EGU 2018: Interactions between tectonics and surface processes from mountain belts to basins

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Abstract submission for the EGU 2018 has just started and is open until 10 Jan 2018, 13:00 CET. The session Interactions between tectonics and surface processes from mountain belts to basins, organized by Dirk Scherler, Alex Whitaker, Taylor Schildgen and me, will address the coupling between tectonics and surface processes. We invite contributions that use geomorphic or sedimentary records to understand tectonic deformation, and we welcome studies that address the interactions and couplings between tectonics and surface processes at a range of spatial and temporal scales. In particular, we encourage coupled catchment-basin studies that take advantage of numerical/physical modeling, geochemical tools for quantifying rates of surface processes (cosmogenic nuclides, low-temperature thermochronology, luminescence dating) and high resolution digital topographic and subsurface data. We also encourage field or subsurface structural and geomorphic studies of landscape evolution, sedimentary patterns and provenance in deformed settings, and invite contributions that address the role of surface processes in modulating rates of deformation and tectonic style.

Please see further information >>here<<.

We look forward to your contributions. See you at the EGU soon!
Wolfgang, Dirk, Taylor and Alex


Two positions in Geomorphology and Cosmogenic Nuclides in Dirk Scherler’s group

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Two positions in Geomorphology and Cosmogenic Nuclides in Dirk Scherler’s group

Dirk Scherler, co-developer of TopoToolbox, has recently been granted the ERC-project “Climate sensitivity of glacial landscape dynamics (COLD)”. The main aim of COLD is to quantify how erosion rates in glacial landscapes vary with climate change and how such changes affect the dynamics of mountain glaciers. Now, he is inviting applications for 2 PhD positions at the German Research Centre for Geosciences (GFZ) in Potsdam.

Please find more details here:

Application deadline is 15th November 2017.

Sensitivity analysis of a highly parameterized landscape evolution model

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Strath and fill terrace in the Bergantes Catchment, Spain.

Today, I came back from an excellent workshop (organized by Darrel Maddy) in Spain focussing on the late Quaternary development of the Bergantes catchment. Located in an extremely beautiful landscape, this catchment features numerous fluvial terraces that were extensively studied and dated by Mark Macklin and Paul Brewer together with four PhD students between 2005 and 2009. A solid chronology together with high resolution terrain and climate data provide the benchmark data against we will test numerical landscape evolution models (LEM).

Assessing the capabilities of LEMs to reconstruct real landscapes, however, involves several challenges among which high parametrization is a severe one. Thus, in order to get a grip on the uncertainty and sensitivity of LEMs, Chris Skinner from the University of Hull led a study in which we assessed the parameter space of CAESAR-Lisflood and its effects on several output metrics derived from hundreds of simulations.

This study has now been accepted for discussion in the journal Geoscientific Model Development and can be accessed here.


Skinner, C. J., Coulthard, T. J., Schwanghart, W., Van De Wiel, M. J., and Hancock, G. (2017): Global Sensitivity Analysis of Parameter Uncertainty in Landscape Evolution Models, Geosci. Model Dev. Discuss., in review. [DOI: 10.5194/gmd-2017-236]

Avoiding edge effects in chi analysis

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Analyses that use upslope area usually demand that catchments are completely covered by the DEM. Values of upslope areas may be too low if catchments are cut along DEM edges, and so are estimates of  discharge. How can we avoid including these catchments into our analyses?

In one of my previous posts on chi analysis, I showed a rather long code to include those catchments that have 20% or less of their outlines on the DEM edges. Here, I’ll be more strict. I’ll remove those parts of the stream network that have pixels on DEM edges in their upslope area. By doing this, we make sure that drainage basins are complete which is vital for chi analysis or estimating discharge.

