Effect of clayey sediment compression on fluoride enrichment in the Quaternary groundwater system of Cangzhou Plain, China.

The effect of clay layer compression on the enrichment of groundwater fluoride remains unknown. Quaternary groundwater with high fluoride levels at the Cangzhou Plain, which has a long history of land subsidence caused by clay layer compression, poses a potential health risk. The spatial distribution and enrichment mechanisms of groundwater fluoride are identified by sample collection, hydrochemical analysis, and geochemical inverse modeling. The results revealed that fluoride concentrations in 82 % of the 122 groundwater samples above the limit in drinking water as 1.0 mg/L in China. Fluoride in shallow groundwater (depth <20 m, ∼average = 2.08 mg/L) was mainly originated from fluorite dissolution and influenced by groundwater HCO3-, pH, and cation exchange levels. Below ∼200 m, the main source of groundwater fluoride (∼average = 3.12 mg/L) was the compression-release of clay pore water with high F- concentration, which was generated by complex water-rock interaction. Based on hydrochemical inverse simulation and end-member mixing models, the pore water released from clayey sediments supplied 53 %-56 % of deep groundwater (>200 m) and contributed 2.07 -2.87 mg/L to F- concentration. The findings of this study provide a theoretical basis for future research on prevention of high fluoride groundwater induced by clayey sediment compression.

Experimental study on soil deformation caused by overexploitation of groundwater.

Due to the overexploitation of deep groundwater, the largest cone of depression in the world has formed in the North China Plain. This led to severe geological hazards, including land subsidence and ground fissures, and also caused economic losses. The prevention and treatment of subsidence needs to rely on the accurate prediction of subsidence amount. According to the one-dimensional consolidation theory and effective stress principle, combined with stratum structure, groundwater flow, stress distribution, and so forth, the high-pressure consolidation test results of 569.6 m deep borehole soil samples are adopted; with a specific focus on stress and deformation parameters under exploitation of groundwater condition, the soil-water coupling prediction model of groundwater level lowering depth and land subsidence has been established. Verification with measured subsidence data near the study sites demonstrated that the predicted curve is consistent with the measured one and the differences between them are acceptable. The model can be applied in different areas after making adjustment based on different regional stratigraphic structures. Its key advantage lies in the ability to provide land subsidence prediction for areas lacking monitoring data, making it highly valuable for widespread application. PRACTITIONER POINTS: There is a compressible stratum structure; it is the internal factors of land subsidence. The groundwater level decline causes the soil body stress to change. It is land subsidence of the external factors. Based on the one-dimensional consolidation theory and by combining stratigraphic structures, groundwater flow, and stress distribution, a ground settlement prediction model was established.

Surface Deformation of Xiamen, China Measured by Time-Series InSAR.

Due to its unique geographical location and rapid urbanization, Xiamen is particularly susceptible to geological disasters. This study employs 80 Sentinel-1A SAR images covering Xiamen spanning from May 2017 to December 2023 for comprehensive dynamic monitoring of the land subsidence. PS-InSAR and SBAS-InSAR techniques were utilized to derive the surface deformation field and time series separately, followed by a comparative analysis of their results. SBAS-InSAR was finally chosen in this study for its higher coherence. Based on its results, we conducted cause analysis and obtained the following findings. (1) The most substantial subsidence occurred in Maluan Bay and Dadeng Island, where the maximum subsidence rate was 24 mm/yr and the maximum cumulative subsidence reached 250 mm over the course of the study. Additionally, regions exhibiting subsidence rates ranging from 10 to 30 mm/yr included Yuanhai Terminal, Maluan Bay, Xitang, Guanxun, Jiuxi entrance, Yangtang, the southeastern part of Dadeng Island, and Yundang Lake. (2) Geological structure, groundwater extraction, reclamation and engineering construction all have impacts on land subsidence. The land subsidence of fault belts and seismic focus areas was significant, and the area above the clay layer settled significantly. Both direct and indirect analysis can prove that as the amount of groundwater extraction increases, the amount of land subsidence increases. Significant subsidence is prone to occur after the initial land reclamation, during the consolidation period of the old fill materials, and after land compaction. The construction changes the soil structure, and the appearance of new buildings increases the risk of subsidence.

