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The slide of granular material in nature and engineering can happen under air (subaerial), under a liquidlike water (submerged), or a transition between these two regimes, where a subaerial slide enters a liquid and becomes submerged. Here, we experimentally investigate these three slide regimes (i.e., subaerial, submerged, and transitional) in two dimensions, for various slope angles, material types, and bed roughness. The goal is to shed light on the complex morphodynamics and flow structure of these granular flows and also to provide comprehensive benchmarks for the validation and parametrization of the numerical models. The slide regime is found to be a major controller of the granular morphodynamics (e.g., shape evolution and internal flow structure). The time history of the runout distance for the subaerial and submerged cases present a similar three-phase trend (with acceleration, steady flow, and deceleration phases) tough with different spatiotemporal scales. Compared to the subaerial cases, the submerged cases show longer runout time and shorter final runout distances. The transitional trends, however, show additional deceleration and reacceleration. The observations suggest that the impact of slide angle, material type, and bed roughness on the morphodynamics is less significant where the material interacts with water. Flow structure, extracted using a granular particle image velocimetry technique, shows a relatively power-law velocity profile for the subaerial condition and strong circulations for the submerged condition. An unsteady theoretical model based on the µ(I) rheology is developed and is shown to be effective in the prediction of the average velocity of the granular mass.

Abstract Landslides, which are the sources of most catastrophic natural disasters, can be subaerial (dry), submerged (underwater), or semi‐submerged (transitional). Semi‐submerged or transitional landslides occur when a subaerial landslide enters water and turns to submerged condition. Predicting the behavior of such a highly dynamic multi‐phase granular flow system is challenging, mainly due to the water entry effects, such as wave impact and partial saturation (and resulted cohesion). The mesh‐free particle methods, such as the moving particle semi‐implicit (MPS) method, have proven their capabilities for the simulation of the highly dynamic multiphase systems. This study develops and evaluates a numerical model, based on the MPS particle method in combination with the μ ( I ) rheological model, to simulate the morphodynamic of the granular mass in semi‐submerged landslides in two and three dimensions. An algorithm is developed to consider partial saturation (and resulting cohesion) during the water entry. Comparing the numerical results with the experimental measurements shows the ability of the proposed model to accurately reproduce the morphological evolution of the granular mass, especially at the moment of water entry.

Ice control structures (ICSs) play a vital role in preventing ice jams and safeguarding communities by either stabilizing ice cover or relocating jams upstream. Understanding and modeling the interaction between ice floes and these structures is crucial for assessing their effectiveness and optimizing their designs. However, simulating these complex multi-physics systems poses challenges for numerical techniques. In this paper, we introduce and evaluate a fully-Lagrangian mesh-free continuum-discrete model based on the Smoothed Particles Hydrodynamics (SPH) method and Discrete Element Method (DEM) for three-dimensional (3D) simulation of ice interactions with control structures. To validate and parameterize the numerical model, we conduct two sets of experiments using real and artificial ice materials: (1) dam-break wave-ice-structure interaction and (2) ice-ICS interaction in an open channel. By comparing numerical and experimental results we demonstrate the capability and relative accuracy of our model. Our findings indicate that real ice generally exhibits faster jam evolution and ice passage through the ICS compared to artificial ice. Moreover, we identify the Froude number and ice material type as important factors influencing jam formation, evolution, and ICS effectiveness. Through sensitivity analysis of material properties, we highlight the significant impact of friction and restitution coefficients.

L’hyperphagie boulimique (HB) représente le trouble alimentaire le plus fréquent avec une prévalence qui est plus élevée que celles combinées de l’anorexie nerveuse et de la boulimie (Hudson et al. , 2007). Bien que l’HB a fait officiellement son entrée dans le DSM-5, il demeure de grandes difficultés concernant l’accessibilité et la disponibilité des traitements s’appuyant sur des données probantes. Le but de cet article est de présenter une recension critique de la littérature qui fera ressortir l’utilisation de la cyberthérapie dans le traitement de l’HB ainsi que les implications au niveau de la pratique clinique et de la recherche. , Binge eating disorder (BED) is the most common eating disorder, with a prevalence higher than the prevalence of anorexia nervosa and bulimia combined (Hudson et a l., 2007). Although BED officially entered the DSM-5, there are still great difficulties regarding the accessibility and availability of evidence-based treatments. The purpose of this paper is to present a critical review of the literature highlighting the use of e-therapy for BED as well as the implications for clinical practice and research.

