An avalanche disaster risk assessment method considering the influence of environmental temperature
By constructing a modified model for the shear strength of the snow layer, taking into account changes in snow layer temperature and moisture content, the problem of the influence of environmental temperature not being considered in existing technologies is solved, and accurate assessment and prediction of avalanche disaster risk is achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- CHINA HYDROELECTRIC ENGINEERING CONSULTING GROUP CHENGDU RESEARCH HYDROELECTRIC INVESTIGATION DESIGN AND INSTITUTE
- Filing Date
- 2026-02-10
- Publication Date
- 2026-06-12
AI Technical Summary
Existing avalanche stability analysis methods fail to effectively consider the influence of ambient temperature, leading to a risk of misjudgment in the assessment results and making it impossible to accurately assess the risk of avalanche disasters.
A modified model for snow layer shear strength was constructed. By considering the changes in snow layer temperature and water content, the shear strength of the snow layer under different ambient temperatures was calculated, and the ratio of the shear strength to the driving shear stress was used as a safety factor to assess the likelihood of avalanche disasters.
It improves the accuracy and precision of avalanche risk assessment, reduces the risk of misjudging stability due to temperature changes, and provides accurate predictions of avalanche disasters.
Smart Images
Figure CN122196626A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of snow disaster prevention and control technology, specifically relating to a method for avalanche disaster risk assessment that takes into account the influence of ambient temperature. Background Technology
[0002] Avalanches are sudden natural disasters common in high-altitude and frigid regions, posing a serious threat to transportation infrastructure, engineering structures, and human lives. In practical engineering scenarios, linear projects often traverse high-altitude and complex terrain areas via tunnels, making avalanches at tunnel entrances and on slopes a significant factor threatening the safety of major projects. Against the backdrop of global warming, avalanches are occurring at increasingly frequent rates. Avalanche outbreaks not only cause tunnel blockages and facility damage but can also pose long-term safety hazards to operating highways and railways. Therefore, risk assessment studies of avalanche hazards in major engineering areas are necessary, along with the development of effective avalanche risk assessment methods to mitigate the substantial threats posed by avalanches to engineering infrastructure.
[0003] Understanding snow mass stability is a crucial part of risk assessment. Existing avalanche stability analysis methods comprehensively consider factors such as slope and valley avalanches, avalanche hazard level, aspect of the most dangerous slope, and snow profile hardness, type of weak layer, grain type, and size. However, they neglect the critical influence of ambient temperature, which is essential for avalanche stability assessment. Extensive engineering observations and field experience show that as snow temperature rises, the bonding strength between snow crystals gradually weakens, leading to a significant decrease in snow shear strength. This results in a significant risk of misjudgment in stability assessments obtained using traditional methods under elevated temperatures. Therefore, it is necessary to consider the impact of ambient temperature and incorporate it into existing assessment models to improve the scientific rigor and accuracy of avalanche stability assessments, thereby further increasing the precision of avalanche risk assessment. Summary of the Invention
[0004] This invention discloses an avalanche disaster risk assessment method that considers the influence of ambient temperature. By taking into account the effects of snow layer temperature and moisture content, the method corrects the snow layer stability coefficient, thereby achieving a refined assessment of avalanche disaster risk and reducing the risk of misjudgment of stability due to temperature changes. This can effectively solve at least one of the technical problems involved in the background art.
[0005] To achieve the above objectives, the technical solution of the present invention is as follows: A method for avalanche hazard risk assessment that takes into account the influence of ambient temperature includes the following steps: Step S1: Obtain topographic data, meteorological data, and snow cover data for the study area, and calculate the driving shear stress generated by the snow layer under the action of gravity. Step S2: The influence of ambient temperature on the snow layer is converted into the process of snow layer temperature and moisture content change. A snow layer shear strength correction model related to snow layer temperature and moisture content is constructed to calculate the shear strength of the snow layer under the current snow layer temperature and moisture content. Step S3: Using the ratio of snow layer shear strength to driving shear stress as a safety factor, assess the likelihood of an avalanche disaster in the snow layer.
[0006] As a preferred improvement, the terrain data includes slope angle; the meteorological data includes ambient temperature, snowfall, and wind speed; and the snow cover data includes snow thickness, snow density, and snow profile temperature.
