Climate change increases the frequency of extreme weather events, intensifying shallow flow-type landslides, soil erosion in mountainous regions, and slope failures in coastal areas. Vegetation and biopolymers are explored for ecological slope protection; however, these approaches often face limitations such as extended growth cycles and inconsistent reinforcement. This study investigates the potential of filamentous fungi and wheat bran for stabilizing loose sand. Triaxial shear tests, disintegration tests, and leachate analyses are conducted to evaluate the mechanical performance, durability, and environmental safety of fungus-treated sand. Results show that the mycelium enhances soil strength, reduces deformation, and lowers excess pore water pressure, with a more pronounced effect under undrained than drained conditions. Mycelium adheres to particle surfaces, forming a durable bond that increases cohesion and shifts the slope of the critical state line, significantly enhancing the mechanical stability of fungus-treated sand. The resulting strength parameters are comparable to those of soils reinforced with plant roots. Fungus-treated sand remains stable after 14 days of water immersion following triaxial shear tests, with no environmental risk from leachate. These findings demonstrated that fungal mycelium provides an effective and eco-friendly solution for stabilizing loose sand, mitigating shallow landslides, and reinforcing coastlines.

In this paper a three-dimensional agro-hydrological model for shallow landslides' prediction is presented. The model is an extension of the CRITERIA-3D free-source model for crop development and soil hydrology, developed by the Hydrometeorological service of the Regional Agency for Environmental prevention and Energy of EmiliaRomagna region (Arpae-simc). The soil-water balance is computed through the coupling of surface and subsurface flows in multi-layered soils over areas topographically characterized by Digital Elevation Model (DEM). The rainfall infiltration process is simulated through a three-dimensional version of Richards' equation. Surface runoff, lateral drainage, capillarity rise, soil evaporation and plant transpiration contribute to the computation of the soil hydrology on an hourly basis. The model accepts meteorological hourly records as input data and outputs can be obtained for any time step at any selected depth of the soil profile. Among the outputs, volumetric water content, soil-water potential and the factor of safety of the slope can be selected. The validation of the proposed model has been carried out considering a test slope in Montue` (northern Italy), where a shallow landslide occurred in 2014 a few meters away from a meteorological and soil moisture measurement station. The paper shows the accuracy of the model in predicting the landslide occurrence in response to rainfall both in time and space. Although there are some model limitations, at the slope scale the model results are highly accurate with respect to field data even when the spatial resolution of the Digital Elevation Model is reduced.

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 assessment of landslide susceptibility often overlooks the influence of forests on shallow landslide mobility, despite its significance. This study delved into the impact of forest presence on shallow landslide mobility during intense rainfall in Mengdong, China. Field investigations were coupled with the analysis of pre- and post-rainfall remote sensing (RS) images to delineate landslides. The ratio of landslide height (H) to travel distance (L) from a digital elevation model (DEM) were used to calculate landslides mobility. Preceding the event, forest coverage was evaluated using the normalized difference vegetation index (NDVI) derived from multiband RS image. The research identified 1531 shallow landslides in the area, revealing a higher concentration of landslides on slopes with elevated NDVI. Results indicated that disparities in soil permeability and cohesion, generating pore water pressure (PWP), triggered clusters of shallow landslides. Shallow landslides exhibit a higher propensity on slopes with elevated NDVI. The dimensions (height and area) of these identified shallow landslides typically exhibit a positive correlation with NDVI, consequently resulting in longer travel distances for landslides occurring on higher NDVI slopes. The average H/L ratio of all identified landslides was about 0.63. H/L generally increases with NDVI and decreases with landslide area. However, due to river channel restrictions, the H/L increases with slope gradient. The findings suggest that the high permeability of areas with tree roots poses a risk to the shallow stability of slopes, yet trees contribute to mitigating landslide mobility.

Root reinforcement, provided by plants in soil, can be exerted by a mechanical effect, increasing soil shear strength for the presence of roots, or by a hydrological effect, induced by plant transpiration. No comparisons have been still carried out between mechanical and hydrological reinforcements on shallow slope stability in typical agroecosystems. This paper aims to compare these effects induced by sowed fields and vineyards and to assess their effects towards the shallow slope staibility. Root mechanical reinforcement has been assessed through Root Bundle Model-Weibull. Root hydrological reinforcement has been evaluated using an empirical relationship with monitored or modelled pore water pressure. Each reinforcement has been inserted in a stability model to quantify their impacts on susceptibility towards shallow landslides. Considering the same environment, corresponding to a typical agroecosystem of northern Italian Apennines, land use has significant effects on saturation degree and pore water pressure, influencing hydrological reinforcement. Root hydrological reinforcement effect is higher in summer, although rainfall-induced shallow landslides rarely occur in this period due to dry soil conditions. Instead, in wet and cold periods, when shallow landslides can develop more frequently, the stabilizing contribution of mechanical reinforcement is on average higher than the hydrological reinforcement. In vineyards, the hydrological reinforcement effect could be observed also during autumn, winter and spring periods, giving a contribution to slope stability also in these conditions. This situation occurs when plants uptake enough water from soil to reduce significantly pore water pressure, guaranteeing values of hydrological reinforcement of 1-3 kPa at 1 m from ground, in agreement with measured mechanical root reinforcement (up to 1.6 kPa). These results suggest that both hydrological and mechanical effects of vegetation deserve high regard in susceptibility towards shallow landslides, helping in selection of the best land uses to reduce probability of occurrence of these failures over large territories.

In the last decades the Valtellina valley (northern Italy) has suffered from several catastrophic rainfall -induced shallow landslide events inducing debris flows. The growing of urban settlements has driven population to colonize areas at risk, where prediction and prevention actions are nowadays a challenge for geoscientists. Debris flows are widespread in mountain areas because occurring along steep slopes covered by loose regolith or soil coverings. Under such conditions, heavy rainfall events might cause slope instabilities due to the increase in pore water pressure depending on hydraulic and geotechnical properties as well as thicknesses of soil covers. Despite the initial small volumes, debris flows hazard is significant due to the sediment entrainment and volume increase of the involved material, high velocity and runout distance. In such a framework, predicting timing and position of slope instabilities as well as paths, volumes, and velocity of potential debris flows is of great significance to assess areas at risk and to settle appropriate countermeasures. In this work, back analyses of debris flows occurred in representative sites of the Valtellina valley were carried out with the aim of understanding their features and providing a methodological basis for slope to valley scale susceptibility mapping. Numerical modeling of slope stability and runout was completed allowing the identification of the detachment, transport, and deposition zones of previously occurred landslides, including other potentially unstable ones. Results from this study emphasize issues in performing distributed numerical modeling depending on the availability of spatially distributed soil properties which hamper the quality of physicsbased models. In the framework of hazard mapping and risk strategy assessments, the approach presented can be used to evaluate the possible runout phase of new potential debris flows recognized by geomorphological evidence and numerical modeling. Furthermore, analyses aimed to the probabilistic assessment of landslide spatial distribution, related to a specific value of rainfall threshold, can be considered as potentially applicable to multi -scale landslide hazard mapping and extendable to other similar mountainous frameworks.