Articles | Volume 8, issue 3
https://doi.org/10.5194/esurf-8-809-2020
© Author(s) 2020. 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-8-809-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| Highlight paper
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25 Sep 2020
Research article | Highlight paper |
| 25 Sep 2020
| 25 Sep 2020
Dominant process zones in a mixed fluvial–tidal delta are morphologically distinct
Mariela Perignon
Department of Civil, Architectural and Environmental Engineering, Cockrell School of Engineering, The University of Texas at Austin, Austin, Texas, USA
Jordan Adams
Community Surface Dynamics Modeling System (CSDMS), Institute of Arctic and Alpine Research, University of Colorado at Boulder, Boulder, Colorado, USA
Science and Math Division, Delgado Community College, New Orleans, Louisiana, USA
Irina Overeem
Community Surface Dynamics Modeling System (CSDMS), Institute of Arctic and Alpine Research, University of Colorado at Boulder, Boulder, Colorado, USA
Department of Geological Sciences, University of Colorado at Boulder, Boulder, Colorado, USA
Paola Passalacqua
CORRESPONDING AUTHOR
Department of Civil, Architectural and Environmental Engineering, Cockrell School of Engineering, The University of Texas at Austin, Austin, Texas, USA
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Tian Gan, Gregory E. Tucker, Eric W. H. Hutton, Mark D. Piper, Irina Overeem, Albert J. Kettner, Benjamin Campforts, Julia M. Moriarty, Brianna Undzis, Ethan Pierce, and Lynn McCready
Geosci. Model Dev., 17, 2165–2185, https://doi.org/10.5194/gmd-17-2165-2024, https://doi.org/10.5194/gmd-17-2165-2024, 2024
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This study presents the design, implementation, and application of the CSDMS Data Components. The case studies demonstrate that the Data Components provide a consistent way to access heterogeneous datasets from multiple sources, and to seamlessly integrate them with various models for Earth surface process modeling. The Data Components support the creation of open data–model integration workflows to improve the research transparency and reproducibility.
Matthew C. Morriss, Benjamin Lehmann, Benjamin Campforts, George Brencher, Brianna Rick, Leif S. Anderson, Alexander L. Handwerger, Irina Overeem, and Jeffrey Moore
Earth Surf. Dynam., 11, 1251–1274, https://doi.org/10.5194/esurf-11-1251-2023, https://doi.org/10.5194/esurf-11-1251-2023, 2023
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In this paper, we investigate the 28 June 2022 collapse of the Chaos Canyon landslide in Rocky Mountain National Park, Colorado, USA. We find that the landslide was moving prior to its collapse and took place at peak spring snowmelt; temperature modeling indicates the potential presence of permafrost. We hypothesize that this landslide could be part of the broader landscape evolution changes to alpine terrain caused by a warming climate, leading to thawing alpine permafrost.
Jayaram Hariharan, Kyle Wright, Andrew Moodie, Nelson Tull, and Paola Passalacqua
Earth Surf. Dynam., 11, 405–427, https://doi.org/10.5194/esurf-11-405-2023, https://doi.org/10.5194/esurf-11-405-2023, 2023
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We simulate the transport of material through numerically simulated river deltas under natural and human-modified (embankment construction and channel dredging) scenarios to understand their impacts on material transport. Human modifications reduce the total area visited by passive particles and alter the amount of time spent within the delta relative to natural conditions. This work can help us understand how future construction may impact land building or ecosystem restoration projects.
Matthew Preisser, Paola Passalacqua, R. Patrick Bixler, and Julian Hofmann
Hydrol. Earth Syst. Sci., 26, 3941–3964, https://doi.org/10.5194/hess-26-3941-2022, https://doi.org/10.5194/hess-26-3941-2022, 2022
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There is rising concern in numerous fields regarding the inequitable distribution of human risk to floods. The co-occurrence of river and surface flooding is largely excluded from leading flood hazard mapping services, therefore underestimating hazards. Using high-resolution elevation data and a region-specific social vulnerability index, we developed a method to estimate flood impacts at the household level in near-real time.
