Avalanche risk evaluation method

Abstract

To provide an avalanche risk evaluation method capable of accurately predicting occurrence of avalanche.SOLUTION: The avalanche risk evaluation method executes processing for determining that risk of occurrence of avalanche is high: when a value obtained by dividing snow depth value (HS) at a prescribed monitoring site on measurement date by an average value of annual maximum snow depth (HSmax) at the monitoring site is a first threshold (0.7) or more; and when a value (avalanche risk (D)) obtained by dividing an integrated value obtained by integrating two-day amount of melted snow and an amount of rainfall at the prescribed monitoring site on the measurement date and the day before by an integrated warmness value obtained by integrating daily mean temperatures of 0°C or higher after generation date of continuous snow cover until the measurement date is second threshold (0.7 mm / (°C day)) or more. The avalanche risk evaluation method can thus accurately predict the occurrence of avalanche.SELECTED DRAWING: Figure 5

Description

[Technical field]

[0001] The present invention relates to an avalanche danger assessment method for assessing the danger of an avalanche disaster and predicting the occurrence of an avalanche. [Background technology]

[0002] Conventionally, there are known methods for predicting and monitoring the occurrence of avalanches using a glide meter, a vibration sensor, and the like (see, for example, Patent Document 1).

[0003] On the other hand, since the locations where avalanches are likely to occur are on slopes, it can be difficult to install glide meters or vibration sensors there. Also, it is practically impossible to install the above-mentioned sensors on every avalanche-prone slope along railway lines, which have long linear extensions. Therefore, there is a demand for technology that can predict and monitor the occurrence of avalanches without using glide meters, vibration sensors, or the like (see, for example, Non-Patent Document 1). This technology quantitatively assesses the risk of avalanches based on a two-stage assessment of snow depth and the amount of snowmelt and precipitation, and predicts the occurrence of avalanches. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] JP 2014-95990 A [Non-patent literature]

[0005] [Non-Patent Document 1] Yasushi Kurihara, Shigehiro Iikura, Megumi Kamata, Shinya Shishido, Daisuke Takahashi, Katsuhisa Kawashima "Avalanche prevention method based on stability index of snow slope area" Railway Technical Research Institute Report Vol.25, No.7, Jul.2011, p19-24 Summary of the Invention [Problem to be solved by the invention]

[0006] However, in the case of the technology of Non-Patent Document 1, avalanche warnings are often issued even when the risk of an avalanche is not high, which leads to unnecessary frequent security. Avalanche security requires the arrangement of security personnel, and there are circumstances specific to railways, such as long distances and travel times to avalanche monitoring locations, so it is desirable to reduce the burden on security by accurately assessing the risk of avalanche occurrence.

[0007] An object of the present invention is to provide an avalanche danger assessment method capable of accurately assessing the danger of avalanche occurrence and predicting the occurrence of an avalanche. [Means for solving the problem]

[0008] In order to achieve the above object, the present invention provides: 1. A method for assessing avalanche danger to predict the occurrence of an avalanche at a predetermined monitoring point, comprising: The risk of an avalanche is determined to be high if the value obtained by dividing the snow depth on the measurement date at the monitoring point by the average annual maximum snow depth at that monitoring point is equal to or greater than a first threshold value, and if the value obtained by dividing the accumulated amount of snowmelt and rainfall for the two days of the measurement date and the previous day at the monitoring point by the accumulated temperature value obtained by accumulating the daily average temperatures of 0°C or higher from the date of the formation of snow cover up to the measurement date is equal to or greater than a second threshold value. The "average annual maximum snow depth" referred to here may also be the "average annual maximum snow depth value maintained by the Japan Meteorological Agency."

[0009] According to the avalanche danger assessment method configured as above, it is possible to predict the occurrence of an avalanche more accurately than the avalanche predictions of the prior art.

[0010] Also preferably, The first threshold is set to 0.7.

[0011] The inventors have found that when the snow depth value (HS) on the measurement day at a specified monitoring point divided by the average annual maximum snow depth (HSmax) is equal to or greater than a first threshold value of 0.7, there is a possibility of an avalanche occurring, and therefore the first threshold value is set to 0.7. It was also found that the possibility of an avalanche occurring was low when the snow depth on the measurement day (HS) divided by the average annual maximum snow depth (HSmax) was less than 0.7.

[0012] Also preferably, The second threshold value is set to 0.7 mm / (°C·day).

