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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

References

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, https://doi.org/10.5194/acp-17-7541-2017, 2017.

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)

 

Geosciences Column: The dangers of an enigmatic glacier in the Karakoram

Geosciences Column: The dangers of an enigmatic glacier in the Karakoram

Nestled among the high peaks of the Karakoram,  in a difficult to reach region of China, lies Kyagar Glacier. It’s trident-like shape climbs from 4800 to 7000 meters above sea level and is made up of three upper glacier tributaries which converge to form an 8 km long glacier tongue.

Until recently, it’s remoteness meant that studying its behaviour relied heavily on the acquisition of data by satellites. The installation, in 2012, of an automated monitoring station yielded photographs and other data which, combined with better satellite observations, give a detailed insight into the nature of an otherwise enigmatic glacier.

The flow of glaciers

Despite their impenetrable fortress-like appearance, glaciers are constantly on the move. Due to the force of gravity acting on the thick pack of ice, glaciers flow, albeit very, very  slowly. The ice deforms under its own enormous weight, creeping slowly down valleys and mountain sides.

The exact position of a glacier’s snout is also affected by the amount of snow that accumulates on its surface. When the rate of evaporation of snow exceeds the amount added to the glacier, it retreats. Rising global temperatures mean that glaciers worldwide are shrinking at unprecedented rates.

Kyagar Glacier on 29 March 2016, as seen from the ESA Sentinel-2A satellite. The glacier-dammed lake of approximately 5 million m3 is visible to the east of the glacier terminus. The curved scale bar up the west branch indicates the longitudinal profile used for surface velocity and elevation analysis, and the inset shows the monitoring station located about 500 m upstream of the glacier terminus. Taken from V.Round et al., 2017 (click to enlarge).

But, the remote glaciers of the Chinese Karakoram are bucking the global trend. Owing to localised increases in winter precipitation between 1999 and 2011, they are maintaining a steady ice-thickness (or even advancing slightly).

The way in which many glaciers of the central Asian mountains flow is also unique. While the majority of glaciers slide down valleys at a relatively steady rate, about 1% experience glacier surges. Long periods of quiescence where flow is extremely slow are punctuated by times (which can last months or years) of accelerated gliding and transport of material.

During active surge periods a glacier’s snout can lengthen and thicken, blocking rivers and forming ice-dammed lakes. If the dam containing the lake fails, a glacial outburst flood (GLOF) occurs, presenting a serious threat to downstream communities.

Mysterious floods

A record of devastating floods along the Yarkand River – which Kyagar Glacier feeds into – exists from as far back as the 1960s. But the origin of the floods remained a mystery for many years. While periods of thickening and advance had been recognised in Kyagar as early as in the 1920s, it wasn’t until 2012 that it was characterised as a surge-type glacier; finally establishing the link between the down valley flood events and the glacier.

In order to manage the hazard presented by future GLOFs,  it is important to fully understand the surge dynamics of Kyagar.  Using a combination of satellite images and data, as well as images and weather records made by the automated observation station, a team of researchers have been able to establish the speed at which Kyagar moved between 2011 and 2016.

The study period also coincided with a recent surge cycle at Kyagar, giving the scientists their first detailed glimpse of how Kyagar moves and forms hazardous ice-dammed lakes.

Kyagar’s surges and GLOFs

Before 2012, Kyagar was in a quiescent phase (which had lasted at least 14 years). During that time the glacier snout was thinning and ice was built up in an area towards the top of the glacial tongue, forming a reservoir.

Glacier surface elevation changes during the surge from subtraction of two TanDEM-X DEMs. (Left) During the quiescent period, snow accumulate in an area towards the top of the glacial tongue, while the snout thinned. (Right) During the surge period this pattern was reversed. Modified from V. Round et al., 2017 (click to enlarge).

Gradually after that, the thickness of ice at the snout began to increase, as ice moved from the reservoir higher up in the glacier where it had accumulated previously.

The velocity with which the glacier moved forward also increased. Between April and May 2014 speeds doubled compared to the maximum speeds recorded before then. Despite a few fluctuations, speeds continued to increase overall, peaking in mid 2015. In that time, Kyagar gained over 60m of ice at its snout.

Photographs from the monitoring station revealed that a lake began to form upstream from the glacier’s terminus in December 2014. It grew steadily throughout the spring and summer and drained, abruptly, through channels carved out below the glacier in July 2015.

By September 2015 the lake began to fill again. Ten months later, in July 2016 it reached its peak volume of 40 million ㎥ (equivalent to the amount of water held in 16,000 olympic sized swimming pools) and drained suddenly shortly after. It refilled over the course of the next month, reaching a volume of 37 million ㎥ , and once again drained abruptly in August 2016.

