On the identification of model structure in hydrological and environmental systems

The paper presents a new recursive estimation algorithm designed expressly for the purpose of model structure identification (not for state estimation or primarily for parameter estimation) and discusses two applications thereof, one to a motivating, hypothetical example and one to data from whole‐pond manipulations designed to explore sediment‐nutrient‐phytoplankton dynamics. The algorithm is the current culmination of a long‐term technical development from state estimation using a Kalman filter, through state parameter estimation using an extended Kalman filter, through a recursive prediction error (RPE) algorithm for parameter estimation cast in the state space and recently modified for estimating time‐varying model parameters, to an RPE algorithm for estimating time‐varying parameters but cast in a parameter space formulation. It is concluded that the algorithm performs well, in the sense of being robust and indeed in revealing specifically where (but less so exactly how) a prior candidate model's structure may be in error.