27 Mar 2024
 | 27 Mar 2024
Status: this preprint is open for discussion.

Evaluating a hierarchy of bias correction methods for ERA5-Land SWE in northern Canada

Neha Kanda and Christopher G. Fletcher

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.

Neha Kanda and Christopher G. Fletcher

Status: open (until 11 May 2024)

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  • RC1: 'Comment on egusphere-2024-639', Anonymous Referee #1, 02 Apr 2024 reply
Neha Kanda and Christopher G. Fletcher
Neha Kanda and Christopher G. Fletcher


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Short summary
For improved water management in snow-dominated regions like Northern Canada, accurate estimates of Snow Water Equivalent (SWE), a metric that quantifies the water in a snowpack are crucial. Our study aims to improve the SWE estimates which were found to be underestimated, particularly in the mountains. We tested four correction techniques and found Random Forest (RF) to be the most effective technique that significantly reduced the errors.