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. Avalanche problems are used in avalanche forecasting to describesnowpack, weather, and terrain factors that require distinct riskmanagement techniques. Although they have become an effective tool forassessing and communicating avalanche hazard, their definitions leaveroom for interpretation and inconsistencies. This study uses conditionalinference trees to explore the application of avalanche problems overeight winters in Glacier National Park, Canada. The influences of weatherand snowpack variables on each avalanche problem type were explored byanalysing a continuous set of weather and snowpack variables producedwith a numerical weather prediction model and a physical snow covermodel. The decision trees suggest forecasters' assessments are based on not onlya physical analysis of weather and snowpack conditions butalso contextual information about the time of season, the location, andinteractions with other avalanche problems. The decision trees showedclearer patterns when new avalanche problems were added to hazardassessments compared to when problems were removed. Despitediscrepancies between modelled variables and field observations, themodel-generated variables produced intuitive explanations for conditionsinfluencing most avalanche problem types. For example, snowfall in the past 72 h wasthe most significant variable for storm slab avalanche problems, skierpenetration depth was the most significant variable for dry looseavalanche problems, and slab density was the most significant variablefor persistent-slab avalanche problems. The explanations for wind slaband cornice avalanche problems were less intuitive, suggesting potentialinconsistencies in their application as well as shortcomings of themodel-generated data. The decision trees illustrate how forecastersapply avalanche problems and can inform discussions about improvedoperational practices and the development of data-driven decision aids.

Examining the operational use of avalanche problems with decision trees and model-generated weather and snowpack variables