Geosciences Column: Flooded by jargon

Geosciences Column: Flooded by jargon

When hydrologists and people of the general public use simple water-related words, are they actually saying the same thing? While many don’t consider words like flood, river and groundwater to be very technical terms, also known as jargon, water scientists and the general public can actually have pretty different definitions. This is what a team of researchers have discovered in recent study, and their results were published in EGU’s open access journal Hydrology and Earth System Sciences. In this post, Rolf Hut, an assistant professor at Delft University of Technology in the Netherlands and co-author of the study, blogs about his team’s findings.

On the television a scientist is interviewed, in a room with a massive collection of books:

“Due to climate change, the once in two years flood now reaches up to…”

“Flood?” interrupts my dad “We haven’t had a flood in fifteen years; how can they talk about a once in two years flood?”

The return period of floods is an often used example to illustrate how statistically illiterate ‘the general public’ is supposed to be. But maybe we shouldn’t focus on the phrase ‘once in two years’, but rather on the term ‘flood’. Because: does my dad know what that scientist, a colleague of mine, means when she says “flood”?

In water-science the words that experts use are the same words that people use in daily life. Words like ‘flood’, ‘dam’ or ‘river’. Because we have been using these words for our entire lives, we may not stop and think that, because of our training as water scientists, we may have a different definition than what people outside our field may have. When together with experts on science communication, I was writing a review paper about geoscience on television[1] when we got into the discussion “what is jargon?”. We quickly found out that within geoscience this is an open question.

Together with a team of Netherlands-based scientists, including part-time journalist and scientist Gemma Venhuizen and professor of science communication Ionica Smeets and assistant professor on soils Cathelijne Stoof and professor of statistics Casper Albers we decided to look for an answer to this question. We conducted a survey where we asked people what they thought words like ‘flood’ meant. People could pick from different definitions. Those definitions were not wrong per se, just different. One might be from Wikipedia and another from a policy document from EU officials. We did not want to test if people were correct, but rather if there was a difference in meaning attached to words between water scientists and lay people. For completeness, we also added picture questions where people had to pick the picture that best matched a certain word.

The results are in. We recently published our findings in the EGU journal Hydrology and Earth System Sciences[2] and will present them at the EGU General Assembly in April 2019 in Vienna. As it turns out: words like ‘groundwater’, ‘discharge’ and even ‘river’ have a large difference between the meaning lay-people have compared to water scientists. For the pictures however, people tend to agree more. The figure below shows the misfit distribution between lay people and water scientists: the bigger the misfit, the more people have different definitions. The numbers on the right are the Bayes factor: bigger than 10 indicates strong evidence that differences between lay people and water scientists are more likely than similarities. The words with an asterisk are the picture questions, showing that when communicating using pictures people are more likely to share the same definition.

Graph showing the posterior distribution of the misfit between laypeople and experts by using a Bayes factor (BF) for every term used in the survey. Pictorial questions are marked with an asterisk. A value of the BF <1∕10 is strong evidence towards H0: it is more likely that laypeople answer questions the same as experts than differently. A value of the BF >10 is strong evidence towards H1: differences are more likely than similarities. In addition to a Bayes factor for the significance of the difference, we also calculated the misfit: the strength of the difference. The misfit was calculated by a DIF score (differential item functioning), in which DIF =0 means perfect match, and DIF =1 means maximum difference. (Figure from

Maybe that scientist talking about floods on the television should have been filmed at a flood site, not in front of a pile of books.

Finally, the term ‘flood’ proved to be one of the words that we do tend to agree on, so maybe dad should take that class in basic statistics afterall…

By dr. ir. Rolf Hut, researcher at Delft University of Technology, the Netherlands

[This article is cross-posted on Rolf Hut’s personal site]


[1] Hut, R., Land-Zandstra, A. M., Smeets, I., and Stoof, C. R.: Geoscience on television: a review of science communication literature in the context of geosciences, Hydrol. Earth Syst. Sci., 20, 2507-2518,, 2016.

[2] Venhuizen, G. J., Hut, R., Albers, C., Stoof, C. R., and Smeets, I.: Flooded by jargon: how the interpretation of water-related terms differs between hydrology experts and the general audience, Hydrol. Earth Syst. Sci., 23, 393-403,, 2019.

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: Don’t throw out that diary – medieval journals reveal the secret of lightning

GeoSciences Column: Don’t throw out that diary – medieval journals reveal the secret of lightning

When 17th century Japanese princess Shinanomiya Tsuneko took note of an afternoon storm in her diary one humid Kyoto summer, she could not have imagined her observations would one day help resolve a longstanding scientific conundrum. Statistical analysis of her journals has revealed a link between lightning strikes and the solar wind – proving that your teenage diary could contain good science, as well as bad poetry.

The mystery of lightning

Lightning has amazed and alarmed weather-watchers since time immemorial. So it may come as a surprise that we still have little idea what sets off one of nature’s most thrilling spectacles.

Any school child will tell you lightning is caused by a difference in electrical charge. Up- and downdrafts cause molecules of air and water to bump against each other, exchanging electrons. When the potential difference is big enough, all those separated charges comes rushing back in one big torrent, superheating the air and turning it into glowing plasma – that’s what we call lightning.

So far, so sensible. But there’s a problem. Air is an insulator – and a very good one at that. To get the current flowing, charged particles need some sort of bridge to travel across. And it’s this bridge that has vexed lightning scientists – fulminologists – for decades.

The most prominent theory points the finger at cosmic rays – heavy, fast-moving particles that impact the Earth from space. Packing energy roughly equivalent to a fast-bowled cricket ball into one tiny atom-sized package, a cosmic ray can shred electrons from their nuclei with ease. The spectacular Northern Lights reveal the effect this can have on the atmosphere: columns of ionised air, perfect conductors for charges to travel along.

