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Geosciences Column: Landslide risk in a changing climate, and what that means for Europe’s roads

Geosciences Column: Landslide risk in a changing climate, and what that means for Europe’s roads

If your morning commute is already frustrating, get ready to buckle up. Our climate is changing, and that may increasingly affect some of central Europe’s major roads and railways, according to new research published in the EGU’s open access journal Natural Hazards and Earth System Sciences. The study found that, in the face of climate change, landslide-inducing rainfall events will increase in frequency over the century, putting central Europe’s transport infrastructure more at risk.  

How do landslides affect us?

Landslides that block off transportation corridors present many direct and indirect issues. Not only can these disruptions cause injuries and heavy delays, but in broader terms, they can negatively affect a region’s economic wellbeing.

One study for instance, published in Procedia Engineering in 2016, examined the economic impact of four landslides on Scotland’s road network and estimated that the direct cost of the hazards was between £400,000 and £1,700,000. Furthermore the study concluded that the consequential cost of the landslides was around £180,000 to £1,400,000.

Such landslides can have a societal impact on European communities as well, as disruptions to road and railway networks can impact access to daily goods, community services, and healthcare, the authors of the EGU study explain.

Modelling climate risk

To analyse climate patterns and how they might affect hazard risk in central Europe, the researchers first ran a set of global climate models, simulations that predict how the climate system will respond to different greenhouse gas emission scenarios. Specifically, the scientists ran climate projections based on the Intergovernmental Panel on Climate Change’s A1B socio-economic pathway, a scenario defined by rapid economic growth, technological advances, reduced cultural and economic inequality, a population peak by 2050, and a balanced reliance on different energy sources.

They then determined how often the conditions in their climate projections would trigger landslide events specifically in central Europe using a climate index that estimates landslide potential from the duration and intensity of rainfall events. The index, established by Fausto Guzzetti of National Research Council of Italy and his colleagues, suggests that landslide activity most likely occurs when a rainfall event satisfies the following three conditions: the event lasts more than three days, total downpour is more than 37.3 mm and at least one day of the rainfall period experiences more than 25.6 mm.

The researchers also incorporated into their models data on central Europe’s road infrastructure as well as the region’s geology, including topography, sensitivity to erosion, soil properties and land cover.

Overview of a particularly risk-prone region along the lowlands of Alsace and the Black Forest mountain range: (a) location of the region in central Europe and median of the increase in landslide-triggering climate events for (b) the near future and (c) the remote future.

The fate of Europe’s roadways

The results of the researchers’ models suggest that the number of landslide-triggering rainfall events will increase from now up until 2100. Their simulations also find while that these hazardous rainfall events slightly increase in frequency between 2021 and 2050, the number of these occurrences will be more significant between 2050 and 2100.  

While the flat, low-altitude areas of central Europe will only experience minor increases in landslide-inducing rainfall activity, regions with high elevation, like uplands and Alpine forests, are most at risk, their findings suggest.

The study found that many locations along the north side of the Alps in France, Germany, Austria and the Czech Republic may face up to seven additional landslide-triggering rainfall events as our climate changes. This includes the Vosges, the Black Forest, the Swabian Jura, the Bergisches Land, the Jura Mountains, the Northern Limestone Alps foothills, the Bohemian Forest, and the Austrian and Bavarian Alpine forestlands.

The researchers go on to explain that much of the Trans-European Transport Networks’ main corridors will be more exposed to landslide-inducing rainfall activity, especially the Rhine-Danube, the Scandinavian-Mediterranean, the Rhine-Alpine, the North Sea-Mediterranean, and the North Sea-Baltic corridors.

The scientists involved with the study hope that their findings will help European policy makers make informed plans and strategies when developing and maintaining the continents’ infrastructure.  

Geosciences Column: Extreme snowfall potentially worsened Nepal’s 2015 earthquake-triggered avalanche

Geosciences Column: Extreme snowfall potentially worsened Nepal’s 2015 earthquake-triggered avalanche

Three years ago, an earthquake-induced avalanche and rockfalls buried an entire Nepalese village in ice, stone, and snow. Researchers now think the region’s heavy snowfall from the preceding winter may have intensified the avalanche’s disastrous effect.

