Abstract
<p>Assimilating surface soil moisture data or GRACE data, retrieved from satellite, into hydrological models has been proven to improve the accuracy of hydrological model estimations and predictions. For data assimilation applications in hydrology, the ensemble Kalm filter(EnKF) is the most commonly used data assimilation(DA) method. Particle filters are a type of non-Gaussian filter that doesn’t need the normality assumption that the EnKF needs. Adding localization defeats the curse of dimensionality that is a problem in normal particle filters. In the present study, we investigated our adaption of the local particle filter based on the Gamma test theory(LPF-GT) to improve discharge estimates by assimilating SMAP satellite soil moisture into the PCR-GLOBWB hydrological model. The study area is the Rhine river basin, driven by forcing data from April 2015 to December 2016. The improved discharge estimates are obtained by using DA to adjust the surface soil moisture in the model. The influence of DA to discharge is not direct but works through the dynamics of the hydrological model.  To explore the potential of LPF-GT, serval sensitivity experiments were conducted to figure out the impact of localization scales and the number of particles on DA's performance. The DA estimates were validated against in situ discharge measurements from gauge stations. To demonstrate the benefit of LPF-GT, EnKF was used as a benchmark in this research. Increases in Nash-Sutcliffe (0.05%– 38%) and decreases in normalized RMSE (0.02%–3.4%) validated the capability of LPF-GT. Results showed that localization scales' impact was substantial. The optimal value of the localization scale was obtained by tuning. LPF-GT achieved a satisfactory performance when only using a few particles, even with as little as five particles. The sample errors posed an adverse impact on the open-loop results. Further improvement could be achieved by considering reduce sample errors due to a small number of particles.</p>
Published Version
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