Home / Security / Avalanches, the most destructive natural hazards threatening human life, and built structures in mountainous regions, require accurate early warning and predicition systems

Avalanches, the most destructive natural hazards threatening human life, and built structures in mountainous regions, require accurate early warning and predicition systems

Snow avalanches are among the most destructive natural hazards threatening human life, ecosystems, built structures, and landscapes in mountainous regions.


Avalanches and landslides are common in Himalayan Kashmir. Avalanches have caused some of the heaviest tolls for the Indian and Pakistani armies camping in the region. Each winter in Colorado, avalanches pose a serious risk to residents, visitors, and travelers. Since 1950, they have killed more people in the state than any other natural hazard, and Colorado accounts for a third of all avalanche deaths in the United States.


The Indian army said  in Feb 2018, that three soldiers were killed when an avalanche struck their Himalayan post in the Indian portion of Kashmir. The army said the soldiers died in the Machhil sector near the Line of Control that divides Kashmir between India and Pakistan. Avalanche  is a rapid flow of snow down a mountainside. In 2016, at least 14 Indian soldiers were killed in two avalanches in the region. In 2012, a massive avalanche in the Pakistan-controlled part of Kashmir killed 140 people, including 129 soldiers.


In order to prevent damage and associated costs, early warning systems have proved highly beneficial. Early warning systems for natural hazards have seen tremendous development in recent years. Early warning systems enable preventative measures and damage reduction by providing early information on an imminent event.


In Switzerland the institute of snow and avalanche has created an early warning system for avalanches, it works by taking regular measurements of the weather surrounding the mountains, the air measurements and the temperature on the ground. They record the measurements every half hour and input the gathered data into a computer which highlights the possibilities of the snow melting and sends an early warning to the people in the area, warning them that the snow might melt if conditions get worse or if skiers cross certain areas. This warning system has been in place for around 13 years because there were many avalanches before designing this system.


Wireless sensor networks are also being used for the purpose of Avalanche monitoring. Sensor nodes can be deployed at avalanche prone sections of the avalanche prone sections of the mountain. This eliminates the need of manual monitoring of the region, thus eliminating human error as well as achieving real time monitoring.

What is snow avalanche?

An avalanche (also called a snowslide) is a rapid flow of snow down a sloping surface. Avalanches are typically triggered in a starting zone from a mechanical failure in the snowpack (slab avalanche) when the forces on the snow exceed its strength but sometimes only with gradually widening (loose snow avalanche).


An avalanche occurs when a relatively stronger layer of snow is on top of a relatively weaker layer on a slope generally of 30 degrees or more, and the layers begin breaking apart and start sliding downhill. The first thing typically needed for avalanche is formation of a crack. “A crack starts to form in the weak layer and that crack tends to be very small,” said Birkeland  director of the Forest Service National Avalanche Center in Bozeman, Montana and one of the leading researchers into the dynamics of snow on a slope. “It might only be on the order of centimeters or less.” If conditions are right, that crack can grow. Once it grows to a critical size, it begins to propagate on its own. “And it’ll grow really rapidly,” says Birkeland. The growth can cause the snow to shatter like pane of glass and begin flowing downhill. “Some of the recent advances are just being able to better understand how that whole process works.”


An avalanche occurs during or after heavy snowfall. After a heavy snowfall all that is necessary for an avalanche is a slope for it to slide down. The vast majority of avalanches (90 per cent) occur on slopes with angles between 30 and 45 degrees and J&K’s upper reaches are prone to such situations.


After initiation, avalanches usually accelerate rapidly and grow in mass and volume as they entrain more snow. Although primarily composed of flowing snow and air, large avalanches have the capability to entrain ice, rocks, trees, and other surficial material.


Avalanches can reach speeds of 80 mph within about 5 seconds. An avalanches can release 2,30,000 cubic meters (300,000 cubic yards) of snow. That is the equivalent of 20 football fields filled 3 meters deep with snow. Avalanche risk is at its greatest 24 hours following a snowfall of 12 inches or more.


Avalanche advisories are helpful, but tend to be general and cover a large area. The process involves cutting out a block of snow of certain dimensions and tapping it in a certain way to determine if the weak snow layer is weak enough to fracture.


Early warning systems

Early warning systems for natural hazards are classified into two categories: alarm and warning systems (Sättele et al., 2012). While warning systems detect precursors of a potential event and allow for early planning of according measures (days to weeks), alarm systems on the other hand detect the actual event itself with a short lead time (few seconds to minutes). Alarm system automatically trigger predefined measures if a certain threshold is exceeded.


Warning systems can detect avalanches which develop slowly, such as ice avalanches caused by icefalls from glaciers. Interferometric Radars, high-resolution Cameras, or motion sensors can monitor instable areas over a long term, lasting from days to years. Experts interpret the recorded data and are able to recognize upcoming ruptures in order to initiate appropriate measures. Such systems (e.g. the monitoring of the Weissmies glacier in Switzerland  can recognize events several days in advance.


