Collapse pits are highly susceptible to secondary hazards such as underground debris flows and slope instability under mining disturbances. These hazards significantly damage the ecological environment of the mining area. To reduce the geological hazards of collapse pits, grouting is used for management. The diffusion pattern and curing mode of slurry under different grouting pressures were investigated through indoor grouting simulation tests, and industrial tests were carried out to assess grouting effects. The results indicate that the slurry is dominated by penetration diffusion and supplemented by splitting diffusion in the moraine. The penetration distance and diffusion radius of the slurry increase linearly with grouting pressure, while the splitting uplift distance and cured volume increase exponentially with grouting pressure. Splitting diffusion consists of three stages: bulging compaction, splitting flow, and passive uplift. Horizontal splitting has a vertical uplift effect on the formation. The slurry primarily consolidates individual moraine particles into a cohesive mass by filling fractures, binding soil particles, and reinforcing interfaces with the rock mass. For different moraine layer structures, full-hole, segmented, and point-based grouting methods were applied. A composite grouting technique, layered grouting with ring solidification, was also introduced, achieving excellent grouting results. This study provides technical support for managing geological hazards in collapse pits caused by block caving mining disturbances and for green mining practices.

Southeast Tibet is characterized by extensive alpine glaciers and deep valleys, making it highly prone to cryospheric disasters such as avalanches, ice/ice-rock avalanches, glacial lake outburst floods, debris flows, and barrier lakes, which pose severe threats to infrastructure and human safety. Understanding how cryospheric disasters respond to climate warming remains a critical challenge. Using 3.3 km resolution meteorological downscaling data, this study analyzes the spatiotemporal evolution of multiple climate indicators from 1979 to 2022 and assesses their impacts on cryospheric disaster occurrence. The results reveal a significant warming trend across Southeast Tibet, with faster warming in glacier-covered regions. Precipitation generally decreases, though the semi-arid northwest experiences localized increases. Snowfall declines, with the steepest decrease observed around the lower reaches of the Yarlung Zangbo River. In the moisture corridor of the lower reaches of the Yarlung Zangbo River, warming intensifies freeze-thaw cycles, combined with high baseline extreme daily precipitation, which increases the likelihood of glacial disaster chains. In northwestern Southeast Tibet, accelerated glacier melting due to warming, coupled with increasing extreme precipitation, heightens glacial disaster probabilities. While long-term snowfall decline may reduce avalanches, high baseline extreme snowfall suggests short-term threats remain. Finally, this study establishes meteorological indicators for predicting changes in cryospheric disaster risks under climate change.

Mountain tunnels built near faults often suffer from significant permanent deformation and structural dislocation during seismic activity. In this paper, we present a rock-fault-tunnel geological model with a transition area between the hanging wall and the foot wall which allows the free slippery growth inside the area. A time-sequenced load based on design code and fault activity is conducted in this model to simulate dynamic seismic input after fault dislocation. In our case, a reverse fault with a tunnel cross has been created with this method. A 30cm fault dislocation is simulated by putting the displacement boundary of the hanging wall with a compression vector and the seismic wave is input from the bottom boundary as acceleration waveform adjusted to 0.4g. The model simulates the uplift of the hanging wall and the growth of the slip surface, and reveals the extension mechanism of the triangular shear zone of shear rupture of the surrounding rock due to the extrusion of the reverse fault during the propagation of the reverse fault. The seismic wave with a three-way acceleration was input after the dislocation process. The simulation indicates that with the gradual uplift of the hanging wall, the rock body of the fracture zone shows a more significant large deformation flow trend and a more significant horizontal slip flow. Under reverse fault thrust, the width of the shear effect influence zone is around 300m. A decreasing trend of accumulated strain can be found at the interface due to acceleration input. Dislocation-seismic time-sequence loading may underestimate its damage effects.

The recent increase of the air temperature due to the global climate change is considered as one of the important reasons for the wildfires increase in the world, even in areas where the wildfires are not that common. In addition to the various physical damages adversely affecting the ecological balance, harmful gases and solid particles are released into the atmosphere due to wildfires, causing serious health problems. In this study, impacts of the most serious forest fire in modern history of the country lasting 16 days from 23rd of July 2022 in the National Park Bohemian Switzerland in the D & ecaron;& ccaron;& iacute;n district, Czech Republic, were investigated using remote sensing satellite datasets by cloud-based Google Earth Engine (GEE) platform. The normalized difference moisture index (NDMI), normalized burn ratio index (NBR), normalized difference vegetation index (NDVI), land surface temperature (LST) and soil moisture index (SMI) were calculated from Landsat-8 Operational Land Imager and Thermal Infrared Sensor (OLI and TIRS) dataset for the dates of 31st October 2021, 18th June 2022, and 31st October 2022. Relationship of the remote sensing indices were calculated to estimate the impacts of the wildfire. Furthermore, distribution of nitrogen dioxide (NO2) was extracted using Sentinel-5P TROPOMI (Tropospheric Monitoring Instrument) to observe changes before and after the forest fire in the study region. The burnt area approximately 13.20 km2 from the total area of 79.28 km2 was detected using different time series of the remote sensing indices in the national park.

