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

• Published in: Hydrological processes Vol. 34; no. 25; pp. 5085 - 5103
• Main Authors: Mortezapour, Marzieh, Menounos, Brian, Jackson, Peter L., Erler, Andre R., Pelto, Ben M.
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 15.12.2020; Wiley Subscription Services, Inc
• Subjects: Atmospheric forcing; Atmospheric models; automation; basins; Canada; Columbia River; Data assimilation; Data collection; Data integration; Fluxes; Glacier mass balance; Glaciers; hydrology; landscapes; LiDAR; Lidar measurements; Mass; Mass balance; Mass balance of glaciers; Meteorological conditions; Meteorological data; modelling; mountains; NLDAS2; Resolution; River basins; Simulation; Snow; Snow accumulation; Snow depth; snow water equivalent; Surveys; watersheds; Weather forecasting; Weather stations; Winter; winter mass balance; WRF; Canada; Columbia River
• ISSN: 0885-6087; 1099-1085
• DOI: 10.1002/hyp.13929

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. In recent decades, snow models have been developed and improved in light of enhanced understanding of snow process physics and growth in computational power, leading to higher complexity of distributed physics‐based snow models. In this study, we evaluate and compare the performance of two snow evolution models with different complexities at simulating winter glacier mass balance using two different datasets. Additionally, we evaluate the performance of two meteorological model datasets (WRF and NLDAS2) that can be used to force snow models as alternatives to weather stations in areas with scarce observations.