3.2.6. Correlation Analysis

To investigate the influence of major driving factors on snow phenology, we calculated the correlation coefficients between these factors and snow phenology:

$$r\_{xy} = \frac{\sum\_{i=1}^{n} (x\_i - \overline{x})(y\_i - \overline{y})}{\sqrt{\sum\_{i=1}^{n} (x\_i - \overline{x})^2 \sum\_{i=1}^{n} (y\_i - \overline{y})^2}} \tag{14}$$

where *xi* and *yi* represent the values in the *i*-th year and *x* and *y* are the average values for all years. If *r* > 0, two variables are positively correlated, and if *r* < 0, it is negatively correlated. When |*r*| ≤ 0.3, two variables are weakly correlated or have no correlation; 0.3 < |*r*| ≤ 0.5 indicates that there is a moderate correlation between two variables; 0.5 < |*r*| ≤ 1 indicates strong correlation [38].
