A method of depth-averaged avalanche motion simulation

Abstract

The application discloses a kind of deep average avalanche motion simulation methods, belong to avalanche disaster prevention and mitigation technical field.The method is based on deep average theory, successively constructs mass, momentum, particle random fluctuation energy and heat energy conservation equation, and establishes the dynamic function relationship between motion resistance parameter and particle random fluctuation energy and water content, realizes that resistance parameter is updated with avalanche internal state self-adapting.The application can more completely depict the dynamics of avalanche motion, dynamically reflect its liquidity change, significantly improve the accuracy and engineering applicability of avalanche motion simulation, and can provide reliable basis for disaster risk assessment and protection design.

Description

Technical Field

[0001] This invention relates to the field of avalanche prevention and mitigation technology, and more specifically, to a depth-averaged avalanche motion simulation method. Background Technology

[0002] Avalanches are one of the most common and destructive natural disasters in high-altitude and cold mountainous areas. They are widely distributed in high mountain valleys and along transportation and power engineering projects, posing a serious threat to personnel safety, transportation infrastructure, and major engineering projects. With the continuous expansion of engineering construction in high-altitude and cold regions, the need for quantitative analysis and risk assessment of avalanche disasters is becoming increasingly prominent. Numerical simulation of avalanche movement processes has become a key technical means in avalanche disaster prevention, mitigation, and engineering protection design.

[0003] Currently, domestic research on avalanche disasters mainly focuses on avalanche hazard zoning, empirical identification, statistical analysis, and protective engineering deployment. The field of dynamic numerical simulation of avalanche movement is still in its early stages. Existing studies often borrow analytical methods from landslides and debris flows, simplifying avalanche movement descriptions. They typically only consider mass and momentum conservation, using fixed resistance parameters to simulate the avalanche process, making it difficult to reflect the dynamic characteristics of avalanches at different development stages. On the other hand, as a high-speed gravitational flow composed of ice and snow particles, avalanches involve significant random particle motion, energy dissipation, frictional heat generation, and snow melting—complex physical processes that significantly influence avalanche mobility, distance traveled, and deposition morphology. However, existing domestic avalanche simulation methods generally fail to characterize the random fluctuations in particle energy within the avalanche and lack systematic consideration of thermodynamic processes such as frictional heat generation, snow melting, and moisture content evolution. This makes it difficult for models to distinguish the movement characteristics of different types of avalanches, such as dry and wet avalanches.

[0004] Furthermore, existing methods often use empirical constants for avalanche resistance parameters, failing to establish a dynamic relationship between these parameters and the internal state variables of the avalanche. This makes it difficult to describe the actual changes in fluidity during avalanche movement as the internal structure and energy state evolve. This, to some extent, limits the reliability and applicability of avalanche simulation results in engineering design and disaster risk assessment.

[0005] Therefore, it is necessary to propose an avalanche motion simulation method that can comprehensively consider the evolution process of avalanche mass, momentum, random particle fluctuation energy, and thermal energy within a deep averaging framework, in order to fill the technological gap in the field of avalanche dynamics numerical simulation in China and improve the scientific and refined level of avalanche disaster prevention and engineering protection design. Summary of the Invention

[0006] The present invention aims to overcome the shortcomings of the prior art and provide a depth-averaged avalanche motion simulation method that provides a more complete physical process and can dynamically reflect changes in avalanche mobility.

[0007] To achieve the above objectives, the present invention adopts the following technical solution:

[0008] A depth-averaged avalanche motion simulation method includes the following steps:

[0009] (1) Divide the digital elevation model of the study area into a computational grid. Each grid serves as a computational unit and stores avalanche motion state parameters, including flow depth, velocity, density, temperature, water content, particle random fluctuation energy, and resistance parameters.

[0010] (2) Establish an avalanche mass conservation equation in the computational domain to describe the spatiotemporal evolution of mass during avalanche movement, and introduce a mass exchange term into the equation to characterize the scraping and entrainment effect of the avalanche on the underlying snow during its movement along the slope.

[0011] (3) Based on the conservation of mass, establish the avalanche momentum conservation equation to calculate the motion response of the avalanche under gravity, terrain constraints and resistance. The resistance term includes the Coulomb resistance term related to the avalanche normal stress and the turbulent resistance term related to the motion velocity.