Risk assessment of land subsidence based on GIS in the Yongqiao area, Suzhou City, China.

This study focuses on the Yongqiao District in Suzhou City, Anhui Province, China, aiming to analyze the current situation of ground settlement and its influencing factors in the area. The selected risk indices include settlement rate, cumulative settlement amount, groundwater level drop funnel, thickness of loose sediment layer, thickness of soft soil layer, and the number of groundwater extraction layers. Additionally, vulnerability indices such as population density, building density, road traffic, and functional zoning are considered. An evaluation index system for assessing land Subsidence risk was established. The risk evaluation of land Subsidence was conducted using the Hierarchical analysis-composite index method and ArcGIS spatial analysis, The evaluation results show that the area of higher risk area is about 2.82 km2, accounting for 0.96% of the total area, mainly distributed in the area of Jiuli village, Sanba Street. The middle risk area is distributed around the higher area, with an area of about 9.18 km2, accounting for 3.13% of the total area. The lower risk areas were distributed in most of the study area, covering an area of 222.24 km2, accounting for 75.82% of the total area. The low risk assessment area is mainly distributed in Bianhe Street and part of Zhuxianzhuang Town, with an area of about 58.88 km2, accounting for 20.09% of the total area. The findings of this study are not only crucial for informing local policies and practices related to land use planning, infrastructure development, and emergency response but also enhance our understanding of the complexities of land Subsidence processes and their interactions with human activities, informing future research and practice in environmental risk assessment and management.

Vulnerability of tidal morphologies to relative sea-level rise in the Venice Lagoon.

The rise in sea level and land subsidence are seriously threatening the diversity of tidal morphologies that have made the Venice Lagoon such a distinctive landscape. Here, we assess the vulnerability of tidal morphologies to relative sea-level rise based on a new conceptual framework that accounts for both above- and below-sea-level zones, sedimentary architecture, and surface morphology. Around 80 % of the lagoon area will face moderate to severe vulnerability by 2050, doubling compared to the 1990s. While the subtidal zone may be relatively less threatened compared to past conditions, the drastic decline in intertidal morphologies is alarming. This contributes to the flattening and deepening of the lagoon topography and thus to the loss of lagoon landscape diversity, likely leading to a decrease in the ecosystem services the tidal morphologies provide. The interconnection of intertidal and subtidal morphologies is crucial for maintaining the overall health and functionality of the lagoon's ecosystem. Any disruption to one aspect can have ripple effects throughout the entire system.

Applying hesitant q-rung orthopair fuzzy sets to evaluate uncertainty in subsidence causes factors.

Land subsidence is a widespread problem impacting communities worldwide. Understanding the causes and factors of land subsidence is crucial for identifying and prioritizing effective mitigation measures. One of the main reasons for prioritizing land subsidence causes is the potential impact on infrastructure and the environment. The main objective of this paper is to emphasize the importance of prioritizing the causes of land subsidence. By understanding and prioritizing the factors contributing to land subsidence based on their impact and urgency, the aim is to develop targeted strategies for mitigation, inform policy decisions, and prevent further exacerbation of this problems. The study comprises three phases, where experts in the field provide their opinions and propose a robust hybrid framework. This framework integrates the Failure Mode and Effect Analysis (FMEA) and Step-wise Weight Assessment Ratio Analysis (SWARA) with Hesitant q-rung orthopair fuzzy set (Hq-ROFS). The performance of the proposed technique was then compared with two other decision-making techniques for evaluating and ranking land subsidence causes. According to the results, extraction of groundwater, excessive irrigation using groundwater, and oxidation and drainage of organic soils were identified as primary drivers of subsidence.

Aerobic and anaerobic decomposition rates in drained peatlands: Impact of botanical composition.

