Relict Landscapes as Archives of Past Climatic and Tectonic Conditions

‘Relict’ landscapes are low-relief, high elevation surfaces that are often interpreted as an archive of previously stable tectonic and/or climatic conditions. These landscapes are commonly recognised in mountain ranges that have been interpreted to be undergoing late-Cenozoic acceleration in tectonic uplift and a rejuvenation by an erosional response (e.g., Clark et al., 2006). Relict topography (and the information it contains about past conditions) will eventually be lost through such erosion (e.g., Whittaker & Boulton, 2012).
These remnants of Earth’s geologic past have been identified across various landscapes on Earth. Several alternative mechanisms have been proposed for their formation including emerging from dynamic reorganisation of drainage networks through divide migration and drainage capture (Yang et al., 2015; Whipple et al., 2017), or due to lateral advection of uplifted topography (Eizenhöfer et al 2019). Yet the mechanisms of formation from the nature of the topography remains unclear. Building on these recent studies, the primary goals of this project are: (i) identifying the processes that can lead to low relief upland; and (ii) deciphering their geomorphological record of past tectonic and climatic conditions across the globe. These goals will be achieved through state-of-the-art, process-based numerical models of landscape evolution.
Understanding the mechanisms to create and preserve such relict landscapes and being able to reconstruct their geomorphological archive of Earth’s past is crucial to understand the interaction of physical processes within the Earth System and to unlock feedbacks between tectonics, climate, and topography. Such knowledge will help to understand spatial landscape responses and response times to changes due to external forcings, improving efforts in topography-driven natural hazard assessments and mitigating the consequences of climate change.

The primary objectives of this PhD project are:
• Implementation of landscape evolution models to establish systematics that promote the emergence of relict landscapes.
• Automated extraction and interpretation of geomorphological metrics across climatically and tectonically distinct regions to establish a global database of relict landscapes.
• Model inversions to identify the range of climatic and tectonic parameters that are archived within the relict landscapes.

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Image Captions

Fig. 1. Landscape response and emergence of ‘relict’ landscapes due to the lateral advection of rock over a mid-crustal, subsurface structural ramp (e.g., Himalaya) modified after Eizenhöfer et al. (2019).


The project will integrate multidisciplinary empirical and modelling data in the fields of palaeo-climate, geomorphology, tectonics, and geodynamics to drive numerical landscape evolution models.

Topographic analyses of commonly available digital elevation models will map geomorphic metrics (river steepness, local relief, knickpoint locations, landscape transience) using the MATLAB software package TopoToolbox and/or LSDTopoTools. Automated algorithms will be developed to identify and characterise relict landscapes worldwide and for three case study areas, i.e., Mongolia, Southern Africa and the Himalaya.

Existing palaeo-climate models (e.g., Mutz & Ehlers, 2018) and proxy-data (e.g., Zachos et al., 2008) will be accessed to quantify climate change over geologic time in the study regions. Geodynamic scenarios will be explored from existing studies. The upper lithospheric tectonic evolution of the three regions will be reconstructed based on literature data.

Numerical landscape evolution models (FastScape) will systematically explore conditions for the preservation of relict landscape with respect to climatic and tectonic parameters. Data obtained for the three case study regions will then inform model inversions to reconstruct climatic and tectonic conditions during and after the formation of relict landscapes.

Project Timeline

Year 1

The student will set up coupled numerical landscape evolution to systematically explore conditions for the emergence of relict landscapes. High resolution numerical landscape evolution models will implement climatic, tectonic, and geodynamic input and evaluate the model output.

Year 2

The student will undertake literature work and geomorphic analyses across the three study regions informed by the numerical models. Remote geomorphological analyses of landscapes will be performed using LSDTopoTools and/or TopoToolbox software packages. The results of this analysis will be placed in relation to the climatic, tectonic, and geodynamic evolution of the study regions.

Year 3

The student will set up inversions of numerical landscape evolution models to reconstruct climatic/tectonic parameter space for the evolution of the three study regions. These will explore climatic, tectonic, and geodynamic conditions that led to the formation of the relict landscapes, and also extract the information archived in the relict landscapes prior to their formation.

Year 3.5

The student will finalise the results, write manuscripts for publications, and complete thesis.

& Skills

The student will be trained by leading experts of geomorphology and tectonics to achieve a holistic understanding of System Earth. This training involves analyses of remote sensing data, as well as data in the fields of climate, tectonics, and geodynamics to reconstruct the mid- to long-term (kyr to Myr) evolution of landscapes. Such data analysis will expose the student to high-level programming environments (e.g., Python, MATLAB, C++, Fortran). Furthermore, the student will apply and develop further process-based numerical models in both forward and inverse modes in a high-performance cluster (HPC) environment. This also implies the statistical evaluation of a large number (>10000) of model runs and big (environmental) data analysis (e.g., misfit analysis, multivariate statistics, geospatial data analysis). Visits to worldwide leading institutions (GFZ Potsdam/Germany and CSDMS Boulder, Colorado / USA) supplement this training. The training also constitutes ‘soft’ skills: project management, scientific writing, grant acquisition, (oral and written) project reporting. These skills make the student highly competitive to a career in computationally driven Earth System science. Beyond academia the student will be able to analyse and manipulate large data sets, apply, and evolve process-based numerical models, make data-driven model predictions towards machine learning capabilities. Hence, the student will be highly employable in the fields of environmental consulting, hazard research, land management and software development.

References & further reading

Clark, M. K., Royden, L. H., Whipple, K. X., Burchfiel, B. C., Zhang, X., & Tang, W. (2006). Use of a regional, relict landscape to measure vertical deformation of the eastern Tibetan Plateau. Journal of Geophysical Research: Earth Surface, 111(F3).
Eizenhöfer, P. R., McQuarrie, N., Shelef, E., & Ehlers, T. A. (2019). Landscape response to lateral advection in convergent orogens over geologic time scales. Journal of Geophysical Research: Earth Surface, 124(8), 2056-2078.
Mutz, S. G., & Ehlers, T. A. (2019). Detection and explanation of spatiotemporal patterns in Late Cenozoic palaeoclimate change relevant to Earth surface processes. Earth Surface Dynamics, 7(3), 663-679.
Whipple, K. X., Forte, A. M., DiBiase, R. A., Gasparini, N. M., & Ouimet, W. B. (2017). Timescales of landscape response to divide migration and drainage capture: Implications for the role of divide mobility in landscape evolution. Journal of Geophysical Research: Earth Surface, 122(1), 248-273.
Whittaker, A. C., & Boulton, S. J. (2012). Tectonic and climatic controls on knickpoint retreat rates and landscape response times. Journal of Geophysical Research: Earth Surface, 117(F2).
Yang, R., Willett, S. D., & Goren, L. (2015). In situ low-relief landscape formation as a result of river network disruption. Nature, 520(7548), 526-529.
Zachos, J. C., Dickens, G. R., & Zeebe, R. E. (2008). An early Cenozoic perspective on greenhouse warming and carbon-cycle dynamics. nature, 451(7176), 279-283.

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