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Towards a 3-Dimensional Model of Forest Heat Contributions to Snowpack Thermodynamics: Determining internal snowpack temperature responses to energy balance drivers in the Australian Alps
  • Andrew Schwartz,
  • Hamish McGowan
Andrew Schwartz
University of Queensland

Corresponding Author:andrew.schwartz@uq.edu.au

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Hamish McGowan
The University of Queensland
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Abstract

Research on modification to snowpacks as a result of forest disturbance has typically focused on spatiotemporal patterns of snow depth and snow water equivalent, snowpack energy fluxes, and melt/ablation characteristics. However, little work has been conducted on relationships between tree trunks and snowpack dynamics. Insight into drivers of internal snowpack thermodynamics around trees and their response to forest disturbance is crucial to understanding hydrological processes in forested regions of the cryosphere, especially as forest disturbance through climate change continues. This work investigates relationships between energy fluxes and thermodynamic patterns surrounding tree trunks and within the greater snowpacks of forest stands in the Snowy Mountains of the Australian Alps. Measurements of vertical and horizontal snowpack temperature profiles and sub-canopy energy fluxes were collected during the 2018 winter season in non-disturbed and fire-disturbed Eucalyptus pauciflora (Snow Gum) stands. Primary heat sources were identified for each measurement location in the snowpack through employing the Random Forest machine learning regression method. Preliminary results indicate that soil heat flux is the dominant control on snowpack temperature at all locations in the un-disturbed forest stand. However, outgoing longwave radiation is shown to be the prevalent driver at numerous locations within the fire-disturbed stand that are close to the snowpack surface and tree well. This work aims to develop the physical basis for a 3-dimensional thermodynamic model of snowpacks contained in forests that could be used in conjunction with existing 1-dimensional snowpack models to determine melt and variability.