Ponding-infiltration processes on wide platforms can readily induce local instability of high-fill slopes during heavy rainfall events. This study aims to clarify the slope hydrological processes under intense y rainfall and to reveal their influences on the local instabilities of the high-fill slopes, thereby providing a scientific basis for engineering design and disaster prevention of this kind of slope. The numerical models for coupling surface water and groundwater processes and simulating the stress-strain state of soil were built through COMSOL Multiphysics. A variety of numerical simulations were carried out to analyze the relationship between the ponding-infiltration processes on the wide platform and the local instability of high-fill slope. Rainfall infiltration forms transient saturated zones in the shallow filling body. When the transient saturated zone expands to the vicinity of the filling interface, the flow direction of groundwater changes, causing groundwater to migrate along the filling interface. It can be observed that the groundwater level rises with time, and that groundwater discharges to surface water near the retaining wall. The increase of slope water content diminishes the soil shear strength, associated with an interface effect, which is especially obvious in the saturated area near the retaining wall (with the maximum reduction of 70.3%). Ponding on the wide platform raises the local groundwater level and increases slope deformation, thereby reducing slope stability. It is found that of the value and duration of the instability of the slope are larger and longer compared with no ponding considered. The installation of anti-seepage geotextiles under the wide platforms can improve the stability of the slope by reducing groundwater seepage. Specifically, the spatial continuity of the transient saturated zones is decreased, reducing the hydraulic connectivity of groundwater seepage channels and the groundwater level at the foot of the slope. Ponding-infiltration processes on wide platforms of high-fill slope can rise the groundwater level, strengthen the interaction of surface water and groundwater, increase the pore water pressure of the shallow soil, and aggravate local deformation, which decrease the stability of the slope. The use of anti-seepage geotextiles under the wide platforms can reduce rainfall infiltration and improve the stability of the slope.
Nonuniform soil profiles can induce seismic ground-motion amplification and deformation incompatibility, thereby intensifying the seismic response and altering the deformation patterns of shield tunnels. To address this critical issue, this study takes a cross-river tunnel project in the lower reaches of the Yangtze River as a case study. Considering the dynamic nonlinearity of riverbed soil and the refined assembly of segments, a 3D refined finite element model with a longitudinal length of 500 m was established. The effects of seismic wave characteristics and the shear wave velocity ratio of adjacent soil layers on the failure mechanism and deformation mode of tunnels crossing longitudinally heterogeneous ground were systematically investigated. The results indicate that under seismic loading, the tunnel exhibits significant longitudinal non-uniform deformation, with the peaks of longitudinal joint opening and staggering concentrated on the soft soil side near the strata interface. The tunnel manifests a distinct “S-shaped” bending deformation at the interface, and the zone significantly affected by the abrupt change in strata extends to a range of 5 times the tunnel diameter into the soft soil side. Furthermore, concrete damage in tunnel segments presents asymmetric evolution. In soft-soil sections, damage is mainly concentrated along conjugate directions of the tunnel cross-section, whereas in hard-soil sections, damage is primarily localized near the springline region. The findings provide useful guidance for the anti-seismic design of cross-river tunnels in heterogeneous ground conditions.
Groundwater inorganic nitrogen contamination is one of the most widespread water environmental issues in the Jianghan Plain.Agricultural activities constitute an important source of inorganic nitrogen in groundwater.However,in agricultural regions characterized by dense river networks,the sources,migration pathways,and transformation mechanisms of inorganic nitrogen in shallow groundwater recharged by precipitation and surface water remain insufficiently understood.Taking the agricultural area of the eastern Jianghan Plain as a case study,the chemical characteristics,the source of inorganic nitrogen,and its migration and transformation mechanism of surface water and groundwater were analyzed by hydrochemistry combined with hydrogen-oxygen and strontium isotopes.The results show that the cations of surface water and groundwater samples in the study area were dominated by Ca2+and Na+,and the anions were dominated by HCO-3.The water chemical type was dominated by HCO3-Ca •Na and HCO3-Ca.The inorganic nitrogen in surface water and groundwater was mainly nitrate,and the concentration of inorganic nitrogen in groundwater was significantly higher than that in surface water.The groundwater with high nitrate concentration was mainly distributed around the northern and southern towns,and the distribution of ammonia nitrogen with high concentration was scattered.Precipitation was the primary source of surface water and groundwater recharge in the region.The chemical characteristics of surface water and groundwater were mainly controlled by silicate and evaporite weathering,and were obviously affected by human activities.The surface water with extreme low concentration of inorganic nitrogen was mainly from atmospheric precipitation,while the groundwater with relatively high concentration of inorganic nitrogen was mainly from domestic sewage and feces in agricultural areas.The mutual transformation of inorganic nitrogen components was affected by the redox conditions.The inorganic nitrogen of the pollution source in the surface layer was mainly ammonia nitrogen,and the concentration of ammonia nitrogen in groundwater is high.Nitrate was gradually enriched by nitrification,accompanied by the downward migration of atmospheric precipitation.This study is of great significance for understanding the causes and migration and transformation mechanism of inorganic nitrogen enrichment in groundwater in agricultural areas with dense water networks,and for the prevention and control of groundwater pollution in Jianghan Plain.
