Data assimilation in operational forecasting systems is a discipline undergoing rapid development. Despite the ever increasing computational resources, it requires efficient as well as robust assimilation schemes to support online prediction products. The parameter considered for assimilation here is water levels from tide gauge stations. The assimilation approach is Kalman filter based and examines the combination of the Ensemble Kalman Filter with spatial and dynamic regularization techniques. Further, both a Steady Kalman gain approximation and a dynamically evolving Kalman gain are considered. The estimation skill of the various assimilation schemes is assessed in a 4-week hindcast experiment using a setup of an operational model in the North Sea and Baltic Sea system. The computationally efficient dynamic regularization works very well and is to be encouraged for water level nowcasts. Distance regularization gives much improved results in data sparse areas, while maintaining performance in areas with a denser distribution of tide gauges.