Real‐time, statistically linearized, adaptive flood routing

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Abstract
The use of nonlinear routing models within real‐time, adaptive, streamflow forecasting has been limited because of the linearity restrictions of the most popular filtering and optimal estimation techniques. This work proposes a linearization methodology suitable for nonlinear multidimensional functions of nearly Gaussian nonstationary processes. Lack of bias and exact preservation of moments are a few of the advantages of the procedure. In order to facilitate computations, simple analytical approximations for the linearization coefficients are offered. A state parameter covariance partitioning algorithm is proposed for real‐time estimation of the states and parameters of the linearized router. An illustrative example of its use, based on data from the Bird Creek basin in Oklahoma, is presented.