The importance of parameter resampling for soil moisture data assimilation into hydrologic models using the particle filter

The Ensemble Kalman filter (EnKF) and the Sequential Importance Resampling (SIR) particle filter are evaluated for their performance in soil moisture assimilation and the consequent effect on discharge. With respect to the resulting soil moisture time series, both filters perform similarly. However, both filters have a negative effect on the discharge due to inconsistency between the parameter values and the states after the assimilation. In order to overcome this inconsistency, parameter resampling is applied along with the SIR filter, to obtain consistent parameter values with the analyzed soil moisture state. Extreme parameter replication, which could lead to a particle collapse, is avoided by the perturbation of the parameters with white noise. Both the modelled soil moisture and discharge are improved if the complementary parameter resampling is applied. The SIR filter with parameter resampling offers an efficient way to deal with biased observations. The robustness of the methodology is evaluated for 3 model parameter sets and 3 assimilation frequencies. Overall, the results in this paper indicate that the particle filter is a promising tool for hydrologic modelling purposes, but that an additional parameter resampling may be necessary to consistently update all state variables and fluxes within the model.