Precipitation comes in various phases, including rainfall, snowfall, sleet, and hail. Shifts of precipitation phases, as well as changes in precipitation amount, intensity, and frequency, have significant impacts on regional climate, hydrology, ecology, and the energy balance of the land-atmosphere system. Over the past century, certain progress has been achieved in aspects such as the observation, discrimination, transformation, and impact of precipitation phases. Mainly including: since the 1980s, studies on the observation, formation mechanism, and prediction of precipitation phases have gradually received greater attention and reached a certain scale. The estimation of different precipitation phases using new detection theories and methods has become a research focus. A variety of discrimination methods or schemes, such as the potential thickness threshold method of the air layer, the temperature threshold method of the characteristic layer, and the near-surface air temperature threshold method, have emerged one after another. Meanwhile, comparative studies on the discrimination accuracy and applicability assessment of multiple methods or schemes have also been carried out simultaneously. In recent years, the shift of precipitation from solid to liquid (SPSL) in the mid-to-high latitudes of the Northern Hemisphere has become more pronounced due to global warming and human activities. It leads to an increase in rain-on-snow (ROS) events and avalanche disasters, affecting the speed, intensity, and duration of spring snow-melting, accelerating sea ice and glacier melting, releasing carbon from permafrost, altering soil moisture, productivity, and phenological characteristics of ecosystems, and thereby affecting their structures, processes, qualities, and service functions. Although some progress has been made in the study of precipitation phases, there remains considerable research potential in terms of completeness of basic data, reliability of discrimination schemes, and the mechanistic understanding of the interaction between SPSL and other elements or systems. The study on shifts of precipitation phases and their impacts will play an increasingly important role in assessing the impacts of global climate change, water cycle processes, water resources management, snow and ice processes, snow and ice-related disasters, carbon emissions from permafrost, and ecosystem safety.

By employing the frequency-wavenumber (FK) method to simulate the propagation of seismic wavefield in the crustal layer, using the spectral element method (SEM) to simulate the propagation of wavefield in the near-surface soil, and using the multi-degree-of-freedom (MDOF) model to simulate the seismic response of building clusters in city, this paper establishes the FK-SE-MDOF approach (a two-step method) for urban earthquake disaster analysis of fault-to-city based on the concept of domain reduction. The approach can simultaneously consider factors such as earthquake source parameters, propagation paths, local site effects, site-city interaction (SCI) effects, and the dynamic nonlinear responses of buildings (hereafter referred to as source-to-city factors) in a physics-based model. Firstly, the theories of the approach were introduced, and the correctness of the approach was verified. Furthermore, the applicability and the necessity of considering source-to-city factors were examined using a building cluster on an ideal sedimentary basin under the action of a point dislocation source. Finally, the seismic response of buildings in a region was simulated using buildings in the Nankai District of Tianjin as examples. This approach avoids the influences caused by expert experience differences in empirical and hybrid methods, establishes a connection between fault rupture and buildings dynamic response, and can more realistically reflect the distribution of seismic wavefields, building seismic responses, and damage state distribution under the earthquake scenario. It can be applied to earthquake disaster simulation for urban buildings at the scale of tens of thousands of buildings, and the simulation results can provide quantitative guidance for urban planning, earthquake-resistant design, risk assessment, post-earthquake rescue, etc.

Karst ground collapse, a geological disaster in karst areas characterized by the sudden subsidence of surface rock and soil, poses significant risks to human life and property owing to its abrupt and frequent occurrence. Karst ground collapses can be classified into soil-cave-type and hourglass-type, based on the viscosity of the overlying layer. Among these, the hourglass-type presents a higher collapse risk owing to the lack of cohesive forces in the overlying layer. This study focused on hourglass-type karst ground collapse, utilizing physical model tests and the discrete element numerical simulations to develop and validate a collapse model. The physical model tests reproduced the collapse process and provided insights into its underlying mechanism. Numerical simulations were employed to evaluate the effects of karst channel conditions and drilling-induced vibrations on hourglass-type collapses. The results indicated that although the length of the karst channel had minimal impact on collapse speed and pattern, a wider karst channel resulted in a faster collapse and a larger final collapse pit. Moreover, vibration loads increased the collapse speed, shifted the collapse pit towards the vibration source, and expanded the scale of the collapse, thereby amplifying the overall damage extent.

