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In recent years, mountainous areas in China have faced frequent geological hazards, including landslides, debris flows, and collapses. Effective simulation of these events requires a solver for shallow water equations (SWEs). Traditional numerical methods, such as finite difference and finite volume, face challenges in discretizing convection flux terms, while theory-based models need to account for various factors such as shock wave capturing and wave propagation direction, demanding a high-level understanding of the underlying physics. Previous deep learning (DL)-based SWE solvers primarily focused on constructing direct input–output mappings, leading to weak generalization properties when terrain data or stress constitutive relations change. To overcome these limitations, this study introduces a novel SWE solver that combines theory and data-driven methodologies. The core idea is to use artificial neural networks to compute convection flux terms, and to reduce modeling complexity. Theory-based modeling is used to tackle complex terrain and friction terms for the purpose of ensuring generalization. Our method surpasses challenges faced by previous DL-based solvers in capturing terrain and stress variations. We validated our solver's capabilities by comparing simulation results with analytical solutions, real-world disaster cases, and the widely used Massflow software-generated simulations. This comprehensive comparison confirms our solver's ability to accurately simulate hazard scenarios and showcases strong generalization on varying terrain and land surface friction. Our proposed method effectively addresses DL-based solver limitations while simplifying the complexities of theory-driven numerical methods, offering a promising approach for hazard dynamics simulation. [ABSTRACT FROM AUTHOR]

A large landslide disaster occurred at Xinmo village of Mao County, Sichuan Province, China on June 24, 2017, and killed 83 persons. The landslide is characterized with high-speed, flow-like movement and strong basal entrainment. A 2-D depth-averaged model based on the Savage-Hutter equations incorporating a basal entrainment relation-ship is developed to stimulate the landslide process. The entrainment relationship is derived from discontinuity formula between the moving mass and the static bed sediment, and the entrainment rate is expressed as a function of the basal shear stress and the bed material resistance. A second-order leap-frog scheme constructed in a staggered grid system is applied to numerically solve the 2D model with high-resolution DEM. An artificial diffusion limiter is introduced on the right-hand side of the continuity equation to keep computational stability. Dry and wet boundary treatment is used to deal with the moving boundary. The simulation results show a good agreement with the on-spot investigation and UAV image interpretation. [ABSTRACT FROM AUTHOR]

On August 8, 2017, an M s 7.0 earthquake occurred at the Jiuzhaigou World Natural and Cultural Heritage, Sichuan Province, southwestern China. Eighty-three earthquake-induced landslides that directly damaged the Jiuzhaigou heritage sites were identified via remote-sensing interpretation and field survey. The coseismic landslides were concentrated primarily within 3 km of the seismogenic fault. Large volumes of sediments were transported into catchments of the Jiuzhaigou Valley, creating a geo-environment conducive to debris flow hazards triggered by post-earthquake heavy rainfall. A methodology combining the calculation of sediment augmentation, particle model, and numerical algorithm is developed to assess the potential dangers of post-quake debris flows. Five typical catchments strongly affected by the earthquake are chosen as study cases. The simulation results at Xiajijiehai Gully show a good agreement with the actual inundation area of the debris-flow event on September 25, 2017. The comparison of pre- and post-earthquake scenarios shows that an increase in sediment supply can greatly increase the debris-flow potential dangers characterized by higher maximum flow depths and larger inundation areas. • Earthquake-induced landslides cause damage to World Heritage Sites. • A methodology is proposed to assess the post-earthquake debris flow potential dangers. • The resulting sediment augmentation affects the frequency and magnitude of debris flows. [ABSTRACT FROM AUTHOR]

• Three-linear hydrological behaviors with both T g and T r thresholds are examined. • Higher stormflow amounts generally occur during the above- T r phase. • Abrupt flood response at the T r is predominantly controlled by rainfall amounts. • The subsurface stormflow is identified as the main contribution to flash floods. Understanding the meanings and identifying the controls for the stormflow generation with complex and nonlinear behaviors is essential for the development of threshold-based hydrological theory, as well as accurate assessment and prediction for flash flood risks. However, the study of catchment emergent patterns with three-linear threshold behaviors associated with hydrological connectivity has received little attention. Therefore, utilizing soil water storage, rainfall, and streamflow data spanning 3 years in a humid forest experimental watershed, Dujiangyan city, China, we elucidated how and where stormflow was generated with nonlinear behaviors, which were affected by antecedent wetness and rainfall amounts. Stormflow threshold behavior was taken as a function of combined gross precipitation and antecedent soil water storage, which was isolated using piecewise regression analysis with the identification of two breakpoints (i.e., generation threshold, T g and rising threshold, T r). It was found that the initial emergent behavior of rainfall-runoff was generally activated at the T g , and then an abrupt shift from slow to fast flood response was possibly triggered at the T r. These processes are important to understand the formation and development of flash floods at the watershed scale. It was noted that, above the T r , considerably higher stormflow amounts generally occurred due to the lateral-connectivity extension of runoff contributing area from stream to neighboring hillslopes. Meanwhile, gravity-driven water movements in soil and better hydrological connectivity during the above- T r phase readily triggered the huge flash flood disasters. The above- T r flash floods with abrupt shifts were predominantly controlled by rainfall amounts, while initial below -T g stormflow generation was mainly controlled by unsaturated soil water storage. More noteworthy, under heavy rainstorm conditions, the above- T r stormflow was dominantly generated by subsurface flow, as was demonstrated at hillslope and watershed scales. These findings contribute to increasing our understanding of the controls on three-linear threshold-based hydrological behaviors, as well as of subsurface stormflow generation mechanism associated with hydrological connectivity in humid forest watersheds. [ABSTRACT FROM AUTHOR]