IAP-24-014

Relict Landscapes as Archives of Past Climate and Tectonics

‘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.

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.
Additional thermochronological analyses from recently collected rock samples from southern Mongolia to be undertaken at the University of Glasgow will further constrain and characterise long-term landscape evolution and the preservation of relict landscapes there.
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 novel model inversions using a combination of statistical emulators and ensemble Kalman filters to reconstruct climatic and tectonic conditions during and after the formation of relict landscapes.

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. At the same time, thermochronological analyses on the new samples from southern Mongolia will be started.

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. Thermochronological analyses will be completed to feed into the landscape evolution model inversions in year 3.

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 take advantage of cutting-edge statistical modelling tools (statistical emulation and ensemble Kalman filtering). The inversion 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.

The student will be trained by leading experts of geomorphology, tectonics and statistical modelling 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, geodynamics and statistics 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 using the new high-performance cluster (HPC) environment at the School of Geographical & Earth Sciences. This also implies big (environmental and statistical) data analysis (e.g., misfit analysis, multivariate statistics, geospatial data analysis, statistical modelling). 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.