The Langtang village, just 70 kilometres from Nepal’s capital Kathmandu, is nestled within a valley under the shadow of the Himalayas. The town was popular amongst trekking tourists, as the surrounding mountains offer breathtaking hiking opportunities.

But in April 2015, a 7.8-magnitude earthquake, also known as the Gorkha earthquake, triggered a massive avalanche and landslides, engulfing the village in debris.

Scientists estimate that the force of the avalanche was half as powerful the Hiroshima atomic bomb. The blast of air generated from the avalanche rushed through the site at more than 300 kilometres per hour, blowing down buildings and uprooting forests.

By the time the debris and wind had settled, only one village structure was left standing. The disaster claimed the lives of 350 people, with more than 100 bodies never located.

Before-and-after photographs of Nepal’s Langtang Valley showing the near-complete destruction of Langtang village. Photos from 2012 (pre-quake) and 2015 (post-quake) by David Breashears/GlacierWorks. Distributed via NASA Goddard on Flickr.

Since then, scientists have been trying to reconstruct the disaster’s timeline and determine what factors contributed to the village’s tragic demise.

Recently, researchers discovered that the region’s unusually heavy winter snowfall could have amplified the avalanche’s devastation. The research team, made up of scientists from Japan, Nepal, the Netherlands, Canada and the US, published their findings last year in the EGU’s open access journal Natural Hazards and Earth System Sciences.

To reach their conclusions, the team drew from various observational sources. For example, the researchers created three-dimensional models and orthomosaic maps, showing the region both before it was hit by the coseismic events and afterwards. The models and maps were pieced together using data collected before the earthquake and aerial images of the affected area taken by helicopter and drones in the months following the avalanche.

They also interviewed 20 villagers local to the Langtang valley, questioning each person on where he or she was during the earthquake and how much time had passed between the earthquake and the first avalanche event. In addition, the researchers asked the village residents to describe the ice, snow and rock that blanketed Langtang, including details on the colour, wetness, and surface condition of the debris.  

Based on their own visual ice cliff observations by the Langtang river and the villager interviews, the scientists believe that the earthquake-triggered avalanche hit Langtang first, followed then by multiple rockfalls, which were possibly triggered by the earthquake’s aftershocks.

A three-dimensional view of the Langtang mountain and village surveyed in this study. Image: K. Fujita et al.

According to the researchers’ models, the primary avalanche event unleashed 6,810,000 cubic metres of ice and snow onto the village and the surrounding area, a frozen flood about two and a half times greater in volume than the Egyptian Great Pyramid of Giza. The following rockfalls then contributed 840,000 cubic metres of debris.  

The researchers discovered that the avalanche was made up mostly of snow, and furthermore realized that there was an unusually large amount of snow. They estimated that the average snow depth of the avalanche’s mountainous source was about 1.82 metres, which was similar to snow depth found on a neighboring glacier (1.28-1.52 metres).

A deeper analysis of the area’s long-term meteorological data revealed that the winter snowfall preceding the avalanche was an extreme event, likely only to occur once every 100 to 500 years. This uncommonly massive amount of snow accumulated from four major snowfall events in mid-October, mid-December, early January and early March.

From these lines of evidence, the team concluded that the region’s anomalous snowfall may have worsened the earthquake’s destructive impact on the village.

The researchers believe their results could help improve future avalanche dynamics models. According to the study, they also plan to provide the Langtang community with a avalanche hazard map based on their research findings.  

Further reading

Qiu, J. When mountains collapse… Geolog (2016).

Roberts Artal, L. Geosciences Column: An international effort to understand the hazard risk posed by Nepal’s 2015 Gorkha earthquake. Geolog (2016).

Geosciences Column: The science behind snow farming

Geosciences Column: The science behind snow farming

For roughly the last decade, some ski resorts and other winter sport facilities have been using a pretty unusual method to ensure white slopes in winter. It’s called snow farming. The practice involves collecting natural or artificially made snow towards the end of winter, then storing the frozen mass in bulk over the summer under a thick layer of sawdust, woodchips, mulch, or other insulating material.

