Stochastic Models in Hydrology

Abstract

The stochastic models that can be used to represent growth and steady state phenomena in hydrology are reviewed. It is shown that there are essentially two types of growth models possible; the cyclic growth model and the random configuration model. For steady state phenomena (time series) we are generally restricted to a Gaussian type of model with or without autocorrelation. Self‐similarity models (fractional Brownian motion) lead to physically absurd conditions if they are extrapolated to high frequencies.