## 4 Results and Discussion
### Vegetation Productivity as a Function of Environmental Drivers  
The interannual spatial variation in vegetation productivity was analyzed as a function of 12 environmental drivers to explain the source of the variation. The contribution of the potential explanatory variables to the interannual variability in vegetation productivity differed across variables. The results of the multiple linear regression approach used to assess the main forces driving  
Figure 6: The spatial distribution of the interannual CoV cumulative NDVI from 1989 to 2014. Figure 7: Box plot showing the variation of CoV of cumulative NDVI among shrubland (**A**), needleleaf forest (**B**), and grassland (**C**). Each box shows upper and lower quartiles along with 75 th and 25 th percentiles, and median. The interannual coefficients of variation are shown on the \(y\)-axis. variability in vegetation productivity are listed in Table 2. The proportion of the interannual variability in vegetation productivity explained by the potential explanatory variables was around 37%. All explanatory variables, except slope and spring temperature, show a significant correlation at 95% confidence level in the study area. Spatial variation in interannual change in vegetation productivity was explained better by climate drivers than by topography. The interseasonal variability of temperature seems to be the most important factor affecting the vegetation productivity in the area. Among all explanatory variables, winter and spring precipitation and fall temperature interannual variability showed strong negative impacts on vegetation productivity. Winter and summer temperature, summer and fall precipitation, as well as the topography factors (aspect and elevation) were negatively correlated with productivity, indicating that the interannual variability in vegetation productivity decreases with increase in the factor. These findings suggest that there is a strong positive feedback between the interannual variability in vegetation productivity and winter and fall precipitation, and fall temperature in the area driven by a strong water demand early in the growing season and a potentially longer growing season moderated by a delayed winter temperature both characteristic of cold weather in the region. Furthermore, topographic and vegetation types showed weak correlation coefficients in the regression model and elevation and vegetation types showed decreasing relationship with the interannual variability in cumulative NDVI. To better understand the uncertainty resulting from the spatial regression model between the interannual variability in response and explanatory variables, the spatial distribution of the regression residuals was generated. Figure 8 shows the spatial distribution of residuals and where the model is strong, weak, and/or where the changes in the interannual CoV-NDVI related to vegetation productivity were controlled by other factors not considered in this study. The residuals of the model obtained vary between \(-0.3\) and \(0.27\) vegetation index (VI) unit over the study area with a standard deviation of \(0.1\) at the 95% confidence level. The range between \([-0.3\) to \(-0.1]\) corresponds to overestimation, while \([0.101\) to \(0.27]\) indicates underestimation. Values between \([-0.1\) to \(0.1]\) correspond to the 95% confidence level and a strong correlation. The model performed well in low elevations where shrubland and grassland are the dominant vegetation types. In general, the relationship between the interannual variability of vegetation productivity was overestimated and no significant correlation was found at high elevation areas where needle forest is the dominant cover type. This implies that the interannual variability in vegetation productivity of shrublands and forest at middle and high elevation is controlled by variables other than the explanatory variables used in this study. Those could be inter-annual variability in snow cover, or other parameters.