The growth and evolution of sinkholes are a considerable proportion of the damage related to subsidence disaster in alluvial areas after ground water extraction for irrigation. In this research it was tried to study the evolution of the sinkholes from the birth point to the stabilization or final step. In the Eqlid-Abarkooh alluvial fan was selected an area about 300 km2 with giant sinkholes where consist; the city of Abarkooh, arable irrigated lands and desert rangelands. The major aspect on the study area was southwest to northeast where it ended to Abarkooh playa. For investigating the formation and evolution of these sinkholes in the study area, field observation for 2 years were done. Soil samples were taken from surface soils (0-25 cm) near and far of the sinkholes. Moreover, 4 soil samples were obtained from the deepest sinkhole as control sample in the study area. Chemical, physical and mechanical soil analyses were performed. Finally, the Ground Penetrating Radar (GPR) method were done for detection subsurface holes to depth of 4 m around the sinkholes. The chemical soil properties results include Electro Conductivity (EC) and the ratio of Ca2+/Mg2+ in lime which was important factors to formation of sinkholes changed from 2.05 to 19.3 dS/m, 0.15 to 6 respectively. The mechanical soil parameters such as Coefficient of Linear Extensibility (COLE) and Plasticity Index (PI) changed from 0.05 to 1.67, 0.99% to 15% respectively. According to sinkhole development, the results obtained that there was a relationship between diameter of sinkhole obtained from 0.6 to 15 m and groundwater extraction quantity changed from 0.18 to 18.14 m3/ha over 25 years. The groundwater level dropped 15 m and sinkhole volume variation obtained 0.014 to 2650 m3 over 25 years. Field discovery and google earth images showed that sinkholes were developed in 3 phases as (1) growth phase (2) mature and (3) steady phases up to about 25 years. The GPR results found some land breaks and a hole underground in the activation and growth phase of sinkhole evolution. Finally, according to some soil parameters and GPR results, the sinkhole hazard map was created in the study area.

In the Cangzhou area of China, groundwater over-exploitation has led to serious land subsidence, and the creep deformation of aquitards has been monitored and found to be closely related to the development of land subsidence. The objective of this paper is to develop a computational model to reflect the creep deformation of aquitards in this area. Firstly, creep tests were conducted on clayey soils with burial depths ranging from 65.7 to 121.7 m. The results show that the total strain consists of three parts: instantaneous strain, primary consolidation strain and creep strain. Creep-time curves and isochronous creep stress-strain curves under stepwise loading were obtained by using the Boltzmann superposition principle, and both types of curves were characterized by nonlinearity, and the creep curves as a whole showed a trend of stable development. Secondly, on the basis of analyzing the advantages and disadvantages of the classical rheological models for clayey soils, a nonlinear creep model of NCE_CS that can take into account the influence of primary consolidation is proposed. The model contains five parameters, which can be solved by using genetic algorithm, and then a simple determination method of the parameters is proposed. Finally, by comparing with the test data and the calculation results of four classical creep models, it is confirmed that the NCE_CS model can fit the creep curves better. The NCE_CS model was also successfully used to estimate the creep behavior in another subsidence area located in Renqiu City in northwest of Cangzhou. This study will provide a basis for quantitative calculation of creep of clayey soils in the Cangzhou area.

