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.

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.

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.

In recent years, frequent flood disasters have posed significant threats to human life and property. From 28 July to 1 August 2023, a basin-wide extreme flood occurred in the Haihe River Basin (23.7 flood). The Gravity Recovery and Climate Experiment satellite can effectively detect the spatiotemporal characteristics of terrestrial water storage anomalies (TWSA) and has been widely used in flood disaster monitoring. However, flood events usually occur on a submonthly scale. This study first utilizes near-real-time precipitation data to illustrate the evolution of the 23.7 extreme flood. We then reconstruct daily TWSA to improve the issues of coarse temporal resolution and data latency and further calculate wetness index (WI) to explore its flood warning. In addition, we analyze soil moisture storage anomalies to provide a comprehensive understanding of flood mechanisms. The study also compares the 2023 floods to a severe flood event in 2021. Results indicate that reconstructed daily TWSA increases by 143.43 mm in 6 days during the 23.7 flood, highlighting the high sensitivity of our approach to extreme events. Moreover, compared to daily runoff data, the WI consistently exceeds warning thresholds 2-3 days in advance, demonstrating the flood warning capability. The flood event 2021 is characterized by long duration and large precipitation extremes, whereas the 2023 flood affects a wider area. This study provides a reference for using daily TWSA to monitor short-term flood events and evaluate the flood warning potential of WI, aiming to enhance near-real-time flood monitoring and support flood prevention and damage mitigation efforts.

Between 23 and 25 January 2020, the Metropolitan Region of Belo Horizonte (MRBH) in Brazil experienced 32 natural disasters, which affected 90,000 people, resulted in 13 fatalities, and caused economic damages of approximately USD 250 million. This study aims to describe the synoptic and mesoscale conditions that triggered these natural disasters in the MRBH and the physical properties of the associated clouds and precipitation. To achieve this, we analyzed data from various sources, including natural disaster records from the National Center for Monitoring and Early Warning of Natural Disasters (CEMADEN), GOES-16 satellite imagery, soil moisture data from the Soil Moisture Active Passive (SMAP) satellite mission, ERA5 reanalysis, reflectivity from weather radar, and lightning data from the Lightning Location System. The South Atlantic Convergence Zone, coupled with a low-pressure system off the southeast coast of Brazil, was the predominant synoptic pattern responsible for creating favorable conditions for precipitation during the studied period. Clouds and precipitating cells, with cloud-top temperatures below -65 degrees C, over several days contributed to the high precipitation volumes and lightning activity. Prolonged rainfall, with a maximum of 240 mm day-1 and 48 mm h-1, combined with the region's soil characteristics, enhanced water infiltration and was critical in triggering and intensifying natural disasters. These findings highlight the importance of monitoring atmospheric conditions in conjunction with soil moisture over an extended period to provide additional information for mitigating the impacts of natural disasters.

Tropical cyclones (TCs) pose a substantial threat to human life and property, with China being among the most affected countries. In this study, a significant increasing trend is detected for TC destructiveness, primarily measured by precipitation, and for TC-induced damage, measured by direct economic losses (DELs), in the inland areas of East China. In contrast, a similar trend cannot be observed in the coastal regions. The rapid increase of TC-induced damage in the inland areas of East China is directly related to an increase of the annual number of disastrous TCs, which is a result of the increased TC landfall frequency and the increased TC decay timescale after landfall. The increase in specific humidity, soil moisture, and the decrease in vertical wind shear in East China favor the survival of TCs inland. Our results highlight the significance of TC disaster prevention in the inland regions.

Most natural disasters result from geodynamic events such as landslides and slope collapse. These failures cause catastrophes that directly impact the environment and cause financial and human losses. Visual inspection is the primary method for detecting failures in geotechnical structures, but on-site visits can be risky due to unstable soil. In addition, the body design and hostile and remote installation conditions make monitoring these structures inviable. When a fast and secure evaluation is required, analysis by computational methods becomes feasible. In this study, a convolutional neural network (CNN) approach to computer vision is applied to identify defects in the surface of geotechnical structures aided by unmanned aerial vehicle (UAV) and mobile devices, aiming to reduce the reliance on human-led on-site inspections. However, studies in computer vision algorithms still need to be explored in this field due to particularities of geotechnical engineering, such as limited public datasets and redundant images. Thus, this study obtained images of surface failure indicators from slopes near a Brazilian national road, assisted by UAV and mobile devices. We then proposed a custom CNN and low complexity model architecture to build a binary classifier image-aided to detect faults in geotechnical surfaces. The model achieved a satisfactory average accuracy rate of 94.26%. An AUC metric score of 0.99 from the receiver operator characteristic (ROC) curve and matrix confusion with a testing dataset show satisfactory results. The results suggest that the capability of the model to distinguish between the classes 'damage' and 'intact' is excellent. It enables the identification of failure indicators. Early failure indicator detection on the surface of slopes can facilitate proper maintenance and alarms and prevent disasters, as the integrity of the soil directly affects the structures built around and above it.