Drained peatlands in temperate climates are under threat from climate change and human activities. The resulting decomposition of organic matter plays a major role in regulating the associated land subsidence rates, yet the determinants of aerobic and anaerobic peat decomposition rates are not fully understood. In this study, we sought to gain insight into the drivers of decomposition rates in botanically diverse peatlands (sedge, reed, wood, and moss dominant) under oxic and anoxic conditions. Peat samples were collected from the anoxic zone and incubated for 24 h (short) and 15 weeks (long) under either oxic or anoxic conditions. CO2 emissions, hydrolytic and oxidative exoenzyme potential activities, phenolic compound concentrations, and several edaphic factors were measured at the end of each incubation period. We found that 15 weeks of oxygen exposure of anoxic peat samples accelerated the average CO2 emissions by 3.9-fold. Reed and sedge peat respired more than wood and moss peat under anoxic conditions. Interestingly, CO2 emissions from anoxic peat layers under permanently anoxic conditions were substantial and given the thickness of peat deposits in the field, such activities may play an important role in long-term land subsidence rates and total CO2 emissions from drained peatlands. The results from the long-term incubations showed that decomposition rates appear to be also controlled by factors other than oxygen intrusion such as substrate availability. In summary, the botanical composition of the peat matrix, incubation conditions and time of incubation are all important factors that need to be considered when predicting peat decomposition and subsequent land subsidence rates.

Declining groundwater and its impacts along Ganga riverfronts using combined Sentinel-1, GRACE, water levels, and rainfall data.

The Indo-Gangetic Plains (IGP) in northern India are vast alluvial tracts with huge shallow aquifers, densely populated and agriculturally productive regions. In the last few decades, IGP has been facing water scarcity driven by erratic monsoon dynamics, anthropogenic activity, and hydroclimatic variability. In urban centers, continuous groundwater withdrawal leads to high stress, affecting surface deformation and a threat to buildings and infrastructures. An attempt has been made to explore the possible linkage and coupling between groundwater level, hydroclimatic variables, and subsidence in the Central Ganga Plains (CGP), in Varanasi metropolis using the combined multisensory multitemporal data, Sentinel-1 (2017-2023), GRACE (2003-2023), groundwater levels (1998-2023), and precipitation (2002-2023). Long-term hydrological response in the CGP shows continuous depletion (14.6 ± 5.6 mm/yr) in response to precipitation variability. Results show spatiotemporal variations between GWS, and precipitation estimate with nonlinear trend response due to associated inter-annual/inter-seasonal climate variability and anthropogenic water withdrawal, specifically during the observed drought years. The significant storage response in the urban center compared to a regional extent suggests the potential impact of exponentially increasing urbanization and building hydrological stress in the cities. The implications of reducing storage capacity show measured land subsidence (∼2-8 mm/yr) patterns developed along the meandering stretch of the Ganga riverfronts in Varanasi. The groundwater level data from the piezometric supports the hydroclimatic variables and subsidence coupling. Considering the vital link between water storage, food security, and socioeconomic growth, the results of this study require systematic inclusion in water management strategies as climate change seriously impacts water resources in the future.

Country-scale assessment of urban areas, population, and households exposed to land subsidence using Sentinel-1 InSAR, and GPS time series.

The increased need for water resources in urban sprawls and intense droughts has forced more aggressive groundwater extraction resulting in numerous urban areas undergoing land subsidence. In most cases, only some large metropolitan areas have been well-characterized for subsidence. However, there is no existing country-wide assessment of urban areas, population, and households exposed to this process. This research showcases a methodology to systematically evaluate urban localities with land subsidence higher than - 2.8 cm/year throughout Mexico. We used Interferometric Synthetic Aperture Radar (InSAR) tools with a dataset of 4611 scenes from European Space Agency's Sentinel-1 A/B SAR sensors acquired from descending orbits from September 2018 through October 2019. This dataset was processed at a supercomputer using InSAR Scientific Computing Environment and the Miami InSAR Time Series software in Python software. The quality and calibration of the resulting velocity maps are assessed through a large-scale comparison with observations from 100 continuous GPS sites throughout Mexico. Our results show that an urban area of 3797 km2, 6.9 million households, and 17% of the total population in Mexico is exposed to subsidence velocities of faster than - 2.8 cm/year, in more than 853 urban localities within 29 land subsidence regions. We also confirm previous global potential estimations of subsidence occurrence in low relief areas over unconsolidated deposits and where groundwater aquifers are under stress. The presented research demonstrates the capabilities for surveying urban areas exposed to land subsidence at a country-scale level by combining Sentinel-1 velocities with spatial national census data. Supplementary Information: The online version contains supplementary material available at 10.1007/s11069-023-06259-5.

Land subsidence susceptibility mapping: a new approach to improve decision stump classification (DSC) performance and combine it with four machine learning algorithms.

