Preprints
https://doi.org/10.5194/egusphere-2024-2378
https://doi.org/10.5194/egusphere-2024-2378
06 Sep 2024
 | 06 Sep 2024
Status: this preprint is open for discussion.

A minimal machine learning glacier mass balance model

Marijn van der Meer, Harry Zekollari, Matthias Huss, Jordi Bolibar, Kamilla Hauknes Sjursen, and Daniel Farinotti

Abstract. Glacier retreat presents significant environmental and social challenges. Understanding the local impacts of climatic drivers on glacier evolution is crucial, with mass balance being a central concept. This study introduces miniML-MB, a new minimal machine learning model designed to estimate annual point surface mass balance (PMB) for very small datasets. Based on an XGBoost architecture, miniML-MB is applied to model PMB at individual sites in the Swiss Alps, emphasizing the need for an appropriate training framework and dimensionality reduction techniques. A substantial added value of miniML-MB is its data-driven identification of key climatic drivers of local mass balance. The best PMB prediction performance was achieved with two predictors: mean air temperature (May–August) and total precipitation (October–February). miniML-MB models PMB accurately from 1961 to 2021, with a mean absolute error (MAE) of 0.417 m w.e. across all sites. Notably, miniML-MB demonstrates similar and, in most cases, superior predictive capabilities compared to a simple positive degree-day (PDD) model (MAE of 0.541 m w.e.). Compared to the PDD model, miniML-MB is less effective at reproducing extreme mass balance values (e.g., 2022) that fall outside its training range. As such, miniML-MB shows promise as a gap-filling tool for sites with incomplete PMB measurements, as long as the missing year's climate conditions are within the training range. This study underscores potential ways for further refinement and broader applications of data-driven approaches in glaciology.

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Marijn van der Meer, Harry Zekollari, Matthias Huss, Jordi Bolibar, Kamilla Hauknes Sjursen, and Daniel Farinotti

Status: open (until 07 Nov 2024)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2024-2378', Signe Hillerup Larsen, 01 Oct 2024 reply
Marijn van der Meer, Harry Zekollari, Matthias Huss, Jordi Bolibar, Kamilla Hauknes Sjursen, and Daniel Farinotti

Interactive computing environment

miniML-MB: Release v.1.1 Marijn van der Meer https://doi.org/10.5281/zenodo.12905503

Marijn van der Meer, Harry Zekollari, Matthias Huss, Jordi Bolibar, Kamilla Hauknes Sjursen, and Daniel Farinotti

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Short summary
Glacier retreat poses big challenges, making understanding how climate affects glaciers vital. But glacier measurements worldwide are limited. We created a simple machine-learning model called miniML-MB, which estimates annual changes in glacier mass in the Swiss Alps. As input, miniML-MB uses two climate variables: average temperature (May–Aug.) and total precipitation (Oct.–Febr.). Our model can accurately predict glacier mass from 1961–2021 but struggles for extreme years (2022 and 2023).