Many winter sport destinations have adopted the practice. In preparation for the 2014 Winter Olympics, Sochi, Russia stockpiled about 800,000 cubic metres of human-made snow during the warmer season, enough snow to fill 320 Olympic-size swimming pools.

Despite the growing trend, there still is little research on snow farming techniques. Recently, a team of scientists from the Institute for Snow and Avalanche Research (SLF) and the CRYOS Laboratory at the École Polytechnique Fédérale in Switzerland examined the success of snow conservation practices and used models to estimate what factors influence covered snow. Their findings were published in the EGU’s open access journal The Cryosphere.

Why store snow for the winter?

The ski industry has been storing snow for many reasons. The practice is a way for winter sports facilities to accommodate training athletes, start ski seasons earlier, and guarantee snow for major sports events. Snow farming can also be seen as a way to adapt to Earth’s changing climate, according to the authors of the study. Indeed, research published last year in The Cryosphere, found that the Alps may lose as much as 70 percent of snow cover by the end of the century if global warming continues unchecked. Snow loss to this degree could severely threaten the $70 billion dollar (57 billion EUR) industry and the alpine communities that depend on ski tourism.

For some ski resorts, the effects of climate change are already visible. For example, in Davos, Switzerland, a popular venue of the International Ski Federation Cross-Country World Cup, winter temperatures have risen over the last century while snow depth in turn has steadily declined.

Snow heap study

The research team studied two snow heaps: one near Davos, Switzerland (pictured here) and another in South Tyrol. Credit: Grünewald et al.

To better understand snow conservation techniques, the research team studied two artificially made snow heaps: one sitting near Davos and another located in South Tyrol. Each pile contained approximately 7,000 cubic metres of snow, about enough ice and powder to build 13,000 1.8-metre tall snowmen. The piles were also each covered with a 40 cm thick layer of sawdust and chipped wood.

Throughout the 2015 spring and summer season, the researchers measured changes in snow volume and density, as well as recorded the two sites’ meteorological data, including air temperature, humidity, wind speed and wind direction. The research team also fed this data to SNOWPACK, a model that simulates snow pile evolution and helps determine what environmental processes likely impacted the snow.

Cool under heat

From their observations, the researchers found that the sawdust and chipped wood layering conserved more than 75 percent of the Davos snow volume and about two thirds of the snow in South Tyrol. Given the high proportion of remaining snow, the researchers conclude that snow farming appears to be an effective tool for preparing for winter.

According to the SNOWPACK model, while sunlight was the biggest source of snow melt, most of this solar radiation was absorbed by the layer of sawdust and wood chips. The simulations suggest that the snow’s covering layer took in the sun’s heat during the day, then released this energy at night, creating a cooling effect on the snow underneath. Even more, the model found that, when the thick layer was moist, the evaporating water cooled the snow as well. The researchers estimate that only nine percent of the sun’s energy melted the snow heaps. Without the insulating layer, the snow would have melted far more rapidly, receiving 12 times as much energy from the sun if uncovered, according to the study.

Images of the South Tyrol snow heap from (a) 19 May and (b) 28 October. The snow depth (HS) is featured in c & d and snow height change (dHS) is shown in e. Credit: Grünewald et al.

The researchers found that the thickness of the covering layer was an important factor for snow conservation. When the team modelled potential snow melt under a 20 cm thick cover, the insulating and cooling effects from the layer had greatly diminished.

The simulations also revealed that, while higher air temperatures and wind speed increased snow melt, this effect was not very significant, suggesting that subalpine areas could also benefit from snow farming practices.

In the face of changing climates and disappearing snow, snow farming may be one solution for keeping winters white and skiers happy.

References

Grünewald, T., Wolfsperger, F., and Lehning, M.: Snow farming: conserving snow over the summer season, The Cryosphere, 12, 385-400, https://doi.org/10.5194/tc-12-385-2018, 2018.

