Improving gridded snow water equivalent products in British Columbia, Canada: multi-source data fusion  by neural network models

Snauffer, Andrew M.; Hsieh, William W.; Cannon, Alex J.; Schnorbus, Markus A.

doi:https://doi.org/10.5194/tc-12-891-2018

Articles | Volume 12, issue 3

https://doi.org/10.5194/tc-12-891-2018

© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 3.0 License.

https://doi.org/10.5194/tc-12-891-2018

© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 3.0 License.

Articles | Volume 12, issue 3

Research article

|

12 Mar 2018

Research article |

| 12 Mar 2018

Improving gridded snow water equivalent products in British Columbia, Canada: multi-source data fusion by neural network models

Andrew M. Snauffer, William W. Hsieh, Alex J. Cannon, and Markus A. Schnorbus

Supplement

https://doi.org/10.5194/tc-12-891-2018-supplement

Data sets

MERRA 2D IAU Diagnostic, Land Only States and Diagnostics Global Modeling and Assimilation Office https://doi.org/10.5067/YL8Z7MICQZF9

MERRA Simulated 2D Incremental Analysis Update (IAU) MERRA-Land reanalysis Global Modeling and Assimilation Office https://doi.org/10.5067/OQ6B1RHOHBI8

GLDAS Noah Land Surface Model L4 3 hourly 0.25 x 0.25 degree V2.0 M. Rodell and H. K. Beaudoing https://doi.org/10.5067/342OHQM9AK6Q

Short summary

Estimating winter snowpack throughout British Columbia is challenging due to the complex terrain, thick forests, and high snow accumulations present. This paper describes a way to make better snow estimates by combining publicly available data using machine learning, a branch of artificial intelligence research. These improved estimates will help water resources managers better plan for changes in rivers and lakes fed by spring snowmelt and will aid other research that supports such planning.