jiehong Chen, Hui Lin and Guangcai Feng
Remote Sens. 2023, 15(13), 3253; https://doi.org/10.3390/rs15133253 - 24 Jun 2023
Cited by 3 | Viewed by 2853
Abstract
Urban land subsidence threatens the safety of urban buildings and people’s lives. The time series interferometric synthetic aperture radar (InSAR) technology can provide us with large-area, high-resolution, and high-precision ground deformation monitoring. In this study, the time series InSAR technology and the strategy [...] Read more.
Urban land subsidence threatens the safety of urban buildings and people’s lives. The time series interferometric synthetic aperture radar (InSAR) technology can provide us with large-area, high-resolution, and high-precision ground deformation monitoring. In this study, the time series InSAR technology and the strategy with long- and short-time baseline networking are used to obtain the surface deformation along the line of sight of Nanchang City based on the six-year (from December 2015 to December 2021) Sentinel-1 data. Longer datasets and better baseline strategies allow us to obtain more stable deformation results of Nanchang City than other researchers. The results of surface deformation show that the overall surface of Nanchang City is stable, but there are several obvious subsidence funnels. We carried out a field survey on four areas with significant surface subsidence. We considered that these subsidence areas may be related to soil compaction, building construction, and groundwater extraction. Based on the surface deformation results around the subway line, we analyzed the impact of subway construction on the surface along the line and identified the sections that need to be focused on by the managers to prevent the deformation area from affecting the surrounding buildings and subway line operation safety. Full article
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<p>Study area. The yellow background area is the main urban area of Nanchang City. The black line in the main figure is the road network. The black dotted box shows the coverage of Sentinel-1 data used in this study. The inside figure shows the location of Nanchang City.</p>
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<p>The spatiotemporal baseline net. The yellow dot shows the image acquisition dates. The green line is the short-time (&lt;48 days) baseline and the blue line is the long-time (356–375 days) baseline.</p>
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<p>Surface deformation in LOS of Nanchang. The background color in the figure is LOS deformation. The black and gray line is the road. The red dotted line is the fault [<a href="#B27-remotesensing-15-03253" class="html-bibr">27</a>].</p>
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<p>LOS surface deformation and fieldwork results in the west of Xiazhuang Lake (Area A). (<b>a</b>) LOS surface deformation of Area A. (<b>b</b>–<b>f</b>) the fieldwork results of Area A.</p>
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<p>LOS surface deformation and fieldwork results in a riverside park (Area B). (<b>a</b>) LOS surface deformation of Area B. (<b>b</b>–<b>d</b>) the fieldwork results of Area B.</p>
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<p>LOS surface deformation and fieldwork results of Shiqi Village resettlement community (Area C). (<b>a</b>) LOS surface deformation of Area C. (<b>b</b>–<b>d</b>) the fieldwork results of Area C.</p>
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<p>LOS surface deformation and fieldwork results of the warehouse logistics park (Area D). (<b>a</b>) LOS surface deformation of Area D. (<b>b</b>) the fieldwork results of Area D.</p>
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<p>Precision and statistical chart of LOS deformation. (<b>a</b>) The distribution of all observation deformation rates. (<b>b</b>) The distribution of fitting accuracy. (<b>c</b>) The figure of fitting accuracy.</p>
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<p>Time series deformation curve of main subsidence area. The time series is the average of observation points within a radius of 50 m. (<b>a</b>–<b>h</b>) Time series deformation of points P1–P8.</p>
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<p>LOS deformation along the Metro. (<b>a</b>) Global LOS deformation along the Metro (<b>b</b>–<b>h</b>) Enlarged view of obvious deformation areas along the subway line.</p>
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<p>Time series deformation curve of subsidence area along the subway lines. The time series is the average of observation points within a radius of 50 m. Time series deformation of points S1–S4.</p>
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19 pages, 7424 KiB  
Article
Identification and Spatiotemporal Migration Analysis of Groundwater Drought Events in the North China Plain
by Jia Huang, Lianhai Cao, Lei Wang, Liwei Liu, Baobao Yu and Long Han
Atmosphere 2023, 14(6), 961; https://doi.org/10.3390/atmos14060961 - 31 May 2023
Cited by 1 | Viewed by 1524
Abstract
Groundwater droughts can explain developments and changes in groundwater from a climatological perspective. The North China Plain (NCP) is a typical underground funnel area. Therefore, groundwater drought studies in the NCP can provide better understanding of the local hydrogeological characteristics from new perspectives. [...] Read more.
