About the Project
Project Background. Glaciers in High Mountain Asia are experiencing mass loss , with implications for the hundreds of millions of people who depend on them for critical water resources . Projections of the likely trajectory of Himalayan glacier mass balance, and associated runoff, are highly uncertain – due in part to lack of knowledge of glacier thickness, which determines glacier response to climate change . With an ever-growing remote-sensing record for the 90,000 glaciers in the region, there is potential to compute thicknesses regionally and model glacier response to climate change , but until now, very few measurements were available to constrain the thickness models. With the completion of the first airborne  ice-thickness survey in the Himalayas, covering the glaciers of the Khumbu basin, these models can finally be constrained.
This project aims to reduce uncertainty in the response of High Mountain Asia glaciers to climate warming by combining new field and remote-sensing data products with advanced computational and physical modelling methods. More specifically,
How do remote sensing observations allow us to improve estimates of glacier thickness, ice volume, and ice ablation in difficult-to-reach regions such as the Himalaya?
How does the improved assessment of glacier thickness and ablation aid in modelling the future behaviour of Asian glaciers in response to climate change?
Using state-of-the-art data-science methods, can the method generated in (1) be built upon to deepen our understanding of other poorly constrained glacier properties?
Methodology. The method for inferring ablation and thickness will be based on a small-scale “proof of concept” inverse model study carried out within the glacial group of the School of GeoSciences. The work of the PhD student will involve automation and refinement of the method, drawing from a number of Level-2 and Level-3 EO datasets: elevation change (WorldView  and ASTER  based geodetic data); and elevation (SRTM); and glacier velocities (NASA ITS_LIVE). State-of-the-art data science methods such as deep learning will be used to optimize the method using helicopter-airborne radar-derived thicknesses provided by BAS supervisors – the first data set of its kind for HMA – and provide an Improved glacier thickness product for the region.
Glacial modelling will involve the use of a glacial simulation software package to allow for application to a large glacier inventory such as the Open Global Glacier Model (oggm.org). In order to represent changes to current ablation due to climate change, climate projection data will be used.
Once “baseline” experiments assessing the impact of the refined thickness have been carried out, the project will delve deeper into constraining glacier properties and physics based on observations. For example,  gained insight into the nature of basal sliding by considering temporal evolution, and similar approaches could be used to refine other processes. The impact of this refinement of processes will be tested using the glacial model developed previously.
CASE Partner: EarthWave Ltd (https://earthwave.co.uk/) is a data science startup that brings together substantial expertise in remote sensing, artifical intelligence and system engineering and will aid on high-level computing and data science aspects of the project.
This PhD is part of the NERC and UK Space Agency funded Centre for Doctoral Training "SENSE": the Centre for Satellite Data in Environmental Science. SENSE will train 50 PhD students to tackle cross-disciplinary environmental problems by applying the latest data science techniques to satellite data. All our students will receive extensive training on satellite data and AI/Machine Learning, as well as attending a field course on drones, and residential courses hosted by the Satellite Applications Catapult (Harwell), and ESA (Rome). All students will experience extensive training on professional skills, including spending 3 months on an industry placement. See http://www.eo-cdt.org
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