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The role of meteorological forcing and snow model complexity in winter glacier mass balance estimation, Columbia River basin, Canada
Author(s) -
Mortezapour Marzieh,
Menounos Brian,
Jackson Peter L.,
Erler Andre R.,
Pelto Ben M.
Publication year - 2020
Publication title -
hydrological processes
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.222
H-Index - 161
eISSN - 1099-1085
pISSN - 0885-6087
DOI - 10.1002/hyp.13929
Subject(s) - snow , glacier , weather research and forecasting model , glacier mass balance , climatology , environmental science , forcing (mathematics) , automatic weather station , lidar , terrain , snowpack , meteorology , atmospheric sciences , geology , geography , remote sensing , geomorphology , cartography
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.