Volume 34, Issue 25 pp. 5085-5103
RESEARCH ARTICLE

The role of meteorological forcing and snow model complexity in winter glacier mass balance estimation, Columbia River basin, Canada

Marzieh Mortezapour

Corresponding Author

Marzieh Mortezapour

Natural Resources and Environmental Studies Institute, Prince George, British Columbia, Canada

Correspondence

Marzieh Mortezapour, University of Northern British Columbia, 4-246, 3333 University Way, Prince George, BC V2N 4Z9, Canada.

Email: [email protected]

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Brian Menounos

Brian Menounos

Natural Resources and Environmental Studies Institute, Prince George, British Columbia, Canada

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Peter L. Jackson

Peter L. Jackson

Natural Resources and Environmental Studies Institute, Prince George, British Columbia, Canada

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Andre R. Erler

Andre R. Erler

Aquanty, Waterloo, Ontario, Canada

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Ben M. Pelto

Ben M. Pelto

Natural Resources and Environmental Studies Institute, Prince George, British Columbia, Canada

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First published: 04 October 2020
Citations: 8

Funding information: The Canada Research Chairs Program; The Columbia Basin Trust; The Engineering and Research Council of Canada

Abstract

Glaciers are commonly located in mountainous terrain subject to highly variable meteorological conditions. High resolution meteorological (HRM) data simulated by atmospheric models can complement meteorological station observations in order to assess changes in glacier energy fluxes and mass balance. We examine the performance of two snow models, SnowModel and Alpine3D, forced by different meteorological data for winter mass balance simulations at four glaciers in the Canadian portion of the Columbia Basin. The Weather Research and Forecasting model (WRF) with resolution of 1 km and the North American Land Data Assimilation System with ~12 km resolution, provide HRM data for the two snow models. Evaluation is based on the ability of the snow models to simulate snow depth at both point locations (automated snow weather stations) and over the entire glacier surface (airborne LiDAR [Light Detection and Ranging] surveys) during the 2015/2016 winter accumulation. When forced with HRM data, both models can reproduce snow depth to within ±15% of observed values. Both models underestimate winter mass balance when forced by HRM data. When driven with WRF data, SnowModel underestimates winter mass balance integrated over the glacier area by 1 and 10%, whilst Alpine3D underestimates winter mass balance by 12 and 22% compared with LiDAR and stake measurements, respectively. The overall results show that SnowModel forced by WRF simulated winter mass balance the best.

DATA AVAILABILITY STATEMENT

The data that support the findings of this study are available from the corresponding author upon reasonable request. Automated snow weather station (ASWS) data over British Columbia are publicly available through: https://www2.gov.bc.ca/gov/content/environment/air-land-water/water/water-science-data/water-data-tools/snow-survey-data/automated-snow-weather-station-data. Weather station data for multiple networks over British Columbia is accessible through: https://data.pacificclimate.org/portal/pcds/map/.

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