Recently natural hazards like earthquakes, landslides, subsidence, glacier bursts and flash floods have severely impacted Himalayan cities, including Joshimath, Uttarakhand. In January 2023, significant ground cracks were observed, leading to the evacuation of nearly 800 buildings. This study investigates the underlying causes of ground failure and building damage through various geotechnical and geophysical tests at 12 sites in Joshimath, including plate load test, dynamic cone penetration test, field direct shear test, multi-channel analysis of surface waves and horizontal-to-vertical spectral ratio. Soil samples are analyzed for natural moisture content and grain size distribution. There is large heterogeneity in the test results which are highly variable. The field tests indicate the soil fabric of Joshimath is a complex mixture of boulders, gravels and soil. Internal erosion in such soils causes the instability of the whole fabric and results in the readjustment of the boulders resulting in subsidence. Internal erosion, driven by subsurface drainage from rainwater, ice melting and wastewater discharge, destabilizes the soil matrix and causes subsidence. It has also been observed that even at greater depths, no clear uniform strata is present and similar heterogeneous strata extend. Lower shear strength and bearing capacity are observed at several sites, potentially contributing to building damage. The study emphasizes that individual test results alone may not adequately capture site conditions. Instead, a combination of multiple test results is essential for a comprehensive assessment. Based on the test results, a vulnerability map of the area is presented.

Globally, land subsidence (LS) often adversely impacts infrastructure, humans, and the environment. As climate change intensifies the terrestrial hydrologic cycle and severity of climate extremes, the interplay among extremes (e.g., floods, droughts, wildfires, etc.), LS, and their effects must be better understood since LS can alter the impacts of extreme events, and extreme events can drive LS. Furthermore, several processes causing subsidence (e.g., ice-rich permafrost degradation, oxidation of organic matter) have been shown to also release greenhouse gases, accelerating climate change. Our review aims to synthesize these complex relationships, including human activities contributing to LS, and to identify the causes and rates of subsidence across diverse landscapes. We primarily focus on the era of synthetic aperture radar (SAR), which has significantly contributed to advancements in our understanding of ground deformations around the world. Ultimately, we identify gaps and opportunities to aid LS monitoring, mitigation, and adaptation strategies and guide interdisciplinary efforts to further our process-based understanding of subsidence and associated climate feedbacks. We highlight the need to incorporate the interplay of extreme events, LS, and human activities into models, risk and vulnerability assessments, and management practices to develop improved mitigation and adaptation strategies as the global climate warms. Without consideration of such interplay and/or feedback loops, we may underestimate the enhancement of climate change and acceleration of LS across many regions, leaving communities unprepared for their ramifications. Proactive and interdisciplinary efforts should be leveraged to develop strategies and policies that mitigate or reverse anthropogenic LS and climate change impacts.

Coal has been crucial in driving economic development and production construction. However, the mining-induced subsidence may cause irreversible damage to the surrounding environment of vegetation growth. Meanwhile, with the worsening of global warming, the frequency and intensity of extreme water-related weather events, such as droughts and excessive rainfall, are on the rise, which leads to heightened impacts on ecosystems and agricultural production. Consequently, extreme water-related weather, the distribution of land subsidence, and its effect on vegetation have attracted significant attention. Based on the Sentinel-1 radar data and Sentinel-2 multispectral data from 2017 to 2022, the SBAS-InSAR technology, the object-oriented classification, and the Normalized Difference Vegetation Index (NDVI) were employed respectively in the study to obtain the spatial-temporal evolution of land subsidence, subsidence-induced water, and crop growth in Tiefa mining area, a representative coal mining area in Northeast China. Moreover, the relationship between land subsidence, subsidence-induced water, and vegetation change was analyzed combined with summer precipitation data. The results showed that: (1) The average cumulative subsidence of the mining area was 256.8 mm, and the subsidence area was 42.525 km2 for the six years. Among them, the heaviest subsidence reached a maximum of 380.5 mm in 2022, and the largest subsidence area was 20.109 km2 in 2017. (2) When the rainfall was excessive, the area of subsidence-induced water would increase sharply, with a proportion jumping to 9.71% from 5.37%, which indicated the subsidence would further amplify the destructive effect of excessive rainfall and waterlogging on land resources. (3) In addition to the existing water pits, ground cracks and shallow subsidence pits appeared under the influence of underground coal mining. The direct impact of ground cracks on crops was not apparent, while the effect of subsidence pits on crops under different rainfall conditions was dual character. In dry years, crops in the subsidence pits could grow better due to higher soil moisture. In wet years, crops in the subsidence pits would suffer the more severe waterlogging. The research results are of great significance for further understanding the influence of coal mining on surface vegetation in mining areas in Northeast China.

