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September 10, 2025The cryosphereOpen Access

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

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Significant improvements in snow water equivalent estimates are achieved with spatially distributed precipitation bias correction.
The multi-year averaged normalized root mean square difference drops from 78% to 33%, enhancing accuracy significantly.
Incorporating snow depth observations further reduces errors at both watershed and pixel levels during the accumulation season.
These bias correction methods notably improve the correlation between simulated snowmelt and observed streamflow, bolstering water management.

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Implication

This study finds improved snow water equivalent estimates through spatially distributed precipitation bias correction, highlighting its significance for streamflow forecasts.

Cite This Study

Kaenel et al. (2025) studied this question.

synapsesocial.com/papers/68c1d24654b1d3bfb60f85b2 https://doi.org/10.5194/tc-19-3309-2025

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Implication

This study finds improved snow water equivalent estimates through spatially distributed precipitation bias correction, highlighting its significance for streamflow forecasts.

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

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Implication

Cite This Study

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

Key Points

Discussion

Implication

Cite This Study

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