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. Previous studies have successfully applied variance decompositionframeworks based on the Budyko equations to determine the relativecontribution of variability in precipitation, potential evapotranspiration(E0), and total water storage changes (ΔS) to evapotranspirationvariance (σET2) on different timescales; however, the effectsof snowmelt (Qm) and vegetation (M) changes have not been incorporatedinto this framework in snow-dependent basins. Taking the arid alpine basinsin the Qilian Mountains in northwest China as the study area, we extendedthe Budyko framework to decompose the growing season σET2 intothe temporal variance and covariance of rainfall (R), E0, ΔS,Qm,and M. The results indicate that the incorporation of Qm could improvethe performance of the Budyko framework on a monthly scale; σET2 was primarily controlled by the R variance with a meancontribution of 63 %, followed by the coupled R and M (24.3 %) and thenthe coupled R and E0 (14.1 %). The effects of M variance or Qmvariance cannot be ignored because they contribute 4.3 % and 1.8 % ofσET2, respectively. By contrast, the interaction of somecoupled factors adversely affected σET2, and theout-of-phase seasonality between R and Qm had the largest effect(−7.6 %). Our methodology and these findings are helpful forquantitatively assessing and understanding hydrological responses to climateand vegetation changes in snow-dependent regions on a finer timescale.

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