To realize the practical value of historical earthquake data in the seismic resilience assessment of urban rail transit network (URTN), it is essential to establish links between resilience metrics and real-world network characteristics, including network topology, service coverage capacity, passenger flow patterns, the spatial distribution of earthquakes, and the vulnerability of civil infrastructure, thereby improving the fidelity of resilience assessment. In this study, taking Shanghai as a case study, a ground-motion estimation model based on historical earthquake data and a civil infrastructure vulnerability model were first developed. These models were then embedded into the URTN topology model, and their coupling was achieved by controlling the failure states of nodes and links in the topology model under earthquake scenarios. For resilience calculation and assessment, fractal dimension was computed using information dimension theory and taken as the functional indicator of network resilience, while the resilience triangle was used to characterize the variation of network resilience over time. Subsequently, this method was applied to obtain the resilience assessment results of Shanghai URTN. Before the earthquake, the fractal dimension was approximately 1.2; under MS 3.0 and MS 4.0 earthquakes, the fractal dimension showed no obvious change; under MS 5.0 and MS 6.0 earthquakes, however, the fractal dimension exhibited significant attenuation, and the corresponding recovery times were 3 400 h and 5 000 h, respectively, both indicating severe resilience losses. After preventive reinforcement measures were implemented, the Shanghai URTN was able to maintain an essentially normal operational state under MS 5.0 earthquakes. Under MS 6.0 earthquakes, the post-earthquake recovery time was shortened by approximately 30%, and the resilience loss was reduced by about 34%. The results show that using fractal dimension for seismic resilience assessment can incorporate more real-world elements and comprehensively and quantitatively evaluate the performance and resilience characteristics of URTN before and after earthquake disasters. Preventive reinforcement measures can also significantly improve the seismic resilience of URTN.
Under complex geological and geostress conditions, the design of mountain-adjacent tunnels in hard rock areas faces numerous challenges, particularly in determining the tunnel’s buried depth. This study proposed a method for determining the buried depth of mountain-adjacent tunnels, comprehensively integrating high-level tectonic stress, the superimposed effects of valley stress fields, and the potential for rockbursts triggered by depth. The specific methods include: (1) Determining the influence range of the superimposed tectonic stress field and valley stress field. This involves initially estimating the influence depths of the tectonic stress and valley stress fields based on measured geostress data from boreholes in the slope zones of canyon areas, and then uses numerical simulation technology to optimize the preliminary estimation results to ensure the accuracy and reliability of the initial obtained range. (2) Determining the limit of severe rockbursts caused by burial depth. Firstly, it calculates the statistical mean value of saturated uniaxial compressive strength (Rc) by rock physical and mechanical tests in the tunnel site area, and then selects the typical geological section of the tunnel site to simulate the stress field. It uses the strength stress ratio method, introducing the σmax=Rc/2 as the basis to judge whether the buried depth may cause severe rockbursts. (3) It should be emphasized that on the basis of the above two steps, the relatively safe buried depth range of the tunnel is determined, while the deep buried depth section (≥1200 m) should be minimized as far as possible in the design process to achieve the final optimization of the tunnel buried depth. This study provides a scientific basis and practical method for the buried depth design of hard rock mountain-adjacent tunnels, and has a wide application prospect and popularization value in the future design of mountain-adjacent tunnels.