Gregory E. Tucker, Eric W. H. Hutton, Mark D. Piper, Benjamin Campforts, Tian Gan, Katherine R. Barnhart, Albert J. Kettner, Irina Overeem, Scott D. Peckham, Lynn McCready, and Jaia Syvitski
Geosci. Model Dev., 15, 1413–1439, https://doi.org/10.5194/gmd-15-1413-2022, https://doi.org/10.5194/gmd-15-1413-2022, 2022
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Scientists use computer simulation models to understand how Earth surface processes work, including floods, landslides, soil erosion, river channel migration, ocean sedimentation, and coastal change. Research benefits when the software for simulation modeling is open, shared, and coordinated. The Community Surface Dynamics Modeling System (CSDMS) is a US-based facility that supports research by providing community support, computing tools and guidelines, and educational resources.
Katherine R. Barnhart, Eric W. H. Hutton, Gregory E. Tucker, Nicole M. Gasparini, Erkan Istanbulluoglu, Daniel E. J. Hobley, Nathan J. Lyons, Margaux Mouchene, Sai Siddhartha Nudurupati, Jordan M. Adams, and Christina Bandaragoda
Earth Surf. Dynam., 8, 379–397, https://doi.org/10.5194/esurf-8-379-2020, https://doi.org/10.5194/esurf-8-379-2020, 2020
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Landlab is a Python package to support the creation of numerical models in Earth surface dynamics. Since the release of the 1.0 version in 2017, Landlab has grown and evolved: it contains 31 new process components, a refactored model grid, and additional utilities. This contribution describes the new elements of Landlab, discusses why certain backward-compatiblity-breaking changes were made, and reflects on the process of community open-source software development.
Kang Wang, Elchin Jafarov, Irina Overeem, Vladimir Romanovsky, Kevin Schaefer, Gary Clow, Frank Urban, William Cable, Mark Piper, Christopher Schwalm, Tingjun Zhang, Alexander Kholodov, Pamela Sousanes, Michael Loso, and Kenneth Hill
Earth Syst. Sci. Data, 10, 2311–2328, https://doi.org/10.5194/essd-10-2311-2018, https://doi.org/10.5194/essd-10-2311-2018, 2018
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Ground thermal and moisture data are important indicators of the rapid permafrost changes in the Arctic. To better understand the changes, we need a comprehensive dataset across various sites. We synthesize permafrost-related data in the state of Alaska. It should be a valuable permafrost dataset that is worth maintaining in the future. On a wider level, it also provides a prototype of basic data collection and management for permafrost regions in general.
M. Liang, N. Geleynse, D. A. Edmonds, and P. Passalacqua
Earth Surf. Dynam., 3, 87–104, https://doi.org/10.5194/esurf-3-87-2015, https://doi.org/10.5194/esurf-3-87-2015, 2015
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In this work we assess the flow-routing component (FlowRCM) of our delta formation model, DeltaRCM. We found that with the level of complexity reduction, FlowRCM is able to produce channel network-scale hydrodynamic details, which provide further insights into the connection between delta flow structures and the morphodynamic outcome.
