Global Attribution of Runoff Variance Across Multiple Timescales

Abstract

Previous findings on the relative importance of driving factors to runoff have been controversial, and the relative contribution of driving factors behind the temporal variance of runoff (Rv) still remains unquantified at a global scale. Here, we present a new framework to quantify the driving factors behind Rv globally by decomposing Rv into the temporal variance of precipitation (P), potential evapotranspiration (E0), total water storage change (∆S), and other factors (represented by n in Budyko equation), such as climate seasonality, land cover, and human impact, at intra‐annual, interannual, and multiannual timescales. The results reveal that P controls Rv at different timescales in most parts of the globe. P has distinctly larger impacts on Rv in humid basins at the multiannual scale, while other factors have larger impacts in arid basins. We highlight the different impacts of driving factors on Rv at multiple timescales, which contradicts previous global assessments that assess the relative importance by sensitivity.

Improved Regional Scale Dynamic Evapotranspiration Estimation Under Changing Vegetation and Climate

“…This study using the Budyko framework follows the well‐established assumption that the change in annual storage is zero as well as that the factors affecting ET are in a steady state during the defined periods. Recent studies have shown that changes in storage are important for moderating streamflow and ET (J. Liu et al., 2019). Time‐varying factors can occur gradually over study periods (Jiang et al., 2015; X. Zhang et al., 2020), such as long term trends in T or snow ratio (Figure ).…”

“…In this study, we focused on the response of ET to vegetation change in the highly disturbed and diverse geographic and climatic region of the central interior of British Columbia, Canada. Based on the work of recent global studies (Berghuijs et al., 2017; Gudmundsson et al., 2016; J. Liu et al., 2019; Z. Liu et al., 2020; Wei et al., 2018; Xu et al., 2013; Zhou et al., 2015) and those in other areas such as China (Jiang et al., 2015; Ning et al., 2020; Shen et al., 2017; Tang & Wang, 2020; D. Yang et al., 2007), Australia (Donohue et al., 2012; H. Li et al., 2012; Teng et al., 2012; L. Zhang et al., 2004), and the USA (Berghuijs et al., 2020; Young et al., 2019; X. Zhang et al., 2020), this study related the watershed parameter m in Fuh's equation to vegetation dynamics as a result of forest disturbance and recovery, and watershed properties, using independent calibration and validation watershed data. With the validated relationships, we investigated how ET has responded to vegetation change through time and across the study region, what the relative contributions of climate and forest change were to ET, and how it might vary in the future under selected climate and forest change scenarios.…”

The changing nature and projection of floods across Australia

“…Another major limitation of this study is that the changes in vegetation due to changing climate and hydrologic conditions are not taken into consideration in the HMs. Variations in vegetation distribution also have consider impacts on streamflow (Liu et al, 2018b(Liu et al, , 2019, especially for the flood generating processes in Australian temperate areas (Zhang et al, 2011).…”

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

“…Further, previous studies have found that ET differs greatly among species, because of the difference in canopy roughness, the timing of physiological functioning, water holding capacity of the soil, and rooting depth of the vegetation (Baldocchi et al, 2004;Bruemmer et al, 2012). Generally, forests had larger ET than grasslands (Ma et al, 2020;Zha et al, 2010). The fraction of forest area is relatively high and thus lead to the higher contributions to ET for the whole basin in the humid region.…”