The mechanical characteristics of porous tunnel construction are complex,and the selection of its construction scheme and stability evaluation are engineering difficulties.This paper takes a shallow-buried,unsymmetrically-loaded four-hole small-clear-distance tunnel in Fujian as the engineering basis.A three-dimensional mechanical model is established to analyze the deformation laws of the tunnel under four excavation sequences.On this basis,a deformation potential energy function based on the cusp catastrophe theory is constructed.By comprehensively considering deformations such as vault settlement and horizontal convergence and their development trends,the stability evaluation of the porous tunnel and the optimization of the construction scheme are completed.Combined with engineering practice,the rationality of the scheme is verified,and stability control measures are proposed.Mechanical analysis shows that when the scheme of"first excavating the two right tunnels with smaller burial depth and then excavating the two left tunnels with larger burial depth"is adopted,the invert heave,vault settlement and horizontal convergence of the tunnel are 18.70,11.31 and 9.80 mm respectively,which are 10.09%-19.01%less than those of other schemes,far less than the specified values and the deformation development is controllable.The evaluation results of the overall stability of the tunnel under different sequences based on the cusp catastrophe theory model show that the scheme of first excavating the two right tunnels with smaller overburden thickness and then excavating the two left tunnels is a relatively optimal tunneling scheme.The overall fitting degree of the calculation results is greater than 0.95,and the prediction accuracy meets the engineering precision requirements.The results of deformation calculation and stability analysis are in good agreement with on-site monitoring and actual construction,which verifies the rationality of the calculation method,evaluation model and construction scheme.It is suggested that during construction,focus should be placed on the horizontal convergence deformation of the left vehicle tunnel,and local geotechnical bodies should be reinforced as needed to reduce the unsymmetric loading effect at the tunnel portal section and the risk of arch foot cracking.The research results provide practical guidance for on-site construction.
The complexity, persistence, and concealment of contamination in groundwater sites pose significant challenges to risk management. To address these challenges, this study develops a knowledge graph-based intelligent decision-making model for managing contaminated groundwater sites and proposes an intelligent risk management framework that integrates technical, economic, and social dimensions. By consolidating a risk management technology repository and a case study database for contaminated groundwater sites, a knowledge graph with semantic reasoning capabilities was constructed. The KG-RF (Knowledge Graph - Random Forest) intelligent decision-making model was designed by integrating knowledge graphs with random forest algorithms to accurately recommend risk management solutions. During the model training phase, the KG-RF model was trained on a dataset of contaminated sites recorded in the knowledge graph and an overall prediction accuracy of 94.86% on the test dataset. A case study on a contaminated groundwater site demonstrates that the KG-RF model, based on key indicators such as hydrogeological conditions and pollutant characteristics, identifies and matches similar historical cases, calculates similarity scores, and recommends optimal risk management solutions. The results indicate that the dual-layer PRB (Permeable Reactive Barrier), consisting of ZVI (zero-valent iron), granular activated carbon, and biofilm, achieves the highest similarity score (0.927 8). This technology excels in terms of technical maturity, applicability, and cost-effectiveness. The findings indicate that knowledge graphs have significant application potential in risk management for contaminated groundwater sites, contributing to the enhancement of intelligent and precise risk management. This study provides new methods and approaches for smart decision-making in complex environmental issues.
Understanding the material sources of the black soil horizons and their parent materials is of great significance for elucidating the formation and evolutionary processes of black soils. However, previous studies have primarily focused on the black soil layer itself, with limited attention given to the continuous evolutionary relationship between black soil and parent material layers, as well as to the sedimentary recycling processes of parent material in black soil regions. This study examined eight representative black soil profiles in the Xingkai Lake area of southern Sanjiang Plain. Grain-size analysis and geochemical measurements were systematically conducted on both black soil and parent material layers to reveal provenance characteristics and climatic implications for black soil formation. Grain size analysis indicates that both the black soil and parent material layers in the study area are dominated by silt-sized particles (approximately 60%). Sedimentary environment discriminants and C-M diagrams indicate that both layers represent aquatic deposits, suggesting weak hydrodynamic characteristics under the region’s gentle topography. Geochemical analysis reveals that the CIA value (72.30) of the black soil layer is significantly higher than that of the parent material layer (64.06), indicating that the black soil formed under a warm and humid climate environment, consistent with the Holocene interglacial warming period background of Northeast China’s black soil development. Both the black soil layer and parent material in the study area share the same source material, medium-to-acidic igneous rocks from the Wanda Mountains, which were transported and deposited via fluvial alluvial processes, undergoing re-sedimentation during deposition. Preliminary findings suggest the formation of southern Sanjiang Plain black soil involved a complex sequence of “weathering in the source area → multi-stage transport → re-sedimentation”, providing geochemical evidence for understanding the formation and evolution of black soils in Northeast China.