Here is the code:

DEM = GRIDobj('srtm_bigtujunga30m_utm11.tif');
FD = FLOWobj(DEM);
S  = STREAMobj(FD,'minarea',1e6,'unit','map');

I  = GRIDobj(DEM,'logical');
I.Z(:,:) = true;
I.Z(2:end-1,2:end-1) = false;

I  = influencemap(FD,I);

The influencemap function takes all edges pixels and derives those pixels that they would influence downstream. Again, this is going to be a logical GRIDobj where true values refer to pixels that might potentially be affected by edge effects. Let’s remove those pixels from the stream network S using the modify function.

S2 = modify(S,'upstreamto',~I);
D  = drainagebasins(FD,S2);
imageschs(DEM,[],'colormap',[1 1 1],'colorbar',false)
[~,x,y] = GRIDobj2polygon(D);
hold on
Edge-unaffected drainage network (blue) and its drainage basins (green). The stream network that is potentially affected is shown as gray solid lines.

Ok, now you have a drainage network that won’t be affected by edge effects for further analysis.

An alternative to along-river swath profiles

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Today, I want to show another application of TopoToolbox’s new smoothing suite that Dirk and I have recently released together with our discussion paper in ESURF. I will demonstrate that using different methods associated with STREAMobj enable you to create plots that are quite similar to swath profiles calculated along rivers. Let’s see how it works.

Swath profiles require a straight or curved path defined by a number of nodes. Each node has a certain orientation defined by one or two of its neighboring nodes. Creating swath profiles then involves mapping values from locations that are perpendicular to that orientation and within a specified distance. We will use a simplified version of mapping values lateral to the stream network which is implemented in the function maplateral. maplateral uses the function bwdist that returns a distance transform from all pixels (the target pixels) to a number of seed pixels in a binary image. In our case, seed pixels are the pixels of the stream network and bwdist identifies the  target pixels from which it calculates the euclidean distance to the stream pixels. Usually, there are several target pixels for each seed pixel so that we require an aggregation function that calculates a scalar based on the vector of values that are associated with each seed pixel. Unlike swath profiles, however, this approach entails that some seed pixels may not have target pixels  up to the maximum distance. We can thus be pretty sure that some pixels may be missing some of the information that we want to extract. While we cannot avoid this problem using this approach, we can at least reduce its impact using the nonparametric quantile regression implemented in the crs function.

Here is the approach. We’ll load a DEM, derive flow directions and a stream network which is in this case just one trunk river. We use the function maplateral to map elevations in a distance of 2 km to the stream network. Our mapping uses the maximum function to aggregate the values. Hence, we will have for each pixel along the stream network the maximum elevation in its 2 km nearest-neighborhood. The swath is plotted using the function imageschs and the second output of maplateral. Then we plot the maximum values along with a profile of the stream network.

DEM = GRIDobj('srtm_bigtujunga30m_utm11.tif');
FD = FLOWobj(DEM);
S  = STREAMobj(FD,'minarea',1e6,'unit','map');
S = klargestconncomps(S);
S = trunk(S);

z = getnal(S,DEM);
[zmax,mask] = maplateral(S,DEM,2000,@max);

imageschs(DEM,mask,'truecolor',[1 0 0],...

hold on
legend('River profile','maximum heights in 2 km distance')


This looks quite ok, but the topographic profile has a lot of scatter and a large number of values seem not to be representative. Most likely, those are the pixels whose closest pixels fail to extend to the maximum distance of 2 km. Rather, I’d expect a profile that runs along the maximum values of the zigzag line. How can we obtain this line? Well, the crs function could handle this. Though it was originally intended to be applied to longitudinal river profiles, it can also handle other data measured or calculated along stream networks. The only thing we need to “switch off” is the downstream minimum gradient that the function uses by default to create smooth profiles that monotoneously decrease in downstream direction. This is simply done by setting the ‘mingradient’-option to nan. I use a smoothing parameter value K=5 and set tau=0.99 which forces the profile to run along the 99th percentile of the data. You can experiment with different values of K and tau, if you like.

zmaxs = crs(S,zmax,'mingradient',nan,'K',5,'tau',0.99,'split',0);
hold on
plotdzshaded(S,[zmaxs z],'FaceColor',[.6 .6 .6])
hold off


This looks much better and gives an impression on the steepness of the terrain adjacent to the stream network. Let’s finally compare this to a SWATHobj as obtained by the function STREAMobj2SWATHobj.

