Hydrological models are commonly used to predict future streamflow. However, the assumption of stationary model parameters obtained through calibration on past conditions may not accurately represent non-stationarity in hydrological system characteristics. Evidence suggests that vegetation adapts its root zone storage capacity in response to changing climate, emphasizing the need to account for non-stationarity in hydrological models. This study examines the effects of long-term climate variability on root zone storage capacity and its consequences on hydrological response. By using the method of Fu (1981), we determine whether the root zone storage capacity has significantly changed and evaluate the sensitivity of hydrological model predictions to these changes. We explain the changes in root zone storage capacity and evaporation using various climate indicators. For two large sample datasets CAMELS GB and CAMELS USA, we confirm that the Fu method can be used with the same omega parameter when transitioning from one decade to the next, indicating small differences in root zone storage capacity. However, for hydrometeorological data from the Meuse basin in Northwest Europe, we observe a trend where the actual evaporation is smaller than expected. This suggests that caution should be exercised when applying the Fu-method. In four different scenarios, we have implemented historical changes in evaporation as altered root zone storage capacity in the wflow flextopo model. The scenarios based on the evaporation trends observed in the Meuse data, result in less evaporation during the summer months (May, June, July) within a range of 0 to -22%, and an increase in streamflow during the autumn months (September, October, November) between 0 and +48%. On the other hand, the scenarios based on evaporation trends observed in all combined data (Meuse, CAMELS GB, CAMELS USA) result in changes around zero of evaporation during summer months between -7% and +5%, and of streamflow in autumn months between -11% and +10%. This study represents a step towards a more reliable and robust estimation of root zone storage capacity in hydrological modelling, thereby enhancing our ability to predict future streamflow.