Groundwater is a critical water resource supporting industrial and agricultural activities in the plain area of the Yongding River Basin.Understanding its hydrochemical characteristics,formation mechanisms,and sources of typical excessive indicators is of great significance for ensuring the water supply security of the Beijing-Tianjin-Hebei urban agglomeration and guiding the rational development and utilization of groundwater resources.Based on 89 groundwater samples collected from May to August 2021,this study systematically analyzed the hydrochemical characteristics,component over-standard status,and spatial distribution patterns.By comprehensively adopting the Piper diagram,Gibbs diagram,ion ratio method,principal component analysis,and Spearman correlation analysis,the hydrochemical formation mechanism of groundwater was elucidated,with an emphasis on revealing the enrichment mechanism and sources of nitrate(NO-3-N)and fluoride(F-).The results show that the groundwater in the study area is weakly alkaline,and most sampling points belong to fresh water.The dominant ions are HCO-3 and Na+,and the hydrochemical type is dominated by the HCO3—Ca type.The mass concentrations of TH,TDS,Ca2+,Mg2+,SO2-4,Cl-,HCO-3,andNO-3-N in phreatic water are significantly higher than those in confined water,showing a decreasing trend with the increase of burial depth.The over-standard rate of F-in confined water is higher than that in phreatic water.The hydrochemical formation is mainly controlled by the weathering and dissolution of carbonate rocks,followed by evaporation and concentration.Stronger cation exchange processes in confined water promoted Na+enrichment and facilitated fluoride release.The over-standard rate ofNO-3-N in phreatic water is relatively high,mainly derived from agricultural activities and domestic sewage discharge.Local high concentrations ofNO-3-N in confined water are caused by interlayer cross-contamination induced by mixed groundwater exploitation and leakage recharge.F-enrichment is predominantly controlled by geological factors;the leaching of fluorine-bearing minerals,the reduced activity of Ca2+under weakly alkaline conditions,and cation exchange and adsorption jointly promote the enrichment of F-.Based on the understanding of hydrochemical formation,targeted risk prevention and control strategies are proposed.It is recommended to restrict phreatic water from being used as a drinking water source,and prioritize confined water for urban water supply with focused monitoring of fluoride pollution risks.Sewage collection and treatment facilities should be promoted in high nitrate risk areas,and water quality early warning thresholds should be established in fluoride over-standard areas.The research results reveal the hydrochemical characteristics and formation mechanisms of groundwater in typical urban agglomerations,providing a scientific basis for groundwater resource protection and water security guarantee in the river basin.
The West Taijinar Lake area in the Qaidam Basin is within a typical arid inland salt-lake environment characterized by complex hydrogeological conditions and strong aquifer heterogeneity. The permeability characteristics play an important role in controlling regional groundwater flow, solute transport, and resource assessment. However, the applicability of existing theoretical and empirical models describing the depth-dependent variation of hydraulic conductivity of aquifer has not yet been systematically examined and evaluated using measured borehole data from the West Taijinar Lake area. In this study, a two-parameter model for depth-dependent attenuation of hydraulic conductivity of aquifer was proposed. Measured hydrogeological data from four boreholes in the West Taijinar Lake area were used to calibrate and validate the proposed model. The depth-dependent variation characteristics of hydraulic conductivity of aquifer in the study area were analyzed, and the performance of the proposed model was further compared with several representative depth-attenuation models reported in previous studies. The nonlinear generalized reduced gradient algorithm was employed for parameter optimization, and model performance was evaluated using the coefficient of determination (R2) and root mean square error. The results indicate that the hydraulic conductivity of the aquifer in the study area presents an overall pronounced decreasing trend with increasing depth, while the measured data show considerable scatter. The proposed two-parameter model for the depth-dependent attenuation of hydraulic conductivity, as well as the single-exponential model and two-parameter logarithmic regression model proposed by previous researchers, all show good applicability in the study area. Among these models, the proposed model exhibits overall higher fitting accuracy and greater parameter stability than the traditional single-exponential model and the logarithmic regression model, and is capable of effectively capturing the characteristic pattern of rapid attenuation of hydraulic conductivity in shallow depths and the transition to more gradual variation at greater depths within salt-lake sedimentary environments. This study can provide theoretical basis for the rational characterization and prediction of deep hydrogeological parameters in inland salt-lake areas, and provide scientific support for groundwater flow simulation and deep brine resource evaluation.