[0013] The inventors have found that when the value (avalanche risk (D)) obtained by dividing the accumulated amount of snowmelt and rainfall for the two days of the measurement date and the previous day at a designated monitoring point by the accumulated warmth value obtained by accumulating the daily average temperatures of 0°C or higher from the day when the snow cover first formed up to the day of the measurement is equal to or greater than the second threshold value of 0.7 mm / (°C·day), there is a possibility of an avalanche occurring, and therefore the second threshold value was set to 0.7 mm / (°C·day). Effect of the Invention

[0014] According to the present invention, the occurrence of an avalanche can be predicted with high accuracy. [Brief description of the drawings]

[0015] [Figure 1] 1 is a conceptual diagram showing an avalanche danger assessment system according to an embodiment of the present invention. [Diagram 2] FIG. 2 is an explanatory diagram regarding the avalanche danger (D) used as an evaluation index in the avalanche danger evaluation method of the present embodiment. [Diagram 3] FIG. 1 is an explanatory diagram showing daily changes in snow depth and avalanche danger (D) at a given monitoring point. [Figure 4] FIG. 1 is an explanatory diagram showing (a) the number of days predicted for an avalanche according to the avalanche danger assessment method of the present embodiment and (b) the number of days predicted for an avalanche according to the prior art. [Diagram 5] 1 is a flowchart illustrating an avalanche danger assessment method according to the present embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0016] Hereinafter, an embodiment of the avalanche risk assessment method according to the present invention will be described in detail with reference to the drawings. However, the embodiment described below is subject to various technically preferable limitations for carrying out the present invention, but the scope of the present invention is not limited to the following embodiment and illustrated examples.

[0017] The avalanche risk assessment system 10 of this embodiment is configured, for example, as shown in Figure 1, to include a snow depth measurement device 1, a snowmelt amount measurement device 2, a rainfall amount measurement device 3, an accumulated warmth measurement device 4, and an avalanche prediction device 5. In this embodiment, an example will be described in which railway workers use the avalanche risk assessment system 10 to monitor avalanches that may affect railway facilities, and predict the occurrence of an avalanche at a specified monitoring point. In particular, the avalanche danger assessment method of this embodiment is a technology developed for predicting the occurrence of a full-depth avalanche.

[0018] The snow depth measuring device 1, the snow melting amount measuring device 2, and the rainfall amount measuring device 3 are installed in an area near a predetermined monitoring point, at a point where they can observe almost the same meteorological data as the monitoring point. Specifically, the snow depth measuring device 1, snow melting amount measuring device 2, and rainfall amount measuring device 3 are installed at locations where no harm will come even if an avalanche occurs at a designated monitoring point, and where almost the same weather data as the monitoring point can be observed. In other words, the snow depth measuring device 1, snow melting amount measuring device 2, and rainfall amount measuring device 3 are configured to observe meteorological data at the locations where they are installed that can be regarded as being substantially the same as that at a specified monitoring location, and measure the data of the respective measurement targets. The accumulated heat measuring device 4 and the avalanche prediction device 5 are installed in a control room of a railway line or the like.

[0019] The snow depth measuring device 1 is, for example, a laser type measuring device, and has a function of measuring snow depth by detecting the reflection of laser light on the snow surface. The snow depth data measured by the snow depth measuring device 1 is transmitted to an avalanche prediction device 5.

[0020] The snowmelt amount measuring device 2 is, for example, a device that performs measurements based on the lysimeter method, and has a function of measuring the amount of melted snow by measuring the amount of melted snow water flowing out from the bottom of the snowpack. Data on the amount of melted snow measured by the snowmelt amount measuring device 2 is transmitted to an avalanche prediction device 5. The amount of snowmelt can also be measured using meteorological observation data for the area. Specifically, it can be calculated by using a model for estimating the amount of snowmelt that uses only temperature and global solar radiation, such as the Temperature-Radiation Index Model shown in the following non-patent document: (Konya, K., Matsumoto, T. and Naruse, R.,: Surface heat balance and spatially distributed ablation modelling at Koryto Glacier, Kamchatka Peninsula, Rusia, Geografiska Annaler, 86A, 337-348.)

