Dynamic parameter estimation for hydrological model

Abstract

Parameters of hydrological models are commonly assumed to be time independent even though some catchment properties are not stationary. The flexibility of hydrological models can be increased by introducing dynamic variation in the parameters of a hydrological model. The purpose of this paper is to develop a methodology to investigate the dynamic nature of model parameters. A robust dynamic parameter estimation (RDPE) algorithm is developed which can be used for model diagnosis as well as for improving model predictions. A moving window approach and simulated annealing are used to optimise the parameters of the HBV hydrological model for each window size. The resulting time series of parameters are used for defining sensitive and insensitive periods in parameter time series. To improve predictions a predictive parameter model is developed. Finally, it is shown that the new methodology leads to more realistic confidence intervals for model simulations and model structure identification.