Physics-informed neural network (PINN) models have been widely applied to forward groundwater modeling problems, such as groundwater head and flow simulations. However, for hydrogeological parameter inversion, the performance of standalone PINN model is often limited by sparse observations and the well-known issue of equifinality, whereby different parameter combinations can produce similar hydraulic responses. These limitations introduce substantial uncertainty into inversion results. To address these challenges and improve the interpretability of hydrogeological parameter inversion, this study integrated PINN with conditional generative adversarial network (CGAN) to develop a physics-informed conditional generative adversarial network (PICGAN) framework. A two-dimensional heterogeneous transient arithmetic model was designed to evaluate the applicability and performance of the proposed model. Under the joint constraints of physical conditions and observed groundwater level, the discriminator of the PICGAN model continuously required the generator to update the global hydrogeological parameter field more in line with the reality until the convergence criteria of the generator and the discriminator were satisfied. At this point, it could be considered that the PICGAN model has completed the inverse solution of the hydraulic conductivity field of the heterogeneous transient confined aquifer, and could also simultaneously simulate and predict the water level. The results show that in the case simulation with 5% sampling rate, the root-mean-square error of water head simulated by the PICGAN model could be stabilized at about 0.95 m, with an accuracy of 89%. The root-mean-square error of hydraulic conductivity field could be stabilized at about 0.69 m/d, with an accuracy of up to 95%, and the distribution form was highly consistent with the reference field. As the sampling rate increased, the global error of the model would be further reduced. After the sampling rate reached 10% or more, the inversion error of the global hydraulic conductivity was reduced to 0.35 m/d, with an accuracy of 97%. In conclusion, the PICGAN model proposed in this study can provide a novel and effective method for bidirectional solution of groundwater problems under small sample conditions, especially for the inversion of heterogeneous hydrogeological parameter fields.
Seismic response prediction at tunnel sites remains challenging because rapid assessment requires both computational efficiency and physically consistent results. This study proposes a physics-constrained Kolmogorov-Arnold and long short-term memory (KAN-LSTM) sequence model for predicting the acceleration time history at the tunnel key point RP1. This method took the site base ground motion acceleration time history as input and, during the training phase, introduced the joint prediction of the three states: displacement, velocity, and acceleration, to enhance the model’s learning of kinematic consistency. However, in the final engineering application, the model only output the acceleration time history at RP1. A Kolmogorov-Arnold network (KAN) was integrated into the LSTM decoder to strengthen nonlinear mapping, and a kinematic derivative-consistency constraint among response quantities was added to the loss function. This constraint improved prediction stability and physical consistency. Training and testing samples were generated from numerical simulations for three representative soils, including soft clay, silty fine sand, and medium-to-coarse sand. Comparisons with GRU, LSTM, Phy-LSTM, and KAN-LSTM show that the proposed model maintains accurate time-history fitting and reduces peak-response bias under strongly nonlinear conditions. This effect improves the consistency of the peak ground acceleration (PGA) metric and increases the 95% confidence interval (CI95) coverage. Under near-linear conditions, differences among models become smaller, and the main benefit is bias control for a limited number of high-intensity records. The results indicate that the proposed approach provides an efficient and physically consistent surrogate for seismic acceleration response prediction at sites with underground structures.
When a TBM tunnel crosses a karst-developed area, its excavation induces disturbances and compressive stress on water-bearing cavities, potentially triggering water inrush hazards. Based on the upper-bound theorem of limit analysis, a failure model is developed for the waterproof rock mass situated ahead and above the tunnel face, conceptualized as a composite of a truncated cone and a wedge. A stability analysis method is formulated using the virtual work principle. An analytical formula for the critical thickness of the waterproof layer is then derived and validated. Finally, the effects of tunnel design parameters, rock mass properties, and cavity characteristics on the critical failure thickness of the waterproof rock mass are systematically analyzed. The results indicate that the mechanical parameters of the surrounding rock (internal friction angle φ and cohesion c, especially φ) have the greatest impact on the critical thickness of the water-resisting rock mass (Hα). This is followed by the tunnel diameter (D), the karst cavity pressure (pw), and the face support pressure (p), with the diameter of the karst cavity (DR) having the least influence. Furthermore, Hα shows a positive linear correlation with D, DR, and pw, and a negative linear correlation with p. In contrast, it exhibits a negative exponential relationship with the surrounding rock’s φ and c. Additionally, when the c and φ of the surrounding rock increase to a certain level (φ>37.5°, c>3 MPa), the reduction in Hα significantly diminishes. This suggests that strengthening the surrounding rock through methods like grouting is only effective within a certain range for preventing karst water inrush in tunnels, with the effect substantially weakening beyond this range. These findings provide a theoretical basis for predicting the critical thickness of waterproof rock mass above TBM tunnels and provide support for the safe construction of TBM tunneling in karst regions.
