### Geo.X Hackathon

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Are you based in Potsdam or Berlin at an institution of the Geo.X network? Are you PostDoc or PhD student? Are you keen on GIS, software development, and programming? If yes, you might like to participate in the Hackathon on visualizing the output of landscape evolution models (LEMs).

Output from LEMs is multidimensional. In fact, it is +4D: three dimensions in space, one in time, and several variables that change over time in space. This makes comparing and visualizing output from LEMs quite difficult. In this hackathon we will attempt to find solutions to this challenge. We will organize in teams to develop solutions and to rapidly prototype software.

Interested? Then apply here. We have only 15 seats and the deadline for application is on Wednesday, December 20, 2017.

### TopoToolbox 2.2 released

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The last weeks had been quite busy to finish version 2.2 which was available as a prerelease for a long while. TopoToolbox users who keep their software constantly updated (for example by using GIT) won’t see much changes.  For those that do not keep pace with the frequent commits to our repository, we encourage them to do so now. There are a lot of new functions and modifications. Benjamin Campforts added TTLEM, the TopoToolbox Landscape Evolution Model. The scope of functions for working with river networks (STREAMobj) has tremendously increased with new plotting functions, low-level chi analysis tools, and tools for geometric modifications. We added new functions to hydrologically correct and smooth river networks and values measured along them (e.g. constrained regularized smoothing (CRS)). TopoToolbox now supports multiple flow directions and there are several new functions for working with grids (GRIDobj). In addition, we consolidated the help sections in each function and increased compatibility with older MATLAB versions. Please see the readme-file for a complete overview of changes.

With version 2.2, we offer TopoToolbox as a MATLAB® toolbox file (mltbx-file). This file will make installation very easy. Simply download it, double-click, and follow the instructions.

Dirk and I met this morning in the train (here we are!) …

… and discussed possible directions for a next version. The number of functions has increased a lot which entails the threat that TopoToolbox might become confusing and even deterrent in particular for new users. Simply adding new functionalities is thus not the way forward. Instead, we decided that a new version should have a better documentation that should be integrated in MATLABs documentation browser. To quote John D’Errico, a long-time and excellent contributor of MATLAB code: Your job as a programmer does not stop when you write the last line of code. If you think so, then you should be fired. You should document your code. Provide help. Otherwise, that code is just a bunch of random bits, useful to nobody else in the world.

With this in mind, let’s go for 2.3.

### Valley-fills as Himalayan Earthquake proxies

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The Himalayan history is rich with a sequence of destructive earthquakes. In the last century, ground-shaking, collapsing houses, and landslides in the wake of earthquakes killed tens of thousand of people, wreaking havoc to the Himalayan nations. The 2015 Gorkha Earthquake was the latest in a series of severe earthquakes to hit Nepal.

Seismic hazard analysis in the Himalayas is based on few instrumental records and a paleoseismic record extending back ~1000 years. Paleoseismology largely relies on rupture histories derived from fault trenches, written accounts, and liquefaction features. Other records derived from e.g. lake sediments are scarce.

In a now published paper in Quaternary Science Reviews, Amelie Stolle et al. documents our research in the Pokhara Valley in Nepal. The valley was massively and repeatedly aggraded by several cubic kilometers of debris in the wake of medieval earthquakes in the region. The paper extends on our 2016 paper in Science, offering new radiocarbon dates and detailing the sedimentology of the infills. Based on our findings, we argue that valley fills in the Himalayas may offer substantial additional evidence for past earthquakes subsidy to the current portfolio of paleoseismological records.

References

Stolle, A., Bernhardt, A., Schwanghart, W., Hoelzmann, P., Adhikari, B.R., Fort, M., Korup, O., 2017. Catastrophic valley fills record large Himalayan earthquakes, Pokhara, Nepal. Quaternary Science Reviews, 177, 88-103. [DOI: 10.1016/j.quascirev.2017.10.015]. << free link to paper until December 26, 2017 >>

### Bayesian Optimization of the mn-ratio

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River profiles are concave upward if they are in a dynamic equilibrium between uplift and incision, and if our simplified assumptions of steady uplift and the stream power incision law (SPL) hold. The concavity derives from the SPL which states that along-river gradients S are proportional to upslope area A exponentiated by the negative mn-ratio.

$S&space;\sim&space;A^{-\frac{m}{n}}$

I have mentioned the mn-ratio several times in this blog. Usually, we calculate it using slope-area plots or chi analysis both of which are included in TopoToolbox. However, these methods usually lack consistent ways to express the uncertainties of the mn-ratio. The lack of consistency is due to fitting autocorrelated data which elude a straightforward statistical analysis.

Today, I want to present a new function that uses Bayesian Optimization with cross-validation to find a suitable mn-ratio. While Bayesian Optimization is designed to find optimal values of objective functions involving numerous variables, solving an optimization problem with mn as the only variable nicely illustrates the approach.

Bayesian Optimization finds a minimum of a scalar-valued function in a bounded domain. In a classification problem, this value could be the classification loss, i.e., the price paid for misclassifications. In a regression problem, this value might refer to the sum of squared residuals. The value might also be derived using cross-validation,  a common approach to assess the predictive performance of a model. Such cross-validation approaches might take into account only random subsets of data, which entails that the value to be optimized might not be the same for the same set of input parameters. Bayesian Optimization can handle stochastic functions.

Now how can we apply Bayesian Optimization for finding the right mn-ratio? The new function mnoptim uses chi analysis to linearize long-river profiles. If there are several river catchments (or drainage network trees), the function will pick a random subset of these trees to fit a mn-ratio and then tests it with another set of drainage basins. This allows us to assess how well an mn-ratio derived in one catchment can actually be applied to another catchment. The goal is to derive a mn-ratio that applies best to other catchments.

Now let’s try this using the new function mnoptim. Here is the code that I’ll use for entire SRTM-3 DEM of Taiwan. I’ll clip the stream network to the area upstream of the 300 m contour to avoid an influence of the alluvial low-lying reaches.

DEM = GRIDobj('taiwan.tif');
FD = FLOWobj(DEM);
S  = STREAMobj(FD,'minarea',1e6,'unit','map');
C  = griddedcontour(DEM,[300 300],true);
S  = modify(S,'upstreamto',C);
A  = flowacc(FD);
[mn,results] = mnoptim(S,DEM,A,'optvar','mn','crossval',true);
% we can refine the results if we need
results = resume(mn);
% and get an optimal value of mn:
bestPoint(results)

ans =

table

mn
_______

0.41482


Now this nicely derives the optimal mn-value of 0.415 which is close to the often reported value of 0.45. Moreover, based on the plot, we gain an impression of the uncertainty of this value. In a transient landscape with frequent knickpoints, the uncertainty about the mn-ratio will be probably larger.

Note that mnoptim requires the latest version of MATLAB: 2017b, as well as the Statistics and Machine Learning Toolbox. It also runs with 2017a, but won’t be able to use parallel computing then.

### 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.

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.

Application deadline is 15th November 2017.

### Sensitivity analysis of a highly parameterized landscape evolution model

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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.

References

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]