Global sensitivity analysis of model parameters is an important step in the development of a hydrological model. If available, time series of different variables are used to increase the number of sensitive model parameters and better constrain the model output. However, this is often not possible. To overcome this problem, we coupled the active subspace method with the discrete wavelet transform. The Haar mother wavelet is the most appropriate for this purpose in case of homoschedastic measurement error, since it avoids any loss of information through the discrete wavelet transform of the signal. With this methodology, we study how the temporal scale dependency of hydrological processes affects the structure and dimension of the active subspaces. We apply the methodology to the LuKARS model of the Kerschbaum spring discharge in Waidhofen a.d. Ybbs (Austria). Our results reveal that the dimensionality of an active subspace increases with increasing hydrologic processes which are affecting a temporal scale. As a consequence, different parameters are sensitive on different temporal scales. Finally, we show that the total number of sensitive parameters identified at different temporal scales is larger than the number of sensitive parameters obtained using the complete spring discharge signal. Hence, instead of using multiple data time series to identify more sensitive parameters, we can also obtain more information about parameter sensitivities from one single, decomposed time series.