Investigation of factors affecting intra-annual variability of evapotranspiration and streamflow under different climate conditions

Abstract

Investigating the factors that affect intra-annual evapotranspiration (ET) and streamflow variability is important to regional hydrological cycles and energy balance research. In this study, ET and streamflow variability (defined as their standard deviations) are attributed to precipitation, potential evapotranspiration (ET0) and total water storage change (TWSC) based on a Budyko-based approach at 282 catchments in China. The results show that the Budyko-based approach satisfactorily simulates the intra-annual ET and streamflow variability (R2 of 0.63–0.84). The dominant contributor to ET variability is ET0 under energy-limited condition (aridity index⩽0.76), whereas the dominant contributor is precipitation under equitant (0.76<aridity index⩽1.35) and water-limited conditions (aridity index⩾1.35). The contribution of ET0 to ET variability decreases with the aridity index, whereas the contribution of precipitation to ET variability increases with the aridity index. However, the dominant contributor to streamflow variability is precipitation under all the three climate conditions, which is unaffected by the aridity index. TWSC enhances ET variability under energy-limited condition and inhibits ET variability under water-limited and equitant conditions. However, TWSC inhibits streamflow variability under all the three climate conditions. In addition, geography and vegetation also influence the contributors to ET and streamflow variability. The effects of geography on the contributors to streamflow variability are larger than that to ET variability. In contrast, the impacts of vegetation on the contributors to ET variability are larger than that to streamflow variability. This study demonstrates that the mechanism of ET variability under different climate conditions is much more complex than that of streamflow variability, suggesting that more attention should be given to ET for water-energy modeling, hydrological predictions and local water management.