Most cosmic rays originate in deep space, hurled at close to the speed of light from distant supernovae. The extreme heat of the sun’s surface also sends more than a few our way – the so-called ‘solar wind’ – but because these particles are more sluggish than galactic cosmic rays, researchers at first doubted they could have much effect on the atmosphere. Lightning’s time in the sun was yet to come.

27 days of summer

Anyone who has lived a year in Japan will be familiar with the country’s long, sultry summers – and its famously methodical Met Agency. It’s a good place to go looking for lightning.

Inspired by some tantalising work out of the UK, Hiroko Miyahara and colleagues across Japan went sifting through their own Met data for patterns that might suggest a connection between solar weather and lightning strikes. They had their eye out for one pattern in particular – the 27-day cycle caused by the sun’s rotation. This is just short enough that the solar wind streaming from any given region of the sun is fairly constant, limiting the impact of solar variability on the data. It’s also short enough to fit comfortably within one season, which helped the authors compare apples with apples over long timespans.

Armed with the appropriate controls, and a clever method they developed for counting lightning strikes that smooths over patchy observations, Miyahara and the team got stuck into the data for Japan circa 1989–2015. Early in 2017, in a paper published in Annales Geophysicae, they presented their results. The 27-day signal stood out to four standard deviations: a smoking-gun proof that solar weather and lightning strikes are connected.

But how is the relatively sluggish solar wind able to influence lightning strikes? The key, according to Miyahara, is the effect the solar wind has on the Earth’s magnetic field – sometimes bolstering and sometimes weakening it, allowing the more potent galactic cosmic rays to wreak their mayhem.

A window into the past

Of course, the 27-day cycle is only the shortest of the major solar cycles. It is well known that the intensity of the sun varies on an 11-year cycle, related to convection rates in the solar plasma. Less understood are the much longer centurial and millennial cycles. The sun passed through one such cycle between the late Middle Ages and now. The so-called Little Ice Age, coinciding with a phase of low sunspot activity known as the Maunder Minimum, precipitated agricultural collapse and even wars across the world – and solar physicists believe we may be due for another such minimum in the near future, if it hasn’t begun already.

Understanding these cycles is a matter of no small importance. Unfortunately, pre-modern data is often scattered and unreliable, hampering investigations. A creative approach is called for – one that blends the disciplines of the human historian and the natural historian. And this is exactly what Miyahara and the team attempted next.

Shinanomiya Tsuneko was born in Kyoto 1642 – just before the Maunder Minimum. A daughter of the Emperor, Shinanomiya became a much-respected lady of the Imperial Court, whose goings-on she meticulously recorded in one of the era’s great diaries. Luckily for Miyhara and his colleagues in the present day, Shinanomiya was also a lover of the weather, carefully noting her observations of all things meteorological – especially lightning.

Figure and text from Miyahara et al, 2017b: “a) Group sunspot numbers around the latter half of the Maunder Minimum. b) Solar cycles reconstructed from the carbon-14 content in tree rings. The red and blue shading denotes the periods of solar maxima and minima, respectively, used in the analyses. c) Periodicity of lightning events during the solar maxima shown in panel (b). The red dashed lines denote 2 and 3 SD during the solar maxima, and the red shaded bar indicates the 27–30-day period. d) Same as in panel c) but for solar minima.”

Shinanomiya’s diary is one of five Miyahara and the team consulted to build a continuous database of lightning activity covering an astonishing 100 years of Kyoto summers. Priestly diaries, temple records, and the family annals of the Nijo clan were all cross-referenced to produce the data set, which preserves a fascinating slice of Earth weather during the sun’s last Grand Minimum.
Analysis of this medieval data revealed the same 27-day cycle in lightning activity observed in more recent times – proof of the influence of the solar wind on lightning frequency. The strength of this signal proved to be greatest at the high points of the sun’s 11-year decadal sunspot cycle. And the signal was almost completely absent between 1668 and 1715 – the era of the Maunder Minimum, when sunspot numbers are known to have collapsed.

Put together, the data provide the strongest proof yet that solar weather can enhance – and diminish – the occurrence of lightning.

Lightning strikes twice

Miyahara and the team now hope to expand their dataset beyond the period 1668 – 1767. With a little luck – and a lot of digging around in dusty old archives – it may be possible to build a record of lightning activity around Japan from before the Maunder Minimum all the way up to the present day. A record like this, covering a grand cycle of solar activity from minimum to maximum and, perhaps soon, back to a minimum again, would help us to calibrate the lightning record, providing a powerful new proxy for solar activity past and future. It may even help us to predict the famously unpredictable – lightning strikes injure or kill a mind-boggling 24,000 people a year.

As for the rest of us, the work of Miyahara and his colleagues should prompt us to look up at the sky a little more often – and note down what we see. Who knows? Three hundred years from now, it could be your diary that sets off a climate revolution – though it may be best to edit out the embarrassing details first.

by Rohan S. Byrne, PhD student, University of Melbourne


Miyahara, H., Higuchi, C., Terasawa, T., Kataoka, R., Sato, M., and Takahashi, Y.: Solar 27-day rotational period detected in wide-area lightning activity in Japan, Ann. Geophys., 35, 583-588,, 2017a.

Miyahara, H., Aono, Y., and Kataoka, R.: Searching for the 27-day solar rotational cycle in lightning events recorded in old diaries in Kyoto from the 17th to 18th century, Ann. Geophys., 35, 1195-1200,, 2017b.

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)