Groundwater droughts can explain developments and changes in groundwater from a climatological perspective. The North China Plain (NCP) is a typical underground funnel area. Therefore, groundwater drought studies in the NCP can provide better understanding of the local hydrogeological characteristics from new perspectives. In this paper, the GRACE groundwater drought index (GGDI) was used to evaluate groundwater drought events in the NCP. Additionally, a new method was proposed in this study for investigating groundwater drought events at the spatiotemporal scale. On this basis, the centroid theory was used to construct an appropriate groundwater drought migration model for the NCP. The results showed that (1) the groundwater drought frequency in the NCP was 24.54%. In addition, the most severe groundwater drought events in the study occurred in March 2020. (2) In total, 49 groundwater drought events occurred in the NCP over the 2003–2020 period. The most intense groundwater drought event occurred over the June 2018–December 2020 period (DE.49), covering the entire study area. DE.29 was the second most intense groundwater drought event over the August 2012–September 2013 period (14 months), resulting in a maximum arid area of 75.57% of the entire study area. (3) The migration of the groundwater drought events was in the southwest–northeast and northeast–southwest directions, which was consistent with the terrain inclination, while most of the groundwater drought centroids were concentrated in Area II. The groundwater drought event identification method and the groundwater drought migration model were effective and reliable for assessing groundwater drought events in the NCP and provided a better understanding of developments and changes in groundwater droughts, which is of great practical significance and theoretical value for the rational development and use of groundwater resources, as well as for guiding industrial and agricultural activities. Full article
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<p>The geographical location of the North China Plain.</p>
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<p>The groundwater drought event identification process (<b>a</b>) shows the drought grid identification, (<b>b</b>) shows the drought event spatial continuity identification, (<b>c</b>,<b>d</b>) shows the drought event temporal continuity identification, and (<b>e</b>–<b>h</b>) shows the drought event spatial-temporal continuity identification.)</p>
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<p>A comparison of the GRACE and GLDAS data from the North China Plain and its various geomorphic areas ((<b>a</b>–<b>d</b>) indicates different partitions).</p>
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<p>The GGDI times series of the North China Plain and its sub-areas.</p>
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<p>The groundwater drought events in the North China Plain.</p>
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<p>The spatial distributions of the groundwater drought event paths ((<b>a</b>) is the migration trajectory of all drought events, and (<b>b</b>–<b>l</b>) is the zoomed-in migration trajectory of each drought event).</p>
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<p>Changes in the DE.49 groundwater drought event characteristics: (<b>a</b>) migration processes and changes in the intensity of arid plasmas; (<b>b</b>) changes in drought areas and drought intensities during drought development.</p>
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<p>Changes in the DE.29 groundwater drought event characteristics: (<b>a</b>) migration processes and changes in the intensity of arid plasmas; (<b>b</b>) changes in drought areas and drought intensities during drought development.</p>
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<p>Changes in the DE.46 groundwater drought event characteristics: (<b>a</b>) migration processes and changes in the intensity of arid plasmas; (<b>b</b>) changes in drought areas and drought intensities during drought development.</p>
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32 pages, 10007 KiB  
Article
Land Subsidence Monitoring and Building Risk Assessment Using InSAR and Machine Learning in a Loess Plateau City—A Case Study of Lanzhou, China
by Yuanmao Xu, Zhen Wu, Huiwen Zhang, Jie Liu and Zhaohua Jing
Remote Sens. 2023, 15(11), 2851; https://doi.org/10.3390/rs15112851 - 30 May 2023
Cited by 3 | Viewed by 2619
Abstract
As a representative city located in the Loess Plateau region of China, Lanzhou is affected by various environmental and engineering factors, such as precipitation, earthquake subsidence, and building construction, which all lead to frequent geological disasters. Obtaining information on land subsidence over a [...] Read more.
