Natural Hazards

Imaggeo on Mondays: Sediments make the colour

Imaggeo on Mondays: Sediments make the colour

Earth is spectacularly beautiful, especially when seen from a bird’s eye view. This image, of a sweeping pattern made by a river in Iceland is testimony to it.

The picture shows river Leirá which drains sediment-loaded glacial water from the Myrdalsjökull glacier in Iceland. Myrdalsjökull glacier covers Katla, one of Iceland’s most active and ice-covered volcanoes.

A high sediment load (the suspended particles which are transported in river water) is typical for these glacial rivers and is visible as the fast-flowing glacial river (on the right of this image) appears light brown in colour. The sediment is gradually lost in the labyrinth of small lakes and narrow, crooked connections between lakes as can be seen as a gradual change in colour to dark blue.

The sediment load, height of the water  and chemistry of this and other glacial rivers are measured partly in real-time by the Icelandic Meteorological Office. This is done for research purposes and in order to detect floods from subglacial lakes that travel up to several tens of kilometers beneath the glacier before they reach a glacial river.

These glacial outburst floods do not only threaten people, livestock and property, but also infrastructure such as Route 1, a circular, national road which runs around the island. They occur regularly due to volcanic activity or localized geothermal melting on the volcano, creating a need for an effective early-warning system.

Advances in the last years include the usage of GPS instruments on top of a subglacial lake and the flood path in order to increase the early-warning for these floods. In 2015, the GPS network, gave scientists on duty at the Icelandic Meteorological Office 3.5 days of warning before one of the largest floods from western Vatnajökull emerged from beneath the ice.

The peak discharge exceeded 2000 m3/s,  which is comparable to an increase in discharge from that of the Thames to that of the Rhine.  This flood was also pioneeringly monitored with clusters of seismometers, so called arrays (from University College Dublin & Dublin Institute for Advanced Studies, Ireland), that enabled an early-warning of at least 20 hours and allowed to track the flood front merely using the ground vibrations it excited. The flood propagated under the glacier at a speed of around 2 km/h; so assuming you can keep up the speed over nearly a day you can escape the flood by walking while it is moving beneath the glacier.

Related publications about the tracking of these subglacial floods will emerge in the published literature soon (real time update available at

By Eva Eibl, researcher at the Dublin Institute for Advanced Studies.

Thanks go to who organised this trip.

Imaggeo is the EGU’s online open access geosciences image repository. All geoscientists (and others) can submit their photographs and videos to this repository and, since it is open access, these images can be used for free by scientists for their presentations or publications, by educators and the general public, and some images can even be used freely for commercial purposes. Photographers also retain full rights of use, as Imaggeo images are licensed and distributed by the EGU under a Creative Commons licence. Submit your photos at


GeoSciences Column: Is smoke on your mind? Using social media to assess smoke exposure from wildfires

GeoSciences Column: Is smoke on your mind? Using social media to assess smoke exposure from wildfires

Wildfires have been raging across the globe this summer. Six U.S. States, including California and Nevada, are currently battling fierce flames spurred on by high temperatures and dry conditions. Up to 10,000 people have been evacuated in Canada, where wildfires have swept through British Columbia. Closer to home, 700 tourists were rescued by boat from fires in Sicily, while last month, over 60 people lost their lives in one of the worst forest fires in Portugal’s history.

The impacts of this natural hazard are far reaching: destruction of pristine landscapes, costly infrastructure damage and threat to human life, to name but a few. Perhaps less talked about, but no less serious, are the negative effects exposure to wildfire smoke can have on human health.

Using social media posts which mention smoke, haze and air quality on Facebook, a team of researchers have assessed human exposure to smoke from wildfires during the summer of 2015 in the western US. The findings, published recently in the EGU’s open access journal Atmospheric Chemistry and Physics, are particularly useful in areas where direct ground measurements of particulate matter (solid and liquid particles suspended in air, like ash, for example) aren’t available.

Particulate matter, or PM as it is also known, contributes significantly to air quality – or lack thereof, to be more precise.  In the U.S, the Environment Protection Agency has set quality standards which limit the concentrations of pollutants in air; forcing industry to reduce harmful emissions.

However, controlling the concentrations of PM in air is much harder because it is often produced by natural means, such as wildfires and prescribed burns (as well as agricultural burns). A 2011 inventory found that up to 20% of PM emissions in the U.S. could be attributed to wildfires alone.

Research assumes that all PM (natural and man-made) affects human health equally. The question of how detrimental smoke from wildfires is to human health is, therefore, a difficult one to answer.

To shed some light on the problem, researchers first need to establish who has been exposed to smoke from natural fires. Usually, they rely on site (ground) measurements and satellite data, but these aren’t always reliable. For instance, site monitors are few and far between in the western US; while satellite data doesn’t provide surface-level concentrations on its own.

To overcome these challenges, the authors of the Atmospheric Chemistry and Physics paper, used Facebook data to determine population-level exposure.

