Mid-European Geomorphology Meeting 2021

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We will have a Mid-European Geomorphology Meeting on Nov. 6-9 in Munich this year. The conference will be hybrid with people attending in person but also virtually. Hence, you may participate if you are fed up with online conferences, but also if you do not have the time or money for travelling to Munich. The organising and scientific committee has elaborated an exciting programme with diverse sessions.

Please read more about here. Hope to see you there!

Hindcasting and nowcasting rainfall-induced landslides – do global precipitation estimates help?

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In July 2017, an unusual torrential storm hit southwestern Japan in Fukuoka Prefecture triggering more than 2000 landslides within 12 hours. Unlike the seemingly large extent of the overall rainfall activity, landslides were concentrated in a small area of about 200 km2, where total rainfall accumulation exceeded 500mm. A year later, trenching rains during a larger weather system for about 5 days affected the entire southwestern Japan. This time, the week-long rainfalls triggered about 8500 landslides in an area of 3000 km2 with spatially highly variable rainfall accumulations. The contrast between these distinct events motivated us to explore the usefulness of global grid rainfall data, such as GPM IMERG (Integrated Multi-satellitE Retrievals for Global Precipitation Measurement) and ERA5 climate reanalysis data, for landslide hindcasting or nowcasting.

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Anatomy of a Himalayan cascading disaster

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It was on the morning of Sunday, the 7th of February 2021, when I checked my Twitter account, and my friend and colleague Basanta Raj Adhikari had noticed me about a massive flash flood in Uttarakhand. With the first images and videos appearing on social media, the scale of the disaster became quickly apparent. As numbers of known fatalaties rose, media outlets started reporting about the event, iterating speculations about a glacial detachment that had caused the flood.

These interpretations were quickly dismissed when Dan Shugar (University of Calgary) tweeted about his interpretation of Planet imagery which clearly revealed a massive landslide as origin of the flood. What then began is a rapid, thorough and comprehensive collaborative effort by 53 scientists worldwide that had gathered via Twitter and joined a Slack channel. Everyone involved contributed with his or her expertise. For example, high resolution satellite imagery were leveraged to map flood marks and boulders, 2 m DEMs before and after the event were calculated and analyzed, seismological data provided insights into the timing of the event, modellers ran simulations with r.avaflow, and the plethora of videos on social media were scoured.

The results of this rapid and collaborative assessment spearheaded by Dan Shugar are now published in Science. The report provides an anatomy of a Himalayan disaster in unprecedented detail. It describes how the cascade of events started as a massive and extremely mobile rock and ice avalanche, that transformed into a debris flow and sediment-laden flood. Moreover, the report discusses potential causes of the event and details its consequences. Importantly, most fatalities occurred at the locations of hydropower projects which were under construction and destroyed as the flood rushed down the Rishiganga and Dhauliganga rivers. As such, the Chamoli disaster forms a sequence together with the Indian Floods in 2013 and the Gorkha Earthquake in 2015 which generated high losses in the water energy sector.

It has been a tremendous experience to work with a huge group of scientists in such a synergetic and efficient way. To this end, let’s hope that our results contribute to avoiding human suffering in the wake of such hazard cascades in the future.

References

Shugar, D., Jacquemart, M., Shean, D., Bhushan, S., Upadhyay, K., Sattar, A., Schwanghart, W., McBride, S., Van Wyk de Vries, M., Mergili, M., Emmer, A., Deschamps-Berger, C., McDonnell, M., Bhambri, R., Allen, S., Berthier, E., Carrivick, J.L., Clague, J.J., Dokukin, M., Dunning, S.A., Frey, H., Gascoin, S., Haritashya, U.K., Azam, M.F., Cook, S.J., Dimri, A.P., Eriksson, M., Farinotti, D., Fiddes, J., Gnyawali, K.R., Harrison, S., Jha, M., Koppes, M., Kumar, A., Leinss, S., Majeed, U., Mal, S., Muhuri, A., Noetzli, J., Paul, F., Rashid, I., Sain, K., Steiner, J., Ugalde, F., Watson, C.S., Westoby, M.J., 2021. A massive rock and ice avalanche caused the 2021 disaster at Chamoli, Indian Himalaya. Science. [DOI: 10.1126/science.abh4455]

Also see press release by the University of Potsdam. and blog post by Dave Petley.

