Clay mineralogy as a crucial factor in badland hillslope processes

Kašanin‐Grubin, Milica

doi:10.1016/j.catena.2012.08.008

Cited by 49 publications

(19 citation statements)

References 36 publications

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“…For example, in a study of the Badland formation often occurs in association with marls, mudstones, shales and similar lithologies (Pulice et al, 2012;Kasanin-Grubin, 2013). One crucial control here is the ESP of the materials (Faulkner, 2013).…”

Section: Fluvial Landformsmentioning

confidence: 96%

Quantification of rock control in geomorphology

Goudie

2016

Earth-Science Reviews

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Section: Fluvial Landformsmentioning

confidence: 96%

Quantification of rock control in geomorphology

Goudie

2016

Earth-Science Reviews

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“…so-called 'popcorn' features) in relation to climate and geological conditions (e.g. Gallart et al, 2002;Nadal-Romero and Regüés, 2010;Kasanin-Grubin, 2013). The DoD is calculated using the geomorphic change detection (GCD) extension for ArcMap (available at http://gcd.joewheaton.org/; see Wheaton et al, 2010) which has the advantage of incorporating uncertainty analysis based on minimum level of detection (minLoD), propagated errors or probabilistic thresholding.…”

Section: Sheet Washingmentioning

confidence: 99%

Geomorphic process signatures reshaping sub‐humid Mediterranean badlands: 1. Methodological development based on high‐resolution topography

Llena

Vericat

Smith

et al. 2020

Earth Surf Processes Landf

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High‐resolution topography data sets have improved the spatial and temporal scales at which we are able to investigate the landscape through the analysis of landform attributes and the computation of topographic changes. Yet, to date, there have been only limited attempts to infer key geomorphic processes in terms of contributions to shaping the landscape. Highly erodible landscapes such as badlands provide an ideal demonstration of such an approach owing to the rapid changes observed over a relatively short time frame. In this technical note we present the Mapping Geomorphic Processes in the Environment (MaGPiE): a new algorithm that allows mapping of geomorphic process signatures through analysis of repeat high‐resolution topography data sets. The method is demonstrated in an experimental badland located in the southern central Pyrenees. MaGPiE is a geographic information system (GIS)‐based algorithm that uses as input: (a) terrain attributes (i.e. Slope, Roughness and Concentrated Runoff Index) extracted from digital elevation models (DEMs), and (b) a map of topographic changes (DEM of difference, DoD). Initial results demonstrate that MaGPiE allows the magnitude and the spatial distribution of the main geomorphic processes reshaping badlands to be inferred for the first time. © 2020 John Wiley & Sons, Ltd.

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“…piping or tunnelling) and mass wasting (i.e. gravitational processes) (Bryan and Yair, ; Clotet‐Perarnau et al, ; Kasanin‐Grubin, ; Moreno‐de las Heras and Gallart, ).…”

Section: Introductionmentioning

confidence: 99%

Geomorphic process signatures reshaping sub‐humid Mediterranean badlands: 2. Application to 5‐year dataset

Llena

Smith

Wheaton

et al. 2020

Earth Surf Processes Landf

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Badland landscapes exhibit high erosion rates and represent the main source of fine sediments in some catchments. Advances in high‐resolution topographic methods allow analysis of topographic changes at high temporal and spatial scales. We apply the Mapping Geomorphic Processes in the Environment (MaGPiE) algorithm to infer the main geomorphic process signatures operating in two sub‐humid badlands with contrasting morphometric attributes located in the Southern Pyrenees. By interrogating a 5‐year dataset of seasonal and annual topographic changes, we examine the variability of geomorphic processes at multiple temporal scales. The magnitude of geomorphic processes is linked to landform attributes and meteorological variables. Morphometric differences between both adjacent badlands allow us to analyse the role of landform attributes in the main geomorphic process reshaping landscapes subjected to the same external forcing (i.e. rainfall and temperature). The dominant geomorphic process signatures observed in both badlands are different, despite their close proximity and the same rainfall and temperature regimes. Process signatures determining surface lowering in the gently sloping south‐facing badland, characterized by lower connectivity and more vegetation cover, are driven by surface runoff‐based processes, both diffuse (causing sheet washing) and concentrated (determining cutting and filling, rilling and gullying). The steeper, more connected north‐facing slopes of the other badland are reshaped by means of gravitational processes, with mass wasting dominating topographic changes. In terms of processes determining surface raising, both mass wasting and cutting and filling are most frequently observed in both badlands. There is a clear near‐balanced feedback between both surface‐raising and ‐lowering processes that becomes unbalanced at larger temporal scales due to the thresholds overcome, as the volume associated with surface lowering becomes higher than that associated with raising‐based processes. Rainfall variables control surface flow processes, while those variables associated with low temperature have a significant relation with mass movement‐based processes and other localized processes such as regolith cohesion loss. Finally, our results point out that morphometry (slope and connectivity) together with vegetation cover are key factors determining geomorphic processes and associated topographic changes. © 2020 John Wiley & Sons, Ltd.

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Clay mineralogy as a crucial factor in badland hillslope processes

Cited by 49 publications

References 36 publications

Quantification of rock control in geomorphology

Quantification of rock control in geomorphology

Geomorphic process signatures reshaping sub‐humid Mediterranean badlands: 1. Methodological development based on high‐resolution topography

Geomorphic process signatures reshaping sub‐humid Mediterranean badlands: 2. Application to 5‐year dataset

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