I am very grateful to Weiheng Shi, who translated the Cheat Sheet into Chinese language. And that’s not all. Weiheng also translated the user guide which provides an introduction to objects in TopoToolbox, a guide towards calculating Ksn as well as a documentation on multiple flow directions. I believe that this is a great resource for students in China and I hope that it will make TopoToolbox much more accessible to many. By the way, Weiheng also translated the documentation of Chad Greene’s Climate Data Toolbox (github link) and the Chinese documentation can be found here.
Cheat sheets are documents that I mainly know from R and its packages. It’s a great resource to quickly get an overview on functions, their syntax, and their application. Inspired by these, I have now created a TopoToolbox cheat sheet (pdf version of the image above). I hope it is useful for beginners and more advanced users alike.
This post is written by Will Struble who is PostDoc at the University of Arizona. Will works on a wide array of problems in geomorphology, tectonics, and surface processes. One of his tools is digital terrain analysis. Thanks for this post, Will!
Many geomorphic analyses focus on fluvial drainage networks. Indeed, metrics such as channel steepness (ksn), χ, and related measurements can provide great insight into how fluvial portions of landscapes respond to climate and tectonics. A significant proportion of the landscape, however, is not composed of fluvial channels. What are we missing? Hillslopes, of course!Read the rest of this entry »
In my previous post, I wrote about calculating the transverse topographic symmetry factor or, short, T-factor. I presented a computational approach to derive the centerline of a basin using (gray-weighted) distance transforms. And in fact, it worked out quite nicely for the Big Tujunga catchment, I think.Read the rest of this entry »
In response to a recent blog post about drainage basin asymmetry, blog reader Allan asked, how one could calculate the transverse topography symmetry factor or T-factor using TopoToolbox. To be honest, I had never heard before about the T-factor, but then I came across Heidi Daxberger’s paper (Daxberger et al. 2014) which contains following description.Read the rest of this entry »
Special issue “Estimating and predicting natural hazards and vulnerabilities in the Himalayan region” in NHESS
Special issues are a great opportunity to collate, review and summarize the current state of research. I am glad to announce that we have agreed with the EGU journal Natural Hazards and Earth System Sciences (NHESS) to schedule a special issue on “Estimating and predicting natural hazards and vulnerabilities in the Himalayan region“. The guest editors are Ankit Agarwal, Kristen Cook, Ugur Öztürk, myself, Roopam Shukla, and Sven Fuchs.
So, if you are looking for a good outlet for your research in the Himalayas, please consider submitting to this Special Issue. If you have questions, please get in touch with us.
Today’s blog post is about drainage basin asymmetry. Drainage basin asymmetry is expressed by the asymmetry factor AF = (Ar / At) where Ar is the area of the basin that is to the right of the trunk of the stream network and At is total area of the basin. Commonly, we would assume that drainage areas are more or less the same to the left and right of the trunk river. However, regional-scale dipping of more resistant rock layers, tectonic tilting or the presence of strike-slip fault zones (or a combination of these factors) may cause that rivers laterally migrate, thus producing different drainage areas to either side of the trunk river.Read the rest of this entry »
Catchments are the most important spatial unit in many geomorphological and hydrological studies. Thus, we often draw drainage basin outlines in maps. Yet, once you have many different lines in your map (e.g. the drainage network, fault lines, roads, etc), your map might become chaotic. Visual clarity is important to convey contents. Thus, here I am going to show a different and perhaps more subtle way to depict drainage outlines.Read the rest of this entry »
Today’s guest blogger is Anshul Yadav. Anshul is a PhD student at the IIT Roorkee and we are working together within the DAAD-funded project Co-PREPARE. Anshul is first-author of a new paper in Journal of Hydrology which he describes here.
The estimates of cross-sectional averaged flow velocity are essential in flood risk assessment for high flows, aquatic habitat protection in case of low flows, and a wide variety of scientific purposes in river research applications. However, direct measurements are difficult in high-gradient rivers, and a flow resistance equation is usually preferred for the velocity estimates. The flow resistance equations provide reliable estimates in low gradient rivers. But in the case of high-gradient rivers, some additional losses occur in case of low relative depth (y/D84), other than the form drag and friction drag. In this work, we hypothesized that the poorly sorted sediments would increase the flow resistance as the protrusions from the bed will increase, leading to distortions of free water surface, increase in spill losses, and turbulent wakes behind the large protruding elements. We tested our hypothesis using the eight well-known flow resistance equations, i.e., Strickler (1923), Keulegan (1938), Hey (1979), Smart and Jaggi (1983), Bathurst (1985), Ferguson (2007), Recking et al. (2008), Rickenmann and Recking (2011). The velocity predictions for the dataset characterized by σg > 7.5 were affected significantly for all the considered equations. Therefore we proposed an empirical approach to assess the additional losses occurring in the case of poorly sorted sediments as:
The coefficients a1, a2, and a3 were obtained by minimizing the difference between the actual observed values and values calculated using the conventional equations for the calibration dataset.
For example, the modified Ferguson (2007) equation would be expressed as:
The modified equations were found to be considerably better than their performances in the conventional form, both in terms of statistical indices and velocity predictions.
For more information, please have a look at our paper:
Yadav, A., Sen, S., Mao, L., & Schwanghart, W. (2022). Evaluation of flow resistance equations for high gradient rivers using geometric standard deviation of bed material. Journal of Hydrology, 605, 127292. [DOI: 10.1016/j.jhydrol.2021.127292] [here is a 50 days free access link]
The hypsometric integral (HI) is a geomorphometric metric that is used to infer temporal stages of geomorphic development. The HI is non-dimensional and is calculated by dividing the difference of the catchments average and minimum elevation by the difference of the maximum and minimum elevation (HI = (Hmean – Hmin)/(Hmax – Hmin)). Values of HI generally range between 0.15 and 0.85, and typically cluster between 0.4 and 0.6. Lowland areas with isolated high-standing surfaces have lower values whereas high values denote landscapes with mainly elevated surfaces which are steeply dissected by valleys.Read the rest of this entry »