The implementation of real-time dynamic monitoring of disaster formation and severity is essential for the timely adoption of disaster prevention and mitigation measures, which in turn minimizes disaster-related losses and safeguards agricultural production safety. This study establishes a low-temperature disaster (LTD) monitoring system based on machine learning algorithms, which primarily consists of a module for identifying types of disasters and a module for simulating the evolution of LTDs. This study firstly employed the KNN model combined with a piecewise function to determine the daily dynamic minimum critical temperature for low-temperature stress (LTS) experienced by winter wheat in the Huang-Huai-Hai (HHH) region after regreening, with the fitting model's R2, RMSE, MAE, NRMSE, and MBE values being 0.95, 0.79, 0.53, 0.13, and 1.716 x 10-11, respectively. This model serves as the foundation for determining the process by which winter wheat is subjected to LTS. Subsequently, using the XGBoost algorithm to analyze the differences between spring frost and cold damage patterns, a model for identifying types of spring LTDs was developed. The validation accuracy of the model reached 86.67%. In the development of the module simulating the evolution of LTDs, the XGBoost algorithm was initially employed to construct the Low-Temperature Disaster Index (LTDI), facilitating the daily identification of LTD occurrences. Subsequently, the Low-Temperature Disaster Process Accumulation Index (LDPI) is utilized to quantify the severity of the disaster. Validation results indicate that 79.81% of the test set samples exhibit a severity level consistent with historical records. An analysis of the environmental stress-mitigation mechanisms of LTDs reveals that cooling induced by cold air passage and ground radiation are the primary stress mechanisms in the formation of LTDs. In contrast, the release of latent heat from water vapor upon cooling and the transfer of sensible heat from soil moisture serve as the principal mitigation mechanisms. In summary, the developed monitoring framework for LTDs, based on environmental patterns of LTD formation, demonstrates strong generalization capabilities in the HHH region, enabling daily dynamic assessments of the evolution and severity of LTDs.

Floods are considered to be among the most dangerous and destructive geohazards, leading to human victims and severe economic outcomes. Yearly, many regions around the world suffer from devasting floods. The estimation of flood aftermaths is one of the high priorities for the global community. One such flood took place in northern Libya in September 2023. The presented study is aimed at evaluating the flood aftermath for Derna city, Libya, using high resolution GEOEYE-1 and Sentinel-2 satellite imagery in Google Earth Engine environment. The primary task is obtaining and analyzing data that provide high accuracy and detail for the study region. The main objective of study is to explore the capabilities of different algorithms and remote sensing datasets for quantitative change estimation after the flood. Different supervised classification methods were examined, including random forest, support vector machine, na & iuml;ve-Bayes, and classification and regression tree (CART). The various sets of hyperparameters for classification were considered. The high-resolution GEOEYE-1 images were used for precise change detection using image differencing (pixel-to-pixel comparison and geographic object-based image analysis (GEOBIA) for extracting building), whereas Sentinel-2 data were employed for the classification and further change detection by classified images. Object based image analysis (OBIA) was also performed for the extraction of building footprints using very high resolution GEOEYE images for the quantification of buildings that collapsed due to the flood. The first stage of the study was the development of a workflow for data analysis. This workflow includes three parallel processes of data analysis. High-resolution GEOEYE-1 images of Derna city were investigated for change detection algorithms. In addition, different indices (normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), transformed NDVI (TNDVI), and normalized difference moisture index (NDMI)) were calculated to facilitate the recognition of damaged regions. In the final stage, the analysis results were fused to obtain the damage estimation for the studied region. As the main output, the area changes for the primary classes and the maps that portray these changes were obtained. The recommendations for data usage and further processing in Google Earth Engine were developed.

Oil pipelines are susceptible to significant hydraulic erosion from mountain torrents during the flood season when passing through the mountain valley area, which can lead to soil erosion on the pipe surface and expose the pipeline. Accordingly, this study centers on investigating the critical issue of the failure mechanism caused by flash flood erosion in the exposed of oil pipelines. Both indoor testing and numerical simulation research methods are employed to analyze the flow field distribution characteristics of flash floods in proximity to an exposed pipeline. This study explores the patterns of soil loss around pipelines of varying pipe diameters, levels of exposure, and pipe flow angles. In addition, the spatial and temporal evolution mechanism of pipelines overhang development under the action of flash floods was elucidated. The experimental observations indicate that as the pipe diameter increases, the failure rate of the soil surrounding the pipe accelerates, while the erosion effect on the soil around the executives becomes more pronounced. Additionally, a larger pipe flow angle leads to a reduced soil loss in the downstream direction of the pipe. During flash flood events, the scouring action on the soil surrounding the pipe leads to rapid compression of the flow field around the pipe, while the vortex at the pipe's bottom exacerbates soil corrosion. Additionally, the maximum pressure exerted on pipeline surfaces at pipeline flow angles of 30 degrees, 60 degrees, and 90 degrees is 14,382 Pa, 16,146 Pa, and 17,974 Pa, respectively. The research results offer valuable insights into pipeline, soil, and water conservation projects in mountain valley regions.