  We report on a large-scale characterization of river discharges by employing
the network framework of the horizontal visibility graph. By mapping daily time
series from 141 different stations of 53 Brazilian rivers into complex
networks, we present an useful approach for investigating the dynamics of river
flows. We verified that the degree distributions of these networks were well
described by exponential functions, where the characteristic exponents are
almost always larger than the value obtained for random time series. The
faster-than-random decay of the degree distributions is an another evidence
that the fluctuation dynamics underlying the river discharges has a long-range
correlated nature. We further investigated the evolution of the river
discharges by tracking the values of the characteristic exponents (of the
degree distribution) and the global clustering coefficients of the networks
over the years. We show that the river discharges in several stations have
evolved to become more or less correlated (and displaying more or less complex
internal network structures) over the years, a behavior that could be related
to changes in the climate system and other man-made phenomena.
