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Articles in press have been peer-reviewed and accepted, which are not yet assigned to volumes /issues, but are citable by Digital Object Identifier (DOI).
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2025, 13(2): 101-115.
doi: 10.26599/JGSE.2025.9280042
Abstract:
Empirical formulas are indispensable tools in water engineering and hydraulic structure design. Derived from meticulous field observations, experiments, and diverse datasets, these formulas help to estimate water leakage in structures such as dams, tunnels, canals, and pipelines. By utilizing a few easily measurable parameters, engineers can employ these formulas to generate preliminary leakage rate estimates before proceeding with more detailed analyses. In this study, a physical model was developed, and a series of experiments were conducted, considering variables such as inflow rate, materials constituting the unsaturated medium, and variations in infiltration trench depth and width. As a result, a novel artificial recharge method was introduced, and an empirical equation, $ {\text{Q}}_{\text{out}} $=0.0066 ×$ {{\mathrm{D}}_{50}}^{0.64} $× L ×$ {\mathrm{P}}^{\;0.36} $, was proposed to estimate the infiltration capacity of the trench. This equation incorporates factors such as the wetted perimeter, mean soil particle diameter, trench length, and a coefficient. A comparative analysis between the observed data from nine Iranian earthen canals and the values calculated using the proposed equation revealed an average relative error of 15% between the two datasets. In addition, the Pearson correlation coefficient was determined to be 0.981 and the Root Mean Square Error (RMSE) was 0.381, demonstrating the strong predictive performance of the equation. The parameters considered in the proposed equation allow for its application across diverse regions. Given its accurate performance, this equation provides a reliable initial estimate of the leakage rate, thereby helping to reduce costs and save time.
Empirical formulas are indispensable tools in water engineering and hydraulic structure design. Derived from meticulous field observations, experiments, and diverse datasets, these formulas help to estimate water leakage in structures such as dams, tunnels, canals, and pipelines. By utilizing a few easily measurable parameters, engineers can employ these formulas to generate preliminary leakage rate estimates before proceeding with more detailed analyses. In this study, a physical model was developed, and a series of experiments were conducted, considering variables such as inflow rate, materials constituting the unsaturated medium, and variations in infiltration trench depth and width. As a result, a novel artificial recharge method was introduced, and an empirical equation, $ {\text{Q}}_{\text{out}} $=
2025, 13(2): 116-132.
doi: 10.26599/JGSE.2025.9280043
Abstract:
Understanding the levels, causes, and sources of fluoride in groundwater is critical for public health, effective water resource management, and sustainable utilization. This study employs multivariate statistical methods, hazard quotient assessment, and geochemical analyses, such as mineral saturation index, ionic activities, and Gibbs diagrams, to investigate the hydrochemical characteristics, causes, and noncarcinogenic risks of fluoride in Red bed groundwater and geothermal water in the Guang'an area and neighboring regions. Approximately 9% of the Red bed groundwater samples contain fluoride concentrations exceeding 1 mg·L−1. The predominant water types identified are Cl-Na and HCO3-Na, primarily influenced by evapotranspiration. Low-fluoride groundwater and high-fluoride geothermal water exhibit distinct hydrochemical types HCO3-Ca and SO4-Ca, respectively, which are mainly related to the weathering of carbonate, sulfate, and fluorite-containing rocks. Correlation analysis reveals that fluoride content in Red bed groundwater is positively associated with Na+, Cl−, SO42−, and TDS (r2 = 0.45–0.64, p < 0.01), while in geothermal water, it correlates strongly with pH, K+, Ca2+, and Mg2+ (r2 = 0.52–0.80, p < 0.05). Mineral saturation indices and ionic activities indicate that ion exchange processes and the dissolution of minerals such as carbonatite and fluorite are important sources of fluoride in groundwater. The enrichment of fluorine in the Red bed groundwater is linked to evaporation, cation exchange and dissolution of fluorite, caused by the lithologic characteristics of the red bed in this area. However, it exhibits minimal correlation with the geothermal water in the adjacent area. The noncarcinogenic health risk assessment indicates that 7% (n = 5) of Red bed groundwater points exceed the fluoride safety limit for adults, while 12% (n = 8) exceed the limit for children. These findings underscore the importance of avoiding highly fluoridated red bed groundwater as a direct drinking source and enhancing groundwater monitoring to mitigate health risks associated with elevated fluoride levels.