Fires during the summer of 2015 in Canada, as well as Idaho, Washington and Oregon, caused poor air quality conditions in the U.S Midwest. The generated smoke plume was obvious in satellite images. The team used this period as a case study to test their idea.

Facebook was mined for posts which contained the words ‘smoke’,’smoky’, ‘smokey’, ‘haze’, ‘hazey’ or ‘air quality’. The results were then plotted onto a map. To ensure the study was balanced, multiple posts by a single person and those which referenced cigarette smoke or smoke not related to natural causes were filtered out. In addition, towns with small populations were weighted so that those with higher populations didn’t skew the results.

The social media results were then compared to smoke measurements acquired by more traditional means: ground station and satellite data.

Example datasets from 29 June 2015. (a) Population – weighted, (b) average surface concentrations of particulate matter, (c) gridded HMS smoke product – satellite data, (d) gridded, unfiltered MODIS Aqua and MODIS Terra satellite data (white signifies no vaild observation), and (e) computer simulated average surface particulate matter. Image and caption (modified) from B.Ford et al., 2017.

The smoke plume ‘mapped out’ by the Facebook results correlates well with the plume observed by the satellites. The ‘Facebook plume’ doesn’t extend as far south (into Arkansas and Missouri) as the plume seen in the satellite image, but neither does the plume mapped out by the ground-level data.

Satellites will detect smoke plumes even when they have lifted off the surface and into the atmosphere. The absence of poor air quality measurements in the ground and Facebook data, likely indicates that the smoke plume had lifted by the time it reached Arkansas and Missouri.

The finding highlights, not only that the Facebook data can give meaningful information about the extend and location of smoke plume caused by wildfires, but that is has potential to more accurately reveal the air quality at the Earth’s surface than satellite data.

The relationship between the Facebook data and the amount of exposure to particular matter is complex and more difficult to establish. More research into how the two are linked will mean the researchers can quantify the health response associated with wildfire smoke. The findings will be useful for policy and decision-makers when it comes to limiting exposure in the future and have the added bonus of providing a cheap way to improve the predictions, without having to invest in expanding the ground monitor network.

By Laura Roberts, EGU Communications Officer


Ford, B., Burke, M., Lassman, W., Pfister, G., and Pierce, J. R.: Status update: is smoke on your mind? Using social media to assess smoke exposure, Atmos. Chem. Phys., 17, 7541-7554,, 2017.

Imaggeo on Mondays: Salt shoreline of the Dead Sea

Imaggeo on Mondays: Salt shoreline of the Dead Sea

This beautiful aerial image (you’d be forgiven for thinking that it was a watercolour) of the Dead Sea was captured by a drone flying in 100m altitude over its eastern coastline.

Climate change is seeing temperatures rise in the Middle East, and the increased demand for water in the region (for irrigation) mean the areas on the banks of the lake are suffering a major water shortage. As a result, the lake is shrinking at an alarming rate. Currently, it is shrinking by over 1m/year. The image was captured as part of a survey in the wider project DESERVE (Kottmeier et al. 2016) addressing the environmental changes accompanying the lake level drop.

In this case, the special focus is to look for e.g. submarine springs or other geomorphological evidence in the shallow lake water that can later turn into hazardous sinkholes (cf. recent publication on that topic Al-Halbouni et. al. 2017). Learn more about the environmental challenges and geohazard risks the region faces in this December 2016 Imaggeo on Mondays post.

The round features see in this image, nevertheless have been identified as salt accumulations following basically the sinusoidal shoreline.

The different colours of the lake indicate water of varying densities, e.g. fresh water floating on top of saltier water and possible sediments inside.

The shoreline appears with different colours each year depending on the sediment mud & evaporite material. Each line represents the retreat of a given year!

[Editor’s note: this image was a finalsit in the 2017 Imaggeo Photo Contest]

By Laura Robert and Djamil Al-Halbouni of the German Research Center for Geosciences, Physics of the Earth, Potsdam, German

Imaggeo is the EGU’s online open access geosciences image repository. All geoscientists (and others) can submit their photographs and videos to this repository and, since it is open access, these images can be used for free by scientists for their presentations or publications, by educators and the general public, and some images can even be used freely for commercial purposes. Photographers also retain full rights of use, as Imaggeo images are licensed and distributed by the EGU under a Creative Commons licence. Submit your photos at


GeoSciences Column: Can seismic signals help understand landslides and rockfalls?

GeoSciences Column: Can seismic signals help understand landslides and rockfalls?

From the top of a small gully in the French Alps, a 472 kg block is launched into the chasm. Every detail of it’s trajectory down the slope is scrutinised by two cameras and a network of seismometers. They zealously record every bounce, scrape and tumble – precious data in the quest to better understand landslides.

What makes landslides tick?