[0012] (4) Introduce the energy conservation equation for random fluctuations of avalanche particles to describe the random fluctuation energy generated by particles in the avalanche during shearing and collision, and consider the transmission and dissipation of this energy in the avalanche.

[0013] (5) Establish the avalanche thermal energy conservation equation to describe the heat accumulation and snow water phase change process caused by friction and energy dissipation during the avalanche movement, so as to obtain the spatiotemporal evolution characteristics of avalanche water content.

[0014] (6) Construct a dynamic functional relationship between the avalanche motion resistance parameters and the random fluctuation energy and water content of the particles. Based on the random fluctuation energy of the particles obtained in step (4) and the water content obtained in step (5), dynamically update the resistance parameters.

[0015] (7) Couple the equations of mass conservation, momentum conservation, random fluctuation energy conservation and thermal energy conservation, and solve them together. The motion resistance term in the avalanche momentum conservation equation is calculated based on the resistance parameters dynamically updated in step (6) to obtain the simulation results of the avalanche flow depth, motion velocity, energy distribution, motion distance and accumulation morphology changing with time.

[0016] Furthermore, in step (2), the avalanche mass conservation equation is:

[0017] ;

[0018] in, This refers to the avalanche flow depth in a dense state. Avalanche density under dense flow conditions; For divergence operators; The average velocity of the avalanche along the slope; Snow density on slope; subscript Indicates time The partial derivatives; This refers to the volume of snow scraped and carried from the slope per unit time. For calibration coefficients, This is the temperature correlation coefficient. This is the slope correlation coefficient.

[0019] Furthermore, in step (3), the avalanche momentum conservation equation is:

[0020] ;

[0021] in, This refers to the internal stress of an avalanche. It is a term for gravitational acceleration; For resistance to motion; The empirical splitting coefficient; This is the mass exchange term caused by the splash effect resulting from the avalanche impacting the snow accumulation on the slope.

[0022] Furthermore, in step (4), the energy conservation equation for random fluctuations is:

[0023] ;

[0024] in, The momentum of avalanche undulations per unit volume; and The coefficient for generating wave energy; Shear work; This refers to the energy loss caused by the scraping process; The energy dissipation coefficient of wave energy.

[0025] Furthermore, in step (5), the thermal energy conservation equation is:

[0026] ;

[0027] in, The heat energy of an avalanche per unit volume; and These are the specific heat capacity of snow cover and temperature on the slope, respectively. This refers to the heat exchange term between avalanche particles and the environment. This is the reduction factor. Avalanche temperature, Ambient air temperature, The thermal conductivity coefficient, For contact area, For the amount of snow dust, The snow particle size; The heat that causes the snow to melt;

[0028] The amount of meltwater is described by the following equation:

[0029] ;

[0030] in, For the quality of meltwater, For the density of water, This is the latent heat of phase transition.

[0031] Furthermore, in step (6), the Voellmy-Salm rheological model is used to characterize the motion resistance:

[0032] ;

[0033] in, Coulomb's coefficient of friction; The coefficient of friction is turbulent. This refers to the avalanche flow density. This refers to the normal stress acting on the slope surface; This is the acceleration due to gravity.

[0034] Furthermore, the dynamic functional relationship is configured to make the Coulomb friction coefficient... Follow and / or The coefficient of turbulent friction decreases monotonically with increasing turbulence, and thus decreases the coefficient of friction of turbulent friction. Follow and / or It increases monotonically with the increase of .

[0035] Furthermore, the Coulomb coefficient of friction and turbulent friction coefficient The function forms are as follows:

[0036] ;

[0037] ;

[0038] in, The minimum Coulomb friction coefficient; The maximum turbulent friction coefficient; It is random fluctuation energy; Moisture content; It is cohesive force; and These are calibration coefficients.

[0039] Furthermore, in step (7), the finite volume method is used for coupled solution; in step (6), when constructing the dynamic function relationship, the coefficients in the dynamic function relationship are determined based on the calibration experimental data for dry and wet avalanche states.

[0040] Compared with existing technologies, the depth-averaged avalanche motion simulation method provided by this invention has the following advantages:

[0041] (1) High model integrity: By simultaneously constructing the conservation equations for mass, momentum, particle random fluctuation energy and thermal energy, and introducing a resistance term that is dynamically related to the internal state into the momentum equation, a more complete avalanche motion simulation framework for the physical process is formed, which can uniformly describe the entire process of avalanche motion, deceleration and accumulation.

