CONICET | Buscador de Institutos y Recursos Humanos

The current understanding of hydro-climatic processes is largely based on time-series analysis of observations such as river discharge. Although records of these variables are often nonlinear and nonstationary, they have been mostly analyzed with classical methods designed for linear and stationary data. This study investigates the possibility of analyzing hydro-climatic time series using a novel data-driven method named Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), which is suitable for nonlinear and nonstationary signals. CEEMDAN is here applied to a monthly-mean discharge record (1904-2010) of the Paraná River (South America). The variability modes (i.e., cycles) and trend of Paraná flow obtained in this way are physically interpreted by comparing them with CEEMDAN decompositions of Paraná-tributary discharges and climate indexes. It is found that Paraná flow modes consist of (i) annual and intrannual oscillations reflecting the rainfall seasonality of different Paraná basin sectors, and (ii) interannual to interdecadal changes linked to climate cycles like El Niño/Southern Oscillation, the North Atlantic Oscillation, and the Interdecadal Pacific Oscillation. A nonlinear trend of Paraná discharge is found and reveals a monotonic increase that could be largely attributed to global warming. The spectral separation of modes obtained using CEEMDAN is cleaner than that achieved by the Ensemble Empirical Mode Decomposition technique. This makes it easier to interpret CEEMDAN results. Hence, CEEMDAN is proposed as a powerful method for extracting physically meaningful information from hydro-climatic data.

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