The Roles of Climate Forcing and Its Variability on Streamflow at Daily, Monthly, Annual, and Long‐Term Scales

Abstract

The temporal variability of precipitation and potential evapotranspiration affects streamflow from daily to long‐term scales, but the relative roles of different climate variabilities on streamflow at daily, monthly, annual, and mean annual scales have not been systematically investigated in the literature. This paper developed a new daily water balance model, which provides a unified framework for water balance across timescales. The daily water balance model is driven by four climate forcing scenarios (observed daily climate and observed daily climate with its intra‐monthly, intra‐annual, and inter‐annual variability removed) and applied to 78 catchments. Daily streamflow from the water balance model is aggregated to coarser timescales. The relative roles of intra‐monthly, intra‐annual, and inter‐annual climate variability are evaluated by comparing the modeled streamflow forced with the climate forcings at two consecutive timescales. It is found that daily, monthly, and annual streamflow is primarily controlled by the climate variability at the same timescale. Intra‐monthly climate variability plays a small role in monthly and annual streamflow variability. Intra‐annual climate variability has significant effects on streamflow at all the timescales, and the relative roles of inter‐annual climate variability are also significant to the monthly and mean annual streamflow, which is often disregarded. The quantitative evaluation of the roles of climate variability reveals how climate controls streamflow across timescales.

New Insights Into Error Decomposition for Precipitation Products

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Precipitation is not only a key variable in climate studies, but also a crucial forcing factor of the hydrological cycle (Do et al, 2020;Jiang & Bauer-Gottwein, 2019;Yao et al, 2020). Currently, various precipitation products are developed through different technologies, such as gauge interpolation, radar observation, satellite retrieval, and assimilation reanalysis (Foufoula-Georgiou et al, 2020;Sun et al, 2018).

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Unanswered questions on the Budyko framework

“…Connecting the Budyko framework to behaviours at other timescales may also help to bridge the gap to more generally understand hydrological behaviours (e.g., Dralle & Thompson, 2016;Laio, Porporato, Ridolfi, & Rodriguez-Iturbe, 2002;D. Wang & Tang, 2014;Yao, Libera, Kheimi, Sankarasubramanian, & Wang, 2020;X. Zhang, Dong, Zhang, & Yu, 2020;L.…”

“…Thus, controls on the long‐term water balance need to be consistent with shorter time‐scale behaviours. Connecting the Budyko framework to behaviours at other time‐scales may also help to bridge the gap to more generally understand hydrological behaviours (e.g., Dralle & Thompson, 2016; Laio, Porporato, Ridolfi, & Rodriguez‐Iturbe, 2002; D. Wang & Tang, 2014; Yao, Libera, Kheimi, Sankarasubramanian, & Wang, 2020; X. Zhang, Dong, Zhang, & Yu, 2020; L. Zhang, Potter, Hickel, Zhang, & Shao, 2008), rather than having unconnected and time‐scale dependent theories.…”

Diagnosis toward predicting mean annual runoff in ungauged basins

“…Mean climate has been identified as the first-order control on mean annual runoff and evaporation and it has been quantified by climate aridity index, which is defined as the ratio between the mean annual potential evapotranspiration (E p ) and precipitation (P ) (Turc, 1954;Pike, 1964). Other controlling factors include the temporal variability of climate (Farmer et al, 2003;Troch et al, 2002;Fu and Wang, 2019), vegetation (Zhang et al, 2001;Donohue et al, 2007;Gentine et al, 2012;Li et al, 2013), soil (Atkinson et al, 2002;Yokoo et al, 2008;Li et al, 2014), and topography (Woods, 2003;Abatzoglou and Ficklin, 2017). Mean annual runoff or evaporation has been modeled as a function of climate aridity index, and the equation is usually called the Budyko equation (Budyko, 1958).…”