The eco-hydrological mismatch between artificial vegetation and available soil water resources in parts of the north-central Loess Plateau has resulted in widespread soil desiccation and the formation of dried soil layers(DSLs).However,limited knowledge of the spatial distribution of DSLs constrains the assessment of soil water sustainability.To quantify the spatial distribution of DSLs in the shrub-grassland regions of the Loess Plateau,identify the dominant environmental factors driving DSL formation,and develop predictive models for DSLs,this study conducted field investigations across 111 sample plots(including farmland,grassland,forestland,and shrubland)to analyze 0-5 m soil profiles.Three key indicators were evaluated:dried soil layer thickness,soil water content within the dried soil layer,and soil water deficit.A total of five types of environmental factors were collected to elucidate the spatial characteristics and driving factors of DSLs in the shrub-grassland regions.Traditional statistical methods and three classical machine learning algorithms were employed to construct predictive models for DSL spatial distribution.The results reveal widespread development of DSLs(0-500 cm depth)in the shrub-grassland regions,with an average DSL thickness of 312 cm,an average soil water content within the DSL of 9.05%,and a mean soil water deficit of 0.59.Vegetation status is identified as the decisive factor influencing the spatial distribution of DSLs,while field capacity emerges as the critical determinant of soil water content in the DSL.Machine learning methods demonstrate strong predictive performance for DSL spatial distribution,particularly the back propagation neural network(BPNN)(except for DSL thickness)and support vector machine(SVM)models.This study quantitatively characterized the spatial patterns of DSLs in the shrub-grassland regions of the Loess Plateau and established vegetation status,soil properties,and meteorological factors as key environmental drivers.Furthermore,the predictive capabilities of machine learning models offer potential for spatial analysis of these findings,enabling improved management and forecasting of soil desiccation across broader regions.
Groundwater hydrochemical background levels (GHBLs) are recognized as a fundamental tool for identifying groundwater contamination and have been widely applied in research on hydrogeology, contaminated hydrogeology, and eco-hydrogeology. This paper systematically reviews the conceptual basis and assessment framework of GHBLs, summarizes key issues in their determination, evaluates approaches for assessing result reliability, and highlights future research directions. Unlike traditional “natural background levels,” GHBLs reflect not only hydrochemical characteristics governed by natural processes but also the legacy effects of long-term anthropogenic activities. The determination of GHBLs generally involves three critical stages: statistical unit delineation, sampling design, and anomaly identification. Statistical units are commonly established based on hydrogeological zoning and may be further refined using hydrogeochemical characteristics; however, standardized quantitative criteria for delineation remain lacking. Sampling design is mainly assessed in terms of spatial representativeness and minimum sample size, although related methodological studies remain limited. Existing anomaly identification methods include preselection, statistical, hydrogeochemical, and influencing-factor-based approaches, each with its own strengths and limitations. Combining multiple methods can improve the robustness and reliability of anomaly identification. However, establishing a unified framework applicable across different regions remains challenging, and anomaly identification for trace constituents is still difficult. Current validation of GHBLs rationality mainly relies on interpreting anomaly sources and identifying hydrogeochemical controls, yet anomaly attribution remains largely qualitative and subject to uncertainty. Future research should strengthen the quantitative evaluation of statistical-unit delineation and sampling design, develop assessment frameworks adaptable to different hydrogeological settings, expand the application of machine learning in anomaly identification, and promote multi-source data integration.