Marty, C., Schlögl, S., Bavay, M., and Lehning, M.: How much can we save? Impact of different emission scenarios on future snow cover in the Alps, The Cryosphere, 11, 517-529, https://doi.org/10.5194/tc-11-517-2017, 2017.

 

 

Geosciences Column: How fast are Greenland’s glaciers melting into the sea?

Geosciences Column: How fast are Greenland’s glaciers melting into the sea?

The Greenland ice sheet is undergoing rapid change, and nowhere more so than at its margins, where large outlet glaciers reach sea level. Because these glaciers are fed by very large reservoirs of ice, they don’t just flow to the coast, but can extend many kilometres out into the ocean. Here, the ice – being lighter than water – will float, but remain connected to the ice on the mainland. This phenomenon is called an ice shelf or, if it is confined to a relatively narrow fjord, an ice tongue. Ice shelves currently exist in Antarctica as well as in high Arctic Canada and Greenland.

Ice shelves already float on the ocean so that their melting does not affect sea level, but they are a crucial part of a glacier’s architecture. The mass of an ice shelf, as well as any contact points with fjord walls, mean that it acts as a buttress for the rest of the glacier, slowing down its flow speed and stabilising it. When ice shelves melt, therefore, this can lead to the whole glacier system behind them flowing faster and thus delivering more land-based ice to the ocean.

Ice shelves lose mass as icebergs calve off at their seaward end, and through melting on their surface – but, unlike glaciers on land, they are with the ocean below. This ice-ocean interface is an important source of melting for a number of glaciers in northern Greenland; instead of the large volume of icebergs produced by many glaciers further south, the large ice tongues reaching into the ocean mean that a lot of ice is instead lost through submarine melting.

This ice-ocean interface is an environment that was, until recently, very difficult to accurately observe and study, and accordingly there is relatively little data on the impact of submarine melting on ice shelves. But the changes that take place here, at the ice-ocean interface, can have important implications for the entire glacier system, as well as for the ice sheet as a whole.

Over the last 30 years, a number of Arctic ice shelves and ice tongues have dramatically shrunk or disappeared entirely. In the Canadian Arctic, the Ellesmere ice shelf broke up into a number of smaller shelves over the course of the 20th century, most of which are continuing to shrink. In Greenland, meanwhile, the dramatic retreat of the Jakobshavn Glacier’s ice tongue during the 2000s has been particularly well documented.

The largest remaining ice tongues in Greenland are now all located in the far north of the island. But even here, at nearly 80°N and beyond, ice tongues are changing rapidly. Warming air temperatures probably play a role in this development, but submarine melting is thought to be the key driver of these rapid changes.

Submarine melting of ice tongues thus appears to be an important variable in ice-sheet dynamics. A new study in the EGU’s open access journal The Cryosphere has now used satellite imagery to produce a detailed map of submarine melt under the three largest ice tongues in northern Greenland. They are the ones belonging to Petermann and Ryder Glaciers in far northwestern Greenland and 79N Glacier – named after the latitude of its location – in the northeast of the island. Each of these ice shelves extends dozens of kilometres from where the glacier stops resting on bedrock and begins to float (the so-called grounding line) and is up to several hundred metres thick.

The locations of Petermann (PG), Ryder (RG) and 79N Glaciers in northern Greenland. From Wilson et al. (2017).

Previous attempts to estimate submarine melt rates relied on an assumption of steady state: that the ice shelf is becoming neither thicker nor thinner. Given the recent changes in all these ice shelves and the glaciers above them, this is not a tenable assumption in this case. Petermann and Ryder Glaciers, in particular, have recently experienced large calving events that were probably related to unusual melt patterns under the waterline.

Lead author Nat Wilson, a PhD student at MIT and Woods Hole Oceanographic Institution, and his colleagues used satellite images spanning four years to create a number of digital elevation models of the Petermann, Ryder and 79N ice shelves. A digital elevation model, or DEM, is a three-dimensional representation of a surface created – in this case – from satellite-based elevation data. By comparing DEMs from different points in time to each other, the team could deduct changes in the height – and therefore volume – of the ice shelves. This method also allowed them to track visible features of the glaciers between images from different years, providing estimates of how fast the ice was flowing down into the ocean.

