Attribution of growing season evapotranspiration variability considering snowmelt and vegetation changes in the arid alpine basins

Abstract. Previous studies have successfully applied variance decomposition frameworks based on the Budyko equations to determine the relative contribution of variability in precipitation, potential evapotranspiration (E₀), and total water storage changes (∆S) to evapotranspiration variance (σ²_ET) on different time-scales; however, the effects of snowmelt (Q_m) and vegetation (M) changes have not been incorporated into this framework in snow-dependent basins. Taking the arid alpine basins in the Qilian Mountains in northwest China as the study area, we extended the Budyko framework to decompose the growing season σ²_ET into the temporal variance and covariance of rainfall (R), E₀, ∆S, Q_m, and M. The results indicate that the incorporation of Q_m could improve the performance of the Budyko framework on a monthly scale; σ²_ET was primarily controlled by the R variance with a mean contribution of 63 %, followed by the coupled R and M (24.3 %) and then the coupled R and E₀ (14.1 %). The effects of M variance or Q_m variance cannot be ignored because they contribute to 4.3 % and 1.8 % of σ²_ET, respectively. By contrast, the interaction of some coupled factors adversely affected σ²_ET, and the out-of-phase seasonality between R and Q_m had the largest effect (−7.6 %). Our methodology and these findings are helpful for quantitatively assessing and understanding hydrological responses to climate and vegetation changes in snow-dependent regions on a finer time-scale.