Understanding the levels, causes, and sources of fluoride in groundwater is critical for public health, effective water resource management, and sustainable utilization. This study employs multivariate statistical methods, hazard quotient assessment, and geochemical analyses, such as mineral saturation index, ionic activities, and Gibbs diagrams, to investigate the hydrochemical characteristics, causes, and noncarcinogenic risks of fluoride in Red bed groundwater and geothermal water in the Guang'an area and neighboring regions. Approximately 9% of the Red bed groundwater samples contain fluoride concentrations exceeding 1 mg·L−1. The predominant water types identified are Cl-Na and HCO3-Na, primarily influenced by evapotranspiration. Low-fluoride groundwater and high-fluoride geothermal water exhibit distinct hydrochemical types HCO3-Ca and SO4-Ca, respectively, which are mainly related to the weathering of carbonate, sulfate, and fluorite-containing rocks. Correlation analysis reveals that fluoride content in Red bed groundwater is positively associated with Na+, Cl−, SO42−, and TDS (r2 = 0.45–0.64, p < 0.01), while in geothermal water, it correlates strongly with pH, K+, Ca2+, and Mg2+ (r2 = 0.52–0.80, p < 0.05). Mineral saturation indices and ionic activities indicate that ion exchange processes and the dissolution of minerals such as carbonatite and fluorite are important sources of fluoride in groundwater. The enrichment of fluorine in the Red bed groundwater is linked to evaporation, cation exchange and dissolution of fluorite, caused by the lithologic characteristics of the red bed in this area. However, it exhibits minimal correlation with the geothermal water in the adjacent area. The noncarcinogenic health risk assessment indicates that 7% (n = 5) of Red bed groundwater points exceed the fluoride safety limit for adults, while 12% (n = 8) exceed the limit for children. These findings underscore the importance of avoiding highly fluoridated red bed groundwater as a direct drinking source and enhancing groundwater monitoring to mitigate health risks associated with elevated fluoride levels.
2025, 13(2): 133-146.
doi: 10.26599/JGSE.2025.9280044
Abstract:
Tai'an city, located in Shandong Province, China, is rich in geothermal resources, characterized by shallow burial, high water temperature, and abundant water supply, making them high value for exploitation. However, corrosion and scaling are main challenges that hinder the widespread application and effective utilization of geothermal energy. This study focuses on the typical geothermal fields in Tai'an, employing qualitative evaluations of the geochemical saturation index with temperature, combined with the corrosion coefficient, Ryznar index, boiler scale, and hard scale assessment, to predict corrosion and scaling trends in the geothermal water of the study area. The results show that the hydrochemical types of geothermal water in the study area are predominantly Na-Ca-SO4 and Ca-Na-SO4-HCO3, with the water being weakly alkaline. Simulations of saturation index changes with temperature reveal that calcium carbonate scaling is dominant scaling type in the area, with no evidence of calcium sulfate scaling. In the Daiyue Qiaogou geothermal field, the water exhibited corrosive bubble water properties, moderate calcium carbonate scaling, and abundant boiler scaling. Feicheng Anjiazhuang geothermal field showed non-corrosive bubble water, moderate calcium carbonate scaling, and significant boiler scaling. The Daidao'an geothermal field presented corrosive semi-bubble water, moderate calcium carbonate scaling, and abundant boiler scaling. The findings provide a foundation for the efficient exploitation of geothermal resources in the region. Implementing anti-corrosion and scale prevention measures can significantly enhance the utilization of geothermal energy.