As a representative city located in the Loess Plateau region of China, Lanzhou is affected by various environmental and engineering factors, such as precipitation, earthquake subsidence, and building construction, which all lead to frequent geological disasters. Obtaining information on land subsidence over a long time series helps us grasp the patterns of change in various types of ground hazard. In this paper, we present the results of using Interferometric Synthetic Aperture Radar (InSAR) to monitor land subsidence in the main urban area of Lanzhou from 26 October 2014 to 12 December 2021. The main influential factors leading to subsidence were analyzed and combined via machine learning simulation to assess the land subsidence risk grade distribution of a building unit. The results show that the annual average deformation rate in Lanzhou ranged from −18.74 to 12.78 mm/yr. Linear subsidence dominated most subsidence areas in Lanzhou during the monitoring period. The subsidence areas were mainly distributed along the Yellow River, the railway, and villages and towns on the edges of urban areas. The main areas where subsidence occurred were the eastern part of Chengguan District, the railway line in Anning District, and the southern parts of Xigu District and Qilihe urban area, accounting for 38.8, 43.5, 32.5, and 51.8% of the area of their respective administrative districts, respectively. The random forest model analysis results show that the factors influencing surface subsidence in Lanzhou were, in order of importance, precipitation, the distribution of faults, the lithology of strata, high-rise buildings, and the distance to the river and railway. Lanzhou experienced excessive groundwater drainage in some areas from 2015 to 2017, with a 1 m drop in groundwater and 14.61 mm surface subsidence in the most critical areas. At the same time, extensive subsidence occurred in areas with highly compressible loess ground and most railway sections, reaching a maximum of −11.68 mm/yr. More than half of the super-tall building areas also showed settlement funnels. The area at a very high risk of future subsidence in Lanzhou covers 22.02 km2, while the high-subsidence-risk area covers 54.47 km2. The areas at greatest risk of future subsidence are Chengguan District and Qilihe District. The city contains a total of 51,163 buildings in the very high-risk area, including about 44.57% of brick-and-timber houses, 51.36% of old housing, and 52.78% of super-tall buildings, which are at especially high risk of subsidence, threatening the lives and properties of the population. The deformation results reveal poor building safety in Lanzhou, providing an essential basis for future urban development and construction. Full article
(This article belongs to the Section Environmental Remote Sensing)
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<p>Location of the study area.</p>
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<p>Spatiotemporal baseline combination of the SBAS-InSAR method.</p>
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<p>Spatiotemporal baseline combination of the PS-InSAR method.</p>
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<p>Maps of the average displacement velocity in the study area. (<b>V</b>) A vertical displacement velocity map of Lanzhou from October 2014 to December 2021, with blue boxes representing the uplifted areas and red boxes representing the subsidence areas according to administrative divisions. (<b>I</b>–<b>IV</b>) Enlarged detailed views of the red boxes in figure (<b>V</b>). Boxes of different colors marked by letters represent division of the settlement area due to the different causes of primary control, as analyzed in <a href="#sec4dot1dot3-remotesensing-15-02851" class="html-sec">Section 4.1.3</a>.</p>
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<p>Time series of cumulative subsidence in the vertical direction. In total, 11 representative images were selected out of 145 view images for temporal comparison.</p>
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<p>Locations of the cross-sectional time series point plots analyzed in <a href="#remotesensing-15-02851-f007" class="html-fig">Figure 7</a>.</p>
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<p>(<b>a</b>–<b>d</b>) Time series plots of subsidence along the west–east cross-section in the four main urban areas, and (<b>e</b>–<b>h</b>) time series point line plots of certain subsidence points in the four main urban areas. (<b>a</b>–<b>e</b>) Ⅰ Anning District, (<b>b</b>–<b>f</b>) Ⅱ Xigu District, (<b>c</b>–<b>g</b>) Ⅲ Qilihe District, and (<b>d</b>–<b>h</b>) Ⅳ Chengguan District.</p>
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<p>A histogram of the rate difference between PS-InSAR and SBAS-InSAR methods.</p>
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<p>Comparison of the vertical velocity correlation estimated using PS-InSAR and SBAS-InSAR methods.</p>
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<p>Groundwater level monitoring point data versus surface deformation. (<b>a</b>) The superimposed locations and their subsidence rates for the three selected groundwater level monitoring points. (<b>b</b>–<b>d</b>) Fold plots of the surface deformation and groundwater level time series for monitoring points Q20, Q62, and Q96, respectively.</p>
