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

Abstract

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.

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

“…The MaGPiE algorithm presented by Llena et al (2020) was used to infer the main geomorphic process signatures controlling topographic changes. Here we present a summary of MaG-PiE, but additional details can be found in Llena et al (2020).…”

“…The CRI was calculated based on the topographic wetness index (TWI) and planform curvature (PC) by means of the expression TWI + (PC × À1), where TWI was computed as ln(A/tan β) -A (m 2 ) refers to the upslope area of a given cell and β (°) is the local gradient. PC represents the normalized (from À1 to 1) planform curvature value obtained from the most recent DEM (see more details in Llena et al, 2020). Finally, topographic changes were obtained by comparison of the DEMs between surveys as described above (i.e.…”

“…It is worth noting that in our case we have considered overlapping processes those yielding a signature distinct from the other identified processes, mainly, in the particular case of our badlands, sheet washing and regolith cohesion loss. For more details about the class boundaries and combinations identified for each geomorphic process, see Llena et al (2020).…”

“…In this paper we analyse multi-temporal repeat highresolution topography obtained during five consecutive years in two morphometrically contrasted sub-humid badlands to untangle the relative importance of different processes in reshaping badlands and exporting sediments downstream, and their link to landform attributes and meteorological variables. Specifically, we have applied the Mapping Geomorphic Processes in the Environment (MaGPiE) algorithm recently developed by Llena et al (2020) to infer the main geomorphic process signatures shaping these badlands through the analysis of changes in form. It is worth mentioning that this paper together with the above-mentioned methodological paper (Llena et al, 2020) are considered a pair.…”

“…Specifically, we have applied the Mapping Geomorphic Processes in the Environment (MaGPiE) algorithm recently developed by Llena et al (2020) to infer the main geomorphic process signatures shaping these badlands through the analysis of changes in form. It is worth mentioning that this paper together with the above-mentioned methodological paper (Llena et al, 2020) are considered a pair. The methodological paper presents the full details of the MaGPiE algorithm.…”