Daily streamflow of Argentinian rivers analysis using information theory quantifiers.

Abstract

AbstractThis paper analyzes the temporal evolution of the streamflow for different rivers in Argentina, based on information quantifiers such as statistical complexity and permutation entropy. The main objective is to identify essential details of the dynamics of the processes to differentiate the degrees of randomness and chaos. The permutation entropy is used with the probability distribution of Ordinal Patterns and the Jensen-Shannon divergence to calculate the disequilibrium and then the statistical complexity. Daily streamflow series at different river stations were analyzed to classify the different hydrological systems. The Complexity Entropy Causality Plane (CEPC) and the representation of the Shannon Entropy and Fisher Information Measure (FIM) show that the daily discharge series could be represented approximately with Gaussian noise, but the variances highlight the difficulty of modeling a series of natural phenomena. An analysis of stations downstream from the Yacyretá dam shows that the operation affects the randomness of the daily discharge series in hydrometric stations near the dam, but when the station is further downstream this effect is attenuated. The size of the basin plays a relevant role in modulating the process, large catchments have smaller values for entropy and the signal is less noisy due to integration over larger time scales. The small and mountain basins present a rapid response that influences the behavior of daily discharge while presenting a higher entropy and lower complexity. The results obtained characterize the behavior of the daily discharge series in Argentinian rivers and provide key information for hydrological modeling.