Item: ENHANCING OPERATIONAL AVALANCHE RISK ASSESSMENT WITH RPAS LIDAR SNOW DEPTH MAPS AND AVALANCHE SIMULATIONS: A CASE STUDY IN WESTERN CANADA
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Title: ENHANCING OPERATIONAL AVALANCHE RISK ASSESSMENT WITH RPAS LIDAR SNOW DEPTH MAPS AND AVALANCHE SIMULATIONS: A CASE STUDY IN WESTERN CANADA
Proceedings: International Snow Science Workshop 2024, Tromsø, Norway
Authors:
- Eirik Ainer-Sharp [ Alpine Solutions ]
- Cam Campbell [ Alpine Solutions ]
- Alexis Tessier [ Alpine Solutions ]
Date: 2024-09-23
Abstract: In this study, we present a novel approach to operational avalanche risk assessment using numerical avalanche models initialized with snow depth maps to simulate the maximum possible avalanche for a given snowpack depth distribution. We demonstrate an application of this approach as part of an operational risk assessment at a mine in British Columbia, Canada, where it was used to determine whether specific snow-pack conditions could generate avalanches large enough to affect worksites and infrastructure. This study also demonstrates the advantages of using Remote Piloted Aircraft Systems (RPAS, colloquially, drones) as a platform for capturing LiDAR data for path-scale snow depth mapping, highlighting their ability to provide high accuracy and repeatable measurements. Assessing potential avalanche magnitudes is critical to operational risk management. Uncertainty in these assessments can lead to overly conservative risk treatments. Our study, which integrates RAMMS (Rapid Mass Movement Simulation) with high-resolution snow depth data from RPAS LiDAR, demonstrates a practical and objective risk assessment tool to support these evaluations. While this approach does not predict avalanche size, it offers valuable information on the upper bounds of potential avalanche runout under specific snowpack conditions. This is particularly useful for assessing the exposure of elements at risk, especially for infrequent avalanches or early and late-season conditions, thereby enabling better-informed risk management decisions.
Object ID: ISSW2024_P1.30.pdf
Language of Article: English
Presenter(s): Eirik Ainer-Sharp
Keywords: RPAS, UAV, drone, LiDAR, RAMMS, forecasting.
Page Number(s): 257 - 263
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