[0021] The rainfall measuring device 3 is, for example, a tipping bucket type rainfall measuring device, which has the function of tipping over when rainwater poured into the tipping bucket placed inside reaches a precipitation level of 0.5 mm, and measuring the amount of precipitation by the number of times it tips over. The rainfall data measured by the rainfall measuring device 3 is transmitted to the avalanche prediction device 5. Rain gauges that are widely and commonly used in cold regions usually have a built-in heater and are designed to measure the amount of precipitation after melting the snow. Here, we want to measure the amount of rain that gradually seeps into the snow layer, but since rain gauges with built-in heaters are designed to melt snow with heat, even if the amount of precipitation is recorded, it may not be possible to determine whether it is rain or snow. Therefore, it is necessary to provide a rainfall determination unit to determine the amount of precipitation that is rain only. Specifically, by determining whether the temperature is within a specified range (for example, 1.5°C or higher), it becomes possible to determine that the precipitation is rain and not snow. Therefore, it is preferable to measure the amount of precipitation that is rain only with a rainfall determination unit.

[0022] The accumulated warmth measuring device 4 is an accumulated warmth calculating device that executes a process of calculating an accumulated warmth by accumulating daily average temperatures of 0° C. or higher from, for example, the beginning of snow cover (the day snow starts to accumulate) up to the day of measurement. The integrated warmth measuring device 4 has a function of measuring the temperature for calculating the daily average temperature. Also, meteorological data on the daily average temperature is inputted through a predetermined communication line. The data of the accumulated temperature measured by the accumulated temperature measuring device 4 (data of the accumulated temperature calculated by the accumulated temperature calculating device) is transmitted to the avalanche prediction device 5. In addition, the daily average temperature can also be obtained by using weather data from the AMeDAS system located at or near the monitoring point.

[0023] The avalanche prediction device 5 is, for example, a computer operated by a railway employee and installed in a control room of the railway line. This avalanche prediction device 5 comprehensively controls the snow depth measurement device 1, snowmelt amount measurement device 2, rainfall measurement device 3, and accumulated warmth measurement device 4, acquires the data transmitted from each device, and performs a process of predicting the occurrence of an avalanche at a designated monitoring point. Specifically, the avalanche prediction device 5 executes a process to determine that there is a high risk of an avalanche if the value obtained by dividing the snow depth on the measurement date at a specified monitoring point by the average annual maximum snow depth at that monitoring point is equal to or greater than a first threshold value, and the value obtained by dividing the accumulated amount of snowmelt and rainfall for the two days of the measurement date and the previous day at the specified monitoring point by the accumulated temperature value obtained by accumulating the daily average temperatures of 0°C or higher from the date when the snow cover first formed up to the measurement date is equal to or greater than a second threshold value.

[0024] Here, the avalanche danger level (D) used by the present inventors to predict the occurrence of an avalanche will be described.

[0025] The inventors focused on the amount of melting snow as an indicator for predicting the occurrence of avalanches, especially full-depth avalanches, and also on the amount of rainfall, which is a factor in promoting the glide of snow. This is because full-depth avalanches occur when the frictional resistance of the ground surface is reduced by the infiltration of melting snow and rainwater under a heavy snow load. On the other hand, while the aforementioned amounts of meltwater and rainwater reflect the weather conditions at the time, the alteration and mechanical properties of the snow accumulated at the monitoring points vary from year to year, even on the same calendar day, because the temperature difference varies from winter to winter. In order to prevent the annual differences in the alteration and mechanical properties of the snow at the monitoring points from having a significant effect on the avalanche risk assessment, we also focused on the accumulated warmth after the snow cover date. The term "accumulated temperature" in this embodiment refers to the accumulated value of daily average temperatures above 0°C, and is calculated by accumulating daily average temperatures above 0°C from the day snow began to accumulate until the day of measurement.

[0026] Furthermore, the present inventors naturally pay attention to snow depth as an index for predicting the occurrence of an avalanche. It is well known that the deeper the snow is, the greater the driving force of the snow, making avalanches more likely to occur. However, the inventors focused on the average annual maximum snow depth as well as the actual snow depth, and investigated how this could be used as an indicator to predict the occurrence of avalanches. The average value of the annual maximum snow depth may be, for example, the average value of the annual maximum snow depth prepared by the Japan Meteorological Agency.

[0027] The inventors then set up a test site in a heavy snowfall area and observed the occurrence of avalanches while measuring snow depth (cm), snowmelt amount (mm), precipitation amount (mm), and accumulated warmth (℃·day) at the test site.