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<p>An overlay of a vertical displacement velocity map of Lanzhou geotechnical zoning between 2014 and 2021. F1 to F4 on the map are faults, F1–1 and F1–2 represent the Jinchengguan Fault, F2 is the Leitan River Fault, F3 is the Siergou Fault, and F4 is the Xijincun Fault.</p>
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<p>Subsidence rate maps for eight profiles near the Leitan River Fault and the Jinchengguan Fault. (<b>a</b>,<b>c</b>) Subsidence rate maps and fault locations; (<b>b</b>,<b>d</b>) subsidence rate profiles from V1 to V8.</p>
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<p>A railway buffer zone map overlaid with a ground deformation map. The four bottom maps, <b>A</b>, <b>B</b>, <b>D</b>, <b>E</b>, enlarge the four areas in red boxes above, where the black dashed lines are the linear intervals of settlement along the railway line after the offset.</p>
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<p>Lanzhou building height and building structure information maps.</p>
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<p>A superimposed deformation map of the Lanzhou building height; red boxes are areas of severe subsidence, and the surface building height is greater than 100 m.</p>
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<p>Subsidence risk maps of the main urban area of Lanzhou. (<b>I</b>–<b>IV</b>) Detailed views of the enlarged red boxes in (<b>V</b>). Except for the new black boxes, the other boxes correspond to those in (<b>I</b>–<b>IV</b>) in <a href="#remotesensing-15-02851-f003" class="html-fig">Figure 3</a>, indicating that subsidence is caused by different causes. The letters beside the boxes are the markings of the boxes. The five new black boxes n1–n5 are new risk areas for possible subsidence after comparing the results of subsidence monitoring in Lanzhou.</p>
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<p>Statistical maps of risk levels by administrative district: (<b>Ⅰ</b>) for Anning District, (<b>Ⅱ</b>) for Xigu District, (<b>Ⅲ</b>) for Qilihe District, and (<b>Ⅳ</b>) for Chengguan District.</p>
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22 pages, 40795 KiB  
Article
Surface Subsidence Characteristics and Causes in Beijing (China) before and after COVID-19 by Sentinel-1A TS-InSAR
by Haiquan Sheng, Lv Zhou, Changjun Huang, Shubian Ma, Lingxiao Xian, Yukai Chen and Fei Yang
Remote Sens. 2023, 15(5), 1199; https://doi.org/10.3390/rs15051199 - 22 Feb 2023
Cited by 4 | Viewed by 1849
Abstract
Surface subsidence is a serious threat to human life, buildings and traffic in Beijing. Surface subsidence is closely related to human activities, and human activities in Beijing area showed a decreasing trend during the Corona Virus Disease 2019 (COVID-19). To study surface subsidence [...] Read more.
Surface subsidence is a serious threat to human life, buildings and traffic in Beijing. Surface subsidence is closely related to human activities, and human activities in Beijing area showed a decreasing trend during the Corona Virus Disease 2019 (COVID-19). To study surface subsidence in Beijing before and after the COVID-19 outbreak and its causes, a total of 51 Sentinel-1A SAR images covering Beijing from January 2018 to April 2022 were selected to derive subsidence information by Time Series Interferometry Synthetic Aperture Radar (TS-InSAR). The results of surface subsidence in Beijing demonstrate that Changping, Chaoyang, Tongzhou and Daxing Districts exhibited the most serious subsidence phenomenon before the COVID-19 outbreak. The four main subsidence areas form an anti-Beijing Bay that surrounds other important urban areas. The maximum subsidence rate reached −57.0 mm/year. After the COVID-19 outbreak, the main subsidence area was separated into three giant subsidence funnels and several small subsidence funnels. During this period, the maximum subsidence rate was reduced to −43.0 mm/year. Human activity decrease with the COVID-19 outbreak. This study effectively analysed the influence of natural factors on surface subsidence after excluding most of the human factors. The following conclusions are obtained from the analysis: (1) Groundwater level changes, Beijing’s geological structure and infrastructure construction are the main reasons for surface subsidence in Beijing. (2) Seasonal changes in rainfall and temperature indirectly affect groundwater level changes, thereby affecting surface subsidence in the area. (3) The COVID-19 outbreak in early 2020 reduced the payload of Beijing’s transportation facilities. It also slowed down the progress of various infrastructure construction projects in Beijing. These scenarios affected the pressure on the soft land base in Beijing and reduced the surface subsidence trend to some extent. Full article
(This article belongs to the Special Issue Applications of SAR Images for Urban Areas)
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Graphical abstract

Graphical abstract