Abstract. This study investigates the ability of long short-term memory (LSTM) neural networks to perform streamflow prediction at ungauged basins. A set of state-of-the-art, hydrological model-dependent regionalization methods are applied to 148 catchments in northeast North America and compared to an LSTM model that uses the exact same available data as the hydrological models. While conceptual model-based methods attempt to derive parameterizations at ungauged sites from other similar or nearby catchments, the LSTM model uses all available data in the region to maximize the information content and increase its robustness. Furthermore, by design, the LSTM does not require explicit definition of hydrological processes and derives its own structure from the provided data. The LSTM networks were able to clearly outperform the hydrological models in a leave-one-out cross-validation regionalization setting on most catchments in the study area, with the LSTM model outperforming the hydrological models in 93 % to 97 % of catchments depending on the hydrological model. Furthermore, for up to 78 % of the catchments, the LSTM model was able to predict streamflow more accurately on pseudo-ungauged catchments than hydrological models calibrated on the target data, showing that the LSTM model's structure was better suited to convert the meteorological data and geophysical descriptors into streamflow than the hydrological models even calibrated to those sites in these cases. Furthermore, the LSTM model robustness was tested by varying its hyperparameters, and still outperformed hydrological models in regionalization in almost all cases. Overall, LSTM networks have the potential to change the regionalization research landscape by providing clear improvement pathways over traditional methods in the field of streamflow prediction in ungauged catchments.

Natural disasters have been demonstrated to cause devastating effects on economic and social development in China, but little is known about the relationship between natural disasters and income at the household level. This study explores the impact of natural disasters on household income, expenditure, and inequality in China as the first study of this nature for the country. The empirical analysis is conducted based on a unique panel dataset that contains six waves of the Chinese Household Income Project (CHIP) survey data over the 1988–2018 period, data on natural disasters, and other social and economic status of households. By employing the fixed effects models, we find that disasters increase contemporaneous levels of income inequality, and disasters that occurred in the previous year significantly increase expenditure inequality. Natural disasters increase operating income inequality but decrease transfer income inequality. Poor households are found to be more vulnerable to disasters and suffer significant income losses. However, there is no evidence to suggest that natural disasters significantly reduce the income of upper- and middle-income groups. These findings have important implications for policies aimed at poverty alleviation and revenue recycling, as they can help improve economic justice and enhance resilience to natural disasters.

Mise en patrimoine des crues et des inondations Sous la direction de Alexis Metzger et Jamie Linton Collection : Géographie et cultures

Agricultural activities can result in the contamination of surface runoff with pathogens, pesticides, and nutrients. These pollutants can enter surface water bodies in two ways: by direct discharge into surface waters or by infiltration and recharge into groundwater, followed by release to surface waters. Lack of financial resources makes risk assessment through analysis of drinking water pollutants challenging for drinking water suppliers. Inability to identify agricultural lands with a high-risk level and implement action measures might lead to public health issues. As a result, it is essential to identify hazards and conduct risk assessments even with limited data. This study proposes a risk assessment model for agricultural activities based on available data and integrating various types of knowledge, including expert and literature knowledge, to estimate the levels of hazard and risk that different agricultural activities could pose to the quality of withdrawal waters. To accomplish this, we built a Bayesian network with continuous and discrete inputs capturing raw water quality and land use upstream of drinking water intakes (DWIs). This probabilistic model integrates the DWI vulnerability, threat exposure, and threats from agricultural activities, including animal and crop production inventoried in drainage basins. The probabilistic dependencies between model nodes are established through a novel adaptation of a mixed aggregation method. The mixed aggregation method, a traditional approach used in ecological assessments following a deterministic framework, involves using fixed assumptions and parameters to estimate ecological outcomes in a specific case without considering inherent randomness and uncertainty within the system. After validation, this probabilistic model was used for four water intakes in a heavily urbanized watershed with agricultural activities in the south of Quebec, Canada. The findings imply that this methodology can assist stakeholders direct their efforts and investments on at-risk locations by identifying agricultural areas that can potentially pose a risk to DWIs.