Articles | Volume 7, issue 2
https://doi.org/10.5194/esurf-7-475-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/esurf-7-475-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
29 May 2019
Research article |
| 29 May 2019
| 29 May 2019
Determining the optimal grid resolution for topographic analysis on an airborne lidar dataset
Institute of Geosciences, Universität Potsdam, Potsdam, Germany
Aljoscha Rheinwalt
Institute of Geosciences, Universität Potsdam, Potsdam, Germany
Bodo Bookhagen
Institute of Geosciences, Universität Potsdam, Potsdam, Germany
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Erosion is a threat for soils with rainfall as the driving force. The annual rainfall erosivity factor quantifies rainfall impact by analysing high-resolution rainfall time series (~ 5 min). Due to a lack of measuring stations, alternatives for its estimation are analysed in this study. The best results are obtained for regionalisation of the erosivity factor itself. However, the identified minimum of 60-year time series length suggests using rainfall generators as in this study as well.
Hajime Naruse and Kento Nakao
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Richard Barnes, Kerry L. Callaghan, and Andrew D. Wickert
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Mariela Perignon, Jordan Adams, Irina Overeem, and Paola Passalacqua
Earth Surf. Dynam., 8, 809–824, https://doi.org/10.5194/esurf-8-809-2020, https://doi.org/10.5194/esurf-8-809-2020, 2020
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Johannes Albert van Hateren, Unze van Buuren, Sebastiaan Martinus Arens, Ronald Theodorus van Balen, and Maarten Arnoud Prins
Earth Surf. Dynam., 8, 527–553, https://doi.org/10.5194/esurf-8-527-2020, https://doi.org/10.5194/esurf-8-527-2020, 2020
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Earth Surf. Dynam., 7, 171–190, https://doi.org/10.5194/esurf-7-171-2019, https://doi.org/10.5194/esurf-7-171-2019, 2019
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Christian Gruber, Sergei Rudenko, Andreas Groh, Dimitrios Ampatzidis, and Elisa Fagiolini
Earth Surf. Dynam., 6, 1203–1218, https://doi.org/10.5194/esurf-6-1203-2018, https://doi.org/10.5194/esurf-6-1203-2018, 2018
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Michael Dietze
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Environmental seismology is the study of the seismic signals emitted by Earth surface processes. This emerging research field is at the intersection of many Earth science disciplines. The overarching scope requires free integrative software that is accepted across scientific disciplines, such as R. The article introduces the R package "eseis" and illustrates its conceptual structure, available functions, and worked examples.
Eric Laloy, Koen Beerten, Veerle Vanacker, Marcus Christl, Bart Rogiers, and Laurent Wouters
Earth Surf. Dynam., 5, 331–345, https://doi.org/10.5194/esurf-5-331-2017, https://doi.org/10.5194/esurf-5-331-2017, 2017
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Over very long timescales, 100 000 years or more, landscapes may drastically change. Sediments preserved in these landscapes have a cosmogenic radionuclide inventory that tell us when and how fast such changes took place. In this paper, we provide first evidence of an elevated long-term erosion rate of the northwestern Campine Plateau (lowland Europe), which can be explained by the loose nature of the subsoil.
Laurent Fouinat, Pierre Sabatier, Jérôme Poulenard, Jean-Louis Reyss, Xavier Montet, and Fabien Arnaud
Earth Surf. Dynam., 5, 199–209, https://doi.org/10.5194/esurf-5-199-2017, https://doi.org/10.5194/esurf-5-199-2017, 2017
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This study focuses on the creation of a novel CT scan methodology at the crossroads between medical imagery and earth sciences. Using specific density signatures, pebbles and/or organic matter characterizing wet avalanche deposits can be quantified in lake sediments. Starting from AD 1880, we were able to identify eight periods of higher avalanche activity from sediment cores. The use of CT scans, alongside existing approaches, opens up new possibilities in a wide variety of geoscience studies.
Daniel E. J. Hobley, Jordan M. Adams, Sai Siddhartha Nudurupati, Eric W. H. Hutton, Nicole M. Gasparini, Erkan Istanbulluoglu, and Gregory E. Tucker
Earth Surf. Dynam., 5, 21–46, https://doi.org/10.5194/esurf-5-21-2017, https://doi.org/10.5194/esurf-5-21-2017, 2017
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Many geoscientists use computer models to understand changes in the Earth's system. However, typically each scientist will build their own model from scratch. This paper describes Landlab, a new piece of open-source software designed to simplify creation and use of models of the Earth's surface. It provides off-the-shelf tools to work with models more efficiently, with less duplication of effort. The paper explains and justifies how Landlab works, and describes some models built with it.
Andrew Valentine and Lara Kalnins
Earth Surf. Dynam., 4, 445–460, https://doi.org/10.5194/esurf-4-445-2016, https://doi.org/10.5194/esurf-4-445-2016, 2016
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Learning algorithms are powerful tools for understanding and working with large data sets, particularly in situations where any underlying physical models may be complex and poorly understood. Such situations are common in geomorphology. We provide an accessible overview of the various approaches that fall under the umbrella of "learning algorithms", discuss some potential applications within geomorphometry and/or geomorphology, and offer advice on practical considerations.
E. Pohl, M. Knoche, R. Gloaguen, C. Andermann, and P. Krause
Earth Surf. Dynam., 3, 333–362, https://doi.org/10.5194/esurf-3-333-2015, https://doi.org/10.5194/esurf-3-333-2015, 2015
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A semi-distributed hydrological model is used to analyse the hydrological cycle of a glaciated high-mountain catchment in the Pamirs.
We overcome data scarcity by utilising various raster data sets as meteorological input. Temperature in combination with the amount of snow provided in winter play the key role in the annual cycle.
This implies that expected Earth surface processes along precipitation and altitude gradients differ substantially.
T. Croissant and J. Braun
Earth Surf. Dynam., 2, 155–166, https://doi.org/10.5194/esurf-2-155-2014, https://doi.org/10.5194/esurf-2-155-2014, 2014
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Short summary
Representing the surface of the Earth on an equally spaced grid leads to errors and uncertainties in derived slope and aspect. Using synthetic data, we develop a quality metric that can be used to compare the uncertainties in different datasets. We then apply this method to a real-world lidar dataset, and find that 1 m data have larger error bounds than lower-resolution data. The highest data resolution is not always the best choice – it is important to consider the quality of the data.
Representing the surface of the Earth on an equally spaced grid leads to errors and...
