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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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https://doi.org/10.5194/hess-2020-535
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-2020-535
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  16 Nov 2020

16 Nov 2020

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This preprint is currently under review for the journal HESS.

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

Tingting Ning1,2, Zhi Li3, Qi Feng1,2, Zongxing Li1,2, and Yanyan Qin1,2 Tingting Ning et al.
  • 1Key Laboratory of Ecohydrology of Inland River Basin, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, 730000, China
  • 2Qilian Mountains Eco-environment Research Center in Gansu Province, Lanzhou, 730000, China
  • 3College of Natural Resources and Environment, Northwest A&F University, Yangling, Shaanxi, 712100, China

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 (σ2ET) on different time-scales; however, the effects of snowmelt (Qm) 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 σ2ET into the temporal variance and covariance of rainfall (R), E0, ∆S, Qm, and M. The results indicate that the incorporation of Qm could improve the performance of the Budyko framework on a monthly scale; σ2ET 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 Qm variance cannot be ignored because they contribute to 4.3 % and 1.8 % of σ2ET, respectively. By contrast, the interaction of some coupled factors adversely affected σ2ET, and the out-of-phase seasonality between R and Qm 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.

Tingting Ning et al.

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Short summary
Previous studies decomposed ET variance in precipitation, potential ET and total water storage changes based on Budyko equations. However, the effects of snowmelt and vegetation changes have not been incorporated in snow-dependent basins. We thus extended this method in the arid alpine basins of Northwest China, and found that ET variance is primarily controlled by rainfall, followed by the coupled rainfall and vegetation. The out-of-phase between rainfall and snowmelt weaken ET variance.
Previous studies decomposed ET variance in precipitation, potential ET and total water storage...
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