# Estimating Snow-Related Daily Change Events in the Canadian Winter Season: A Deep Learning-Based Approach

## TL;DR

This paper introduces a deep learning model to detect daily snow water equivalent changes in Canada, showing how climate factors affect snowmelt patterns.

## Contribution

A novel Siamese Attention U-Net model is proposed for detecting daily SWE change events with high accuracy.

## Key findings

- Daily SWE change events increased between 1979 and 2018, especially in March and April.
- Low temperature and low precipitation anomalies reduce the frequency of SWE change events.
- February had the highest frequency of zero-change events.

## Abstract

Snow water equivalent (SWE), an essential parameter of snow, is largely studied to understand the impact of climate regime effects on snowmelt patterns. This study developed a Siamese Attention U-Net (Si-Att-UNet) model to detect daily change events in the winter season. The daily SWE change event detection task is treated as an image content comparison problem in which the Si-Att-UNet compares a pair of SWE maps sampled at two temporal windows. The model detected SWE similarity and dissimilarity with an F1 score of 99.3% at a 50% confidence threshold. The change events were derived from the model’s prediction of SWE similarity using the 50% threshold. Daily SWE change events increased between 1979 and 2018. However, the SWE change events were significant in March and April, with a positive Mann–Kendall test statistic (tau = 0.25 and 0.38, respectively). The highest frequency of zero-change events occurred in February. A comparison of the SWE change events and mean change segments with those of the northern hemisphere’s climate anomalies revealed that low temperature and low precipitation anomalies reduced the frequency of SWE change events. The findings highlight the influence of climate variables on daily changes in snow-related water storage in March and April.