Land subsidence is a worldwide threat. In arid and semiarid lands, groundwater depletion is the main factor that induce the subsidence resulting in environmental damages and socio-economic issues. To foresee and prevent the impact of land subsidence, it is necessary to develop accurate maps of the magnitude and evolution of the subsidences. Land subsidence susceptibility maps (LSSMs) provide one of the effective tools to manage vulnerable areas and to reduce or prevent land subsidence. In this study, we used a new approach to improve decision stump classification (DSC) performance and combine it with machine learning algorithms (MLAs) of naïve Bayes tree (NBTree), J48 decision tree, alternating decision tree (ADTree), logistic model tree (LMT), and support vector machine (SVM) in land subsidence susceptibility mapping (LSSSM). We employ data from 94 subsidence locations, among which 70% were used to train learning hybrid models and the other 30% were used for validation. In addition, the models' performance was assessed by ROC-AUC, accuracy, sensitivity, specificity, odd ratio, root-mean-square error (RMSE), kappa, frequency ratio, and F-score techniques. A comparison of the results obtained from the different models reveals that the new DSC-ADTree hybrid algorithm has the highest accuracy (AUC = 0.983) in preparing LSSSMs as compared to other learning models such as DSC-J48 (AUC = 0.976), DSC-NBTree (AUC = 0.959), DSC-LMT (AUC = 0.948), DSC-SVM (AUC = 0.939), and DSC (AUC = 0.911). The LSSSMs generated through the novel scientific approach presented in our study provide reliable tools for managing and reducing the risk of land subsidence.

An interpretable deep learning model to map land subsidence hazard.

The main goal of this research is the interpretability of deep learning (DL) model output (e.g., CNN and LSTM) used to map land susceptibility to subsidence hazard by means of different techniques. For this purpose, an inventory map of land subsidence (LS) is prepared based on fieldwork and a record of LS presence points, and 16 features controlling LS were mapped. Thereafter, 11 effective features controlling LS were identified by means of a particle swarm optimization (PSO) algorithm, which was then used as input in the CNN and LSTM predictive models. To address the inherent black box nature of DL models, six interpretation methods (feature interaction, permutation importance plot (PFIM), bar plot, SHapley Additive exPlanations (SHAP) main plot, heatmap plot, and waterfall plot) were used to interpret the predictive model outputs. Both models (CNN and LSTM) had AUC > 90 and therefore provided excellent accuracy for mapping LS hazard. According to the most accurate model-the CNN predictive model-the range from very low to very high hazard classes occupied 20%, 20%, 25%, 16.3%, and 18.7% of the study area, respectively. According to three plots (bar plot, SHAP main plot, and heatmap plot), which were constructed based on the SHAP value, distance from the well, GDR and DEM were identified as the three most important features with the highest impact on the DL model output. The results of the waterfall plot indicate two effective features consisting of distance from the well and coarse fragment, and two effective features comprising landuse and DEM, contributed negatively and positively to LS, respectively. Overall, these explanation techniques can address critical concerns with respect to the interpretability of sophisticated DL predictive models.

Slowly but surely: Exposure of communities and infrastructure to subsidence on the US east coast.

Coastal communities are vulnerable to multihazards, which are exacerbated by land subsidence. On the US east coast, the high density of population and assets amplifies the region's exposure to coastal hazards. We utilized measurements of vertical land motion rates obtained from analysis of radar datasets to evaluate the subsidence-hazard exposure to population, assets, and infrastructure systems/facilities along the US east coast. Here, we show that 2,000 to 74,000 km2 land area, 1.2 to 14 million people, 476,000 to 6.3 million properties, and >50% of infrastructures in major cities such as New York, Baltimore, and Norfolk are exposed to subsidence rates between 1 and 2 mm per year. Additionally, our analysis indicates a notable trend: as subsidence rates increase, the extent of area exposed to these hazards correspondingly decreases. Our analysis has far-reaching implications for community and infrastructure resilience planning, emphasizing the need for a targeted approach in transitioning from reactive to proactive hazard mitigation strategies in the era of climate change.

Unveiling the driving factors of urban land subsidence in Beijing, China.

