Articles | Volume 19, issue 8
https://doi.org/10.5194/tc-19-3309-2025
https://doi.org/10.5194/tc-19-3309-2025
Research article
 | 
27 Aug 2025
Research article |  | 27 Aug 2025

Improved modelling of mountain snowpacks with spatially distributed precipitation bias correction derived from historical reanalysis

Manon von Kaenel and Steven A. Margulis

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Cited articles

Aalstad, K., Westermann, S., Schuler, T. V., Boike, J., and Bertino, L.: Ensemble-based assimilation of fractional snow-covered area satellite retrievals to estimate the snow distribution at Arctic sites, The Cryosphere, 12, 247–270, https://doi.org/10.5194/tc-12-247-2018, 2018. 
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Bair, E. H., Abreu Calfa, A., Rittger, K., and Dozier, J.: Using machine learning for real-time estimates of snow water equivalent in the watersheds of Afghanistan, The Cryosphere, 12, 1579–1594, https://doi.org/10.5194/tc-12-1579-2018, 2018. 
Bair, E. H., Rittger, K., Skiles, S. M., and Dozier, J.: An Examination of Snow Albedo Estimates From MODIS and Their Impact on Snow Water Equivalent Reconstruction, Water Resour. Res., 55, 7826–7842, https://doi.org/10.1029/2019WR024810, 2019. 
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Short summary
Accurate snow water equivalent (SWE) estimates are crucial for water management in snowmelt-dependent regions, but bias and uncertainty in precipitation data make this challenging. Here, we leverage insights from a historical SWE data product to correct these biases and yield more accurate SWE estimates and streamflow predictions. Incorporating snow depth observations further boosts accuracy. This study demonstrates an effective method to downscale and bias-correct global mountain precipitation.
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