[0007] As a preferred improvement, the driving shear stress is calculated using the following formula: In the formula, Snow density; Represents gravitational acceleration; Indicates the thickness of the snow layer; Indicates the slope angle.
[0008] As a preferred improvement, the construction process of the snow layer shear strength correction model includes the following steps: Step S21, Snow layer temperature correction: Starting from the reference shear strength at the reference temperature, define a first function model characterizing the change of shear strength with temperature. The first function model is calibrated with the actual shear strength at different snow layer temperatures to obtain the temperature correction model. Step S22, Moisture content correction: Based on the temperature correction model, a moisture content term is introduced, and a second function model characterizing the change of shear strength with moisture content is defined. The second function model is calibrated with the actual shear strength under different moisture contents to obtain the moisture content correction model, which is the snow layer shear strength correction model.
[0009] As a preferred improvement, the temperature correction model is an exponential model or a linear model.
[0010] As a preferred improvement, the exponential model of the temperature correction model is expressed as: In the formula, Indicates the base snow layer temperature The baseline shear strength; This indicates the actual snow layer temperature; Indicates the temperature of the snow layer Shear strength at the following levels; This represents the snow layer temperature correction factor.
[0011] As a preferred improvement, the linear model of the temperature correction model is expressed as: In the formula, Indicates the base snow layer temperature The baseline shear strength; This indicates the actual snow layer temperature; Indicates the temperature of the snow layer Shear strength at the following levels; This represents the snow layer temperature correction factor.
[0012] As a preferred improvement, the shear strength correction model is expressed as: In the formula, Indicates snow layer temperature Moisture content The shear strength of the snow layer at that time; This represents the moisture content correction factor.
[0013] As a preferred improvement, the safety factor Represented as: .
[0014] As a preferred improvement, the evaluation process is expressed as follows: Safety factor A value greater than 1 indicates a stable snow layer, making avalanches less likely; the safety factor is [missing information]. <1 indicates snow layer instability, making avalanches more likely; safety factor =1 indicates that the snow layer is in a critical state.
[0015] The beneficial effects of this invention are as follows: This invention considers the influence of ambient temperature on snow layer stability, converting ambient temperature into snow layer temperature and moisture content for characterization. It constructs a snow layer shear strength correction model related to snow layer temperature and moisture content, accurately calculating the actual shear strength of the snow layer. Then, using the ratio of snow layer shear strength to driving shear stress as a safety factor, it assesses the likelihood of avalanche disasters, thereby achieving accurate and reliable avalanche prediction. This provides strong technical support for avalanche risk assessment under temperature change conditions, effectively reducing avalanche disaster risks. Attached Figure Description
[0016] 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: Figure 1 This is a flowchart illustrating an avalanche disaster risk assessment method that takes into account the influence of ambient temperature, provided by the present invention. Detailed Implementation
[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.
[0018] Please see Figure 1 This invention proposes a method for avalanche disaster risk assessment that considers the influence of ambient temperature, comprising the following steps: Step S1: Obtain topographic data, meteorological data, and snow cover data for the study area, and calculate the driving shear stress generated by the snow layer under the action of gravity.
[0019] The factors directly influencing avalanche occurrence are topographic data and snow accumulation data. Topographic data includes slope angle; a slope angle that is too small makes it difficult for snow to slide, while a slope angle that is too large makes it difficult to accumulate large amounts of snow. Slope angles of 30-45 degrees are the most common areas for avalanches. Snow accumulation data includes snow layer thickness, snow layer density, and snow layer temperature. This data directly reflects the physical properties of the snow and is directly related to shear stress and shear strength.
[0020] The meteorological data includes ambient temperature, snowfall, and wind speed. This data influences snow cover data through various mechanisms: snowfall characterizes the formation and accumulation of snow layers, significantly affecting snow thickness and density; wind speed affects snow redistribution and snow layer structure, influencing the spatial distribution and internal structure of the snow layer, and reflecting back to changes in snow density, thickness, and temperature. The meteorological data collected in this invention is not directly used for risk assessment, but rather to assist in determining the reasonableness of the collected snow cover data and improve the accuracy of avalanche risk assessment.