Radar backscatter images of the glacier terminus showing the lake (a) 11 days before drainage, (b) just after the start of drainage, and (c) after the lake drainage. Lake drainage clearly occurred through subglacial channels, rather than through dam collapse or overtopping. Images from TanDEM-X data provided by DLR. Taken from V.Round et al., 2017 (click to enlarge).

What causes Kyagar to surge?

Not unlike other surge-type glaciers, Kyagar seems to have an inefficient drainage system at its base. It is particularly poor at transporting ice from its reservoir to its snout in a regular manner. Instead, it does is cyclically, through surges.

During quiescent periods interconnected tunnels at the base of the glacier carry water away efficiently. During surge periods, the tunnels turn to cavities which are poorly connected by very narrow passages, meaning material isn’t carried away from the glacier easily. This leads to a pressure build-up, and only when the pressure is high enough, is water released, lubricating the glacier bed and encouraging sliding over large areas.

Scientists aren’t certain what causes Kyagar to behave in this way. It is likely a combination of factors: the glacier tongue is relatively flat compared to the steeper slopes in the accumulation area, while the underlying geology and regional climatic conditions also play a role.

What does the future hold?

Historical records of glacial advance and lake formation at Kyagar suggest surge periods occur every 15 to 20 years. Unless there are major changes to the rate at which snow accumulates on the glacier, the nest quiescent period is expected to last until, at least, 2030.

The current risk of GLOFs remains high and will remain so for the next few years, as the glacier snout is still slightly higher than normal and transport of ice from the reservoir is ongoing.

Whether a lake will form (and how large it will grow) during future surge periods depends on the height of the ice dam and how efficiently water is drained away through the subglacial channels.

Regular satellite images, taken during summer periods, are needed to continually assess the risk of GLOFs and to prepare downstream communities.

By Laura Roberts Artal, EGU Communications Officer

References

Round, V., Leinss, S., Huss, M., Haemmig, C., and Hajnsek, I.: Surge dynamics and lake outbursts of Kyagar Glacier, Karakoram, The Cryosphere, 11, 723-739, doi:10.5194/tc-11-723-2017, 2017.

Gardelle, J., Berthier, E., Arnaud, Y., and Kääb, A.: Region-wide glacier mass balances over the Pamir-Karakoram-Himalaya during 1999–2011, The Cryosphere, 7, 1263-1286, doi:10.5194/tc-7-1263-2013, 2013.

Geosciences Column: How El Niño triggered Indonesia corals die-off

Geosciences Column: How El Niño triggered Indonesia corals die-off

In the glistening waters of Indonesia, shallow corals – the rain forests of the sea – teem with life.  Or at least they did once. Towards the end of 2015 the corals started to die, leaving a bleak landscape behind. An international team of researchers investigated the causes of the die-off. Their findings, published recently in the EGU’s open access journal, Biogeosciences, are rather surprising.

Globally, corals face tough times. Increasing ocean-water temperatures (driven by a warming climate) are disrupting the symbiotic relationship between corals and the algae that live on (and in) them.

The algae, known as zooxanthellae, provide a food source for corals and give them their colour. Changing water temperatures and/or levels, the presence of contaminants or overexposure to sunlight, put corals under stress, forcing the algae to leave. If that happens, the corals turn white – they become bleached – and are highly susceptible to disease and death.

Triggered by the 2015-2016 El Niño, water temperatures in many coral reef regions across the globe have risen, causing the National Oceanic and Atmospheric Administration (NOAA) to declare the longest and most widespread coral bleaching event in recorded history. Now into its third year, the mass bleaching event is anticipated to cause major coral die-off in Australia’s Great Barrier Reef for the second consecutive year.

The team of researchers studying the Indonesian corals found that, unlike most corals globally, it’s not rising water temperatures which caused the recent die-off, but rather decreasing sea level.

While conducting a census of coral biodiversity in the Bunaken National Park, located in the northwest tip of Sulawesi (Indonesia), in late February 2016, the researchers noticed widespread occurrences of dead massive corals. Similar surveys, carried out in the springs of 2014 and 2015 revealed the corals to be alive and thriving.

In 2016, all the dying corals were found to have a sharp horizontal limit above which dead tissue was present and below which the coral was, seemingly, healthy. Up to 30% of the reef was affected by some degree of die-off.