Where and why do submarine canyons remain connected to the shore during sea-level rise?

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Bathymetric and topographic model of the northern Mediterranean Sea. Data from opentopography.org.

The world’s shelfs are dissected by steep canyons which act as conduits of terrestrial sediments to marine-depocenters. The spatial distribution of these canyons, however, is neither uniform nor completely spatial random along the continental margins. In particular, there are profound spatial differences in the occurrence of canyons that extend back to the shore line and those that have their heads close to the continental slope.

In our latest open-access paper, Anne Bernhardt and I investigate where and why submarine canyons remain connected to the shore during post-glacial sea-level rise. We mapped canyon heads and used a Bayesian Poisson regression to identify controls on their distance to the coast.

We show that shore-connected canyons primarily occur along continental margins with narrow and steep shelves found on the Mediterranean active margin, and the Pacific coast of Central and South America. In addition, we find that shore-connected canyons occur downstream of catchments that have resistant bedrock and high water discharge. This likely indicates that these catchments deliver coarse-grained tools which are able to increase erosion of the canyon head.

References

Bernhardt, A., Schwanghart, W., 2021. Where and Why Do Submarine Canyons Remain Connected to the Shore During Sea-level Rise? – Insights from Global Topographic Analysis and Bayesian Regression. Geophysical Research Letters, 48, e2020GL092234. [DOI: 10.1029/2020GL092234]

Note that there is also a data repository associated with the publication:

Bernhardt, A., Schwanghart, W. 2021: Global dataset of submarine canyon heads combined with terrestrial and marine topographic and oceanographic parameters. GFZ Data Services. [DOI: 10.5880/fidgeo.2021.008]

Ksn anomaly plot with confidence bounds (or how to distinguish knickzones from knicknoise)

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In response to my previous post, Philippe Steer from the University of Rennes came up with an idea that I’d like to explore a bit more here. In brief, I posted a code and figure that showed an anomaly plot of Ksn and Philippe suggested to have confidence bounds around the mean so that one could identify Ksn values that go beyond the noise inherent in data of longitudinal river profiles. To this end, such analysis would be needed if you want to objectively distinguish knickzones from the noise.

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Ksn anomaly plot

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This is going to be a very short blog post.

I am sure you came across climate anomaly plots before (like this one). How about plotting a steepness anomaly plot? And how could this be done using TopoToolbox? Now here is code to plot anomalies of Ksn along the Big Tujunga.

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Point patterns on stream networks (1)

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Our new paper on point patterns on stream networks just got published in Earth Surface Processes and Landforms and is open-access (thanks to the DEAL agreement). In this post, I’d like to talk about what the new class PPS is and what it is good for.

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Finding the right mn-ratio using mnoptimvar

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River profile analysis based on the stream-power model usually requires finding the right mn ratio (also termed the concavity index theta). While many researchers often refer to the standard value of 0.45, there is not much reason to believe that this value is universally valid. And the issue is crucial. Many metrics such as ksn or chi are very sensitive to changes in the mn-ratio (see the recent paper by Boris Gailleton and colleagues).

TopoToolbox features a function that uses Bayesian Optimization to find a suitable value of the mn-ratio (mnoptim, see this blog post). If the study area consists of several drainage basins, the function finds an optimal value using cross-validation. This means, it finds a good value in a subset of the basins and tests it with the remaining catchments. This approach works well when the majority of basins is in steady state.

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A river is rarely straight…

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I just watched Ajay Limaye‘s Landscapes Live talk about river meandering. Wondering, how to calculate sinuosity using TopoToolbox, I then realized that I had actually written a function that does the job a while ago. It’s called – guess what – sinuosity.

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Labelling composite figures quickly

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In one of my previous posts, I wrote that I am increasingly using MATLAB to make publication-ready figures. In fact, MATLAB offers everything that you need to create maps and figures that can be exported to any format and quality required by journals (see also Martin Trauth’s blog post on this).

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