Tai'an city, located in Shandong Province, China, is rich in geothermal resources, characterized by shallow burial, high water temperature, and abundant water supply, making them high value for exploitation. However, corrosion and scaling are main challenges that hinder the widespread application and effective utilization of geothermal energy. This study focuses on the typical geothermal fields in Tai'an, employing qualitative evaluations of the geochemical saturation index with temperature, combined with the corrosion coefficient, Ryznar index, boiler scale, and hard scale assessment, to predict corrosion and scaling trends in the geothermal water of the study area. The results show that the hydrochemical types of geothermal water in the study area are predominantly Na-Ca-SO4 and Ca-Na-SO4-HCO3, with the water being weakly alkaline. Simulations of saturation index changes with temperature reveal that calcium carbonate scaling is dominant scaling type in the area, with no evidence of calcium sulfate scaling. In the Daiyue Qiaogou geothermal field, the water exhibited corrosive bubble water properties, moderate calcium carbonate scaling, and abundant boiler scaling. Feicheng Anjiazhuang geothermal field showed non-corrosive bubble water, moderate calcium carbonate scaling, and significant boiler scaling. The Daidao'an geothermal field presented corrosive semi-bubble water, moderate calcium carbonate scaling, and abundant boiler scaling. The findings provide a foundation for the efficient exploitation of geothermal resources in the region. Implementing anti-corrosion and scale prevention measures can significantly enhance the utilization of geothermal energy.
2025, 13(2): 147-155.
doi: 10.26599/JGSE.2025.9280045
Abstract:
Accurately simulating water flow movement in vadose zone is crucial for effective water resources assessment. Richards' equation, which describes the movement of water flow in the vadose zone, is highly nonlinear and challenging to solve. Existing numerical methods often face issues such as numerical dispersion, oscillation, and mass non-conservation when spatial and temporal discretization conditions are not appropriately configured. To address these problems and achieve accurate and stable numerical solutions, a finite analytic method based on water content-based Richards' equation (FAM-W) is proposed. The performance of the FAM-W is compared with analytical solutions, Finite Difference Method (FDM), and Finite Analytic Method based on the pressure Head-based Richards' equation (FAM-H). Compared to analytical solution and other numerical methods (FDM and FAM-H), FAM-W demonstrates superior accuracy and efficiency in controlling mass balance errors, regardless of spatial step sizes. This study introduces a novel approach for modelling water flow in the vadose zone, offering significant benefits for water resources management.
Accurately simulating water flow movement in vadose zone is crucial for effective water resources assessment. Richards' equation, which describes the movement of water flow in the vadose zone, is highly nonlinear and challenging to solve. Existing numerical methods often face issues such as numerical dispersion, oscillation, and mass non-conservation when spatial and temporal discretization conditions are not appropriately configured. To address these problems and achieve accurate and stable numerical solutions, a finite analytic method based on water content-based Richards' equation (FAM-W) is proposed. The performance of the FAM-W is compared with analytical solutions, Finite Difference Method (FDM), and Finite Analytic Method based on the pressure Head-based Richards' equation (FAM-H). Compared to analytical solution and other numerical methods (FDM and FAM-H), FAM-W demonstrates superior accuracy and efficiency in controlling mass balance errors, regardless of spatial step sizes. This study introduces a novel approach for modelling water flow in the vadose zone, offering significant benefits for water resources management.
2025, 13(2): 156-169.
doi: 10.26599/JGSE.2025.9280046
Abstract:
Lake wetlands play a crucial role as global carbon sinks, significantly contributing to carbon storage and ecological balance. This study estimates the quarterly carbon storage in the Dongting Lake wetland for the years 2010, 2015, and 2020, using MODIS remote sensing imagery and the InVEST model. A Structural Equation Model (SEM) was then employed to analyze the driving factors behind changes in carbon storage. Results show that intra-annual carbon storage increases and then decreases, with maximum level in the third quarter (average of 34.242 Tg) and a minimum one in the first quarter (average of 21.435 Tg). From 2010 to 2020, inter-annual carbon storage variations initially exhibited an increasing trend before decreasing, with the peak annual average carbon storage reaching 32.230 Tg in 2015. Notably, the coefficient of variation for intra-annual carbon storage increased from 8.5% in 2010 to 25.8% in 2020. Key driving factors that influence carbon storage changes include surface solar radiation, temperature, and water level, with carbon storage positively correlated with surface solar radiation and temperature, and negatively correlated with water level. These findings reveal the spatiotemporal evolution characteristics of carbon storage in the Dongting Lake wetland, offering scientific guidance for wetland conservation and regional climate adaptation policies.