Land subsidence (LS) and pipe collapse (PC) as the major types of geomorphologic hazards lead to noticeable changes in landscape alterations, land damage, loss of soil and water, surface erosion, and sediment buildup in affected areas. To overcome this, the susceptibility to LS and CP was investigated using three deep learning convolutional neural network (DL-CNN) architectures, including Res-Net, AlexNet, and VGG-Network. We used various predictor variables, and then, trained and tested our DL-CNN models using ReLu, Cross-Entropy, and Adam as activation, loss, and optimization functions, respectively. Our findings showed that DL-CNN models achieved an overall accuracy of 0.9836, 0.9721, and 0.9642 for the Res-Net, AlexNet, and VGG-Network, respectively, for CP sensitivity detection. In addition, the Res-Net, AlexNet, and VGG-Network with an overall accuracy of 0.9698, 0.9654, and 0.9519, respectively, showed satisfying performances for LS detection. We also applied univariate summary statistics, including L(r), the pair correlation function (g(r)), and the O-ring function (O(r)), to investigate the spatial pattern and distribution of CP and LS. The L(r) function graph showed that the spatial patterns of CP and LS were clustered across all the investigated distance scales. The value of this function fell outside the Monte Carlo range, indicating that the accumulation of CP and LS at the mentioned distance scale was statistically significant. The results of the O(r) function for the distribution pattern of CP in the study area indicated that this phenomenon was mostly distributed next to each other, implying the facilitating effect of CP on the creation and expansion of each other across all the investigated distance scales. Similarly, the univariate function g(r) also showed the dispersed distribution of subsidence LS at all distances next to each other. In summary, the results of this research revealed that much of the study area was susceptible to CP and LS. The proposed methodology and findings of this study would be useful for land managers, stakeholders, and researchers.

The development of land subsidence has seriously affected the safe operation of Beijing-Tianjin high-speed railway. The South-to-North Water Diversion Project Central Route (SNWDP-CR) was officially put into operation in December 2014. It has changed the water supply pattern in Beijing and provided conditions for reducing groundwater exploitation and controlling land subsidence. In this paper, the time-series interferometric data, in situ monitoring data of recent 20 years and the basic geological datasets are combined to compare and analyze the changes of groundwater level, land subsidence and the main subsidence layers along the Beijing-Tianjin high-speed railway before and after the SNWDP-CR. The effects of the environment of Quaternary sedimentary, groundwater exploitation and soil deformation of different lithology on land subsidence along the high-speed railway under the background of new water conditions are revealed. The main conclusions are as follows: 1) The serious land subsidence area along the Beijing-Tianjin high-speed railway always concentrated in the of DK11-DK23. After the operation of SNWDP-CR, the land subsidence along the railway generally showed a slowing trend. The maximum subsidence rate was reduced from 80 mm/yr to 49 mm/yr. The length of subsidence rate that more than 50 mm/yr of the was reduced from 8.0 km to 0 km. 2) The groundwater level of different aquifer groups along the Beijing-Tianjin high-speed railway rose and declined before and after the SNWDP-CR. in eastern part of the plain, the groundwater level of each aquifer group has changed from a continuous decline (range 0.13-1.82 m) to a gradual rise (range 0.45-1.87 m) since 2017. However, in the southeast of the plain, the groundwater level still showed a continuous decline trend, with an average annual decline of 1.2-1.8 m. 3) From 2006 to 2019, the subsidence of the first, second and third compression layer group along the railway accounted for 2.71%, 28.29% and 69%, respectively. The third compression layer group (monitoring layer 94-182 m) had the largest subsidence proportion and was the main subsidence layer. 4) The land subsidence along the Beijing-Tianjin high-speed railway is controlled by the basement structure. The difference of groundwater exploitation intensity led to differences in the spatial distribution of land subsidence along the railway. The subsidence of the soil layer below the bearing layer (about 50 m) of the high-speed railway pile foundation exhibited the characteristics of viscoplastic or viscoelastic plasticity deformation. This of strata is a key layer that needs to be considered for land subsidence control along the Beijing-Tianjin high-speed railway in the future.