Alarm systems: Radar station for avalanche monitoring in Zermatt

Modern radar technology enables the monitoring of large areas and the localization of avalanches at any weather condition, by day and by night. Complex alarm systems are able to detect avalanches within a short time in order to close (e.g. roads and rails) or evacuate (e.g. construction sites) endangered areas. An example of such a system is installed on the only access road of Zermatt in Switzerland. Two radars monitor the slope of a mountain above the road. The system automatically closes the road by activating several barriers and traffic lights within seconds such that no persons are harmed.


Chandigarh-based research organisation developing technology for real-time avalanche monitoring

The Chandigarh-based Snow and Avalanche Study Establishment (SASE) is developing the Wireless Sensor Technology (WST) for gathering information from remote locations that will ensure uninterrupted data streaming to base station on near realtime basis. Even today, SASE issues the avalanche warning, but with the WST providing real time updates, the SASE will be able to provide last minute alerts to soldiers manning such posts so that they can evacuate before an avalanche is triggered.


“The army posts are dictated by strategic and tactical requirements and the threat of an avalanche comes secondary. Sometimes these posts are located in high avalanche-prone areas. We have mapped the avalanche prone areas and keep updating the army about these locations, but in the absence of the real time predictions the danger of getting swept away in an avalanche persists for the soldiers posted in these locations,” said a SASE spokesperson, associated with the project.


Evaluation of snowpack stability over a particular slope for prediction of avalanche is a challenging and complicated task. “This project is a systematic effort in this direction and will contribute towards providing a state-of-the-art autonomous platform for acquiring in-situ snow-met parameters, avalanche occurrence information and improving upon the accuracy in predicting stability of snowpack in an avalanche prone mountain slope,” said the spokesperson.


Wireless Sensor Networks are low-power, multi-hopping systems that combine multiple wireless nodes into an extendable network environment with non-line-of-sight coverage and a self-healing data path that provide ubiquitous sensing of any environment in the monitoring of parameters that may lead to natural disasters.


An efficient wireless sensor network will have characteristics like multi-hop communication, self-configuring, self-healing, dynamic routing, ease of use, scalability and bi-directional communication, which make them state-of-the-art as compared to any other network.


When combined with battery power management, these characteristics allow sensor networks to be long-lived, easily deployable, and resilient to the unpredictable wireless channel. With WSN, the vision of pervasive and fine-grained sensing becomes reality particularly in remote or hazardous environments where many fundamental processes have never been studied in detail due to inaccessibility, informed the spokesperson.


Avalanche predicition

Scientists at the Colorado Avalanche Information Center (CAIC) analyze current weather conditions to predict when and where avalanches are most likely to occur, especially along highways. By assigning avalanche danger ratings and issuing warnings, the center helps reduce the chance that people will get injured or killed by avalanches.


The center also issues forecasts of the avalanche hazard in transportation corridors for the Colorado Department of Transportation (CDOT) to improve public safety on the state’s highways. Road crews mitigate avalanche risk by using explosives to release any looming packs of snow and then safely clear the debris off highways.


Esri partner Earth Analytic, Inc., developed SmartMountain, a set of web-based and mobile apps that allows users to gather, organize, and share data that is critical to keeping snowy environments safe. For about a year now, CAIC and CDOT have been collaboratively using a customized version of the solution to gain a better understanding of how new snow, wind, and persistently weak layers of old snow contribute to the occurrence of avalanches.


With geoprocessing tools, scientists at CAIC can identify the elevation and degree of all the slopes within the surrounding terrain. Once the system processes that data, it outputs feature layers that can be used to display and query the data. “Our Python machine learning algorithms dig into existing and newly collected field data,” explained Cooperstein. “We can add much more data about the topography of the path and weather conditions that affect each path’s own avalanche behaviors. Machine learning algorithms explore individual path data and calculate predictions.”

The point at which conditions cause an avalanche release is what forecasters call the threshold value. To determine these threshold values, they rely on historic data about each avalanche path and the conditions present when the avalanche occurred. CAIC scientists have written Python scripts to determine threshold values as XML. They then color-code certain avalanche paths that will reach threshold within 12, 24, and 36 hours. Finally, CAIC scientists generate risk-level visualizations and distribute them online to CDOT’s forecasters, who use them to see high-risk areas along Colorado’s highways so they can target their mitigation activities.


Despite advancements in the field, such as Birkeland and colleagues understanding of fracture and release dynamics, prediction remains tricky. “No one has yet developed a computer model that can accurately predict the complexities of an avalanche. Folks are getting closer and better,”says Brandon Scwartz, director of the Sierra Avalanche Center, which services the back country around Tahoe. “But it’s not like weather modeling or the stock market or so many other things that are very accurately computer-modeled at this point.”


A promising tool is the open source SNOWPACK model, out of Switzerland, where it is used in some forecasting.




About Rajesh Uppal

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