Application of particle filtering methods to a conceptual rainfallrunoff model

The Bayesian total error analysis (BATEA) framework allows model calibration and prediction informed by estimates of data and model uncertainty. However, full BATEA applications are currently limited to studies with relatively short record lengths which do not require real-time updating of model predictions. This is due to the use of batch calibration strategies, which rapidly become computationally expensive when input and/or model errors are inferred directly. This study seeks to develop a recursive implementation of the BATEA framework based on particle filters that efficiently manage time invariant parameters and stochastic state variables. For real-time updating, recursive estimation techniques can be considerably faster than batch methods, facilitating the application of BATEA to applications such as forecasting. It is shown how particle filtering techniques, traditionally used in automatic control and signal processing applications, can be adapted to provide a robust recursive implementation of BATEA. This study assesses the performance of the resample-move particle filter using noise models that preserve physical constraints when applied to the calibration of a conceptual rainfall-runoff model with time-invariant parameters and time-varying model states.