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<p>The digital elevation model (DEM) map of Beijing, China. The black curve shows the administrative division of Beijing. The dark green box is the extent of the SAR imagery, and the light green box is the main study area, which covers most of the Beijing plain. The red star represents that it is China’s capital.</p>
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<p>PS-InSAR flowchart.</p>
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<p>(<b>a</b>) Space–time baseline plot from January 2018 to March 2020. (<b>b</b>) Space–time baseline plot from February 2020 to April 2022.</p>
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<p>Surface subsidence rate in Beijing from January 2018 to March 2020 (before the COVID-19 outbreak). ‘CP’ is Changping District, ‘HD’ stands for Haidian District, ‘CY’ is located in Chaoyang District, ‘TZ’ means Tongzhou District and ‘DX’ is Daxing District.</p>
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<p>Surface subsidence rate in Beijing from February 2020 to April 2022 (after the COVID-19 outbreak). ‘CP’ is Changping District, ‘HD’ stands for Haidian District, ‘CY’ is located in Chaoyang District, ‘TZ’ means Tongzhou District and ‘DX’ is Daxing District.</p>
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<p>Time series of cumulative subsidence before the COVID-19 outbreak.</p>
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<p>Time series of cumulative subsidence after the COVID-19 outbreak.</p>
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<p>(<b>a</b>). Probability density of PS point subsidence information quality for January 2018 to March 2020. (<b>b</b>). Probability density of PS point subsidence information quality for February 2020 to April 2022.</p>
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<p>The vector map of Beijing. The red star represents that it is China’s capital. The red dot is the Beiyuan Street Weather Monitoring Station, and the red square is the groundwater burial depth monitoring station.</p>
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<p>Comparison of groundwater level and average cumulative deformation variables. (<b>a</b>–<b>d</b>) are the changes in groundwater level wells (<b>a</b>–<b>d</b>), respectively.</p>
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<p>(<b>a</b>). Plot of cumulative surface deposition versus rainfall before the COVID-19 outbreak. (<b>b</b>). Selected reference points before the COVID-19 outbreak. (<b>c</b>). Plot of cumulative surface deposition versus rainfall after the COVID-19 outbreak. (<b>d</b>). Selected reference points after the COVID-19 outbreak. The red column is the rainfall data with a 1-day interval; the grey broken line is the sequential cumulative subsidence of the reference point with an interval of 1 month. The green horizontal line is the 50 mm line of rainfall.</p>
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<p>(<b>a</b>) Planar distribution of geological formations in Beijing. The black box is the study area in this work, and the black folded lines in the black box are the cross-sectional lines. (<b>b</b>) Cross-sectional view of geological formations in Beijing.</p>
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<p>(<b>a</b>). Surface subsidence rate from January 2018 to March 2020 and geological horizontal distribution. (<b>b</b>). Surface subsidence rate from February 2020 to April 2022 and geological horizontal distribution.</p>
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<p>(<b>a</b>). Temperature data with surface deposition information from January 2018 to March 2020; the accumulated subsidence is shown in <a href="#remotesensing-15-01199-f011" class="html-fig">Figure 11</a>a. (<b>b</b>). Temperature data with surface deposition information for February 2020 to April 2022.</p>
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<p>(<b>a</b>,<b>b</b>). Subsidence rate scenario for G1 between January 2018 and March 2020. (<b>c</b>,<b>d</b>). Subsidence rate scenario for G1 between February 2020 and April 2022.</p>
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<p>(<b>a</b>). Beijing section of the Beijing–Harbin Expressway. The red curve is the G1 motorway, the two white boxes indicate two important transport hubs and the blue box represents the newly constructed bridges. (<b>b</b>). Subsidence rate in the construction area from January 2018 to March 2020. (<b>c</b>). Subsidence in the construction area from February 2020 to April 2022; the blue line is G1. Other images are the conditions of this area at different times.</p>
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15 pages, 3701 KiB  
Article
Optimizing the Cropland Fallow for Water Resource Security in the Groundwater Funnel Area of China
by Hong Chen, Sha Chen, Runjia Yang, Liping Shan, Jinmin Hao and Yanmei Ye
Land 2023, 12(2), 462; https://doi.org/10.3390/land12020462 - 12 Feb 2023
Cited by 2 | Viewed by 2212
Abstract
Excessive exploitation of groundwater for agricultural irrigation has resulted in groundwater funnel, causing land subsidence, water pollution, and vegetation degradation. The cropland fallow is an effective tool to maintain groundwater by reducing water consumption from agricultural irrigation. However, the cropland fallow program of [...] Read more.
