In recent years, it has been documented that climatic variability influences hydrological processes; however, these influences, such as hydrologic dynamics, have not yet been incorporated into models, which have been assumed as stationary with regard to climatic conditions. In this study, the temporal variability of hydrological processes and their influence on the water balance of a mountainous and data-sparse catchment in Chile are observed and modeled through the comparison of a stationary (time-invariant parameters) and dynamic (time-variant parameters) model. Since conceptual models are the most adequate option for a data-scarce basin, a conceptual model integrated with the Monte Carlo Analysis Toolbox is used to perform the analyses. Simple analyses aimed at increasing the amount of information obtained from models were used. The General and Dynamic Identifiability Analyses were used to perform stationary and dynamic calibration strategies, respectively. As a result we concluded that the dynamic model is more robust than the stationary one. Additionally, DYNIA helped us to observe the temporal variability of hydrological processes. This analysis contributed to a better understanding of hydrological processes in a data-sparse Andean catchment and thus could potentially help reduce uncertainties in the outputs of hydrological models under scenarios of climate change and/or variability.