Land subsidence, an insidious and gradual geological phenomenon, presents a latent threat to future urban development and socio-economic progress. Beijing City, renowned for its high population density, has encountered significant challenges associated with land subsidence. In this study, we leverage time-series interferometric synthetic aperture radar (time-series InSAR) method to analyze the spatio-temporal patterns of land subsidence in Beijing. Furthermore, we quantify the contributions of natural and anthropogenic factors to land subsidence. Our findings reveal that land subsidence primarily occurs in the plain area of Beijing, exhibiting an average rate of -5.6 mm/year (Positive values indicate uplift, while negative values indicate subsidence.). Notably, several large-scale subsidence centers are identified, with the maximum subsidence rate reaching an alarming -232.7 mm/year. The assessments indicate that geological factors, specifically fault activity, account for 33 % of the observed land subsidence, while human activities contribute to the remaining 67 %, with groundwater overexploitation playing a prominent role. The insights gained from this study provide a foundation for understanding the causative factors behind urban land subsidence and can aid in the formulation of effective intervention policies targeting this critical issue.

Machine learning-based techniques for land subsidence simulation in an urban area.

Understanding and mitigating land subsidence (LS) is critical for sustainable urban planning and infrastructure management. We introduce a comprehensive analysis of LS forecasting utilizing two advanced machine learning models: the eXtreme Gradient Boosting Regressor (XGBR) and Long Short-Term Memory (LSTM). Our findings highlight groundwater level (GWL) and building concentration (BC) as pivotal factors influencing LS. Through the use of Taylor diagram, we demonstrate a strong correlation between both XGBR and LSTM models and the subsidence data, affirming their predictive accuracy. Notably, we applied delta-rate (Δr) calculus to simulate a scenario with an 80% reduction in GWL and BC impact, revealing a potential substantial decrease in LS by 2040. This projection emphasizes the effectiveness of strategic urban and environmental policy interventions. The model performances, indicated by coefficients of determination R2 (0.90 for XGBR, 0.84 for LSTM), root-mean-squared error RMSE (0.37 for XGBR, 0.50 for LSTM), and mean-absolute-error MAE (0.34 for XGBR, 0.67 for LSTM), confirm their reliability. This research sets a precedent for incorporating dynamic environmental factors and adapting to real-time data in future studies. Our approach facilitates proactive LS management through data-driven strategies, offering valuable insights for policymakers and laying the foundation for sustainable urban development and resource management practices.

Projection of land susceptibility to subsidence hazard in China using an interpretable CNN deep learning model.

Land subsidence is a worldwide geo-environmental hazard. Clarifying the influencing factors of land subsidence hazards susceptibility (LSHS) and their spatial distribution are critical to the prevention and control of subsidence disasters. In this study, we selected natural and anthropogenic features or variables on LSHS and used the interpretable convolutional neural network (CNN) method to successfully construct a LSHS model in China. The model performed well, with AUC and F1-score testing set accuracies reaching 0.9939 and 0.9566, respectively. The interpretable method of SHapley Additive exPlanations (SHAP) was use to elucidate the individual contribution of input features to the predictions of CNN model. The importance ranking of model variables showed that population, gross domestic product (GDP) and groundwater storage (GWS) change are the three major factors that affect China's land subsidence. During year 2004-2016, an area of 237.6 thousand km2 was classified as high and very high LSHS, mainly concentrated in the North China Plain, central Shanxi, southern Shaanxi, Shanghai and the junction of Jiangsu and Zhejiang. There will be 333.82-343.12 thousand km2 of areas located in the high and very high LSHS in the mid-21st century (2030-2059) and 361.9-385.92 thousand km2 of areas in the late-21st century (2070-2099). Future population exposure to high and very high LSHS will be 252.12-270.19 million people (mid-21st century) and 196.14-274.50 million people (late-21st century), respectively, compared with the historical exposure of 210.99 million people. The proportion of future railway and road exposure will reach 14.63 %-14.89 % and 11.51 %-11.82 % in the mid-21st century, and 15.46 %-17.12 % and 12.35 %-13.11 % in the late-21st century, respectively. Our findings provide an important information for creating regional adaptation policies and strategies to mitigate damage induced by subsidence.

Hydrogeological characteristics and water chemistry in a coastal aquifer of Korea: implications for land subsidence.

