EGU24-13710, updated on 09 Mar 2024
EGU General Assembly 2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

A glacier ice volume modeling framework based on generative adversarial networks and graph neural networks

Niccolò Maffezzoli1,2,3, Gianluca Lagnese3,4, Sebastiano Vascon1, Troels Petersen5, Carlo Barbante1, and Eric Rignot2,6
Niccolò Maffezzoli et al.
  • 1Ca' Foscari University of Venice, Venice, Italy
  • 2University of California Irvine, Irvine, USA
  • 3Institute of Polar Sciences, National Research Council (CNR-ISP), Venice, Italy
  • 4Jožef Stefan Institute, Ljubljana, Slovenia
  • 5Niels Bohr Institute, University of Copenhagen, Copenhagen, Denmark
  • 6Jet Propulsion Laboratory, Pasadena, USA

Estimating the ice volume of Earth's glaciers is a key challenge in Earth System science, crucial for understanding their evolution, quantifying future global sea level rise and freshwater resources in climate-sensitive regions. Given current global warming that causes glaciers mass loss, precise ice volume estimates become a top priority in face of future climate scenarios. 

Here we present the SKYNET project, which aims to develop a general modeling framework for estimating ice volumes of Earth’s glaciers using generative deep learning. 

The modeling framework comprises two main networks. The first network, a generative adversarial network, reconstructs glacier bedrocks using elevation maps of surrounding ice-denuded regions. Trained on over 1 million Digital Elevation Maps, the model learns key geometrical features and patterns of Earth’s mountain regions. The challenge is addressed as an image inpainting problem, in which the objective is to reconstruct the bedrock altitude in a missing portion of the image, using surrounding information. 

The second network leverages existing ice thickness measurements. Such a network is trained on local features such as ice velocity, slope, distance from glacier boundaries, and other glacier statistics to predict local ice thickness. We use the Glacier Ice Thickness Dataset (GlaThiDa) and other input products as training dataset. We employ a graph neural network (GNN) with an architecture that explicitly accounts for the connectivity of the data. The GNN’s local ice thickness estimates serve as a prior to refine the inpainting network’s generated ice thickness maps. 

We introduce the model, discuss its concept, advantages, limitations, current challenges and present preliminary results and tests on diverse continental glaciers across the globe. 

How to cite: Maffezzoli, N., Lagnese, G., Vascon, S., Petersen, T., Barbante, C., and Rignot, E.: A glacier ice volume modeling framework based on generative adversarial networks and graph neural networks, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13710,, 2024.