EGU General Assembly 2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Inverting fluvial network topology to understand landscape dynamics

Stuart Grieve1, Simon Mudd2, Fiona Clubb3, Michael Singer4, Katerina Michaelides5, and Shiuan-An Chen5
Stuart Grieve et al.
  • 1School of Geography, Queen Mary University of London, London, United Kingdom (
  • 2School of GeoSciences, University of Edinburgh, Edinburgh, United Kingdom
  • 3Department of Geography, Durham University, Durham, United Kingdom
  • 4School of Earth and Ocean Sciences, Cardiff University, Cardiff, United Kingdom
  • 5School of Geographical Sciences, University of Bristol, Bristol, United Kingdom

The topology of fluvial networks has long been studied, with Horton's laws describing relationships between stream order, stream density, and stream length often cited as fundamental governing principles of drainage basin development. Building upon these principles, small scale studies have identified patterns of self-similarity in drainage networks in the continental USA, suggesting that to some extent, river networks self-organise in a scale invariant manner. More stringent measures of self-similarity have also been developed, which quantify the fractal nature of side branching structures in fluvial networks. Using such metrics, studies have identified similarities between leaf vein structures and fluvial networks, and have identified a potential climatic signature in North American river topology.

The appeal of such techniques over traditional methods of channel analysis using topographic data is that in self-similar networks, the precise location of channel heads is unimportant, allowing analysis to be performed at unprecedented scales, and in locations where data quality is limited. Here, we attempt to reconcile these two suites of techniques to understand the potential and limitations of network topology as an indicator of broader landscape dynamics. We achieve this through the analysis of fluvial networks extracted at a global scale from the Shuttle Radar Topography Mission dataset alongside other global earth observation data.

How to cite: Grieve, S., Mudd, S., Clubb, F., Singer, M., Michaelides, K., and Chen, S.-A.: Inverting fluvial network topology to understand landscape dynamics, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8740,, 2020

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Presentation version 1 – uploaded on 02 Apr 2020
  • CC1: Comment on EGU2020-8740, Andrew Gunn, 04 May 2020

    Hi Stuart,

    This poster looks great - I have 3 questions:

    What is the data you're using for the networks?

    Not resolving the smallest channels impacts your fit to 'c', but does that impact change as a function of the network size?

    It looks like there's an inverse trend between 'c' and drainage area, where the variance in 'c' gets larger with decreasing drainage area - I'm struggling for some physical intuition on that, do you suspect any mechanism for it?

    All the best,


    • AC1: Reply to CC1, Stuart Grieve, 04 May 2020

      Hi Andrew,

      Thank you for your comment. To answer your questions:

      1) The channel networks are being extracted from SRTM 30m resolution DEMS, available from OpenTopography(, using the LSDTopoTools topographic analysis software ( The extraction procedure is outlined in Chen et al. (2020).

      2) In theory, if the networks are perfectly Tokunaga Self-Similar, then ignoring the smallest channels will have no influence on the fit. In reality however this is asking a lot - as we know that real data will always be noisy to some degree. I have performed some sensitivity analyses on this global dataset, varying the channel extraction threshold by 2 orders of magnitude and found that it has no significant impact on the results.

      3) My intuition on these patterns is that in larger basins, channels are likely to be more stable and established, thereby taking longer to respond to a new forcing (be that climate or tectonics). Whereas in smaller basins, channels may be more ephemeral and have a shorter response time - leading to more rapid adjustments in response to forcings, driving larger variability in c than we see in larger basins, which respond more slowly and are therefore less likely to diverge from the 'expected' c value. I have no data to support this hypothesis, however, it is something I am working on at present.

      Many thanks,


      Chen, S., Michaelides, K., Grieve, S.W.D. et al. Aridity is expressed in river topography globally. Nature 573, 573–577 (2019).