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Soil moisture modeling is necessary for many hydrometeorological and agricultural applications. One of the ways in which the modeling of soil moisture (SM) can be improved is by assimilating SM observations to update the model states. Remotely sensed SM observations are prone to being riddled with data discontinuities, namely in the horizontal and vertical spatial, and temporal, dimensions. In this study, a set of synthetic experiments were designed to assess how much impact each of these individual components of spatiotemporal gaps can have on the modeling performance of SM, as well as streamflow. The results show that not having root-zone SM estimates from satellite derived observations is most impactful in terms of the modeling performance. Having temporal gaps and horizontal spatial gaps in the satellite SM data also impacts the modeling performance, but to a lesser degree. Real-data experiments with the remotely sensed Soil Moisture Active Passive (SMAP) product generally brought improvements to the SM modeling performance in the upper soil layers, but to a lesser degree in the bottom soil layer. The updating of the model SM states with observations also resulted in some improvements in the streamflow modeling performance during the synthetic experiments, but not during the real-data experiments.