[0021] The driving shear stress is calculated using the following formula: In the formula, Snow density (kg / m³) 3 ); Expressing gravitational acceleration (m / s²) 2 ); h Indicates the thickness of the snow layer (m); Indicates the slope angle.
[0022] Step S2: The influence of ambient temperature on the snow layer is transformed into the process of snow layer temperature and moisture content change. A snow layer shear strength correction model related to snow layer temperature and moisture content is constructed, and the shear strength of the snow layer under the current snow layer temperature and moisture content is calculated.
[0023] Ambient temperature refers to the atmospheric temperature above the snow layer. It acts on the snow layer through heat exchange, altering its temperature. An increase in ambient temperature leads to a corresponding increase in snow layer temperature, and vice versa. Furthermore, changes in snow layer temperature affect its internal water content. As snow layer temperature rises, some snow melts, forming liquid water, increasing water content. Conversely, as snow layer temperature decreases, some liquefied water solidifies, decreasing water content. Therefore, snow layer temperature and water content are directly controlled by changes in ambient temperature. This invention uses snow layer temperature and water content as variables representing ambient temperature to quantify the effects of ambient temperature.
[0024] Snow layer temperature and moisture content reflect the driving effect of ambient temperature on the internal state of the snow layer. When the snow layer temperature and moisture content change, the physical properties of the snow layer (such as density and thickness) also change accordingly, resulting in changes in the shear strength of the snow layer. Therefore, this invention constructs a snow layer shear strength correction model related to snow layer temperature and moisture content to calculate the shear strength of the snow layer under different snow layer temperatures and moisture contents, thereby improving the accuracy of the calculation.
[0025] The construction process of the snow layer shear strength correction model includes the following steps: Step S21, Snow layer temperature correction: Starting from the baseline shear strength at the reference temperature, define a first function model characterizing the change of shear strength with temperature. The first function model is calibrated using the actual shear strength at different snow layer temperatures to obtain the temperature correction model.
[0026] The temperature correction model is used to describe the change in the shear strength of the snow layer with temperature. It can be selected as an exponential model or a linear model, wherein: When the exponential model is selected, it is represented as follows: In the formula, Indicates the base snow layer temperature The baseline shear strength; This indicates the actual snow layer temperature; Indicates the temperature of the snow layer Shear strength at the following levels; This represents the snow layer temperature correction coefficient, which is a coefficient to be calibrated and is used to characterize the degree of influence of snow layer temperature changes on the shear strength of the snow layer.
[0027] When a linear model is selected, it is represented as follows: In the formula, This represents the snow layer temperature correction factor.
[0028] The type of temperature correction model can be selected according to different engineering scenarios.
[0029] Step S22, Moisture content correction: Based on the temperature correction model, a moisture content term is introduced, and a second function model characterizing the change of shear strength with moisture content is defined. The second function model is calibrated with the actual shear strength under different moisture contents to obtain the moisture content correction model, which is the snow layer shear strength correction model.
[0030] When the temperature of the snow layer approaches or reaches the melting condition, some of the snow will melt, increasing the water content and weakening the shear strength. Therefore, the influence of water content needs to be considered, and the temperature-corrected model needs to be corrected again.
[0031] The moisture content correction model is expressed as follows: In the formula, Indicates snow layer temperature Moisture content The shear strength of the snow layer at that time; This represents the moisture content correction factor.
[0032] The coefficients of the exponential model Coefficients of the linear model and the coefficients of the shear strength correction model The model is fitted using conventional calibration methods in the field, such as linear regression or gradient descent. The final snow layer shear strength correction model is the aforementioned water content correction model, which has undergone snow layer temperature and water content correction, and simultaneously considers the influence of snow layer temperature and water content on snow layer shear strength, thereby improving the accuracy of avalanche risk assessment.
[0033] Step S3: Using the ratio of snow layer shear strength to driving shear stress as a safety factor, assess the likelihood of an avalanche disaster in the snow layer.
[0034] Safety factor Represented as: The evaluation process is represented as follows: Safety factor A value greater than 1 indicates a stable snow layer, making avalanches less likely; the safety factor is [missing information]. <1 indicates snow layer instability, making avalanches more likely; safety factor =1 indicates that the snow layer is in a critical state.
