A machine learning approach to constraining ice volume and potential loss in High Mountain Asia

Project Background. Glaciers in High Mountain Asia (HMA) are
experiencing mass loss [1], with implications for the hundreds of millions
of people who depend on them for critical water resources [2]. 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 [3]. With
an ever-growing remote-sensing record for the 90,000 glaciers in the
region [e.g., 4], there is potential to compute thicknesses regionally and
model glacier response to climate change [5], but until now, very few
measurements were available to constrain the thickness models. With the
completion of the first airborne [6] ice-thickness survey in the Himalayas,
covering the glaciers of the Khumbu basin, these models can finally be constrained.
This project will investigate HMA glacier sensitivity to climate warming by combining new field and
satellite data products with advanced modelling and machine learning methods. More specifically,
1. Can ML-trained models assimilate/invert for HMA thickness data from satellite data?
2. How do field observations inform and improve such inverse models?
3. How does the improved assessment of glacier thickness and ablation aid in modelling the future
behaviour of Asian glaciers in response to climate change?
Methodology. The method for inferring thickness will be based around the python assimilation
framework of [5], which makes use of the Instructed Glacier Model, a deep learning emulator [7].
The framework has been applied successfully to Alpine glaciers, but not to HMA glaciers where type
and availability of observations differs. The work of the PhD will involve modifying the framework for
application to HMA glaciers; preparing and experimenting with inputs based on potential Level-2 and
Level-3 EO datasets: elevation change (WorldView [8] and ASTER [9] and Cryosat [1] based data);
as well as elevation and glacier velocities (ITS_LIVE). Airborne thickness measurements of select
glaciers will be provided by BAS supervisors, allowing validation and refinement of the methodology.
Importantly, as IGM has only been trained on and applied to Alpine glaciers, its performance will
also be tested on a small subset against a physical glacier model [10], with potential to improve the
IGM through further deep learning. The impacts of the improved thickness on future glacier loss will
be examined through multidecadal modelling using the IGM.
Context: This PhD project will engage strongly with The Big Thaw, a recently-funded BAS-led,
cross-institutional NERC Highlight Topics grant which aims to fill key gaps in knowledge of global
mountain water resources, but does not encompass novel ML approaches to thickness estimation.
The efforts of this PhD will feed into and inform The Big Thaw, and the student will be strongly
involved in project meetings and discussions, enabling strong interaction with scientists at BAS,
Leeds, and CEH that extend beyond the supervisory team and industry partner.