Excessive exploitation of groundwater for agricultural irrigation has resulted in groundwater funnel, causing land subsidence, water pollution, and vegetation degradation. The cropland fallow is an effective tool to maintain groundwater by reducing water consumption from agricultural irrigation. However, the cropland fallow program of fallow areas and fallow locations based on the protection of water resources at county level is unclear. The objective of this study is to improve the efficiency of cropland fallows under the premise of ensuring regional food security. In this study, we assessed the fallow urgency using IPLI (irrigation profit/loss index) and SGDCR (shallow groundwater depth change rate) and analyzed the cropland fallow areas and cropland fallow locations in Quzhou County, which is located in the world’s largest groundwater funnel area. The results showed that winter wheat’s irrigation water is in short supply (IPLI value is 0.1173), while that of summer maize and cotton’s irrigation water are in excessive supply (−0.9849 and −0.0071, respectively), and the depth to groundwater is deeper in the south and east in Quzhou County. The GM (1,1) gray prediction model showed that the cropland area that can be fallowed is 4089.288 hm2, 1189.288 hm2 larger than the current cropland fallow area (2900 hm2) according to official figures. In addition, two townships in southeast Quzhou county (Yizhuang and Houcun town) should be given high priority for cropland fallow; this is different from the current fallow cropland plots, distributed in eight townships (Yizhuang, Houcun, Nanliyue, Huaiqiao, Disituan, Henantuan, Baizhai, and Quzhou town). These results were useful to improve the cropland fallow program with the actual needs of the groundwater funnel area and develop the cropland fallow program from the aspects of “quality”, “quantity”, and “positioning” at county level. Full article
(This article belongs to the Section Land Environmental and Policy Impact Assessment)
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<p>Location of Quzhou County in Hebei Province, China. Note: This map of China is from the standard map system of the Ministry of Natural Resources of China.</p>
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<p>The overall framework of the study.</p>
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<p>Shallow groundwater depth change rate (SGDCR) in Quzhou County in 2018 (multi-year average of shallow groundwater depth (MASGD)).</p>
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<p>Classification of the urgency situation (<b>a</b>) and urgency level (<b>b</b>) of cropland fallow in Quzhou County in 2018.</p>
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<p>The total cropland, predicted fallow cropland, and current cropland in Quzhou County in 2018.</p>
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<p>Spatial distribution of the existing fallow cropland (<b>a</b>) and the selected fallow cropland (<b>b</b>) in Quzhou County in 2018.</p>
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18 pages, 6950 KiB  
Article
Land Subsidence Characteristics and Numerical Analysis of the Impact on Major Infrastructure in Ningbo, China
by Feng Gao, Tuanzhi Zhao, Xuebin Zhu, Lingwei Zheng, Wenjun Wang and Xudong Zheng
Sustainability 2023, 15(1), 543; https://doi.org/10.3390/su15010543 - 28 Dec 2022
Cited by 3 | Viewed by 2235
Abstract
For the construction and safe operation of major infrastructure in coastal cities, the impact of regional land subsidence that has occurred or is slowly proceeding deserves attention. Previous studies have mainly focused on the surrounding land subsidence caused during construction or operation, as [...] Read more.
For the construction and safe operation of major infrastructure in coastal cities, the impact of regional land subsidence that has occurred or is slowly proceeding deserves attention. Previous studies have mainly focused on the surrounding land subsidence caused during construction or operation, as well as the superposition effect of land subsidence caused by groundwater extraction. However, research on the different impacts of damage due to land subsidence in the construction and operation of urban infrastructure needs to be carried out according to the actual geological environmental conditions, reflected in parameters such as the soil properties and common loads. Numerical simulation cannot fully reflect the details of reality; however, it can avoid the influence of other conditions to focus on different factors influencing land subsidence and thus highlight the contribution of a single factor influencing land subsidence. Therefore, in this paper, we adopt field measurement data and carry out a numerical simulation analysis of different influencing factors. First, taking the Ningbo Jiangdong subsidence center (now located in Yinzhou District) as an example, area growth, cumulative subsidence and the occurrence and development of the subsidence rate of a typical urban subsidence funnel area are analyzed. Then, taking the Ningbo Chunxiao–Meishan area as an example, based on the physical and mechanical characteristics of the main soil layers in the coastal reclamation area, a numerical analysis of the self-weight/backfill and surcharge consolidation settlement of the soil layer (considering the water permeability/impermeability of the bottom surface) and a numerical analysis of the nonuniform settlement caused by pile foundation engineering are carried out. Finally, the Ximenkou–Gulou area is taken as the analysis object. Numerical simulation of metro tunnel pipeline deformation is carried out considering uniform/nonuniform settlement. The results show that the comprehensive prohibition of groundwater exploitation is beneficial to slow the land subsidence rate, while the sedimentation of silty clay in Layer 4 (muddy clay) is the largest among all the soil layers. Compared with uniform settlement, nonuniform settlement is more likely to cause connection failure between tunnel segments. The above research results can provide references for the prevention and control of land subsidence and thus the safe operation of major infrastructure. Full article