[0042] (2) The internal state is well characterized: By introducing and solving the energy conservation equation of random fluctuation of particles, the random motion intensity of particles inside the avalanche is quantified for the first time in the depth-averaged model, which makes up for the shortcomings of traditional models that rely only on average motion parameters.

[0043] (3) The ability to distinguish between thermodynamic processes and types: By establishing and solving the thermal energy conservation equation that includes phase change processes, it is possible to simulate the frictional heat generation, energy dissipation and snow water phase change process in avalanche movement, thereby obtaining the spatiotemporal evolution of avalanche water content, enabling the model to distinguish and simulate the different motion characteristics of dry avalanches and wet avalanches.

[0044] (4) Dynamic adaptation of resistance parameters: By constructing a dynamic functional relationship between avalanche motion resistance parameters and particle random fluctuation energy and water content, the resistance parameters are adaptively updated with the internal energy state and wetting degree of the avalanche, overcoming the limitations of using fixed resistance parameters in existing methods, so that the simulated avalanche flow can more realistically reflect the dynamic changes in its actual motion process.

[0045] (5) Strong engineering applicability: The depth-averaged frame and finite volume solution method adopted in this invention have high computational efficiency and are suitable for simulation analysis at the engineering scale. The calibration coefficients involved in the model can be determined experimentally, which facilitates localized calibration of the model in combination with actual observation data, providing a more reliable and flexible technical tool for hazard analysis, risk assessment and protection engineering design of avalanche disasters. Attached Figure Description

[0046] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort, wherein:

[0047] Figure 1 The flowchart illustrates the calculation process of the depth-averaged avalanche motion simulation method provided in this embodiment of the invention.

[0048] Figure 2 The avalanche volume evolution over time is shown in the embodiment of the present invention.

[0049] Figure 3 The evolution of the Coulomb friction coefficient over time is shown in the embodiment of the present invention.

[0050] Figure 4 The graph shows the change in avalanche meltwater volume over time, as provided in an embodiment of the present invention.

[0051] Figure 5 The diagram shows the simulation results of the avalanche deposition process provided in the embodiment of the present invention.

[0052] Figure 6 This is a maximum velocity distribution diagram of avalanche motion provided in an embodiment of the present invention. Detailed Implementation

[0053] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this invention, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0054] Please see Figure 1 As shown, this invention provides a depth-averaged avalanche motion simulation method, comprising the following steps:

[0055] (1) Divide the digital elevation model of the study area into a computational grid. Each grid serves as a computational unit and stores avalanche motion state parameters, including flow depth, velocity, density, temperature, water content, particle random fluctuation energy, and resistance parameters.

[0056] (2) Establish an avalanche mass conservation equation in the computational domain to describe the spatiotemporal evolution of mass during avalanche movement, and introduce a mass exchange term into the equation to characterize the scraping and entrainment effect of the avalanche on the underlying snow during its movement along the slope.

[0057] (3) Based on the conservation of mass, establish the avalanche momentum conservation equation to calculate the motion response of the avalanche under gravity, terrain constraints and resistance. The resistance term includes the Coulomb resistance term related to the avalanche normal stress and the turbulent resistance term related to the motion velocity.

[0058] (4) Introduce the energy conservation equation for random fluctuations of avalanche particles to describe the random fluctuation energy generated by particles in the avalanche during shearing and collision, and consider the transmission and dissipation of this energy in the avalanche.

[0059] (5) Establish the avalanche thermal energy conservation equation to describe the heat accumulation and snow water phase change process caused by friction and energy dissipation during the avalanche movement, so as to obtain the spatiotemporal evolution characteristics of avalanche water content.

[0060] (6) Construct a dynamic functional relationship between the avalanche motion resistance parameters and the random fluctuation energy and water content of the particles. Based on the random fluctuation energy of the particles obtained in step (4) and the water content obtained in step (5), dynamically update the resistance parameters.

[0061] (7) Couple the equations of mass conservation, momentum conservation, random fluctuation energy conservation and thermal energy conservation, and solve them together. The motion resistance term in the avalanche momentum conservation equation is calculated based on the resistance parameters dynamically updated in step (6) to obtain the simulation results of the avalanche flow depth, motion velocity, energy distribution, motion distance and accumulation morphology changing with time.