In response to no mature analytical model for the jet from flowing artesian wells, including well discharge and jet height, systematic research was conducted. First, based on the principles of well flow, jet flow, and water flow continuity, a steady flow analytical model for jet from a flowing artesian well was established, and a closed form solution was derived, upon which its practical implications were discussed. Second, to characterize jet status, the concept of jet potential of a flowing artesian well was proposed, encompassing both apparent jet potential and substantial jet potential. For the quantitative analysis of substantial jet potential, the expression of jet power was derived. Third, through systematic theoretical deduction, an in-depth analysis was conducted on the impact of three fundamental hydraulic factors (flowing hydraulic head, specific well discharge, and wellhead area) on the jet potential of a flowing artesian well. Finally, the theoretical results are validated through application to a practical case. The results indicate that: (1) Jet is entirely determined by three fundamental hydraulic factors. (2) The apparent jet potential provides an intuitive measure of jet status and serves as an external functional indicator of flowing artesian well, while the substantial jet potential can more accurately characterize the status of the well from a mechanistic perspective. (3) The three fundamental hydraulic factors are sufficient and necessary conditions for the jet. Among them, the flowing hydraulic head is the source of energy and supply, whose impact on jet is positively correlated and unbounded. The specific well discharge reflects the runoff channel and has a positive correlation and bounded impact on the jet potential. The area of the wellhead represents the drainage capacity of a flowing artesian well, and is also the “main switch” and “regulator” for jet, whose impact on jet potential is “bidirectional”; theoretically, there exists a “maximum jet potential wellhead area”. (4) The prectical case studies have demanstrated that both forward and invevse analysis can be conducted by this research with high precision (the relative error of well flow discharge is within 1%,the absolute error of jet height is only 0.03 m, and the relative error of specific well discharge is within 1%). At the same time, it also explains from the perspective of caculation and analysis that three basic hydraulic factors have a significant impact on the formation of the jet potential characteristics and working status of the flowing artesian well. Systematic theoretical derivation and case application demonstrate that this study not only reveals the hydraulic essence of jet in theory but also provides important guidance for the design and operation of flowing artesian wells, as well as for the inversion of hydrogeological parameters by jet observations.
The Zhangjiakou-Chengde(Zhang-Cheng)district is an important ecological barrier and water conservation area in the Beijing-Tianjin-Hebei region.Groundwater-dependent ecosystems(GDEs)are therefore crucial for regional ecological stability and water security.Groundwater-dependent vegetation(GDV)constitutes the core component of GDEs.However,studies on the identification of GDEs and GDV distributions and on groundwater depth thresholds required to sustain GDV in the Zhang-Cheng district remain limited.Based on multi-source remote sensing,meteorological,and groundwater monitoring data from 2018 to 2024,the year 2023,a drought year,was selected as the reference year for identification.Available precipitation was calculated using a surface energy balance model and satellite precipitation data.Potential GDEs were delineated by integrating land-use data and groundwater depth data.Within the potential GDE areas,NDVI and groundwater depth were standardized using Z-scores.Areas where the standardized NDVI and groundwater depth showed a significant negative linear correlation were identified as GDV.A quantile-based statistical method was then used to determine groundwater depth thresholds associated with vegetation degradation.GDV in the Zhang-Cheng district was mainly distributed in northeastern and southwestern Chengde,accounting for 1.5%of the total regional area.In the Bashang area,GDV was dominated by meadow vegetation,whereas in the Baxia area,it was mainly composed of meadows,shrubs,shrublands,and birch forests.Vegetation in the Bashang area,Baxia area,and the entire Zhang-Cheng district was more likely to decline when groundwater depth exceeded the local mean by 0.78 standard deviations.The corresponding regional risk thresholds of groundwater depth for vegetation degradation were 5.71 m,8.00 m,and 7.41 m,respectively.These risk thresholds were consistent with the actual thresholds associated with unhealthy vegetation conditions.Among different vegetation types,meadow had the shallowest actual groundwater depth threshold for degradation,at 5.48 m,whereas aspen had the deepest threshold,at 16.60 m.The ecological thresholds of GDV in the Zhang-Cheng district exhibited pronounced spatial heterogeneity.The actual groundwater depth thresholds corresponding to different health states of typical vegetation types were consistent with their rooting depths.This study provides a simple approach for delineating GDV distribution and confirms the relationship between rooting depth and groundwater table depth for groundwater-dependent vegetation in the Zhang-Cheng district.The findings offer a quantitative basis for groundwater resource management and ecological restoration in the region.