Lake wetlands play a crucial role as global carbon sinks, significantly contributing to carbon storage and ecological balance. This study estimates the quarterly carbon storage in the Dongting Lake wetland for the years 2010, 2015, and 2020, using MODIS remote sensing imagery and the InVEST model. A Structural Equation Model (SEM) was then employed to analyze the driving factors behind changes in carbon storage. Results show that intra-annual carbon storage increases and then decreases, with maximum level in the third quarter (average of 34.242 Tg) and a minimum one in the first quarter (average of 21.435 Tg). From 2010 to 2020, inter-annual carbon storage variations initially exhibited an increasing trend before decreasing, with the peak annual average carbon storage reaching 32.230 Tg in 2015. Notably, the coefficient of variation for intra-annual carbon storage increased from 8.5% in 2010 to 25.8% in 2020. Key driving factors that influence carbon storage changes include surface solar radiation, temperature, and water level, with carbon storage positively correlated with surface solar radiation and temperature, and negatively correlated with water level. These findings reveal the spatiotemporal evolution characteristics of carbon storage in the Dongting Lake wetland, offering scientific guidance for wetland conservation and regional climate adaptation policies.
2025, 13(2): 170-179.
doi: 10.26599/JGSE.2025.9280047
Abstract:
Developing a rapid and precise method for trace element analysis in geothermal water is crucial due to its high total dissolved solids and salinity, which can impact element determination. In this study, we optimized the determination of ferrum, manganese, strontium and barium in geothermal water samples collected from different regions. A matrix matching method was established for accurate quantification using Inductively Coupled Plasma Optical Emission Spectroscopy (ICP-OES). Instrumental conditions and experimental parameters were optimized, and the influence of storage medium and storage duration on measurement accuracy were evaluated. The results demonstrated that storing geothermal water samples in 1% nitric acid had no significant impact on measurement results over an eight-week period. Calibration curve correlation coefficients exceeded 0.9998 for all target elements. The detection limits of this method ranged from 0.0002 mg/L to 0.0031 mg/L, with Relative Standard Deviations (RSD) were 0.07%–2.33%, and spike recovery rates were from 96.8% to 103.2%. The obtained data were consistent with results from the standard addition method and dilution method, demonstrating the reliability of this approach. This method offers low detection limits, high precision and excellent recovery rate, providing a robust reference for the accurate determination of ferrum, manganese, strontium and barium in geothermal water, thereby laying a solid foundation for the development and utilization of geothermal resources.
Developing a rapid and precise method for trace element analysis in geothermal water is crucial due to its high total dissolved solids and salinity, which can impact element determination. In this study, we optimized the determination of ferrum, manganese, strontium and barium in geothermal water samples collected from different regions. A matrix matching method was established for accurate quantification using Inductively Coupled Plasma Optical Emission Spectroscopy (ICP-OES). Instrumental conditions and experimental parameters were optimized, and the influence of storage medium and storage duration on measurement accuracy were evaluated. The results demonstrated that storing geothermal water samples in 1% nitric acid had no significant impact on measurement results over an eight-week period. Calibration curve correlation coefficients exceeded 0.9998 for all target elements. The detection limits of this method ranged from 0.0002 mg/L to 0.0031 mg/L, with Relative Standard Deviations (RSD) were 0.07%–2.33%, and spike recovery rates were from 96.8% to 103.2%. The obtained data were consistent with results from the standard addition method and dilution method, demonstrating the reliability of this approach. This method offers low detection limits, high precision and excellent recovery rate, providing a robust reference for the accurate determination of ferrum, manganese, strontium and barium in geothermal water, thereby laying a solid foundation for the development and utilization of geothermal resources.
2025, 13(2): 180-192.
doi: 10.26599/JGSE.2025.9280048
Abstract:
Groundwater (GW) is a vital freshwater resource extensively exploited in the Vietnamese Mekong Delta, especially during the dry seasons. This study applies the Cumulative Rainfall Departure (CRD) method to estimate GW recharge in deep aquifers of Soc Trang Province, located in the southernmost region of Vietnam under a tropical climate. Monthly rainfall records and daily GW level data of the aquifers from 2010 to 2020 were used. The Pearson correlation between observed GW levels and CDR model GW levels exceeds 0.995, indicating high model accuracy. The analysis reveals that the CRD fractions for the Upper Pleistocene (qp3), Middle Pleistocene (qp2-3), Lower Pleistocene (qp1), and Middle Pliocene (n22) aquifers are 0.085%, 0.104%, 0.130%, and 0.180%, respectively, totaling approximately 0.5% of the annual rainfall. This corresponds to an annual GW recharge of 25.86 million m3, or 70,850 m3/day, equivalent to 70% of the current GW abstraction rate of 101,000 m3/day. Given the critical role of GW as a freshwater source, implementing an enhanced GW recharge program using surface water and rainwater is strongly recommended. Additionally, the analysis suggests that the decline in GW levels due to abstraction corresponds to 0.85 times the mean annual precipitation, a finding that warrants further investigation.