Land subsidence is the gradual or sudden dropping of the ground surface developed by increasing the total stress. Most studies have discussed the relationship between land subsidence with groundwater level. However, there is a lack of discussion on groundwater environmental changes after occurring land subsidence. This study aimed to evaluate the hydrogeological and water chemistry characteristics of construction sites with land subsidence. Land subsidence in the Yangyang coastal area occurred suddenly on August 3, 2022, when the retaining wall of the construction collapsed. The groundwater level was measured three times, and water samples were collected twice between August 5, 2022, and September 5, 2022, for laboratory analysis. After land subsidence occurred, the average groundwater level was - 19.91 m ground level (GL) on August 9, 2022, and finally decreased to - 19.21 m GL on September 05, 2022. The groundwater levels surrounding the construction site gradually increased for a month. The electrical conductivity value measured at the monitoring wells ranged from 89 to 7800 µS/cm, and four wells exceeded the measurement limit near the groundwater leaked points. The highest mixing ratio of leaked water samples, collected on August 9, 2022, was 27.6%. Furthermore, the fresh groundwater-saltwater interface depth was estimated to be above the construction bottom. Although groundwater levels recovered, the groundwater quality continuously is affected by saltwater. This finding could contribute to understanding the hydrogeological characteristics surrounding construction sites with land subsidence and provide insight into the hydrochemical evolution process during declined groundwater levels in coastal aquifers.

Coupled processes of groundwater dynamics and land subsidence in the context of active human intervention, a case in Tianjin, China.

To address the crisis of water shortage in the North China Plain, the Chinese government implemented the South-to-North Water Transfer Project (SNWTP). In this context, Tianjin, one of the main beneficiaries of this project, has been relieved from water shortages and begun to implement Groundwater Management Plans (GMP) since 2018, which undoubtedly have a significant effect on the groundwater recovery. Meanwhile, this provides a good case for studying the coupled process of ground settlement and groundwater dynamics, especially the soil deformation pattern driven by groundwater level (GWL) rebound. To analyze these issues in detail, field well data was collected to depict groundwater flow field. Moreover, geodetic data was also collated, including leveling, GPS, and InSAR, so that a vertical deformation field with high spatiotemporal resolution could be generated. The results reveal that the GWL of the third confined aquifer which is the main exploitation layer in Tianjin recovered significantly since 2018 with a rate of 2.1 m/yr. The dynamic deformation patterns indicate that the area of land subsidence cones in Tianjin has reduced significantly, accompanied by a sharply declining subsidence rate (decreased from -32.2 mm/yr to -4.5 mm/yr.). Particularly, a significant poroelastic rebound has occurred in the Wuqing and Beichen districts since 2020. Furthermore, due to the delayed pore pressure dissipation in the aquitard, we find a time delay of 0.3-5.5 years between land subsidence and GWL time series, which is far less than that estimated by hydrogeological parameters, as the latter ignored the recharge and recovery capacity of the aquifer system. Finally, an evolution models in Tianjin was presented to illustrate interactive process among the deformation, pore pressure, and hydraulic head. In general, the SNWDP and the GMP has restored the pore pressure of aquifer, reduced the land subsidence, and alleviated the groundwater storage depletion of Tianjin.

Electrical resistivity imaging data for hydrogeological and geological hazard investigations in Taiwan.

This data article presents electrical resistivity imaging (ERI) data and inverted models with the objectives of hydrogeological characterization, land subsidence studies, and geological structural detections in Taiwan. The ERI data for hydrogeological studies includes 5 ERI profiles from Changhua, 33 from Yunlin, 36 from Yilan, 23 from Taichung, 23 from Chiayi and Tainan, and 23 from Taipei basins. In addition, time-lapse ERI profiles are presented for 10 ERI from Yilan, 10 ERI from Pingtung, 11 ERI from Taichung, and 31 ERI from Minzu basins. Moreover, 10 ERI data were used to detect the Rusui Fault, 12 for the Qishan Fault, 13 for the Yuli Fault, and 25 for the Shanyi Fault. This data article contains 265 ERI profiles with a total survey length of 59,905 m. A single ERI profile contains hundreds to thousands of subsurface apparent resistivity data points. The data was collected between 2010 and 2022 from different regions of Taiwan. The main findings from the ERI data consisted here were reported by Lin et al. [1] for the Pingtung basin, Chang et al. [2] for the Minzu basin, and Jordi et al. [3] for the Taichung basin in order to estimate hydraulic parameters and characterize the aquifer systems. The ERI data presented here can be used for a variety of hydrogeological, geological, engineering, and environmental applications, and it can be further interpreted using machine learning and statistical methods. Therefore, the ERI data will helps in various subsurface applications, academic research, and educational purposes.