Bunaken reef flats. (a)Close-up of one Heliopora coerula colony with clear tissue mortality on the upper part of the colonies; (b)same for a Porites lutea colony; (c) reef flat Porites colonies observed at low spring tide in May 2014. Even partially above water a few hours per month in similar conditions, the entire colonies were alive. (d) A living Heliopora coerula (blue coral) community in 2015 in a keep-up position relative to mean low sea level, with almost all the space occupied by corals. In that case, a 15 cm sea level fall will impact most of the reef flat. (e–h) Before–after comparison of coral status for colonies visible in (c). In (e), healthy Poritea lutea (yellow and pink massive corals) reef flat colonies in May 2014, observed at low spring tide. The upper part of colonies is above water, yet healthy; (f) same colonies in February 2016. The white lines visualize tissue mortality limit. Large Porites colonies (P1, P2) at low tide levels in 2014 are affected, while lower colonies (P3) are not. (g) P1 colony in 2014. (h) Viewed from another angle, the P1 colony in February 2016. (i) Reef flat community with scattered Heliopora colonies in February 2016, with tissue mortality and algal turf overgrowth. Taken from E. E. Ampou et al. 2016.

The confinement of the dead tissue to the tops and flanks of the corals, lead the scientists to think that the deaths must be linked to variations in sea level rather than temperature, which would affect the organisms ubiquitously. To confirm the theory the researchers had to establish that there had indeed been fluctuations in sea level across the region between the springs of 2015 and 2016.

To do so they consulted data from regional tide-gauges. Though not located exactly on Bunaken, they provided a good first-order measure of sea levels over the period of time in question. To bolster their results, the team also used sea level height data acquired by satellites, known as altimetry data, which had sampling points just off Bunaken Island. When compared, the sea level data acquired by the tidal gauges and satellites correlated well.

Sea-level data from the Bitung (east North Sulawesi) tide-gauge, referenced against Bako GPS station. On top, sea level anomalies measured by the Bitung tide-gauge station (low-quality data), and overlaid on altimetry ADT anomaly data for the 1993– 2016 period. Note the gaps in the tide-gauge time series. Middle: Bitung tide-gauge sea level variations (high-quality data, shown here from 1986 till early 2015) with daily mean and daily lowest values. Bottom, a close-up for the 2008–2015 period. Taken from E. E. Ampou et al. 2016.

The data showed that prior to the 2015-2016 El Niño, fluctuations in sea levels could be attributed to the normal ebb and flow of the tides. Crucially, between August and September 2015, they also showed a sharp decrease in sea level: in the region of 15cm (compared to the 1993-2016 mean). Though short-lived (probably a few weeks only), the period was long enough that the corals sustain tissue damage due to exposure to excessive UV light and air.

NOAA provides real-time Sea Surface Temperatures which identify areas at risk for coral bleaching. The Bunaken region was only put on alert in June 2016, long after the coral die-off started, therefore supporting the crucial role sea level fall played in coral mortality in Indonesia.

The link between falling sea level and El Niño events is not limited to Indonesia and the 2015-2016 event. When the researchers studied Absolute Dynamic Topography (ADT) data, which provides a measure of how sea level has change from 1992 to 2016, they found sea level falls matched with El Niño years.

The results of the study highlight that while all eyes are focused on the consequences of rising ocean temperatures and levels triggered by El Niño events, falling sea levels (also triggered by El Niño) could be having a, largely unquantified, harmful effect on corals globally.

By Laura Roberts Artal, EGU Communications Officer

References and resources

Ampou, E. E., Johan, O., Menkes, C. E., Niño, F., Birol, F., Ouillon, S., and Andréfouët, S.: Coral mortality induced by the 2015–2016 El-Niño in Indonesia: the effect of rapid sea level fall, Biogeosciences, 14, 817-826, doi:10.5194/bg-14-817-2017, 2017

Varotsos, C. A., Tzanis, C. G., and Sarlis, N. V.: On the progress of the 2015–2016 El Niño event, Atmos. Chem. Phys., 16, 2007-2011, doi:10.5194/acp-16-2007-2016, 2016.

What are El Niño and La Niña? – a video explainer by NOAA

Coral Reef Watch Satellite Monitoring by NOAA

Global sea level time series – global estimates of sea level rise based on measurements from satellite radar altimeters (NOAA/NESDIS/STAR, Laboratory for Satellite Altimetry)

El Niño prolongs longest global coral bleaching event – a NOAA News item

NOAA declares third ever global coral bleaching event – a NOAA active weather alert (Oct. 2015)

The 3rd Global Coral Bleaching Event – 2014/2017 – free resources for media and educators

What is coral bleaching? – an infographic by NOAA
The ENSO (El Niño–Southern Oscillation) Blog by Climate.gov (a NOAA resource)

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