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<p>Growth of the land subsidence funnel area of the Jiangdong subsidence center.</p>
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<p>Variation in the cumulative subsidence (<b>a</b>) and subsidence rate (<b>b</b>) of the Jiangdong subsidence center.</p>
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<p>Variation in the cumulative subsidence (<b>a</b>) and subsidence rate (<b>b</b>) of the Jiangdong subsidence center.</p>
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<p>Development of land subsidence with time under the self-weight consolidation condition.</p>
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<p>Development of land subsidence for each soil layer with time under the self-weight consolidation condition. (<b>a</b>) Impermeable surface. (<b>b</b>) Permeable surface.</p>
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<p>Development of land subsidence for each soil layer with time under the self-weight consolidation condition. (<b>a</b>) Impermeable surface. (<b>b</b>) Permeable surface.</p>
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<p>Development of land subsidence with time under backfill and surcharge.</p>
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<p>Development of land subsidence for each soil layer with time under backfill and surcharge. (<b>a</b>) Impermeable surface. (<b>b</b>) Permeable surface.</p>
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<p>Development of land subsidence for each soil layer with time under backfill and surcharge. (<b>a</b>) Impermeable surface. (<b>b</b>) Permeable surface.</p>
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<p>Finite element model and mesh division of a single pile. (<b>a</b>) Finite element model. (<b>b</b>) Mesh division.</p>
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<p>Finite element model and mesh division of a single pile. (<b>a</b>) Finite element model. (<b>b</b>) Mesh division.</p>
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<p>Subsidence pattern of the consolidated soil.</p>
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<p>Variation in nonuniform soil settlement with time.</p>
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<p>Typical Ximenkou–Gulou geological section in Ningbo.</p>
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<p>Plane strain finite element model of the metro tunnel. (<b>a</b>) Finite element model. (<b>b</b>) Mesh division.</p>
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<p>Relationship between tunnel inner diameter shrinkage and land subsidence (uniform).</p>
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<p>Von Mises stress patterns of the tunnel (deformation amplification factor = 50).</p>
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<p>Vertical displacement patterns of the stratum (deformation amplification factor = 200).</p>
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<p>Vertical displacement and von Mises stress of the tunnel segment (deformation amplification factor = 200). (<b>a</b>) Vertical displacement. (<b>b</b>) Von Mises stress.</p>
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<p>Vertical displacement and von Mises stress of the tunnel segment (deformation amplification factor = 200). (<b>a</b>) Vertical displacement. (<b>b</b>) Von Mises stress.</p>
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<p>Relationship between the tunnel inner diameter shrinkage and the ground subsidence (nonuniform).</p>
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13 pages, 5586 KiB  
Article
Effect of Groundwater Depression Cone on the Hydrochemical Evolution Process in the People’s Victory Canal Irrigation Area, China
by Shaoyi Feng, Zhongpei Liu, Yuping Han, Lu Wang, Zhipeng Hu and Mingkun Qi
Processes 2022, 10(12), 2563; https://doi.org/10.3390/pr10122563 - 1 Dec 2022
Cited by 3 | Viewed by 1592
Abstract
The over-exploitation of shallow groundwater in the People’s Victory Canal irrigation area has led to the continuous decline in the groundwater level. The formation of a groundwater drawdown cone has changed the original runoff conditions and hydrochemical environment. Based on the groundwater data [...] Read more.
The over-exploitation of shallow groundwater in the People’s Victory Canal irrigation area has led to the continuous decline in the groundwater level. The formation of a groundwater drawdown cone has changed the original runoff conditions and hydrochemical environment. Based on the groundwater data in the irrigated area from 1996 to 2022, multivariate statistical analysis, traditional hydrochemical methods, and inverse geochemical modeling were used to reveal the impact of the formation of the groundwater depression cone on hydrochemical evolution. The results show that the formation of the groundwater depression cone near the central area in 2003 changed the direction of the canal head flowing to the northwest area, making the groundwater flow from the canal head and the northwest area to the central area. The change in the hydrodynamic fields also caused the groundwater with high salinity in the northwest region to flow to the funnel area, and the ion concentration of groundwater along the pathway area to increase. The groundwater type in the runoff area changes, gradually evolving from Group 1 to Group 2 groundwater. Analysis of the hydrochemical characteristics of groundwater in the runoff area for many years shows that after the formation of the central funnel area in 2003, the groundwater with high SO42 ion in the northwest area flows to the funnel area, and the correlation between total dissolved solids and SO42 ions in the groundwater along the way is significantly enhanced. The inverse geochemical modeling shows that the main water–rock action along the runoff direction is the dissolution of halite and gypsum. In addition, the study area has a strong cation exchange reaction. Full article
(This article belongs to the Section Chemical Processes and Systems)
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<p>Study area map.</p>
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<p>Location map showing sampling site of the study area. ((<b>a</b>) is the distribution of all sampling points, (<b>b</b>) is sampling points of runoff path selected for inverse geochemical modeling in 2022).</p>