K. R. Barnhart, I. Overeem, and R. S. Anderson
The Cryosphere, 8, 1777–1799, https://doi.org/10.5194/tc-8-1777-2014, https://doi.org/10.5194/tc-8-1777-2014, 2014
B. Hudson, I. Overeem, D. McGrath, J. P. M. Syvitski, A. Mikkelsen, and B. Hasholt
The Cryosphere, 8, 1161–1176, https://doi.org/10.5194/tc-8-1161-2014, https://doi.org/10.5194/tc-8-1161-2014, 2014
Related subject area
Cross-cutting themes: Quantitative and statistical methods in Earth surface dynamics
Introducing standardized field methods for fracture-focused surface process research
Full four-dimensional change analysis of topographic point cloud time series using Kalman filtering
Comparison of rainfall generators with regionalisation for the estimation of rainfall erosivity at ungauged sites
Inverse modeling of turbidity currents using an artificial neural network approach: verification for field application
Automated quantification of floating wood pieces in rivers from video monitoring: a new software tool and validation
Particle size dynamics in abrading pebble populations
Computing water flow through complex landscapes – Part 3: Fill–Spill–Merge: flow routing in depression hierarchies
A photogrammetry-based approach for soil bulk density measurements with an emphasis on applications to cosmogenic nuclide analysis
Identifying sediment transport mechanisms from grain size–shape distributions, applied to aeolian sediments
Determining the optimal grid resolution for topographic analysis on an airborne lidar dataset
Systematic identification of external influences in multi-year microseismic recordings using convolutional neural networks
Earth's surface mass transport derived from GRACE, evaluated by GPS, ICESat, hydrological modeling and altimetry satellite orbits
The R package “eseis” – a software toolbox for environmental seismology
Bayesian inversion of a CRN depth profile to infer Quaternary erosion of the northwestern Campine Plateau (NE Belgium)
A new CT scan methodology to characterize a small aggregation gravel clast contained in a soft sediment matrix
Creative computing with Landlab: an open-source toolkit for building, coupling, and exploring two-dimensional numerical models of Earth-surface dynamics
An introduction to learning algorithms and potential applications in geomorphometry and Earth surface dynamics
Sensitivity analysis and implications for surface processes from a hydrological modelling approach in the Gunt catchment, high Pamir Mountains
Constraining the stream power law: a novel approach combining a landscape evolution model and an inversion method
Martha Cary Eppes, Alex Rinehart, Jennifer Aldred, Samantha Berberich, Maxwell P. Dahlquist, Sarah G. Evans, Russell Keanini, Stephen E. Laubach, Faye Moser, Mehdi Morovati, Steven Porson, Monica Rasmussen, and Uri Shaanan
Earth Surf. Dynam., 12, 35–66, https://doi.org/10.5194/esurf-12-35-2024, https://doi.org/10.5194/esurf-12-35-2024, 2024
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All rocks have fractures (cracks) that can influence virtually every process acting on Earth's surface where humans live. Yet, scientists have not standardized their methods for collecting fracture data. Here we draw on past work across geo-disciplines and propose a list of baseline data for fracture-focused surface process research. We detail the rationale and methods for collecting them. We hope their wide adoption will improve future methods and knowledge of rock fracture overall.
Lukas Winiwarter, Katharina Anders, Daniel Czerwonka-Schröder, and Bernhard Höfle
Earth Surf. Dynam., 11, 593–613, https://doi.org/10.5194/esurf-11-593-2023, https://doi.org/10.5194/esurf-11-593-2023, 2023
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We present a method to extract surface change information from 4D time series of topographic point clouds recorded with a terrestrial laser scanner. The method uses sensor information to spatially and temporally smooth the data, reducing uncertainties. The Kalman filter used for the temporal smoothing also allows us to interpolate over data gaps or extrapolate into the future. Clustering areas where change histories are similar allows us to identify processes that may have the same causes.
Ross Pidoto, Nejc Bezak, Hannes Müller-Thomy, Bora Shehu, Ana Claudia Callau-Beyer, Katarina Zabret, and Uwe Haberlandt
Earth Surf. Dynam., 10, 851–863, https://doi.org/10.5194/esurf-10-851-2022, https://doi.org/10.5194/esurf-10-851-2022, 2022
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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
Earth Surf. Dynam., 9, 1091–1109, https://doi.org/10.5194/esurf-9-1091-2021, https://doi.org/10.5194/esurf-9-1091-2021, 2021
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This paper proposes a method to reconstruct the hydraulic conditions of turbidity currents from turbidites. We investigated the validity and problems of this method in application to actual field datasets using artificial data. Once this method is established, it is expected that the method will elucidate the generation process of turbidity currents and will help to predict the geometry of resultant turbidites in deep-sea environments.
Hossein Ghaffarian, Pierre Lemaire, Zhang Zhi, Laure Tougne, Bruce MacVicar, and Hervé Piégay
Earth Surf. Dynam., 9, 519–537, https://doi.org/10.5194/esurf-9-519-2021, https://doi.org/10.5194/esurf-9-519-2021, 2021
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Quantifying wood fluxes in rivers would improve our understanding of the key processes in river ecology and morphology. In this work, we introduce new software for the automatic detection of wood pieces in rivers. The results show 93.5 % and 86.5 % accuracy for piece number and volume, respectively.
