Item: DATA-DRIVEN PREDICTION OF SATELLITE-OBSERVED AVALANCHE ACTIVITY FROM SNOWPACK SIMULATIONS
-
-
Title: DATA-DRIVEN PREDICTION OF SATELLITE-OBSERVED AVALANCHE ACTIVITY FROM SNOWPACK SIMULATIONS
Proceedings: Proceedings, International Snow Science Workshop, Whistler, BC, Canada, 2026
Authors:
- Jakob Grahn [ NORCE Research, Tromsø, Norway ]
- Filippo Maria Bianchi [ NORCE Research, Tromsø, Norway ] [ UiT The Arctic University of Norway, Tromsø, Norway ]
- Bert Kruyt [ Norwegian Water Resources and Energy Directorate, Oslo, Norway ]
- Karsten Müller [ Norwegian Water Resources and Energy Directorate, Oslo, Norway ]
Date: 2026-09-28
Abstract: Avalanche forecasting requires knowledge of the snowpack and recent avalanche activity, but both are difficult to keep track of across large mountain regions. Field observations are essential but often sparse, which limits regional monitoring and development of numerical or statistical prediction models. Synthetic aperture radar (SAR) can repeatedly map avalanche debris over large areas, opening up new opportunities for data-driven approaches for avalanche forecasting. In this study, we take a first step in this direction. We constructed two large datasets for five winters with two operational Sentinel-1 satellites and a typical six-day repeat interval. First, we mapped avalanche debris in Sentinel-1 images across Norway and parts of Sweden. Secondly, we ran the SNOWPACK model forced by numerical weather predictions on a 20×20 km grid, at different elevations and predefined slope angles. A transformer was then trained to use five days of SNOWPACK outputs to predict activity mapped by SAR for the following day. We represented activity with the SAR-detected Avalanche Activity Index (SAR-AAI), a study-specific index that gives larger debris more weight, spreads detections across possible occurrence dates and normalises by modelled runout area. We trained the transformer on four winters and evaluated it on one. Regional mean predicted and reference SAR-AAI had a Pearson correlation of 0.803 when averaged over complete six-day periods, with each value placed at the midpoint of its period. The model followed broad changes in time and space, but produced smoother predictions and underestimated the strongest activity. At the 20 km cell scale, agreement after the same six-day averaging was weaker (r = 0.549). The result is based on a single training run of the machine-learning model and has not been tested on an untouched winter. The SAR dataset is incomplete and contains detection errors and uncertain timing. Thus, the results do not yet show operational forecast skill. Still, predictions based on regional SNOWPACK simulations followed broad changes mapped by Sentinel-1. This is a promising first step towards using SAR avalanche detections with snowpack modelling for avalanche forecasting.
Object ID: ISSW2026_P2.46.pdf
DOI: https://doi.org/10.15788/1790099016
Language of Article: English
Presenter(s): Jakob Grahn
Keywords: avalanche activity; synthetic aperture radar; SNOWPACK; deep learning; remote sensing
Page Number(s): 1627 - 1633
-