An increase in precipitation due to climate change has given rise to the number of landslide occurrences. Vetiver, which is a perennial grass, is becoming increasingly popular all over the world as a vegetation-based soil bioengineering tool for preventing landslides. Sunshine Vetiver grass, also known as Chrysopogon zizanioides is noninvasive and does not compete with other indigenous plants growing in the area. Even though it is a tropical grass, Vetiver can grow in a wide range of climate conditions, including those that are quite harsh in terms of both soil and climate. The roots can grow up to 3 m in length in a dense bushy root network under optimal conditions. In this review, the authors have studied the impact of Vetiver on landslide mitigation as a climate-adaptive slope repair tool based on the research undertaken so far. Furthermore, the authors have addressed the future potential and constraints associated with the use of Vetiver for landslide mitigation. It is seen that the use of Vetiver reduces pore water pressure. The high tensile strength of Vetiver roots provides reinforcement for slopes and enhances soil shear strength. Vetiver increases saturated hydraulic conductivity and reduces surface runoff and slip surface depth. Being a vegetation-based climate-adaptive technology, this grass exhibits great promise in its ability to effectively address landslide problems. However, the magnitude of the root impact diminishes as the depth increases, rendering Vetiver a more promising remedy for shallow landslide occurrences. In addition, Vetiver grass has a wide range of practical uses due to its unique characteristics, which provide additional benefits. Employment of Vetiver is cost-effective compared with traditional engineering methods, and it requires less initial maintenance, which implies that community-based initiatives can effectively address landslide prevention through Vetiver implementation. Vetiver grass has a long bushy network of roots that can grow up to 3 m in length. The Sunshine Vetiver grass is not invasive and does not compete with indigenous plants. Although Vetiver is a tropical grass, this grass can survive in various climates and soil conditions. Vetiver is a vegetation-based climate-adaptive technology that can prevent slope failure and reduce surface runoff. Additionally, growing Vetiver can generate income for local communities because the fragrant roots can be utilized in the extraction of essential oils for the perfume industry and from the manufacture and trade of other commodities derived from Vetiver. The grass's green leaves contribute to the aesthetic appeal of the landscape. Implementing Vetiver on slopes does not require heavy machinery and is cost-effective compared with traditional engineering methods. It also requires less initial maintenance, making it an ideal solution for community-based initiatives aiming to address slope failure prevention through Vetiver implementation.

Grasshopper disasters threaten grassland animal husbandry, and overgrazing is widely recognized as one of the main causes of locust infestation in grassland regions. However, the impact of overgrazing on grasshopper disasters remains unclear. To address this knowledge gap, this study interviewed 541 households living in locust-prone areas in Inner Mongolia, China. The generalized Poisson model and OLS regression examined the relationship between herders' production behavior and locust disasters. The results showed that 42% of the herders had suffered from locusts more than three times over the past 15 years, with an average of 49 ha of grassland damaged per household. In addition, with the increase in grazing rates, the scale of locust disasters decreased before it increased. The results also showed that operating grassland areas and feeding forage reduced locust disasters significantly, while renting grassland areas and grazing rates worked oppositely. These results suggest that grazing intensity can make a significant difference in the occurrence of locust disasters.

Context The failure of the Fund & atilde;o dam devastated a large area of the Atlantic Forest, causing damage to and loss of riparian forests. Considering all the ecological roles of a terrestrial and freshwater community, it is necessary to understand the functioning of riparian forests and their regenerative potential, which will be decisive in selecting actions to restore these ecosystems, especially Atlantic Forest remnants. Aims We evaluated the flora and structure of the regenerating stratum in three riparian vegetation remnants along the Rio Doce basin to support the propagation and restoration of the affected environments. Methods Plots of 5 m x 5 m were made in each area, totalling 77 sampling units. In these plots, all woody individuals with a diameter at soil height (DSH) of at least >= 1 cm and diameter at breast height (1.3 m from the soil) of at least <5 cm were marked, measured (in height and DSH), sampled and identified. Key results A total of 275 species distributed in 47 families were sampled, with Fabaceae the most diverse family and Siparuna guianensis Aubl. the most abundant species. Variation in beta diversity was significant, and composition analysis showed that plots of each area tended to cluster. Principal component analysis and linear models showed that the edaphic parameters were not related to the richness and abundance of species in the sampled areas. Conclusions The areas sampled here serve as a reference for the restoration of impacted areas. Implications This study represents an important step towards knowing the species in reference areas for an active and efficient restoration in impacted areas.