András A. Sipos, Gábor Domokos, and János Török
Earth Surf. Dynam., 9, 235–251, https://doi.org/10.5194/esurf-9-235-2021, https://doi.org/10.5194/esurf-9-235-2021, 2021
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Abrasion of sedimentary particles is widely associated with mutual collisions. Utilizing results of individual, geometric abrasion theory and techniques adopted in statistical physics, a new model for predicting the collective mass evolution of large numbers of particles is introduced. Our model uncovers a startling fundamental feature of collective particle dynamics: collisional abrasion may either focus size distributions or it may act in the opposite direction by dispersing the distribution.
Richard Barnes, Kerry L. Callaghan, and Andrew D. Wickert
Earth Surf. Dynam., 9, 105–121, https://doi.org/10.5194/esurf-9-105-2021, https://doi.org/10.5194/esurf-9-105-2021, 2021
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Existing ways of modeling the flow of water amongst landscape depressions such as swamps and lakes take a long time to run. However, as our previous work explains, depressions can be quickly organized into a data structure – the depression hierarchy. This paper explains how the depression hierarchy can be used to quickly simulate the realistic filling of depressions including how they spill over into each other and, if they become full enough, how they merge into one another.
Joel Mohren, Steven A. Binnie, Gregor M. Rink, Katharina Knödgen, Carlos Miranda, Nora Tilly, and Tibor J. Dunai
Earth Surf. Dynam., 8, 995–1020, https://doi.org/10.5194/esurf-8-995-2020, https://doi.org/10.5194/esurf-8-995-2020, 2020
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In this study, we comprehensively test a method to derive soil densities under fieldwork conditions. The method is mainly based on images taken from consumer-grade cameras. The obtained soil/sediment densities reflect
truevalues by generally > 95 %, even if a smartphone is used for imaging. All computing steps can be conducted using freeware programs. Soil density is an important variable in the analysis of terrestrial cosmogenic nuclides, for example to infer long-term soil production rates.
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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In this paper, we introduce a new technique that can be used to identify how sediments were transported to their place of deposition (transport mode). The traditional method is based on the size of sediment grains, ours on the size and the shape. A test of the method on windblown sediments indicates that it can be used to identify the transport mode with less ambiguity, and therefore it improves our ability to extract information, such as climate from the past, from sediment deposits.
Taylor Smith, Aljoscha Rheinwalt, and Bodo Bookhagen
Earth Surf. Dynam., 7, 475–489, https://doi.org/10.5194/esurf-7-475-2019, https://doi.org/10.5194/esurf-7-475-2019, 2019
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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.
Matthias Meyer, Samuel Weber, Jan Beutel, and Lothar Thiele
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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Monitoring rock slopes for a long time helps to understand the impact of climate change on the alpine environment. Measurements of seismic signals are often affected by external influences, e.g., unwanted anthropogenic noise. In the presented work, these influences are automatically identified and removed to enable proper geoscientific analysis. The methods presented are based on machine learning and intentionally kept generic so that they can be equally applied in other (more generic) settings.
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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By using a set of evaluation methods involving GPS, ICESat, hydrological modelling and altimetry satellite orbits, we show that the novel radial basis function (RBF) processing technique can be used for processing the Gravity Recovery and Climate Experiment (GRACE) data yielding global gravity field models which fit independent reference values at the same level as commonly accepted global geopotential models based on spherical harmonics.
Michael Dietze
Earth Surf. Dynam., 6, 669–686, https://doi.org/10.5194/esurf-6-669-2018, https://doi.org/10.5194/esurf-6-669-2018, 2018
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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
We propose a machine learning approach for the classification and analysis of large delta systems. The approach uses remotely sensed data, channel network extraction, and the analysis of 10 metrics to identify clusters of islands with similar characteristics. The 12 clusters are grouped in six main classes related to morphological processes acting on the system. The approach allows us to identify spatial patterns in large river deltas to inform modeling and the collection of field observations.
We propose a machine learning approach for the classification and analysis of large delta...
