IAP-24-049

Glacial valleys in a changing climate: understanding links between glacial valley morphology, sediment dynamics, and climate variations

The melting of mountain glaciers have changed the sediment delivery to downstream rivers, posing threats to infrastructure, food security, and ecological stability in mountainous regions and beyond (Zhang et al., 2022). However, uncertainties remain in projections of future sediment delivery under ongoing climate change.

Sediment dynamics in glacial landscapes are controlled by the morphology of glacial valleys. Past glaciations have carved distinctive U-shaped valleys throughout mountainous regions worldwide. With ongoing climate warming, more of these glacially carved valleys are being exposed. These valleys serve as critical, temporary sediment reservoirs, capturing material as it is transported from valley walls to the oceans. The capacity of these valleys to retain sediment is fundamentally governed by their unique morphological characteristics, particularly the valley floor morphology. Meanwhile, valley walls act as essential sediment sources, with their morphology directly affecting sediment production rates. Furthermore, glacial valley morphology preserves valuable records of past sediment production during their formation. Given the critical role of glacial valleys in sediment production and storage, it is essential to understand the formation and evolution of glacial valley morphology in response to climate fluctuations.

This project aims to address this question by integrating topographic analysis, numerical modeling, and machine learning approaches. Building on recently developed automated geospatial techniques (Clubb et al., 2022; Hilley et al., 2020; Hurst et al., 2012), the student will generate datasets capturing the morphological characteristics of glacial valleys. These datasets will then be combined with numerical landscape evolution modeling (Lai and Anders, 2021) and/or machine learning approaches (Mey et al., 2015) to investigate the interactions among valley morphology, sediment dynamics, and climate variability.

This multidisciplinary project offers the student opportunities to develop expertise in geospatial data analysis, numerical simulations, and machine learning techniques. The student will be supervised by leading experts in geomorphology, glaciology, sedimentology, and computational geoscience. Additionally, the project will provide opportunities to collaborate with international researchers and visit leading institutions worldwide.

Automated geomorphological mapping:
Building on recently developed automated topographic analysis techniques, this project will develop methods to extract key valley morphological metrics, including valley floor width, valley wall slope, and hillslope curvature. These methods will be applied to glacial landscapes worldwide, such as the Alps, the Himalayas, and New Zealand, to generate comprehensive datasets of glacial valley morphology.

Statistical analysis and machine learning:
To understand connections between glacial valley morphology, sediment dynamics, and climate, this project will integrate the newly generated morphological datasets with climate and sedimentology data and investigate their correlations through statistical and machine learning techniques.

Numerical landscape evolution modeling:
This project will also use numerical landscape evolution models to investigate the evolution of valley morphology in response to climate variations and to evaluate the impact of different valley morphological characteristics on sediment dynamics. Model results will be integrated with analyses of valley morphology datasets to investigate the mechanisms driving interactions among glacial valley morphology, sediment dynamics, and climate.

Year 1

The student will conduct literature review to identify key valley morphological metrics that are sensitive to climate variations and/or important to sediment dynamics. These metrics will be extracted using automated geomorphological mapping methods to form a dataset of valley morphology. The student will visit Durham to work with co-supervisor to develop automated mapping methods.

Year 2

The student will collect relevant climate and sedimentology datasets from the literature and perform statistical and/or machine learning methods to these datasets to reveal connections among glacial valley morphology, sediment dynamics, and climate.

Year 3

Building on previous analyses, the student will set up landscape evolution models and conduct simulations. These results will be investigated to understand the evolution of valley morphology under various climate scenarios and their impact on sediment dynamics. The student will have opportunities to visit the GFZ Potsdam in Germany or the University of Lausanne in Switzerland to learn numerical modeling of landscape evolution and glacier dynamics.

Year 3.5

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

This multidisciplinary project provides the student with various opportunities to develop professional and transferable skills. Through relevant research work, the student will develop expertise in geomorphology, glaciology, and sedimentology. Automated data collection, large-scale geospatial analysis, and numerical simulations will offer the student opportunities to gain experience in programming, big data analysis, machine learning, data-driven research, high-performance computing, etc. The student will also develop soft skills including critical thinking project management, communication, and teamwork.

The supervisors will work with the student to design a flexible training plan tailored to their individual needs. A Personal Development Plan will be implemented and regularly reviewed to address key knowledge and skill gaps, ensuring continuous progress in fulfilling academic goals and personal development needs. Additionally, the student will have access to a range of professional and career development programs at the University of Glasgow.

The School of Geographical and Earth Sciences at the University of Glasgow provides a lively environment that maximize the student’s opportunity to learn, grow and develop. The student will be integrated into the “Earth, Climate & Biosphere Dynamics” and “Computational Earth Systems​” research clusters, interacting with experts from diverse fields of Earth system science. The school and the university offer opportunities to participate in research seminars and networking events. More broadly, the student will have opportunities to work with international researchers and visit leading institutions worldwide.