Surface and atmospheric water balances analysis for Amazon River basin at monthly time scale are performed with an emphasis on the linkages between the Andes and low-lying Amazonia. Analyses are performed using observational and reanalysis data covering five different periods of analysis (depending on length of evapotranspiration data sets) for the entire basin, major subcatchments, and for the Andes and low-lying Amazonia. Results for the entire basin show a state close to balance within tolerance, while the spatial disaggregation produced a less clear picture, presenting major discrepancies between observations and reanalysis data sets. The atmospheric budget exhibits no closure and positive residuals regardless of data set, with the magnitude of residuals for the entire Amazon basin highly dependent on evapotranspiration data source. The imbalance between the two water budgets (14%-16%) is driven by higher values of runoff and by an abrupt change in runoff when changing from dry to wet seasons in the Amazon. We unveil two shortcomings of the available data, namely poor quality in the representation of surface processes by reanalysis, and flaws and scarcity of information in the high Andes that induce uncertainties and errors in both water budgets. The results of the present study highlight the importance of the Andean region for the hydrological integrity of the entire Amazon River basin. Our results confirm the paramount importance of the joint analysis of atmospheric and surface water budgets at the basin level with respect to achieving a complete understanding of the whole hydrological cycle at the continental scale.

Identifying causal relations from observational data sets has posed great challenges in data-driven causality inference studies. One of the successful approaches to detect direct coupling in the information theory framework is transfer entropy. However, the core of entropy-based tools lies on the probability estimation of the underlying variables. Here we propose a data-driven approach for causality inference that incorporates recurrence plot features into the framework of information theory. We define it as the recurrence measure of conditional dependence (RMCD), and we present some applications. The RMCD quantifies the causal dependence between two processes based on joint recurrence patterns between the past of the possible driver and present of the potentially driven, excepting the contribution of the contemporaneous past of the driven variable. Finally, it can unveil the time scale of the influence of the sea-surface temperature of the Pacific Ocean on the precipitation in the Amazonia during recent major droughts.

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