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Abstract. Precise estimates of Snow Water Equivalent (SWE) are crucial for informed decision-making in regions like Northern Canada, where snow cover significantly contributes to springtime discharge. However, the sparse nature of the existing SWE monitoring network poses a challenge to comprehensively understanding the SWE distribution and variability. Reanalysis products like ERA5-Land provide long-term continuous SWE estimates, but our evaluation identified a negative bias (-61 mm) in the estimated SWE and maximum underestimation was observed at high elevation (>1500 m) areas. To correct these biases, we applied four correction methods: Mean Bias Subtraction (MBS), Simple Linear Regression (SLR), Multiple Linear Regression (MLR), and Random Forest (RF). RF exhibited the highest performance, reducing the Root Mean Square Error (RMSE) by 78 % and minimizing the annual mean bias from 61.2 mm to 0.01 mm. However, RF did not produce reliable SWE estimates for unseen spatial and temporal domains due to its limitation of not extrapolating beyond the training data.
Kanda et al. (Wed,) studied this question.