In 2016, fatalities caused by landslides tipped 2,250 people. The United States Geological Survey (USGS) estimates that between 25 and 50 people are killed, annually, by landslides in the United States alone. Quantifying the economic losses caused by landslides is no easy task, but the costs are known to be of economic significance.

It is paramount that the mechanisms which govern landslides are better understood in hopes that the knowledge will lead to improved risk management in the future.

But landslides and rockfalls are rarely observed in real-time. Deciphering an event, when all you have left behind is a pile of debris, is no easy task. The next best thing (if not better than!) to witnessing a landslide (from a safe distance) is having a permanent record of its movement as it travels down a slope.

Although traditionally used to study earthquakes, seismometers have now become so sophisticated they are able to detect the slightest ground movements; whether they come from deep within the bowels of the planet or are triggered by events at the surface. For some year’s now they have been an invaluable tool in detecting mass movements (an all-encompassing term for the movement of bed rock, rock debris, soil, or mud down a slope) across the globe.

More recently, processing recorded seismic signals triggered by large catastrophic events has not only allowed to identify when and where they occurred, but also their force, how quickly they travel, gain speed and their direction of movement.

This approach gives only a limited amount of data for scientists to work with. After all, large, catastrophic, mass movements represent only a fraction of the landslide and rockfall events that occur worldwide. To gain a fuller understanding of landslide processes, information about the smaller events is needed too.

So, what if scientists could use a seismic signal which is generated by all mass movements, independent of their size?

The high-frequency seismic signal

A high-frequency seismic signal is generated as the individual particles, which combined make up a landslide or rockfall, bounce and tumble against the underlying layer of rock. Would it be possible to, retrospectively, find out information about the size and speed at which individual particles traveled from this seismic signal alone?

This very question is what took a team of scientists up into the valleys of the French Alps.

At a place where erosion carves gullies into lime-rich muds, the researchers set-up two video cameras and network of seismometers. They then launched a total of 28 blocks, of weights ranging from 76 to 472 kg, down a 200 m long gully and used the data acquired to reconstruct the precise trajectory of each block.

The impacts of each block on the underlying geology, as seen on camera, were plotted on a 3D representation of the terrain’s surface. From the time of impact, block flight time and trajectory, the team were able to find out the velocity at which the blocks travelled and the energy they carried.

View from (a) the first and (b) the second video cameras deployed at the bottom of the slope. The ground control points are indicated by blue points. (c) Trajectory reconstruction for block 4 on the DEM, built from lidar acquisition, superimposed on an orthophoto
of the Rioux-Bourdoux slopes. Each point indicates the position of an impact and the colour gradient represents the chronology of these impacts (blue for the first impact and red for the last one). K2 is a three-component short-period seismometer and K1, K3 and K3 are vertical-only seismometers. CMG1 is a broad-band seismometer. From Hibert, C. et al., 2017. (Click to enlarge)

As each block impacted the ground, it generated a high-frequency seismic signal, which was recorded by the seismometers. The signals were processed to see if information about the (now known) properties of the blocks could be recovered.

Following a detailed analysis, the team of scientists, who recently published their results in the EGU’s open access journal Earth Surface Dynamics, found a correlation between the amplitude (the height of the wave from it’s resting position), as well as the energy of the seismic signals and the mass and velocities of the blocks before impact. This suggests that indeed, these high-frequency seismic signal can be used to find out details about rockfall and landslide dynamics.

But much work is left to be done.

There is no doubt that the type of substrate on which the particles/blocks bounce upon play a large part in governing the dynamics of mass movements. In the case of the French Alps experiment, the underlying geology of lime-rich muds was very soft and absorbed some of the energy of the impacts. Other experiments (which didn’t use single blocks), performed in hard volcanic and metamorphic rocks, found energy absorption was lessened. To really get to the bottom of how much of a role the substrate plays, single-block, controlled release experiments, like the one described in the paper, should be performed on a variety of rock types.

At the same time, while this experiment certainly highlights a link between seismic signals and individual blocks, rockfalls and landslides are made up of hundreds of thousands of particles, all of which interact with one another as they cascade down a slope. How do these complex interactions influence the seismic signals?

By Laura Roberts Artal, EGU Communications Officer

References and resources:

Hibert, C., Malet, J.-P., Bourrier, F., Provost, F., Berger, F., Bornemann, P., Tardif, P., and Mermin, E.: Single-block rockfall dynamics inferred from seismic signal analysis, Earth Surf. Dynam., 5, 283-292, doi:10.5194/esurf-5-283-2017, 2017.

USGS FAQs: How many deaths result from landslides each year?

The human cost of landslides in 2016 by David Petley, published, 30 January 2017 in The Landslide Blog, AGU Blogosphere.

[Paywalled] Klose M., Highland L., Damm B., Terhorst B.: Industrialized Countries: Challenges, Concepts, and Case Study. In: Sassa K., Canuti P., Yin Y. (eds) Landslide Science for a Safer Geoenvironment. Springer, Cham, (2014)



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