Complexity and chaotic behavior of the U.S. Rivers and estimation of their prediction horizon

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