[0035] Under natural conditions, avalanches occur because the shear stress (sliding force) inside the snow exceeds its shear strength (cohesion). Using the ratio of the two as a safety factor, the magnitude of shear stress and shear strength can be intuitively judged, thereby assessing the possibility of avalanche disasters in the snow layer.
[0036] Example 1 This embodiment is applied to an avalanche-prone section of a national highway in a high-altitude, cold mountainous area. The slope above this section ranges from 32° to 38°, and slab avalanches occur frequently in winter, posing a serious threat to road safety. The method provided by this invention is used to conduct an avalanche disaster risk assessment.
[0037] Multiple snow layer monitoring points were set up on the target snow slope, and relevant parameters were obtained through manual measurement and sensor data acquisition. To ensure the accuracy of the measurement data, multiple measurements were taken at the same location, and the average value was calculated. The snow layer thickness was obtained as h = 1.94m, and the slope inclination angle was [not specified]. =35°, average snow density =280kg / m³. Temperature sensors are also embedded inside the snow layer to obtain the snow layer temperature T in real time.
[0038] Based on the obtained parameters, the driving shear stress is calculated according to the theoretical formula. : The shear strength of the snow layer was measured through on-site shear tests at a reference temperature of T0 = -10℃. =3.5kPa, real-time snow layer temperature is monitored as T=-2℃. The exponential temperature correction model provided in this invention is adopted: Wherein, β is obtained from historical data. The selected historical lowest temperature T1 and highest temperature T2 are -12℃ and -1℃, respectively, yielding corresponding shear strengths of 212.3 Pa and 162.5 Pa. Therefore, β can be determined as: The corrected shear strength of the snow layer is: Calculate the stability safety factor : because The value is less than 1, therefore the snow slope is in an unstable state and a warning signal needs to be issued.
[0039] Example 2 This embodiment is applied to the advanced ski slope area of a high-altitude ski resort. This area has a large slope and frequent personnel activities, so the real-time and accuracy requirements for snow slope stability analysis are high.
[0040] By combining drone mapping with manual measurement, the snow layer thickness h=0.9m and the slope angle were obtained. =34°, snow density =260kg / m 3 Meanwhile, temperature monitoring nodes were set up along the ski slope to obtain real-time data on changes in snow temperature.
[0041] Based on the real-time collected physical parameters of the snow layer, the driving shear stress is calculated. : Measured under low morning temperatures: =4.0 kPa, =−12℃ As daytime temperatures rise, the real-time temperature T increases to -1℃, and a linear temperature correction model is adopted: The shear strength of the snow layer is dynamically corrected. The values are calibrated based on historical data. Selecting temperatures of -20℃ and -5℃, the corresponding snow layer shear strengths are 315.6 Pa and 150.5 Pa, respectively, thus obtaining... The value is: Then the shear strength of the snow layer The FS value is calculated in real time, and its trend over time is recorded. The snow layer shear strength is calculated as the peak strength, and the maximum value of the stability coefficient is expressed as: because A value greater than 1 indicates that, under ideal conditions, an avalanche will not occur on the slope.
[0042] Example 3 This example is applied to a temporary snow-covered slope at a hydropower construction site in a high-altitude, cold mountainous area. During the spring snowmelt season, there is a significant risk of wet snow slippage. The snow layer thickness on the slope is h=1.5m, and the slope angle is... =30°, snow layer density =320kg / m 3 At the same time, temperature and moisture content sensors were deployed to monitor the snow layer temperature in real time. Snow layer liquid water content .
[0043] Calculate the driving shear stress according to the standard formula: Measured under reference conditions: =3.8kPa, =−8℃. When the snow layer temperature is monitored to be close to 0℃ and the water content is... When the temperature and moisture content continue to increase, a joint correction model based on temperature and moisture content is used: In the formula, The values were obtained through calibration using historical data. The lowest and highest temperatures were selected as -30°C and -1.5°C, respectively, corresponding to avalanche shear strengths of 350 Pa and 220 Pa, and the snow layer water content at -1.5°C was also considered. If it is 0.06, then The value is represented as: On-site monitoring revealed that when the snow layer temperature reached 0℃, the water content was 0.7, therefore the shear strength of the wet snow layer was... Calculate the stability coefficient based on the corrected shear strength: because =0.7 < 1, which is below the safety threshold. Therefore, the snow slope is in an unstable state and an early warning signal needs to be issued to suspend slope operations, clear loose snow, and strengthen on-site safety protection.