In recent years,the increasing intensity of infrastructure development in mountainous regions,coupled with the rising frequency of extreme meteorological events,has led to a growing incidence of rockfall hazards,particularly in northwestern China.Weathering processes often cause unstable rock masses to fragment upon detachment.However,the current rockfall risk assessment methods mostly ignore the impact of rockfall fragmentation and debris shape characteristics on the rockfall trajectory and energy dissipation,which significantly affects the reliability of assessments.Consequently,quantifying the risk of rockfalls to mountainous towns has become a critical issue.Focusing on the high and steep back-mountain collapse in Chengguan Town,Zhouqu County,Gansu Province,this study proposed a quantitative risk assessment method integrating 3D modeling with fracture shape characteristics.Potential hazardous rock masses were identified through UAV close-range photogrammetry and point cloud data.A 3D model was reconstructed using Blender software,while a fracture simulation program was developed based on the PhysX physics engine.Additionally,the Tyson polygon fractal fracture model was employed to dynamically simulate the shape evolution of rockfall fragmentation.The results indicate that the volume distribution of fragmented rocks follows a power-law distribution(R2=0.918),and the shape parameters demonstrate a flattening trend of fragments(mean sphericity=0.75).For instance,hazardous rock W1 generates the highest impact energy(3 314.7 kJ)on structure C1 within the disaster area on the left side of the slope foot,with a medium risk level.In contrast,hazardous rocks W2 and W3 exhibit the greatest impact on structures C2-C3 in the central road area.This study provides essential data support for the refined prevention and control of fractured collapse hazards.
The accumulation layer landslide mainly composed of the slope deposits of the fourth system collapse is currently the most common type of landslide. The movement characteristic of after instability such as movement velocity and accumulation range are two important indicators for evaluating its hazard. The particle discrete element PFC3D has obvious advantages in simulating the instability, sliding and accumulation of this type of landslide, however, some problems such as too many mesoscopic parameters which are difficult to select reasonably still exist. So, an in-depth study has been carried out on them. Firstly, taking Shidaguan accumulation layer landslide as an example, the unstable movement process is reproduced with PFC3D, and compared with the on-site investigation results to validate the numerical model. Then, with this model, the influence of the friction coefficient of the sliding surface, local damping and viscous damping on the movement distance and accumulation range of the sliding body is mainly discussed. The increase of the friction coefficient of the sliding surface reduces the sliding speed and movement distance of the sliding body, and decreases the range of the accumulation body. However, the degree of influence decreases with the increase of the friction coefficient. The increase in the volume of the sliding body will lead to an increase in the accumulation range and the maximum movement distance. The increase of local damping will cause greater hindrance to the movement of particles, reducing the sliding speed and the degree of damage. The influence degree of viscous damping on the particle motion decreases with the increase of the damping coefficient. The increase of normal viscous damping will increase the particle velocity without affecting the motion distance, while the increase of tangential viscous damping will simultaneously reduce the particle velocity and the motion distance. When conducting landslide simulation with PFC3D, reasonable friction and damping coefficients should be set simultaneously based on the duration of the actual landslide occurrence, the accumulation range of the landslide mass, the average velocity and the displacement value. The research results can provide useful references for the simulation of the unstable motion range of similar landslides.
Clustered landslides induced by earthquake and rainfall are characterized by large quantity, high frequency, uneven spatial distribution, and extensive impact areas. Traditional landslide identification relies primarily on visual interpretation of remote sensing imagery, which depends on the expertise of professionals and the analysis of pre- and post-event image textures. Although these methods can yield reasonable accuracy, they are labor-intensive and struggle to provide rapid and large-scale assessments, resulting in inefficiency and high operational costs. To date, with the rapid development of artificial intelligence, a semantic segmentation method based on data-driven and deep learning has been widely used in the automatic detection of clustered landslides. Taking Niangniangba Town, Qinzhou District, Tianshui City, Gansu Province, as a case study, this study established a comprehensive landslide inventory containing 3026 rainfall-triggered landslides through systematic visual interpretation of Google Earth satellite imagery. The full-scene images were subsequently cropped into 3026 patches (1200×1200 pixels). These patches were then stratified and randomly allocated into training (2526), validation (250), and test sets (250). The pyramid scene parsing network （PSPNet） semantic segmentation model was employed to automatically identify rainfall-induced landslides in the study area. The model obtained at the 87000th iteration was selected as the best model. On the validation set, the Precision, F1 score, Recall, and intersection over union (IoU) were 0.903, 0.821, 0.752, and 0.696, respectively. On the test set, these metrics were 0.920, 0.865, 0.816, and 0.761. Among the test samples, 204 landslides were correctly identified, 18 were falsely detected, and 28 were missed. The proposed intelligent recognition model based on semantic segmentation demonstrates high accuracy in identifying clustered landslides of varying scales and morphologies, with strong generalization capability. This approach provides an effective and efficient solution for the rapid and precise identification of clustered landslides.
