## 6 Conclusions  
Drifters have a long history of use in oceanographic, climate research and weather forecasting; however, they have only been introduced into estuarine environments in the last few years. Accordingly, many drifter types exist. Recently, drifters with Global Positioning System (GPS) receivers have drawn considerable attention due to their ability to obtain data with a large spatiotemporal coverage at a relatively lower cost compared to traditional Eulerian instruments. This work provides the first examination into the use of low-cost, low-resolution Lagrangian drifters for improving the accuracy and understanding of flow field dynamics of a micro-tidal estuary (Currimundi Lake) through data assimilation into a hydrodynamic model. The real velocities derived from the Lagrangian drifter data in addition to Eulerian velocities were assimilated into a two-dimensional version of Delft3D-FM which solves shallow water equations through a finite element method. Assimilation experiments were conducted for 3 h, using the flow data collected during the 28 April 2015 experiment. The EnKF was applied through the OpenDA assimilation toolbox. We tested the system for different data configurations and model setups. The problem of assimilation of Lagrangian drifter data in Eulerian models is circumvented by applying a pseudo-Lagrangian approach through which the corresponding Eulerian velocity is constructed. OpenDA, which is an open interface standard, was used to evaluate the efficiency of an Ensemble Kalman Filter (EnKF) data assimilation scheme to improve the velocity field and residual currents of the study domain. The pivotal elements influencing DA performance were examined. The robustness of the EnKF method was assessed by varying several parameters, such as the number of ensembles, frequency of assimilation and number of drifters. We found that the ensemble size plays a key role in reducing model error. The results also show that DA performance is highly sensitive to the assimilation time interval, where a more frequent DA time interval of 1 min led to the largest improvement (i.e., 28%). Increasing the time interval to 10 min showed nearly no improvement because of velocity information loss. Assimilation of Lagrangian drifter data, even with a limited number of drifters, improves velocity estimates. Therefore, even a small number of Lagrangian sensors can be effective for collecting water flow information to be used in an EnKF-driven assimilation process. The Lagrangian assimilation algorithm was examined by conducting a series of DA experiments. A base experiment (Base-Test) was performed using data from eight drifters in the model. The results obtained by Lagrangian assimilation were compared with Eulerian and Lagrangian plus Eulerian assimilation. Results demonstrated the efficacy of DA performance because a significant deviation was gained in velocity maps before and after assimilation. We found that both Lagrangian and Eulerian data improve assimilation of our system dynamics with Lagrangian data being the most important. The results also indicate that concurrent assimilation of Lagrangian drifter and Eulerian ADV velocities lead to a higher deviation between assimilated and non-assimilated models. Our results suggest that a desirable alternative to fixed Eulerian devices such as ADVs and ADCPs are drifters. Furthermore, drifters can be promptly deployed in any specific domain under study, particularly in the case that unforeseen events occur. Lagrangian measurements are especially beneficial in the areas where Eulerian instruments are either sparse or unreliable. It is evident, however, that directly assimilating Lagrangian positions is relatively difficult in Eulerian-based hydrodynamic models because of the differences in data and model frameworks. In addition, raw Lagrangian data are rather challenging to use because particle flow is generally affected by local flow perturbations, which are induced by different physical processes, such as turbulence and surface wind.