Investigating watershed hydrology from a data-driven causal perspective consists of an attractive opportunity to characterize and understand relationships between water storages and fluxes. Previous studies have focused on assessing causal interactions of individual hydrologic processes along with their environmental drivers. Here we assess integrally how the water balance components interact with themselves, aiming to find relevant time-lags or dependency patterns. Granger causality test and time-lagged mutual information were used in a pairwise approach to examine cause-effect relationships between precipitation, streamflow, groundwater levels under different land-covers, and evapotranspiration data (daily timescale) from 2009 to 2019 in a Brazilian watershed (5200 ha), located in a recharge area of the Guarani Aquifer System. A verification assessment using synthetic datasets shows that the methods are effective to identify the underlying generating mechanisms. Statistically significant causal connections were confirmed in practically all pairs of observed data. Granger’s causality indicates that groundwater and streamflow responses are influenced by precipitation even with a lag of 1-day, while evapotranspiration can take more than 200 days to influence groundwater responses, depending on the water table depth and surrounding land-cover. From the mutual information curves, the first local peaks are possibly associated with a physical mechanism, while other peaks, despite resulting statistically significant, lack a reasonable interpretation and require further research. The causal analysis provides a complementary view of the hydrological system’s functioning and challenges us to develop predictive models that reproduce not only the target variables but also the diverse time-lagged dependencies observed in environmental data.