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<p>Spatial distribution of groundwater level ((<b>a</b>–<b>f</b>) are 1996, 2001, 2003, 2013, 2016, and 2022, respectively).</p>
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<p>Spatial distribution of groundwater level ((<b>a</b>–<b>f</b>) are 1996, 2001, 2003, 2013, 2016, and 2022, respectively).</p>
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<p>Dendrogram of the Q-mode hierarchical cluster analysis. ((A–F) represent groundwater sampling points in 1996, 2001, 2003, 2013, 2016, and 2022, respectively).</p>
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<p>Box diagram of main physical and chemical indicators of cluster group ((<b>a</b>–<b>c</b>) represent the groundwater ion concentrations of G1, G2, and G3, respectively) and Piper diagram (<b>d</b>).</p>
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<p>Box diagram of main physical and chemical indicators of cluster group ((<b>a</b>–<b>c</b>) represent the groundwater ion concentrations of G1, G2, and G3, respectively) and Piper diagram (<b>d</b>).</p>
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<p>Spatial distribution of clusters representing years ((<b>a</b>–<b>f</b>) are 1996, 2001, 2003, 2013, 2016, and 2022, respectively).</p>
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<p>The ion concentration of groundwater sampling points changes over the years ((<b>a</b>), N17 sampling sites are located in the northwest region; (<b>b</b>), N01 sampling points located in the canal head area; (<b>c</b>,<b>d</b>) are N53, N02 sampling points located in the runoff area, and (<b>e</b>), N05 sampling point is located in the funnel area).</p>
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<p>The ion concentration of groundwater sampling points changes over the years ((<b>a</b>), N17 sampling sites are located in the northwest region; (<b>b</b>), N01 sampling points located in the canal head area; (<b>c</b>,<b>d</b>) are N53, N02 sampling points located in the runoff area, and (<b>e</b>), N05 sampling point is located in the funnel area).</p>
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14 pages, 13685 KiB  
Article
Evaluation of Groundwater Flow Changes Associated with Drainage within Multilayer Aquifers in a Semiarid Area
by Li Chen, Xiaojun Wang, Gelong Liang and Haicheng Zhang
Water 2022, 14(17), 2679; https://doi.org/10.3390/w14172679 - 29 Aug 2022
Cited by 2 | Viewed by 1952
Abstract
In order to evaluate the impact of groundwater drainage on groundwater flow, the Hetaoyu coal field was taken as a case study in the Longdong area, China, where the coal seam was covered with multilayer aquifers. A three-dimensional unsteady groundwater flow model and [...] Read more.
In order to evaluate the impact of groundwater drainage on groundwater flow, the Hetaoyu coal field was taken as a case study in the Longdong area, China, where the coal seam was covered with multilayer aquifers. A three-dimensional unsteady groundwater flow model and a one-dimensional fracture water flow model were calculated by joint equations for changing hydrogeological structures under coal mining. According to the results, mine construction had greatly affected groundwater reserves in the Quaternary phreatic aquifer, Cretaceous Huanhe confined aquifer, and Luohe confined aquifer. The groundwater drainage was mainly from the Cretaceous aquifer, in which the aquifer reserves of the Luohe Formation decreased by 30,861.8 m3/m, accounting for about 92% of the total changes in local groundwater reserves. A drop funnel with an area of about 2.3 km2 would be formed under the groundwater discharge of 187.6 m3/h for the main inclined shaft excavation of the Hetaoyu coal mine. With the continuation of mining activities, the mine water flow will reach 806.83 m3/h and would result in descending funnel area of about 4.5 km2, the groundwater level drawdown at least 16 m, which would exceed the limited value regulated by the government. Therefore, in order to ensure the safety of coal mining and protect groundwater resources, the Hetaoyu Coal Mine departments should take some water loss prevention and control projects to reduce the drawdown of groundwater. Full article
(This article belongs to the Special Issue Flow and Transport Processes in Groundwater Systems)
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<p>The location of the Hetaoyu coal field.</p>
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<p>The schematic diagram of the study area and the isolevel map of the Luohe Formation aquifer.</p>
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<p>Diagram of the hydrogeological section of the mining area.</p>
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<p>Schematic diagram of calculation nodes of the groundwater flow model.</p>
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<p>Time variations of groundwater drainage with coal mining in Hetaoyu Coal Mine.</p>
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<p>Temporal variations of groundwater level under groundwater drainage in Hetaoyu Coal Mine. (<b>a</b>) Luohe aquifer (1#) and mixed groundwater level (2#). (<b>b</b>) Huanhe aquifer (3#).</p>
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<p>The correlation under different lag-time for #1 and #1.</p>
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<p>The water table contour map of the Cretaceous Luohe Formation aquifers under the groundwater discharge was 187.6 m<sup>3</sup>/h.</p>
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<p>The time variation of aquifer reserve reduction under coal mining.</p>
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<p>Groundwater level drawdown under the different groundwater drainage.</p>
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<p>Prediction of the mine water inflow caused by the water-conducting fracture zone under coal mining.</p>
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<p>The groundwater level contour map under the groundwater drainage rate was 806.83 m<sup>3</sup>/h: (<b>a</b>) the Huanhe Formation aquifers; (<b>b</b>) the Luohe Formation aquifer.</p>
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