[0028] The present inventors set the condition of the following formula (1) as an index for predicting the occurrence of an avalanche based on snow depth (HS) and the average annual maximum snow depth (HSmax). Snow depth (HS) / average annual maximum snow depth (HSmax) ≥ 0.7 (1) The constant 0.7 in this formula (1) is a value that the inventors obtained empirically in the process of observing and predicting the occurrence of avalanches. The inventors have discovered that when formula (1) is satisfied (when the value obtained by dividing the snow depth on the measurement day (HS) by the average annual maximum snow depth (HSmax) is 0.7 or greater), there is a high possibility of an avalanche occurring. In addition, it was found that the possibility of an avalanche occurring is low when snow depth (HS) is less than 70% of the average annual maximum snow depth (snow depth (HS) < average annual maximum snow depth (HSmax) × 0.7; formula (1) is not satisfied). The first condition for predicting the occurrence of an avalanche was that the snow depth on the day of measurement (HS) divided by the average annual maximum snow depth (HSmax) be 0.7 or greater.

[0029] The inventors also decided to set an avalanche danger level (D) as a new index for predicting the occurrence of an avalanche based on the amount of melting snow, the amount of precipitation, and the accumulated warmth. This avalanche danger level (D) was defined as the cumulative amount of snowmelt and rainfall at a designated monitoring point for the two days of the measurement date and the previous day, divided by the cumulative warmth from the day the snow cover began to cover the entire area until the measurement date (see formula (2) below). Avalanche danger level (D; mm / (℃·day)) = 2-day cumulative snowmelt and rainfall amount (mm) / cumulative temperature from the day of snow cover to the day of snow cover (℃·day) (2)

[0030] Figure 2 shows the accumulated snowmelt and rainfall amounts over the two days leading up to the avalanche, based on measurements of snow depth, snowmelt amount, rainfall amount, and accumulated warmth, as well as the trends in accumulated warmth after the day the snow became permanent. In Figure 2, observation days on which the value of the snow depth on the measurement day (HS) divided by the average annual maximum snow depth (HSmax) is 0.7 or greater (satisfying formula (1)) are indicated with a black circle (●). In addition, in Figure 2, observation days on which the value of the snow depth on the measurement day (HS) divided by the average annual maximum snow depth (HSmax) is less than 0.7 (observation days that do not satisfy equation (1)) are shown with a white circle (○).

[0031] In addition, in Figure 2, the dashed line indicates a baseline where the value obtained by dividing the two-day accumulated value of snowmelt and rainfall by the accumulated warmth value after the snow cover date is 0.7 (a baseline with a slope of 0.7 mm / (℃·day)). This reference line indicates the line where the avalanche danger level (D) expressed by equation (2) is 0.7 mm / (℃·day). In the observations conducted to measure the data shown in Figure 2, an avalanche was confirmed to have occurred on March 12th. As a result of continuing to measure such data and observe the occurrence of avalanches, the inventors discovered that avalanches are likely to occur when the avalanche danger level (D) is 0.7 mm / (℃·day) or higher. The second condition for predicting the occurrence of an avalanche was that the avalanche danger level (D) be 0.7 mm / (℃·day) or more. This second condition corresponds to satisfying the following formula (3). Avalanche danger level (D) ≥ 0.7 mm / (℃·day) ··· (3)

[0032] Next, FIG. 3 shows the results of measurements of snow depth, snowmelt amount, precipitation amount, and accumulated warmth, based on the first and second conditions for predicting the occurrence of an avalanche set by the inventors. This data was collected at a location near a railway facility, which serves as a monitoring point to predict future avalanches.

[0033] As shown in Figure 3, when snow depth (HS) is less than 70% of the average annual maximum snow depth (snow depth (HS) < average annual maximum snow depth (HSmax) × 0.7), the first condition is not met, and the risk of avalanche occurrence is predicted to be zero and plotted on the graph.

[0034] Then, the avalanche danger level (D) for the period from around mid-January to around late March, when the first condition is met, was calculated and plotted on a graph. In particular, for days when avalanches were observed during the data measurement process, the avalanche conditions were classified and plotted. As shown in Figure 3, the data is plotted here according to the days when avalanches flowed onto the railroad tracks, the days when avalanches flowed onto roads near the railroad tracks, and the days when avalanches occurred but did not reach the railroad tracks. Avalanches occurred on 11 days in total, with snow flowing onto the railroad tracks on two days, on roads on two days, and not reaching the railroad tracks on seven days.

