Flash floods are often responsible for deaths and damage to infrastructure. The objective of this work is to create a data-driven model to understand how predisposing factors influence the spatial variation of the triggering factor (rainfall intensity) in the case of flash floods in the continental area of Portugal. Flash floods occurrences were extracted from the DISASTER database. We extracted the accumulated precipitation from the Copernicus database by considering two days of duration. The analysed predisposing factors for flooding were extracted considering the whole basin where each occurrence is located. These factors include the basin area, the predominant lithology, drainage density, and the mean or median values of elevation, slope, stream power index (SPI), topographic wetness index (TWI), roughness, and four soil properties. The Random Forest algorithm was used to build the models and obtained mean absolute percentage error (MAPE) around 19%, an acceptable value for the objectives of the work. The median of SPI, mean elevation and the area of the basin are the top three most relevant predisposing factors interpreted by the model for defining the rainfall input for flash flooding in mainland Portugal.

Slope failure, as a natural disaster, can cause extensive human suffering and financial losses worldwide. This paper introduces a new soil moisture extended cohesive damage element (SMECDE) method to predict railway slope failure under heavy rainfall. A correlation between rainfall intensity and soil moisture content is first established to create an equivalence between the two. Considering slope failure mechanisms dominated by the loss of soil or the cohesion of slope materials due to heavy rainfall infiltration, the soil moisture decohesion model (SMDM) is developed using previous experimental data to express how soil cohesion varies with different soil moistures and depths. The SMDM is incorporated into the extended cohesive damage element (ECDE) method to fundamentally study slope failure mechanisms under varying soil moisture levels and depths. The proposed SMECDE approach is used to predict the failure propagation of a selected railway embankment slope at the critical soil moisture or rainfall intensity. This SMECDE failure prediction is validated using relevant data from previous fieldwork and meteorological reports on the critical rainfall intensity at the site. Additionally, the corresponding slope damage scale prediction is validated with a large plastic deformation analysis using the commercial FEM package ABAQUS.

Currently, the destabilization mechanisms of slopes due to rainfall infiltration are not fully understood. We conducted physical model tests to measure displacement and pore water pressure from rainfall, using the data to validate numerical models. This study explores how rainfall intensity and duration affect these measures across loess slopes with varying steepness. The goal is to understand slope responses to different rainfall conditions. Our findings indicate that steeper gradients see modest increases in displacement and pore water pressure at the top and mid-slope, but these increases are more pronounced at the toe. The changes at the toe and mid-slope are driven by infiltrated rainwater volume and soil compressive behavior, while top-slope displacement is primarily due to infiltration. Continuous deformation was observed during and after the rainfall events. Post-rain, pressure from saturated soil at the slope's apex amplifies pore water pressure at the toe, influenced by gravitational forces and retained water pressure. This underscores the complex interactions affecting slope stability in wet conditions. Understanding loess slopes' responses can improve predictive models and mitigation strategies, reducing infrastructure and safety risks in these vulnerable areas.

Soil hydraulic properties are mainly governed by the soil's heterogeneity, anisotropy, and discontinuous structural characteristics, primarily when connected soil macropores characterize the structures. Therefore, researchers must document reliable hydrological models to elucidate how the soil medium affects the movement of soil water. This study, utilizing a field-scale staining tracer test, distinguishes between matrix flow and preferential flow areas in the seepage field of Xi'an loess. The Xi'an loess's soil water characteristic curve (SWCC) was explored through field investigations and laboratory analyses. A dual-permeability model that couples matrix and macropore flow was developed using the Hydrus-2D model, enabling simulations of water migration under varying initial soil water content, rainfall intensity, and crack width. The results showed that (1) The SWCC of macropores in the preferential flow area exhibits a bimodal distribution, and the Fredlund & Xing model is applied for sectional fitting to obtain the corresponding soil water characteristic parameters. (2) Initial soil water content and rainfall intensity significantly influence water distribution, while crack width has a relatively minor effect. (3) The cumulative flux under the preferential flow is significantly higher than in the matrix area, and the wetting front depth increases with higher initial water content and rainfall intensity. This study reveals the key characteristics of preferential flow and moisture migration in the matrix zone and their influencing factors in loess. It constructs a two-domain infiltration model by integrating loess's diverse structural characteristics and pore morphology. This model provides a theoretical basis and technical support for simulating preferential flow and studying the moisture dynamics of loess profiles.

Rainfall erosion can cause environmental and economic damage by decreasing the storage capacity of water reservoirs because of the detachment of soil particles. The purpose of this study was to develop a one-dimensional physicomathematical model that can help predict the effects of rainfall erosion on the banks of water reservoirs. The model was developed using the Mein-Larson model to describe water infiltration, the kinematic wave approximation to represent overland flow generation, and the steady state sediment continuity equation to estimate sediment transport. The model was validated using rainfall simulator tests and lateritic soil samples with a bimodal soil-water retention curve. The results showed conformity with the experimental data, identifying a threshold in the models for discharge per unit area and sediment yield rate, as well as a linear increase in the models for total runoff and sediment load per unit area. However, the model failed to capture the peak in sediment yield rate owing to raindrop impact during the initial minutes of rainfall. Parametric analysis highlighted the impact of increasing the calibration constant of splash erosion, erodibility coefficient, and critical shear stress on the slope of the sediment load per unit area model. Despite its limitations, the model demonstrates satisfactory predictive capability for sediment load per unit area under high-intensity rainfalls, achieving an R2 greater than 0.92 in five of the six cases examined.