Groundwater (GW) is a vital freshwater resource extensively exploited in the Vietnamese Mekong Delta, especially during the dry seasons. This study applies the Cumulative Rainfall Departure (CRD) method to estimate GW recharge in deep aquifers of Soc Trang Province, located in the southernmost region of Vietnam under a tropical climate. Monthly rainfall records and daily GW level data of the aquifers from 2010 to 2020 were used. The Pearson correlation between observed GW levels and CDR model GW levels exceeds 0.995, indicating high model accuracy. The analysis reveals that the CRD fractions for the Upper Pleistocene (qp3), Middle Pleistocene (qp2-3), Lower Pleistocene (qp1), and Middle Pliocene (n22) aquifers are 0.085%, 0.104%, 0.130%, and 0.180%, respectively, totaling approximately 0.5% of the annual rainfall. This corresponds to an annual GW recharge of 25.86 million m3, or 70,850 m3/day, equivalent to 70% of the current GW abstraction rate of 101,000 m3/day. Given the critical role of GW as a freshwater source, implementing an enhanced GW recharge program using surface water and rainwater is strongly recommended. Additionally, the analysis suggests that the decline in GW levels due to abstraction corresponds to 0.85 times the mean annual precipitation, a finding that warrants further investigation.
2025, 13(2): 193-208.
doi: 10.26599/JGSE.2025.9280049
Abstract:
Seismic data plays a pivotal role in fault detection, offering critical insights into subsurface structures and seismic hazards. Understanding fault detection from seismic data is essential for mitigating seismic risks and guiding land-use plans. This paper presents a comprehensive review of existing methodologies for fault detection, focusing on the application of Machine Learning (ML) and Deep Learning (DL) techniques to enhance accuracy and efficiency. Various ML and DL approaches are analyzed with respect to fault segmentation, adaptive learning, and fault detection models. These techniques, benchmarked against established seismic datasets, reveal significant improvements over classical methods in terms of accuracy and computational efficiency. Additionally, this review highlights emerging trends, including hybrid model applications and the integration of real-time data processing for seismic fault detection. By providing a detailed comparative analysis of current methodologies, this review aims to guide future research and foster advancements in the effectiveness and reliability of seismic studies. Ultimately, the study seeks to bridge the gap between theoretical investigations and practical implementations in fault detection.
Seismic data plays a pivotal role in fault detection, offering critical insights into subsurface structures and seismic hazards. Understanding fault detection from seismic data is essential for mitigating seismic risks and guiding land-use plans. This paper presents a comprehensive review of existing methodologies for fault detection, focusing on the application of Machine Learning (ML) and Deep Learning (DL) techniques to enhance accuracy and efficiency. Various ML and DL approaches are analyzed with respect to fault segmentation, adaptive learning, and fault detection models. These techniques, benchmarked against established seismic datasets, reveal significant improvements over classical methods in terms of accuracy and computational efficiency. Additionally, this review highlights emerging trends, including hybrid model applications and the integration of real-time data processing for seismic fault detection. By providing a detailed comparative analysis of current methodologies, this review aims to guide future research and foster advancements in the effectiveness and reliability of seismic studies. Ultimately, the study seeks to bridge the gap between theoretical investigations and practical implementations in fault detection.
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Editor-in-chief1.7
Impact Factor(2023)
2.8
CiteScore 2023
Editor-in-ChiefHOU Chun-tang
Sponsors
Institute of Hydrogeology and Environmental Geology (IHEG), CAGS
China Chapter, International Association of Hydrogeologists (IAH-CC)
Commission on Hydrogeology, Geological Society of China(GSC-CH)
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