However, using digital elevation models in a marine setting is not always a straightforward matter. Tides can affect the elevation of ice shelves by a significant amount, especially as the distance from the grounding line increases, and their effect needed to be accounted for in the results. Similarly, the team had to account for the changes on the surface of the ice shelf, where snowfall and melting can affect its volume.

What Wilson and his colleagues were left with was a map of melt rates across the ice shelves. In some respects, the findings were unsurprising. Melt rates were greatest near the coast, where the ice shelves were thickest, because at these points they would be in contact with the ocean at depths of several hundred metres. At such depths, fjords around Greenland often contain warm, dense water that flows in from the continental shelf and contributes to rapid ice melt. As the ice shelves thin towards their outer edges, they are in contact with shallower, colder water that doesn’t melt the ice as quickly.

Submarine melt rates at Greenland’s largest ice tongues are shown in colour shading; the arrows show the direction of ice flow. PG – Petermann Glacier; RG -Ryder Glacier. From Wilson et al. (2017).

All three ice shelves lost between 40-60m per year to submarine melting at their thickest points, while this decreased to about 10m per year in thinner sections. This equates to billions of tonnes of ice melting in contact with the ocean. Each of the ice shelves lost at least five times as much ice to melting underwater than to melting on the surface. This highlights what an important contribution submarine melting makes to the mass balance of Greenland’s ice shelves, and that this remote environment is deserving of our interest and study.

The team found that at Ryder Glacier’s ice shelf, mass loss from melting (from both above and below) is not significantly greater than the amount of ice entering the ice shelf from land: the ice shelf appears to be relatively stable for the time being. The situation is similar at Petermann Glacier, although its ice shelf has been in rapid retreat and lost some 250 km in the decade leading up to 2010. With the extra submarine melting from that area, melting would likely have exceeded incoming ice! It remains to be seen whether Petermann Glacier and its ice shelf will stabilise in their new configuration.

Finally, at 79N Glacier, the results indicate the ice shelf is losing mass faster than it is replenished from upstream. The ice tongue loses some 1.3% of its mass to melting each year – and that’s before iceberg calving is included in the equation. This finding is consistent with satellite imagery that suggests that the ice shelf at 79N has been thinning in recent decades.

This new study shows that there is considerable variability in submarine melting of ice shelves, both in space and in time. 79N glacier’s ice shelf – the biggest one remaining in Greenland – exhibited the highest mass deficit in this study, suggesting that we may see major changes in this glacier in future. With this type of melt making up for the bulk of mass loss of northern Greenland’s ice shelves, its accurate prediction plays an important role in understanding how these huge glaciers – and the whole ice sheet itself – will change in coming years.

By Jon Fuhrmann, freelance science writer

References

Wilson, N., Straneo, F., and Heimbach, P.: Satellite-derived submarine melt rates and mass balance (2011–2015) for Greenland’s largest remaining ice tongues, The Cryosphere, 11, 2773-2782, https://doi.org/10.5194/tc-11-2773-2017, 2017.

Hodgson, D. A. First synchronous retreat of ice shelves marks a new phase of polar deglaciation. Proc. Natl. Acad. Sci. U. S. A. 108, 18859-18860, doi:10.1073/pnas.1116515108 (2011).

Münchow, A., L. Padman, P. Washam, and K.W. Nicholls. 2016. The ice shelf of Petermann Gletscher, North Greenland, and its connection to the Arctic and Atlantic OceansOceanography 29(4):84–95, https://doi.org/10.5670/oceanog.2016.101.

Reeh N. (2017) Greenland Ice Shelves and Ice Tongues. In: Copland L., Mueller D. (eds) Arctic Ice Shelves and Ice Islands. Springer Polar Sciences. Springer, Dordrecht. https://doi.org/10.1007/978-94-024-1101-0_4

Truffer, M., and R. J. Motyka, Where glaciers meet water: Subaqueous melt and its relevance to glaciers in various settings, Rev. Geophys., 54, 220– 239. doi:10.1002/2015RG000494,  (2016)