An adaptive algorithm for analyzing short‐term structural and parameter changes in hydrologic prediction models

An adaptive unbiased recursive prediction algorithm, based upon the state space description of hydrologic systems, is discussed. Discrete linear systems with white Gaussian disturbances are considered. The algorithm allows for short‐term structural and parameter changes due to random environmental effects. A prediction model is set up from a representation of the rainfall‐runoff processes with the unknown parameters modeled by a random walk. The predictions are obtained by the use of linear Kaiman filters where the unknown noise covariance matrices are also adaptively estimated. The behavior of the adaptive prediction algorithm is illustrated by a real‐world example taken from rainfall‐runoff flood forecasting.