[0035] As shown in Figure 3, of the 11 days on which avalanches occurred, there were 10 days that met the second condition, that is, the avalanche danger level (D) was 0.7 mm / (℃·day) or higher, and of these, there were 4 days when the avalanche danger level (D) was 1.1 mm / (℃·day) or higher. As a result of continuing to measure such data and observe the occurrence of avalanches, the inventors have confirmed that it is appropriate to predict the occurrence of an avalanche when the avalanche danger level (D) is 0.7 mm / (℃·day) or higher, since there is a high possibility of an avalanche occurring.

[0036] For example, of the 151 days during the five months from December to April when the data shown in Figure 4(a) was collected, there were 27 days that met both the first and second conditions, and these 27 days correspond to the days when avalanches were predicted to have a high probability of occurring. Of the 27 days when both the first and second conditions were met, an avalanche actually occurred on 10 days. However, of the 123 days when neither the first nor second conditions were met, there were 122 days when an avalanche did not actually occur. The accuracy rate of predicting whether an avalanche would occur or not was 88%.

[0037] In contrast, when predicting the occurrence of avalanches using the conventional technology of Non-Patent Document 1 mentioned above, as shown in FIG. 4(b), avalanches were predicted to occur on 85 days out of 151 days in the five months from December to April. Of these, avalanches actually occurred on 11 days. On the other hand, there were 66 days when avalanches were predicted not to occur and did not actually occur. The accuracy rate of avalanche occurrence or non-occurrence was 51%.

[0038] As described above, when predicting whether or not an avalanche will occur using conventional technology, the accuracy rate of the avalanche prediction of this embodiment, which satisfies both the first and second conditions, is only about 50%, which shows that the accuracy of predicting whether or not an avalanche will occur using conventional technology is low.

[0039] Through this kind of verification, the inventors have come to propose an avalanche risk assessment method which determines that there is a high risk of an avalanche occurring when the value obtained by dividing the snow depth on the measurement date at a designated monitoring point by the average annual maximum snow depth at that monitoring point is equal to or greater than a first threshold value (first condition met), and the value obtained by dividing the accumulated amount of snowmelt and rainfall over the two days of the measurement date and the previous day at a designated monitoring point by the accumulated warmth value obtained by accumulating the average daily temperatures of 0°C or higher since the date on which permanent snow formed up to the measurement date is equal to or greater than a second threshold value (second condition met). The first threshold in this avalanche danger assessment method is 0.7, and the second threshold is 0.7 mm / (℃·day).

[0040] Next, the avalanche danger assessment method according to this embodiment will be described with reference to the flowchart shown in FIG.

[0041] First, snow depth, amount of melted snow, amount of precipitation, and accumulated warmth are measured at a specified monitoring point (a point in an area near the specified monitoring point where weather data that can be regarded as the same as that of the monitoring point can be observed) (step S1). The various measured data are input to the avalanche prediction device 5 by being transmitted thereto, for example.

[0042] Next, as a judgment process based on snow depth, it is judged whether the value obtained by dividing the snow depth value (HS) on the measurement day by the average annual maximum snow depth (HSmax) is equal to or greater than the first threshold value of 0.7 (step S2). This judgment process is performed by the avalanche prediction device 5 by determining whether the above formula (1) is satisfied. If it is determined that the above formula (1) is not satisfied (step S2; NO), the process returns to step S1. If it is determined that the above formula (1) is satisfied (step S2; YES), the process proceeds to step S3.

[0043] Next, in step S3, a judgment process based on the avalanche danger level (D) is performed to judge whether the avalanche danger level (D) is equal to or greater than a second threshold value of 0.7 mm / (℃·day) (step S3). This judgment process is performed by the avalanche prediction device 5 by determining whether the above-mentioned formula (3) is satisfied. If it is determined that the above formula (3) is not satisfied (step S3; NO), the process returns to step S1. If it is determined that the above formula (3) is satisfied (step S3; YES), the process proceeds to step S4.

[0044] Next, in step S4, a message is displayed on the monitor screen of the avalanche prediction device 5 indicating that an avalanche is predicted to occur at a specified monitoring point, thereby informing railway workers of the risk of an avalanche (step S4). Specifically, when equation (1) relating to the first condition is satisfied (step S2; YES) and equation (3) relating to the second condition is satisfied (step S3; YES), it is determined that there is a high risk of an avalanche occurring at a specified monitoring point, and in step S4, railway employees are notified of the avalanche risk.

[0045] A notice regarding the risk of this avalanche is given (step S4), and the process regarding the avalanche prediction is terminated. When a warning is issued regarding the risk of an avalanche, railway employees will patrol designated monitoring locations to determine whether or not there is a risk of an avalanche, and will then consider, for example, whether or not to change the train operation schedule.