Geospatial analytics of driving mechanism of land subsidence in Gulf Coast of Texas, United States.

Land subsidence has been an ongoing issue for over a century along the Gulf Coast of Texas in the United States. This study assesses and models the factors contributing to land subsidence covering fifty-six (56) counties along the Gulf of Mexico coastline from Louisiana to the border of Mexico, approximately 300,000 km2. Geospatial statistical techniques and regression models were employed to investigate and predict the fundamental causes of land subsidence by integrating multiple datasets such as Global Navigation Satellite System (GNSS) (147 stations), groundwater extraction (78,420 wells), hydrocarbon production (84,424 wells), precipitation, and population growth. In the last two decades, the overall population rose by 33 % and the compound annual population growth rate increased from 2.08 to 4.10 % in Montgomery, Waller, Fort Bend, and Chambers counties. Emerging hotspot analysis reveals that the groundwater level is persistently declining and the regression model (R2 = 0.92) tested over Harris County predicts that the population growth significantly contributes to land subsidence in this region. The groundwater withdrawal rate is increased from 23 to 96.6 billion gallons in Harris, Montgomery, and Fort Bend counties in the last two decades. A prolonged drought from 2010 to 2015 due to low precipitation affected all fifty-six counties. Oil production increased eightfold and a high extraction rate of 19.5 to 40.1 million bbl/yr of oil in Karnes County was recorded within the last 20 years. The regression model (R2 = 0.73) over this county suggests that oil extraction is a primary contributing factor to the observed subsidence. Although the gas extraction rates for all counties are decreasing over time, some counties in the southern part of the Gulf Coast Aquifer show relatively higher extraction rates. For the first time, this research determines that all fifty-six counties along the Gulf Coast of Texas are undergoing land subsidence and experiencing high population growth, groundwater withdrawal, and hydrocarbon extraction.

Scrutinization of land subsidence rate using a supportive predictive model: Incorporating radar interferometry and ensemble soft-computing.

Land subsidence is a huge challenge that land and water resource managers are still facing. Radar datasets revolutionize the way and give us the ability to provide information about it, thanks to their low cost. But identifying the most important drivers need for the modeling process. Machine learning methods are especially top of mind amid the prediction studies of natural hazards and hit new heights over the last couple of years. Hence, putting an efficient approach like integrated radar-and-ensemble-based method into practice for land subsidence rate simulation is not available yet which is the main aim of this research. In this study, the number of 52 pairs of radar images were used to identify subsidence from 2014 to 2019. Then, using the simulated annealing (SA) algorithm the key variables affecting land subsidence were identified among the topographical parameters, aquifer information, land use, hydroclimatic variables, and geological and soil factors. Afterward, three individual machine learning models (including Support Vector Machine, SVM; Gaussian Process, GP; Bayesian Additive Regression Tree, BART) along with three ensemble learning approaches were considered for land subsidence rate modeling. The results indicated that the subsidence varies between 0 and 59 cm in this period. Comparing the Radar results with the permanent geodynamic station exhibited a very strong correlation between the ground station and the radar images (R2 = 0.99, RMSE = 0.008). Parsing the input data by the SA indicated that key drivers are precipitation, elevation, percentage of fine-grained materials in the saturated zone, groundwater withdrawal, distance to road, groundwater decline, and aquifer thickness. The performance comparison indicated that ensemble models perform better than individual models, and among ensemble models, the nonlinear ensemble approach (i.e., BART model combination) provided better performance (RMSE = 0.061, RSR = 0.42, R2 = 0.83, PBIAS = 2.2). Also, the distribution shape of the probability density function in the non-linear ensemble model is much closer to the observations. Results indicated that the presence of significant fine-grained materials in unconsolidated aquifer systems can clarify the response of the aquifer system to groundwater decline, low recharge, and subsequent land subsidence. Therefore, the interaction between these factors can be very dangerous and intensify subsidence.