[0044] The embodiments of the present invention have been described above with reference to the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other modifications under the guidance of the present invention without departing from the spirit of the present invention, and all of these modifications are within the protection scope of the present invention.
Claims
1. A method for avalanche hazard risk assessment that considers the influence of ambient temperature, characterized in that, Includes the following steps: Step S1: Obtain topographic data, meteorological data, and snow cover data for the study area, and calculate the driving shear stress generated by the snow layer under the action of gravity. Step S2: The influence of ambient temperature on the snow layer is converted into the process of snow layer temperature and moisture content change. A snow layer shear strength correction model related to snow layer temperature and moisture content is constructed to calculate the shear strength of the snow layer under the current snow layer temperature and moisture content. Step S3: Using the ratio of snow layer shear strength to driving shear stress as a safety factor, assess the likelihood of an avalanche disaster in the snow layer.
2. The avalanche disaster risk assessment method considering the influence of ambient temperature according to claim 1, characterized in that, The terrain data includes slope angle; the meteorological data includes ambient temperature, snowfall, and wind speed; and the snow cover data includes snow thickness, snow density, and snow profile temperature.
3. The avalanche disaster risk assessment method considering the influence of ambient temperature according to claim 1, characterized in that, The driving shear stress is calculated using the following formula: In the formula, Snow density; Represents gravitational acceleration; Indicates the thickness of the snow layer; Indicates the slope angle.
4. The avalanche disaster risk assessment method considering the influence of ambient temperature according to claim 3, characterized in that, The construction process of the snow layer shear strength correction model includes the following steps: Step S21, Snow layer temperature correction: Starting from the reference shear strength at the reference temperature, define a first function model characterizing the change of shear strength with temperature. The first function model is calibrated with the actual shear strength at different snow layer temperatures to obtain the temperature correction model. Step S22, Moisture content correction: Based on the temperature correction model, a moisture content term is introduced, and a second function model characterizing the change of shear strength with moisture content is defined. The second function model is calibrated with the actual shear strength under different moisture contents to obtain the moisture content correction model, which is the snow layer shear strength correction model.
5. The avalanche disaster risk assessment method considering the influence of ambient temperature according to claim 4, characterized in that, The temperature correction model is either an exponential model or a linear model.
6. The avalanche disaster risk assessment method considering the influence of ambient temperature according to claim 5, characterized in that, The exponential model of the temperature correction model is expressed as follows: In the formula, Indicates the base snow layer temperature The baseline shear strength; This indicates the actual snow layer temperature; Indicates the temperature of the snow layer Shear strength at the following levels; This represents the snow layer temperature correction factor.
7. The avalanche disaster risk assessment method considering the influence of ambient temperature according to claim 5, characterized in that, The linear model of the temperature correction model is expressed as follows: In the formula, Indicates the base snow layer temperature The baseline shear strength; This indicates the actual snow layer temperature; Indicates the temperature of the snow layer Shear strength at the following levels; This represents the snow layer temperature correction factor.
8. The avalanche disaster risk assessment method considering the influence of ambient temperature according to claim 6 or 7, characterized in that, The shear strength correction model is expressed as follows: In the formula, Indicates snow layer temperature Moisture content Shear strength of snow layer at that time; This represents the moisture content correction factor.
9. The avalanche disaster risk assessment method considering the influence of ambient temperature according to claim 8, characterized in that, Safety factor Represented as: 。 10. The avalanche disaster risk assessment method considering the influence of ambient temperature according to claim 1, characterized in that, The evaluation process is represented as follows: Safety factor A value greater than 1 indicates a stable snow layer, making avalanches less likely; the safety factor is [missing information]. <1 indicates snow layer instability, making avalanches more likely; safety factor =1 indicates that the snow layer is in a critical state.