Global sea level rise (SLR) has emerged as a pressing concern because of its impacts, especially increased vulnerability of coastal urban areas flooding. This study addresses the pressing concern of SLR and flood vulnerability in the East Coast of North Sumatra (ECNS) and Medan City. We employ a data-driven approach integrating multicriteria analysis, analytical hierarchy process (AHP)-based weighting, and spatial modeling within a geographic information system framework. The analysis considers crucial factors such as slope, land use, soil type, SLR, and land deformation. The study expands the existing framework by incorporating SLR and land subsidence, acknowledging their significant roles in exacerbating flood vulnerability. Future flood-intensity scenarios are simulated based on SLR projections. Data for spatial analysis primarily originated from multisensor satellite imagery, secondary sources from published literature, and field surveys. We validated the consistency of the variable weightings assigned for vulnerability analysis using a consistency ratio threshold (<0.1). Finally, the established flood vulnerability model was validated by comparing its predictions with recorded flood events in the ECNS and Medan City. The ECNS and Medan City areas were classified as very high and highly vulnerable to flooding, respectively.

Land subsidence is an environmental geological phenomenon mainly caused by groundwater overexploitation. Long-term overexploitation of groundwater not only causes compaction of aquifer thickness and surface deformation but also leads to the loss of aquifer water storage capacity. The skeleton water storage coefficient (S-k) is an important parameter for evaluating the water storage capacity of aquifer groups. This article proposes a new research framework for obtaining the S-k of different aquifer groups: combining permanent scatter for SAR interferometry technology and a multiscale geographic weighted regression model to obtain subsidence information for different aquifer groups, inverting the S-k of different aquifer groups from the spatial scale, and discussing the deformation characteristics of soil layers under different water head change modes to evaluate the deformation and water storage characteristics of different aquifer groups. This framework is applied to the land subsidence region of the Beijing Plain. We calculated that the settlement proportions of different compression layer groups were 14.75%, 23.65%, 33.44%, and 28.16%. Due to the different lithological compositions and groundwater exploitation of different aquifers, the S-k values exhibit different spatial distribution characteristics. With the continuous development of subsidence, the water storage performance of the aquifer group is continuously declining. These findings contribute to managing the sustainable use of groundwater resources and controlling subsidence. It is demonstrated that the research framework proposed in this article can serve as an effective tool for obtaining settlement information and the S-k of different aquifer groups.

Recently, global urbanization and industrialization have resulted in excessive groundwater exploitation, and the occurrence and damage of land subsidence to human life and property are increasing accordingly. The present study assessed the impact of tunnel excavation on the groundwater system and potential land subsidence in a Korean metropolitan area. A numerical model was established to predict groundwater level variations with tunnel excavation, and the mechanisms and cases of land subsidence worldwide were reviewed. The established model adequately represented the groundwater system in the study area. The tunnel excavation decreased the groundwater level along the tunnel line, and significant groundwater drawdown primarily occurred up to 6.1 m at locations with high permeability, leading to the temporary development of a large depression cone. The study area has favorable conditions for land subsidence, and curved tunnel excavation may induce ground disturbance, resulting in groundwater inflow and soil loss in the region. In addition, the risk of land subsidence is expected to persist owing to the lag time caused by the creep phenomenon, even though the groundwater level recovers. Efforts to effectively reduce land subsidence damage in urban areas are crucial, requiring measures such as controlling land subsidence occurrence and implementing prevention measures through early recognition, including artificial recharge of groundwater, underground cavity observation with ground penetrating radar, land subsidence observation with a borehole extensometer, and groundwater level monitoring systems.