Adaptive state updating in real-time river flow forecasting—a combined filtering and error forecasting procedure

A new robust, accurate and efficient data assimilation procedure based on a general filtering update combined with error forecasting at measurement points is presented. The filtering update procedure is based on a predefined, time invariant weighting (gain) function that is used to distribute model errors at measurement points to the entire state of the river system. The error forecast models are used to propagate model errors at measurement points in the forecast period. The procedure supports a general linear and non-linear formulation of the error forecast models, and fully automatic parameter estimation techniques have been implemented to estimate the parameters of the models based on the observed model errors prior to the time of forecast. The parameter estimates are automatically updated, which allow the error forecast models to adapt to the prevailing conditions at the time of forecast, hence accounting for any structural differences in the model errors in the transition between different flow regimes. The developed procedure is demonstrated in an operational flood forecasting setup of Metro Manila, the Philippines. The results showed significantly improved forecast skills for lead times up to 24 h as compared to forecasting without updating. Erroneous conditions imposed at the downstream boundary were effectively corrected by utilising the harmonic behaviour of the model error in the error forecast model; a situation where the usually applied autoregressive error forecast models would fail.