  A streamflow time series encompasses a large amount of hidden information and
reliable prediction of its behavior in the future remains a challenge. It seems
that the use of information measures can significantly contribute to
determining the time horizon of rivers and improving predictability. Using the
Kolmogorov complexity (KC) and its derivatives (KC spectrum and its highest
value), and Lyapunov exponent (LE), it has previously been shown that the
degree of streamflow predictability depends on human activities, environmental
factors, and natural characteristics. This paper applied the KC and LE measures
to investigate the randomness and chaotic behavior of monthly streamflow of
1879 rivers from the United States for a period from 1950 to 2015 and evaluated
their time horizons via the Lyapunov and Kolmogorov time (LT and KT,
respectively).
