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Lagrangian particle-based smoothed particle hydrodynamics (SPH) is increasingly widely used in landslide modelling. This paper investigates four important issues not addressed by previous studies on SPH modelling of large-scale landslides, i.e., convergence property, influence of constitutive parameters, scale effect and friction reduction, and influence of different treatments of the viscous effect. The GPU-acceleration technique is employed to achieve high-resolution three-dimensional (3D) modelling. The Baige landslide is investigated by comparing numerical results with field data, and detailed analyses on the four issues are provided. Suggestions on particle resolution, constitutive parameter, and formulations of viscous discretization are also presented for future SPH modelling of large-scale landslides. Key words: large-scale landslides, smoothed particle hydrodynamics, constitutive model, graphics processing unit (GPU) acceleration, convergence. L'hydrodynamique particulaire lissee (SPH) basee sur des particules lagrangiennes est de plus en plus utilisee dans la modelisation des glissements de terrain. Cet article analyse quatre questions importantes qui n'ont pas ete abordees par les etudes precedentes sur la modelisation SPH des glissements de terrain a grande echelle, a savoir la propriete de convergence, l'influence des parametres constitutifs, de l'effet d'echelle et de la reduction du frottement, et l'influence de differents traitements de l'effet visqueux. La technique d'acceleration par GPU est utilisee pour realiser une modelisation 3D a haute resolution. Le glissement de terrain de Baige est etudie en comparant les resultats numeriques avec les donnees de terrain, et des analyses detaillees sur les quatre questions sont fournies. Des suggestions sur la resolution des particules, les parametres constitutifs et les formulations de la discretisation visqueuse sont egalement presentees pour la modelisation SPH future des glissements de terrain a grande echelle. [Traduit par la Redaction] Mots-cles : glissements de terrain a grande echelle, hydrodynamique des particules lissees, modele constitutif, acceleration par processeur graphique (GPU), convergence., 1. Introduction Large-scale landslides are a major socio-economic threat in mountainous regions, as they usually lead to continuous damage of infrastructures and, in many cases, causalities. Various numerical methods were [...]

The conventional numerical solvers for partial differential equations encounter a formidable challenge, as their computational efficiency and accuracy are heavily contingent on grid size. Recently, machine learning (ML) has exhibited substantial promise in addressing partial differential equations. Nevertheless, substantial hurdles persist in practical applications. In this work, we endeavor to establish a deep learning framework founded on the Fourier neural operator (FNO) for resolving the intricacies of simulating real landslide dynamic processes. Our findings demonstrate that the current FNO approach adeptly replicates landslide dynamic processes and boasts exceptional computational efficiency. Additionally, it is noteworthy that this data‐driven ML methodology can seamlessly incorporate data from other experimental sources or numerical simulation techniques. Consequently, this work underscores the significant potential of utilizing ML methodologies to supplant conventional numerical simulation methods. Plain Language Summary: There are great challenges involved in leveraging machine learning methods to learn realistic physical dynamic processes. When it comes to the real landslide movement across intricate terrains, it is meaningful to validate the capacities of machine learning in tackling the complicated problem. This study aims to propose an innovative solution of modeling of landslide dynamic processes from a machine learning perspective. Here, we introduce a data‐driven framework based on Fourier neural operator to predict the dynamic behavior of actual landslides. Following an exhaustive assessment, the superior performance of our suggested model in real landslide situations and its versatility in adapting to landslides across various geographical regions have been confirmed. This study explores a new approach to modeling landslide dynamic processes and highlights the great potential of data‐driven approaches to address dynamic process challenges present in real physical world. Key Points: The data‐driven deep learning method based on FNO can achieve fast prediction of real landslide accumulation processThe proposed data‐driven method for predicting landslide dynamic processes can be extended to new areas after transfer learningWe provide numerical datasets of landslide dynamics, which can serve as the foundational resources for ML‐based landslide forecasting tasks [ABSTRACT FROM AUTHOR]

The Shishapangma region, situated in the middle of the Himalayas, is rich in glacial lakes and glaciers. Hence, glacial lake outburst floods (GLOFs) have become a top priority because of the severe threat posed by GLOFs to the downstream settlements. This study presents a comprehensive analysis of GLOF hazards using multi-source remote sensing datasets and designs a flood model considering the different breaching depths and release volumes for the Galong Co region. Based on high-resolution optical images, we derived the expanding lake area and volume of glacial lakes. We monitored deformation velocity and long-term deformation time series around the lake dam with Small BAseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR). The glacier thinning trend was obtained from the difference in the Digital Elevation Model (DEM). We identified potential avalanche sources by combining topographic slope and measurable deformation. We then carried out flood modeling under three different scenarios using the hydrodynamic model HEC-RAS for Galong Co, which is formed upstream of Nyalam. The results show that the Nyalam region is exposed to high-intensity GLOFs in all scenarios. The larger breaching depth and release volumes caused a greater flow depth and peak discharge. Overall, the multiple remote sensing approaches can be applied to other glacial lakes, and the modeling can be used as a basis for GLOF mitigation. [ABSTRACT FROM AUTHOR]

