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Annual to millennial landslide inventories and hillslope erosion in NW Argentine Andes

Subject Area Palaeontology
Term from 2018 to 2023
Project identifier Deutsche Forschungsgemeinschaft (DFG) - Project number 356194137
 
Final Report Year 2023

Final Report Abstract

The Quebrada del Toro in the Eastern Andes is a natural laboratory where high magnitude and dynamic erosion processes can be regularly observed. The seasonal climate and monsoon activity provides a driving force for mass movements in this area. This projected supported research from space and on-the-ground field work. First, data from radar and optical satellites were used to better understand the spatial distribution of landslides and debris flows in the steep gorge at the eastern flanks of the Central Andean Plateau. The radar-based coherence assessment documents the seasonal activity of debris flows and that these data can be used to understand transport in the hillslope and fluvial regime. We identify the time series of radar coherence data as a proxy for landscape denudation rates: a robust correlation with cosmogenic radionuclide-based denudation rates is shown. This provides an alternative to estimate millennial-timescale erosion rates and landscape steepness assessments. High-resolution optical data from different viewing angles allow to create 3-m digital elevation models that can be used to better describe channels and their debris flow activity. A debris-flow similarity index was developed that documents the impact of structural preconditioning and geologic history on massmovement generation. Both (optical and radar) satellite-based assessment provide opportunities to measure mass-transport rates and perform a natural hazard assessment. The intersection of satellite-based data with ground validation is an important component of successful satellite-based assessments. An important component of quantifying and understanding processes at the earth surface are digital elevation models. This projected supported several field campaigns that collected detailed topographic validation data that was used in several peer-reviewed studies. New approaches to develop high-resolution topography from stereo-satellite images were explored. In a second step, this project investigated cm-scale observations on the ground with specific focus on the description of sediment-transport processes. A new method was developed to measure large quantities of pebbles (i.e., digital grain sieving) through high-resolution stereo-photogrammetry. These data indicate that there are significant changes only in the highest percentile of the grain-size distributions. This tools is an open-source project and is used by other researcher to perform grain-size measurements. Further development of this tool is ongoing and approaches with convolutional neural networks within machine learning frameworks are explored. This project was a close international collaboration with colleagues at academic institutions in northwestern Argentina. Joint field studies and personal exchanges were carried out that fostered knowledge transfer and education on both sides.

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