SW = STREAMobj2SWATHobj(S,DEM,'width',4000);
xlabel('Distance upstream [m]');
ylabel('Elevation [m]');


Our previous results should be similar to the maximum line shown in the SWATHobj derived profile. And I think they are. In fact, the SWATHobj derived profiles also shows some scatter that is likely due to the sharp changes of orientation of the swath centerline.  While we can remove some of this scatter by smoothing the SWATHobj centerline, I think that the combination of maplateral and the CRS algorithm provides a convenient approach to sketch along-river swath profiles.

4th Summer School funded by the Volkswagen Foundation, Deadline 1 October 2017

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This post is written by Martin Trauth who is a professor for paleoclimate dynamics at the University of Potsdam. Consider applying for this summer school. Deadline is soon! It will be a great opportunity to learn a lot in short time and to expand your network. Please find all information below.

We are pleased to announce two fully sponsored consecutive summer school sessions on “Earth Surface Dynamics Understanding Processes at the Earth’s Vulnerable Skin” for 25 doctoral students from geosciences, environmental sciences and related fields such as biology, chemistry and physics. These summer schools funded by the VolkswagenFoundation will be designed for doctoral students, aiming (1) to improve their skills to understand the complex interaction of the processes shaping the Earth’s surface at different temporal and spatial scales, (2) to monitor, model and predict the results of these interactions, and (3) to identify and mitigate risks of natural and human-caused interference in these processes in an interdisciplinary and intercultural environment. The two summer schools each comprise three modules with each module covering a week, taking place at different locations in Germany (Rügen Island, Wandlitz, 2x Potsdam, Neustadt an der Weinstrasse, Garmisch-Partenkirchen/Zugspitze). These locations are representative of typical settings for Earth surface processes, from the coast to lowlands and from continental rifts to high mountains. The FIRST SET OF MODULES will focus on types of signals and noise commonly encountered at the Earth’s surface, and methods of acquiring, processing and analyzing data with non-destructive physical surveying methods. The SECOND SET OF MODULES will be about the examination and modeling of the processes underlying the data collected at the Earth’s surface.

The summer school fellowship covers all costs for transportation, accommodation and meals during the summer school. Full or partial support for travel expenses to/from Potsdam will be granted according to necessity. The fellowship does not cover, though these things are necessary for participation, costs for (1) a computer laptop, (2) hiking boots and clothing, and (3) insurance and medical costs. No daily allowance will be paid.

The intense, multifaceted science training program of the summer school will help participants to acquire knowledge and understanding of the processes shaping the Earth’s vulnerable skin and to define premier research topics to study processes at the Earth’s dynamic surface. Participants in the summer school are expected to form part of a new generation of researchers, practitioners and lecturers with the necessary background and scientific tools to evaluate and mitigate the effects of present-day and future environmental change.

More information and how to apply can be viewed here. There is also a summer school flyer. Please feel free to forward this message to interested people. Please send your questions about the summer school to

Kind regards, Martin

One tool to download them all

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Some changes and additions to TopoToolbox are subtle and may not be visible at first glance. Hence, I report them here. One such addition is part of version 2.2 (which Dirk and I are about to finalize shortly) and is found in the folder IOtools (input/output tools): readopentopo. The function uses opentopography’s RESTful Web service for global raster data access for seamless download of global digital elevation models. readopentopo offers access to SRTM-3, SRTM-1, and ALOS 3D World data at your finger tips. By default, the downloaded *.tif-file is stored in your system’s temporary folder and is deleted as soon as it is read into MATLAB. If you want to permanently save the file on your hard drive make sure to define a file path and to set the ‘deletefile’-option to false. The tif-file comes in geographic coordinates. Hence, don’t forget to reproject the DEM to UTM (see reproject2utm) before you start working with it.

Many thanks to members of the opentopography-team who make the data and service available. Please use these services reasonably and don’t excessively download data!