Articles | Volume 24, issue 10
Hydrol. Earth Syst. Sci., 24, 4887–4902, 2020
https://doi.org/10.5194/hess-24-4887-2020
Hydrol. Earth Syst. Sci., 24, 4887–4902, 2020
https://doi.org/10.5194/hess-24-4887-2020

Research article 14 Oct 2020

Research article | 14 Oct 2020

Application of machine learning techniques for regional bias correction of snow water equivalent estimates in Ontario, Canada

Fraser King et al.

Data sets

Snow Data Assimilation System (SNODAS) Data Products at NSIDC, Version 1, SWE Data Subset National Operational Hydrologic Remote Sensing Center https://doi.org/10.7265/N5TB14TC

Canadian Meteorological Centre (CMC) Daily Snow Depth Analysis Data R. Brown and B. Brasnett https://doi.org/10.5067/W9FOYWH0EQZ3

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Short summary
Snow is a critical contributor to our water and energy budget, with impacts on flooding and water resource management. Measuring the amount of snow on the ground each year is an expensive and time-consuming task. Snow models and gridded products help to fill these gaps, yet there exist considerable uncertainties associated with their estimates. We demonstrate that machine learning techniques are able to reduce biases in these products to provide more realistic snow estimates across Ontario.