Shallow landslides are often unpredictable and seriously threaten surrounding infrastructure and the ecological environment. Traditional landslide prediction methods are time-consuming, labor-intensive, and inaccurate. Thus, there is an urgent need to enhance predictive techniques. To accurately predict the runout distance of shallow landslides, this study focuses on a shallow soil landslide in Tongnan District, Chongqing Municipality. We employ a genetic algorithm (GA) to identify the most hazardous sliding surface through multi-iteration optimization. We discretize the landslide body into slice units using the dynamic slicing method (DSM) to estimate the runout distance. The model's effectiveness is evaluated based on the relative errors between predicted and actual values, exploring the effects of soil moisture content and slice number on the kinematic model. The results show that under saturated soil conditions, the GA-identified hazardous sliding surface closely matches the actual surface, with a stability coefficient of 0.9888. As the number of slices increases, velocity fluctuations within the slices become more evident. With 100 slices, the predicted movement time of the Tongnan landslide is 12 s, and the runout distance is 5.91 m, with a relative error of about 7.45%, indicating the model's reliability. The GA-DSM method proposed in this study improves the accuracy of landslide runout prediction. It supports the setting of appropriate safety distances and the implementation of preventive engineering measures, such as the construction of retaining walls or drainage systems, to minimize the damage caused by landslides. Moreover, the method provides a comprehensive technical framework for monitoring and early warning of similar geological hazards. It can be extended and optimized for all types of landslides under different terrain and geological conditions. It also promotes landslide prediction theory, which is of high application value and significance for practical use.

The behavior of ice particles impacting against a rigid surface is a topic that has gained more and more relevance in the field of transportation safety, particularly in the automotive and aerospace sectors. The present review outlines the various controlled experimental approaches used to study artificial ice particle collisions, describing the setup configurations, particle release mechanisms, and the selection of materials for impact surfaces. It also assesses fundamental studies that measure the coefficient of restitution (CoR or en) and the fragmentation regime of ice upon impact, clarifying how ice particles react when they strike a surface. The review also includes analytical and empirical formulas that describe the critical impact velocity, which determines the different impact outcomes, like bouncing, sticking, or fragmentation and fragmentation distribution of the ice particles. Lastly, it summarizes how particle size, temperature, and material properties affect the impact responses. In addition, the review proposes a visual representation of the different models and how they compare. The visual representation highlights the differences between each model and the transition from elastic to plastic impact responses, and it is instrumental in understanding the conditions under which ice particles leave a residual mass on the impact surface. The insights gained from this review are vital for better understanding the impact of the ice particle phenomenon and mapping the state of the art in this branch of research.

Icing in cut slopes is a serious risk to transportation safety in cold regions. Research on the occurrence process and mechanism of icing is a prerequisite for proposing effective management measures. We took the cut slopes of the K162 of the Beihei Highway as the research object. We used a combination of field investigation, geological exploration, monitoring, and simulation to study and analyze the power source, occurrence process, and triggering mechanism of icing in cut slopes. The results show that the geologic type of this cut slope is a mudstone-sandstone interaction stratum. Abundant shallow groundwater is the source of water for icing. The excavation of cut slopes extends the effect of negative temperatures on groundwater flow during the winter period. The process of ice formation in cut slopes can be described as follows: As the environmental temperature drops, the surface soil begins to freeze, resulting in a gradual narrowing of the water channel; then, the groundwater flow is blocked, so that the internal pressure begins to rise. When the internal pressure of the pressurized groundwater exceeds the strength of the frozen soil, groundwater overflows from the sandstone layer to the surface, forming icing. The high pore water pressure inside the cut slope is the precursor for the occurrence of icing. The dynamic pressure of the pore water pressure is the main driving force for the formation of icing in cut slopes. The obstruction of the water channel due to ground freezing is the triggering condition for ice formation in cut slopes.

This study aims to investigate the economic effects of stormwater best management practices (BMPs) on housing sale prices in Washington, D.C., BMPs play a significant role in mitigating multiple threats, such as water pollution, soil erosion, and property damage. While studies on the economic value of BMPs were limited, literature addresses that housing sale prices can be affected by nearby stormwater BMPs. This study addresses the following research questions: Do stormwater BMPs positively impact housing sale prices? How do proximity and number of structural BMPs affect the housing sale prices? We used the hedonic pricing method by applying multiple linear regression models to determine whether a set of independent variables significantly improved the models. Our primary findings indicate that BMPs have positive, negative, or no effects on housing sale prices. The proximity of BMPs inside of parks increased housing sale prices in all buffers. In contrast, the proximity of BMPs outside of parks and impervious roads decreased housing sale prices in all buffers. Percent tree canopy coverage negatively linked to a 50 m buffer and had no relationship with other buffers on housing sale prices. This study implies that BMPs impact housing prices and can be improved by landscape architects, policymakers, and stakeholders.

Aufeis is a common phenomenon in cold regions of the Northern Hemisphere that develops during winter by successive water overflow and freezing on ice-covered surfaces. Most studies on aufeis occurrence focus on regions in North America and Siberia, while research in High Mountain Asia (HMA) is still in an exploratory phase. This study investigates the extent and dynamics of icing processes and aufeis in the Tso Moriri basin, eastern Ladakh, India. Based on a combination of 235 Landsat 5 TM/8 OLI and Sentinel-2 imagery from 2008 to 2021 the occurrence of icing and aufeis was classified using a random forest classifier. A total of 27 frequently occurring aufeis fields with an average maximum extent of 9 km(2) were identified, located at a mean elevation of 4,700 m a.s.l. Temporal patterns show a distinct accumulation phase (icing) between November and April, and a melting phase lasting from May until July. Icing is characterized by high seasonal and inter-annual variability. Successive water overflow mainly occurs between January and March and seems to be related to diurnal freeze-thaw-cycles, whereas higher daytime temperatures result in larger icing areas. Aufeis feeding sources are often located within or in close vicinity to wetland areas, while vegetation is largely absent on surfaces with frequent aufeis formation. These interactions require more attention in future research. In addition, this study shows the high potential of a machine learning approach to monitor icing processes and aufeis, which can be transferred to other regions.

Aufeis are sheets of ice unique to cold regions that originate from repeated flooding and freezing events during the winter. They have hydrological importance associated with summer flows and winter insulation, but little is known about the seasonal dynamics of the unfrozen sediment layer beneath them. This layer may support perennial groundwater flow in regions with otherwise continuous permafrost. For this study, ground penetrating radar (GPR) were collected in September 2016 (maximum thaw) and April 2017 (maximum frozen) at the Kuparuk aufeis field on the North Slope of Alaska. Supporting surface nuclear magnetic resonance data were collected during the maximum frozen campaign. These point-in-time geophysical data sets were augmented by continuous subsurface temperature data and periodic Structure-from-Motion digital elevation models collected seasonally. GPR and difference digital elevation model data showed up to 6 m of ice over the sediment surface. Below the ice, GPR and nuclear magnetic resonance identified regions of permafrost and regions of seasonally frozen sediment (i.e., the active layer) underlain by a substantial lateral talik that reached >13-m thickness. The seasonally frozen cobble layer above the talik was typically 3- to 5-m thick, with freezing apparently enabled by relatively high thermal diffusivity of the overlying ice and rock cobbles. The large talik suggests that year-round groundwater flow and coupled heat transport occurs beneath much of the feature. Highly permeable alluvial material and discrete zones of apparent groundwater upwelling indicated by geophysical and ground temperature data allows direct connection between the aufeis and the talik below.