Gully head erosion causes serious land degradation in semiarid regions. The existing studies on gully head erosion are mainly based on measuring the gully volume in small-scale catchments, which is a labor-intensive and time-consuming approach. Therefore, it is necessary to explore an accurate method quantitatively over large areas and long periods. The objective of this study was to develop a model to assess gully head erosion in the Loess Plateau of China using a method based on the SBAS-InSAR technique. The gully heads were extracted from the digital elevation model and validated by field investigation and aerial images. The surface deformation was estimated with SBAS-InSAR and 22 descending ALOS PALSAR datasets from 2007 to 2011. A gully head erosion model was developed; this model can incorporate terrain factors and soil types, as well as provides erosion rate predictions consistent with the SBAS-InSAR measurements (R² = 0.889). The results show that gully head erosion significantly depends on the slope angle above the gully head, slope length, topographic wetness index, and catchment area. The relationship between these factors and the gully head erosion rate is a power function, and the average rate of gully head erosion is 7.5 m³/m²/year, indicating the high erosional vulnerability of the area. The accuracy of the model can be further improved by considering other factors, such as the stream power factor, curvature, and slope aspect. This study indicates that the erosion rate of gully heads is almost unaffected by soil type in the research area. An advantage of this model is that the gully head area and surface deformation can be easily extracted and measured from satellite images, which is effective for assessing gully head erosion at a large scale in combination with SBAS-InSAR results and terrain attributes.

The micro pores in loess show regional variation in structure on the Loess Plateau and greatly influence the physical properties and macro behaviors of loess. In this study, the 3D microstructures of Malan loess from Lanzhou (LZ), Qingyang (QY), Hengshan (HS) and Jingyang (JY) were established based on μ-CT scanning, and the corresponding microstructural parameters were compared and analyzed quantitatively. The results indicate that the LZ and HS loess both show overall homogeneous structures with dominant inter-particle pores, while the QY and JY loess have more intra-aggregate and constricted pores. Overall, the LZ loess has the largest pore size, followed by the JY loess, QY loess and HS loess, which is consistent with the throat sizes of the four loess samples. The average coordination numbers (CNs) of the LZ and HS loess are lower than those of the QY and JY loess, while the throat lengths of the former two loess are larger than those of the latter two loess. Analysis of the correlation between the micropore parameters and macro behaviors of the loess suggests that the void ratio is the precondition for loess collapse, but it shows weak relevance to collapsibility; meanwhile, the size of pores contributing to the major pore space presents a strong positive correlation. The throat length representing the pore structure is more closely related to loess permeability compared with the void ratio, average CN and throat size.

The rainfall-induced landslide early warning model (LEWM) is an important means to mitigate property loss and casualties, but the conventional discriminant matrix-based LEWM (DLEWM) leaves room for subjectivity and limits warning accuracy. Additionally, it is important to employ appropriate indicators to evaluate warning model performance. In this study, a new method for calculating the spatiotemporal probability of rainfall-induced landslides based on a Bayesian approach is proposed, and a probabilistic-based LEWM (PLEWM) at the regional scale is developed. The method involves four steps: landslide spatial probability modeling, landslide temporal probability modeling, coupling of spatial and temporal probability models, and the conversion method from the spatiotemporal probability index to warning levels. Each step follows the law of probability and is tested with real data. At the same time, we propose the idea of using economic indicators to evaluate the performance of the multilevel LEWM and reflect its significant and unique aspects. The proposed PLEWM and the conventional DLEWM are used to conduct simulate warnings for the study area day-by-day in the rainy season (July-September) from 2016 to 2020. The results show that the areas of the 2nd-, 3rd-, and 4th-level warning zones issued by the PLEWM account for 60.23%, 45.99%, and 43.98% of those of the DLEWM, respectively. The investment in issuing warning information and the losses caused by landslides account for 54.54% and 59.06% of those of the DLEWM, respectively. Moreover, under extreme rainfall conditions, the correct warning rate of the PLEWM is much higher than that of the DLEWM.

Mine waste debris flows are a type of man-made debris flow that commonly lead to major disasters. In this study, the Xiaotong Gully, which is located in the Xiaoqinling gold mining area in China and contains a typical mine waste debris flow gully, was selected as the study area. Since a debris flow can be classified as either a geotechnical debris flow or hydraulic debris flow based on its initiation mode, we conducted 46 experimental model tests to explore the initiation conditions of these two different types of debris flows. According to our tests, the initiation conditions of hydraulic debris flows were mainly affected by the flume gradient, the water content of the mine waste, the inflow discharge, the water supply modes, and the clay particle content. A larger flume gradient and higher mine waste water content were more conducive to initiating a hydraulic debris flow. However, the influence of the water supply mode on the initiation of a hydraulic debris flow was complex (influenced by factors such as water content of mine waste, runoff discharge rate and rainfall intensity). The critical runoff of a hydraulic debris flow, which starts with a parabolic relationship to the clay particle content of the mine waste, decreased with increasing clay particle content and then increased. There was a minimum critical runoff when the clay content of the mine waste was 30%. The initiation conditions of a geotechnical debris flow were mainly affected by the flume gradient, the water content, and the clay particle content. The critical gradient of a geotechnical debris flow decreased with increasing water content and had a parabolic relationship to the clay particle content. In tests 31–46 of this study, the second and third critical slopes both decreased and then increased with increasing clay particle content. These preliminary research results provide a scientific reference for subsequent research on the prevention and mitigation of mine waste debris flows.

Global climate change has led to a marked increase in the frequency of record-breaking extreme rainfall events, which often surpass historical benchmarks and pose significant challenges to conventional geological hazard risk assessment methods. This study used a record-breaking extreme rainfall event in Zhenba County, Shaanxi Province, in July 2023 as a case study to develop a tailored risk assessment framework for geological hazards under extreme rainfall conditions. By integrating high-resolution Planet satellite imagery, millimeter-scale surface deformation data derived from SBAS-InSAR, and detailed field investigation results, a comprehensive disaster inventory containing 1012 landslides was compiled. The proposed framework integrates cumulative extreme rainfall metrics with subtle ground deformation indicators and applies four advanced machine learning algorithms—DNN, XGBoost, RF, and LightGBM—for multidimensional hazard assessment. Among these, the DNN model exhibited the highest performance, achieving an AUC of 0.82 and Kappa coefficients of 0.833 (training) and 0.812 (prediction). Further analysis using SHAP values identified distance to rivers, cumulative rainfall, and the Topographic Wetness Index (TWI) as the most influential factors governing landslide occurrence under extreme rainfall conditions. Validation using representative case studies confirmed that the framework effectively identifies high-hazard zones, particularly in areas severely impacted by debris flows and landslide deformation zones. These findings provide a robust scientific foundation and technical basis for early warning, disaster prevention, and mitigation strategies in geologically complex regions increasingly affected by extreme rainfall events.

In this study, we investigated the effect of particle size distribution on the shear properties of sand. Direct shear tests were conducted using four types of sand samples with different particle size distributions obtained from standard sand produced by Xiamen ISO Co. Ltd. The results show that the influence of particle size distribution on the internal friction angle was significant. Typically, the internal friction angle increases with increasing the coefficient of non-uniformity (Cu) and decreasing the curvature coefficient (Cc). The discrete element results show that the initial particle size distribution significantly affects the porosity, coordination number, and particle slide fraction. In addition, the grey relation analysis revealed that the sliding fraction and coordination number have the greatest correlation with the internal friction angle. The research results of this study help to understand the changes in particle contact, internal stress, and particle sliding during the shear failure process of sand.