The efficiency and accuracy of grid-based computational fluid dynamics methods are strongly dependent on the chosen cell size. The computational time increases exponentially with decreasing cell size. Therefore, a grid coarsing technology without apparent precision loss is essential for various numerical modeling methods. In this article, a physical adaption neural network (PANN) is proposed to optimize coarse grid representation from a fine grid. A new convolutional neural network is constructed to achieve a significant reduction in computational cost while maintaining a relatively accurate solution. An application to numerical modeling of dynamic processes in landslides is firstly carried out, and better results are obtained compared to the baseline method. More applications in various flood scenarios in mountainous areas are then analyzed. It is demonstrated that the proposed PANN downscaling method outperforms other currently widely used downscaling methods. The code is publicly available and can be applied broadly. Computing by PANN is hundreds of times more efficient, meaning that it is significant for the numerical modeling of various complicated Earth-surface flows and their applications. [ABSTRACT FROM AUTHOR]

Numerical prediction of landslide runout and deposition is important for estimating landslide risk and developing mitigation plans. The choice of a suitable model and its parameters and a confident calibration strategy are crucial for numerical simulations. Here, we evaluated two constitutive models with a three‐dimensional smoothed particle hydrodynamics (SPH) method by simulating the catastrophic 11 October 2018 Baige landslide. The results indicate that both the soil mechanic and fluid models can capture the dynamic runout and deposition morphology while using different values of input parameters. A point‐wise comparison of deposit elevation can minimize the calibration error. Numerical models were constrained accurately by utilizing both the static observation data and dynamic seismic signals. The effects of friction on deep‐seated landslides motion and deposition are more significant than cohesion. The 3D model includes the effects of shear stresses and velocities inside the material body, resulting in a reduced friction coefficient compared to the 2D model (e.g., depth‐averaged model). Our study highlights the potential of the 3D SPH method for modeling large‐scale complex landslides. Plain Language Summary: Landslides belong to a type of earth surface process recognized by their high damage potential. Computer models can simulate the landslides' movement to predict speed, forces, and deposition, which can help to delineate areas at risk and to design mitigation measures. We simulated the 2018 Baige landslide in China with a method known as smoothed particle hydrodynamics (SPH), to test whether it is suitable for landslide simulation and to investigate protocols for model calibration when including seismic data in addition to information on the landslide's deposit. In this method, the landslide body is represented by millions of small, moving, interacting particles, which offers advantages over established models in terms of computation time and details in the simulation. We find that SPH is suitable for modeling large‐scale natural landslides. The seismic data are more valuable in the model calibration than landslide deposit observations. The new approach yields landslide simulations that deliver more details on particle velocities within the landslide body, and their spatial and temporal distribution. These details can be used to infer the properties of landslides during the sliding process, which helps to better understand landslides in general and to set up models for events where little data are available. Key Points: A three‐dimensional mesh‐less graphics processing unit‐accelerated smoothed particle hydrodynamics formulation is suitable for modeling large‐scale natural landslidesThe selection of an appropriate constitutive model depends on the landslide features and materialsIncluding dynamic seismic signals into numerical model calibration routines in addition to topographic changes improves calibration [ABSTRACT FROM AUTHOR]

Wildfires are becoming a global natural disaster under climate change. A worthwhile concern is that the rainfall thresholds that trigger debris flows in catchments affected by recent wildfires are much lower than those that trigger debris flows in catchments with similar environments but that have not experienced wildfires. In this study, we investigated a major post‐fire debris flow event in Xiangjiao catchment triggered by heavy rainfall in 2021. Through the combination of field observation, laboratory experiments and several numerical models (rainfall interception, infiltration, erosion and runoff models), the physical mechanistic analysis and numerical simulation of the post‐fire debris flow in Xiangjiao catchment was carried out. Results show that the simulated deposited area, erosion depth, and the occurrence time of debris‐flow initiation correlate well with the actual situation. It is demonstrated that the numerical simulation method used in this study can be used to effectively describe the characteristics of the initial and propagation stages of the post‐fire debris flow generated by runoff and provide important insights and guidance for the prediction and prevention of debris‐flow disasters in catchments affected by wildfire. Plain Language Summary: Wildfires are becoming more and more common under more frequent extreme weather. After the wildfire, the overall environment would be substantially changed, which will lead to more frequent debris‐flow disasters. Consequences of these changes are worthy to be deeply investigated. In this study, we investigated a major 2021 post‐fire debris flow event in Xiangjiao catchment, China, triggered by heavy rainfall. Through the combination of field observation, laboratory experiments, and numerical models, the physical mechanistic analysis and numerical simulation of the post‐fire debris flow in Xiangjiao catchment was carried out. Key Points: The mechanism of post‐fire debris flows caused by runoff in Xiangjiao catchment is analyzedUsing multiple numerical models and multi‐source data, the 2021 Xiangjiao post‐fire debris flow is simulatedThe numerical simulation results are analyzed and correlated well with the field investigation [ABSTRACT FROM AUTHOR]