[0062] In step (2), the avalanche mass conservation equation is:

[0063] ;

[0064] in, This refers to the avalanche flow depth in a dense state. Avalanche density under dense flow conditions; For divergence operators; The average velocity of the avalanche along the slope; Snow density on the slope; This refers to the volume of snow scraped and carried from the slope per unit time. For calibration coefficients, This is the temperature correlation coefficient. The slope correlation coefficient is used. By introducing a mass exchange term, a dynamic description of the key mass source—scraping and carrying snow from the slope—is achieved during avalanche movement, making the mass evolution more consistent with the actual physical process.

[0065] In step (3), the avalanche momentum conservation equation is:

[0066] ;

[0067] in, This refers to the internal stress of an avalanche. It is a term for gravitational acceleration; For resistance to motion; The empirical splitting coefficient; This is the mass exchange term caused by the splash effect resulting from the avalanche impacting the snow-covered slope. This equation comprehensively considers gravity-driven forces, internal stresses, Coulomb and turbulent drag, as well as the impact splash effect, enabling a more accurate simulation of the acceleration, deceleration, and turning processes of avalanches in complex terrain.

[0068] In step (4), the energy conservation equation for random fluctuations is:

[0069] ;

[0070] in, The momentum of avalanche undulations per unit volume; and The coefficient for generating wave energy; Shear work; This refers to the energy loss caused by the scraping process; The energy dissipation coefficient of the wave energy is introduced. This equation quantifies the intensity of random motion of particles inside an avalanche, making up for the shortcomings of traditional continuous medium models that only consider average velocity, and providing a key internal state quantity for the subsequent dynamics of drag parameters.

[0071] The thermal energy conservation equation mentioned in step (5) is:

[0072] ;

[0073] in, The heat energy of an avalanche per unit volume; and These are the specific heat capacity of snow cover and temperature on the slope, respectively. This refers to the heat exchange term between avalanche particles and the environment. This is the reduction factor. Avalanche temperature, Ambient air temperature, The thermal conductivity coefficient, For contact area, For the amount of snow dust, The snow particle size; The heat that causes the snow to melt;

[0074] The amount of meltwater is described by the following equation:

[0075] ;

[0076] in, For the quality of meltwater, For the density of water, The latent heat of phase change is considered. This equation system takes into account frictional heat generation, energy dissipation, heat exchange with the environment, and the phase change process of snow water. It can simulate the spatiotemporal evolution of avalanche water content, thereby effectively distinguishing and simulating the differences between dry and wet avalanches in terms of fluidity and thermodynamic characteristics.

[0077] In step (6), the Voellmy-Salm rheological model is used to characterize the drag force:

[0078] ;

[0079] in, Coulomb's coefficient of friction; The coefficient of friction is turbulent. This refers to the avalanche flow density. This refers to the normal stress acting on the slope surface; The friction coefficient is determined by gravitational acceleration. The classic Voellmy-Salm model is extended so that it is no longer a constant but is correlated with the internal state, laying the foundation for dynamic simulation of fluidity.

[0080] Furthermore, the dynamic functional relationship is configured to make the Coulomb friction coefficient... Follow and / or The coefficient of turbulent friction decreases monotonically with increasing turbulence, and thus decreases the coefficient of friction of turbulent friction. Follow and / or The resistance increases monotonically with the increase of the particle's random motion or increased water content. This configuration reflects the physical nature of "intensified random particle motion or increased water content leading to enhanced avalanche fluidity," enabling the model to adaptively simulate the dynamic changes in resistance throughout the entire avalanche process, from initiation and high-speed movement to deceleration and accumulation.

[0081] Furthermore, the Coulomb coefficient of friction and turbulent friction coefficient The function forms are as follows:

[0082] ;

[0083] ;

[0084] in, The minimum Coulomb friction coefficient; The maximum turbulent friction coefficient; It is random fluctuation energy; Moisture content; It is cohesive force; and These are calibration coefficients. This exponential function form can smoothly characterize the nonlinear relationship between the friction coefficient and the internal state, is computationally stable, and the coefficients with clear physical meanings can be calibrated experimentally, enhancing the engineering practicality of the model.