[0046] In this way, the avalanche risk assessment method of this embodiment can measure snow depth, amount of melted snow, amount of precipitation, and accumulated warmth at specified monitoring points, and perform a judgment process based on this measurement data to predict the occurrence of an avalanche. Specifically, when the conditions discovered by the inventors satisfy both equation (1) relating to the first condition and equation (3) relating to the second condition, it is possible to determine that there is a high risk of an avalanche occurring at a specified monitoring point, and to predict the occurrence of an avalanche at that monitoring point. The avalanche risk assessment method of this embodiment, which satisfies equation (1) relating to the first condition and equation (3) relating to the second condition, can predict the occurrence of an avalanche more accurately than, for example, the conventional technology of Non-Patent Document 1 mentioned above. Furthermore, if avalanches can be predicted with high accuracy, it will be possible to reduce the number of surveillance patrols that end up being unsuccessful, thereby reducing the need for unnecessary security personnel and the time required for security.

[0047] In particular, to measure snow depth, amount of snowmelt, amount of rainfall, and accumulated warmth at a designated monitoring point, the necessary weather data can be measured by installing measuring equipment not at the designated monitoring point, but at a point in the area near the designated monitoring point where weather data that can be regarded as the same as that at the monitoring point can be observed, so that even if an avalanche occurs at the designated monitoring point, there will be no damage to measuring equipment, etc. Furthermore, the snow depth, amount of snowmelt, amount of rainfall, and accumulated temperature at a designated monitoring point can be measured with approximately the same meteorological data if the monitoring point is not very far geographically from the designated monitoring point and is in an area nearby the designated monitoring point with approximately the same topography, sunlight, etc. Therefore, rather than railway employees having to install new measuring equipment, it is possible to implement the avalanche risk assessment method of this embodiment using meteorological data released by the Japan Meteorological Agency or the like for areas near the designated monitoring point.

[0048] As described above, the avalanche risk assessment method of this embodiment can accurately predict the occurrence of an avalanche if the value obtained by dividing the snow depth value (HS) on the measurement date at a specified monitoring point by the average annual maximum snow depth at that monitoring point (HSmax) is equal to or greater than a first threshold value (0.7), and the value obtained by dividing the accumulated amount of snowmelt and rainfall for the two days on the measurement date and the previous day at the specified monitoring point by the accumulated warmth value obtained by accumulating the daily average temperatures of 0°C or higher since the date on which snow cover formed up to the measurement date (avalanche risk (D)) is equal to or greater than a second threshold value (0.7mm / (°C·day)).

[0049] In the above embodiment, the snow depth measuring device 1, snow melting amount measuring device 2, rainfall measuring device 3, and accumulated warmth measuring device 4 are described as examples in which they transmit measurement data to the avalanche prediction device 5 via communication, but the present invention is not limited to this. For example, a railway employee may operate the avalanche prediction device 5 to input data measured by each device.

[0050] In the above embodiment, the first threshold value is set to 0.7 and the second threshold value is set to 0.7 mm / (°C·day) as an example. However, the present invention is not limited to this. For example, the first threshold value and the second threshold value may be set through further refinement to enable more accurate avalanche prediction.

[0051] In addition, other specific details of the structure can of course be modified as appropriate. [Explanation of symbols]

[0052] 1. Snow depth measuring device 2. Snowmelt amount measuring device 3. Rainfall measuring device 4. Accumulative temperature measuring device 5. Avalanche prediction device 10 Avalanche danger assessment system

Claims

1. 1. A method for assessing avalanche danger to predict the occurrence of an avalanche at a predetermined monitoring point, comprising: This method for evaluating the risk of an avalanche determines that there is a high risk of an avalanche if the value obtained by dividing the snow depth on the measurement date at the monitoring point by the average annual maximum snow depth at that monitoring point is equal to or greater than a first threshold value, and if the value obtained by dividing the accumulated amount of snowmelt and rainfall for the two days of the measurement date and the previous day at the monitoring point by the accumulated warmth value obtained by accumulating the average daily temperatures of 0°C or higher since the date on which snow cover formed up to the measurement date is equal to or greater than a second threshold value.

2. 2. The method for assessing avalanche danger according to claim 1, wherein the first threshold value is 0.

7.

3. 3. The method for assessing avalanche danger according to claim 1, wherein the second threshold value is 0.7 mm / (° C.·day).

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