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Groundwater head and stream discharge is assimilated using the ensembletransform Kalman filter in an integrated hydrological model with the aim ofstudying the relationship between the filter performance and the ensemblesize. In an attempt to reduce the required number of ensemble members, anadaptive localization method is used. The performance of the adaptivelocalization method is compared to the more common distance-basedlocalization. The relationship between filter performance in terms ofhydraulic head and discharge error and the number of ensemble members isinvestigated for varying numbers and spatial distributions of groundwaterhead observations and with or without discharge assimilation and parameterestimation. The study shows that (1) more ensemble members are needed whenfewer groundwater head observations are assimilated, and (2) assimilatingdischarge observations and estimating parameters requires a much largerensemble size than just assimilating groundwater head observations. However,the required ensemble size can be greatly reduced with the use of adaptivelocalization, which by far outperforms distance-based localization. Thestudy is conducted using synthetic data only

hydrology and earth system sciences

Data assimilation in integrated hydrological modeling using ensemble Kalman filtering: evaluating the effect of ensemble size and localization on filter performance