[0085] Preferably, the finite volume method is used for coupled solution in step (7).

[0086] The depth-averaged avalanche motion simulation method provided by the present invention will be described in detail below with specific embodiments.

[0087] Example 1

[0088] This embodiment 1 takes a potential avalanche path in a high-altitude cold mountainous area as the research object and applies the method of the present invention to simulate it.

[0089] First, perform step (1): obtain the digital elevation model (DEM) of the study area through remote sensing image interpretation, divide it into a 5m precision calculation grid, and store the initialized avalanche motion state parameters in each grid cell, including flow depth, velocity, density, temperature, water content, turbulent energy, friction coefficient, etc.

[0090] Subsequently, steps (2)-(5) are performed to establish and initialize each conservation equation.

[0091] In step (2), the avalanche mass conservation equation and its parameter settings are as follows:

[0092] ;

[0093] Among them, dense avalanche density Set at 450 kg / m³, slope snow density Set to 150 kg / m³, calibration factor Temperature correlation coefficient In this embodiment, the slope snow surface temperature The temperature was set to -5℃. This step simulated the scraping effect on snow accumulation during an avalanche through dynamic mass exchange, allowing the avalanche volume to increase from the initial 7.6 × 10⁻⁶. 4 m³ gradually increases, and the evolution process is as follows: Figure 2 As shown.

[0094] In step (3), the parameters of the avalanche momentum conservation equation are set as follows: empirical splitting coefficients. .

[0095] In step (4), the parameters of the wave energy conservation equation are set as follows: wave energy generation coefficient , Wave energy dissipation coefficient This step enables the quantitative calculation of the intensity of random motion of particles inside an avalanche, providing a key input for dynamic updates of resistance.

[0096] In step (5), the parameters of the heat energy conservation equation are set as follows: specific heat capacity of snow cover on slope. Ambient air temperature Avalanche initial temperature Heat exchange reduction coefficient latent heat This step simulates frictional heat generation and the possible snow melting process, and the calculated change in meltwater volume is as follows: Figure 4 As shown, state variables are provided to distinguish between dry and wet avalanches.

[0097] Next, perform the core step (6): construct a dynamic resistance model.

[0098] This embodiment uses the Voellmy-Salm frame and incorporates the Coulomb friction coefficient. and turbulent friction coefficient This can be expressed in the following function form:

[0099] ;

[0100] ;

[0101] Wherein, the initial friction coefficient is input. , ;set up , Cohesion Calibration coefficient , This step is the core of the invention, as it enables the resistance parameters to be obtained in real time based on calculations. and Dynamic adjustment. For example... Figure 3 As shown, During exercise and The increase in avalanche flow significantly reduces the avalanche flow, which intuitively reflects the physical process of enhanced avalanche mobility.

[0102] Finally, step (7) is executed: the coupled equations are discretized and solved using the finite volume method. In each time step, the mass, momentum, wave energy, thermal energy, and drag parameters are updated sequentially until the simulation ends. Through iterative solving, the final result is as follows: Figure 5 The simulation diagram of the avalanche deposition process shown, and as follows Figure 6 The maximum flow velocity distribution is shown in the diagram.

[0103] The calibration coefficients involved in the model, such as the mass exchange calibration coefficients. Wave energy generation coefficient and Energy dissipation coefficient and the calibration coefficient in the dynamic resistance function , These parameters can be determined through field observations, laboratory experiments, or back-analysis of historical cases for typical dry and wet avalanche conditions. For example, by comparing simulation results with actual observed avalanche movement distance, deposition morphology, and flow velocity data, optimization algorithms can be used to inversely calibrate the aforementioned coefficients. This calibration process combines the physical model of this invention with the avalanche characteristics of a specific region, and is a crucial step in ensuring the accuracy of the simulation results and their engineering applicability. Those skilled in the art, based on the teachings of this invention, are able to complete such calibration work.

