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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]

The evolution of the volume fraction of solid phase, basal entrainment and interactions between the solid and fluid phase have significant impacts on rheological behavior, dynamic characteristics, and volume amplification in some earth-surface flows, including debris flows, hyper-concentration flows, dam-break flows. In this study, we propose a new two-phase flow dynamic model based on coupling of the depth-integrated continuum method and discrete element method (DEM). The fluid phase is computed by the depth-integrated continuum method, while the solid phase is modeled by the discrete element method. The data exchange between the two phases is efficiently handled by setting shared variables between the separate computational frameworks. The basal topography is simultaneously updated by numerically monitoring when solid particles are entrained into the fluid or deposited from the fluid. The current method provides a guiding solution for the common need to couple computations among multiple models. We examine several computational cases and compare the results with the theoretical solution and the experimental results. We conclude that the computational framework has unique advantages, placing it in a position to solve some ticklish issues in the field of fluid-solid interactions. • A new fast-computing two-phase model by depth-integrated continuum and DEM is proposed • A new erosion rate computational model directly from discrete particles is proposed. • Several computational cases compared with experiments are well calculated. [ABSTRACT FROM AUTHOR]

• Ill-considered urban planning around Niwan gully may multiply the human and economic losses caused by debris flows. • A depth-integrated model was adopted to determine the hazard zones of debris flows with different recurrence intervals. • The debris flow inundation and evolutionary trajectory are shown to be reasonably comparable with historical debris flows. • This study established a relatively complete system to implement hazard and risk assessment in the study area. The highly geologically active, structurally complex Bailongjiang river basin, Longnan City, Gansu Province hosts many debris-flow gullies. The recent Wenchuan and Jiuzhaigou earthquakes have increased the amount of source material available in these debris flow gullies, and the disturbed nature of the slopes makes them prone to failure under high rainfall, increasing the risk of large-scale debris flows. Moreover, poor urban planning in the region may multiply the human and economic losses caused by debris flows. Therefore, it is necessary to determine the potential hazard zones associated with such debris flows and to implement debris flow hazard and risk assessment. In this paper, Niwan gully is chosen as the study object, as it is characterized by repeated debris flow outbreaks and an ill-considered urban planning at the outlet of the gully. A depth-integrated continuum method is adopted to determine the potential hazard zones of debris flows with different recurrence intervals. The debris flow inundation and evolutionary trajectory are shown to be reasonably comparable with historical debris flows. A two-factor model that couples maximum flow depth with momentum is used to better classify the hazard zones. This study also provides a complete and efficient means to determine the values of debris flow parameters and to implement a hazard and risk assessment based on the solid material reserve method and a probabilistic method. This proposed approach efficiently generated a debris flow risk distribution map that can be used for effective disaster prevention in the Bailongjiang River basin. [ABSTRACT FROM AUTHOR]

Jiuzhaigou County in Sichuan, China is renowned for its beautiful natural scenery, recognized as a World Heritage Site and World Biosphere Reserve by the UNESCO. On August 8, 2017, a M s 7.0 (M w 6.5) earthquake struck the region, causing severe damage and casualties. The earthquake triggered thousands of landslides and the collapse of the natural travertine dam impounding Huohua Lake, an important tourist attraction in the reserve. In this study, the dam failure process was investigated through field surveys and remote sensing observations. The propagation of the flood surge was analyzed by Massflow, a depth-integrated continuum model based on the MacCormack-TVD finite difference algorithm. The model results were validated against vegetation maps evaluated from satellite multispectral data recorded before and after the event. The relation between changes in vegetation coverage and computed flooded areas, flow depth, and velocities was elucidated. This study is preliminary in nature, yet the proposed approach could be used, after further validation, in flood hazard and vegetation damage assessments, which may be especially valuable in nature reserves. • A travertine dam collapsed during an earthquake in Jiuzhaigou's natural reserve. • A deep-integrated model was used to simulate the propagation of the flood surge. • A method was proposed to estimate flooded areas based on vegetation damage. [ABSTRACT FROM AUTHOR]