[0104] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A depth-averaged avalanche motion simulation method, characterized in that, Includes the following steps: (1) Divide the digital elevation model of the study area into a computational grid. Each grid serves as a computational unit and stores avalanche motion state parameters, including flow depth, velocity, density, temperature, water content, particle random fluctuation energy, and resistance parameters. (2) Establish an avalanche mass conservation equation in the computational domain to describe the spatiotemporal evolution of mass during avalanche movement, and introduce a mass exchange term into the equation to characterize the scraping and entrainment effect of the avalanche on the underlying snow during its movement along the slope. (3) Based on the principle of mass conservation, an avalanche momentum conservation equation is established to calculate the motion response of an avalanche under gravity, terrain constraints, and drag. The drag term includes a Coulomb drag term related to the avalanche normal stress and a turbulent drag term related to the motion velocity. The avalanche momentum conservation equation is as follows: ； in, This refers to the internal stress of an avalanche. It is a term for gravitational acceleration; For resistance to motion; The empirical splitting coefficient; The mass exchange term is caused by the splash effect resulting from the avalanche impacting snow accumulation on the slope. (4) Introduce the energy conservation equation for random fluctuations of avalanche particles to describe the random fluctuation energy generated by particles in the avalanche during shearing and collision, and consider the transmission and dissipation of this energy in the avalanche. (5) Establish the avalanche thermal energy conservation equation to describe the heat accumulation and snow water phase change process caused by friction and energy dissipation during the avalanche movement, so as to obtain the spatiotemporal evolution characteristics of avalanche water content. (6) Construct a dynamic functional relationship between avalanche motion resistance parameters and the random fluctuation energy and water content of the particles. Based on the random fluctuation energy of the particles obtained in step (4) and the water content obtained in step (5), dynamically update the resistance parameters. The Voellmy-Salm rheological model is used to characterize the motion resistance. ； in, Coulomb's coefficient of friction; The coefficient of friction is the turbulent friction coefficient. This refers to the avalanche flow density. This refers to the normal stress acting on the slope surface; It is the acceleration due to gravity; The dynamic functional relationship is configured to: make the Coulomb friction coefficient... Follow and / or The coefficient of turbulent friction decreases monotonically with increasing turbulence, and thus decreases the coefficient of friction of turbulent friction. Follow and / or The coefficient of friction increases monotonically with increasing ; Coulomb friction coefficient and turbulent friction coefficient The function forms are as follows: ； ； in, The minimum Coulomb friction coefficient; The maximum turbulent friction coefficient; It is random fluctuation energy; Moisture content; It is cohesive force; and These are calibration coefficients; (7) Couple the equations of mass conservation, momentum conservation, random fluctuation energy conservation and thermal energy conservation, and solve them together. The motion resistance term in the avalanche momentum conservation equation is calculated based on the resistance parameters dynamically updated in step (6) to obtain the simulation results of the avalanche flow depth, motion velocity, energy distribution, motion distance and accumulation morphology changing with time.

2. The depth-averaged avalanche motion simulation method according to claim 1, characterized in that, In step (2), the avalanche mass conservation equation is: ； in, This refers to the avalanche flow depth in a dense state. Avalanche density under dense flow conditions; For divergence operators; The average velocity of the avalanche along the slope; Snow density on the slope; This refers to the volume of snow scraped and carried from the slope per unit time. For calibration coefficients, This is the temperature correlation coefficient. This is the slope correlation coefficient.

3. The depth-averaged avalanche motion simulation method according to claim 2, characterized in that, In step (4), the energy conservation equation for random fluctuations is: ； in, The momentum of avalanche undulations per unit volume; and The coefficient for generating wave energy; Shear work; This refers to the energy loss caused by the scraping process; The energy dissipation coefficient of wave energy.

4. The depth-averaged avalanche motion simulation method according to claim 3, characterized in that, In step (5), the thermal energy conservation equation is: ； in, The heat energy of an avalanche per unit volume; and These are the specific heat capacity of snow cover and temperature on the slope, respectively. This refers to the heat exchange term between avalanche particles and the environment. This is the reduction factor. Avalanche temperature, The ambient air temperature, The thermal conductivity coefficient, For contact area, For the amount of snow dust, The snow particle size; The heat that causes the snow to melt; The amount of meltwater is described by the following equation: ； in, For the quality of meltwater, For the density of water, This is the latent heat of phase transition.

5. The depth-averaged avalanche motion simulation method according to claim 1, characterized in that, In step (7), the finite volume method is used for coupled solution; in step (6), when constructing the dynamic function relationship, the coefficients in the dynamic function relationship are determined based on the calibration experimental data for dry and wet avalanche states.

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