## 1 Introduction
Some studies have also indicated that Lagrangian DA can significantly improve model estimates for small-scale water systems, e.g., rivers [4; 15; 17]; however, the application of these data in shallow estuaries and inland waters is in its infancy up to this time. Despite the value of using Lagrangian drifters to study the hydrodynamics of estuaries, particularly where fixed sensor availability is limited, there are some major challenges linked to using these data. Lagrangian drifter motions can be affected by local perturbations due to physical processes, such as wind-induced currents and turbulence, and they are restricted to the free water surface [18]. Because these small-scale features cannot be effectively resolved in hydrodynamic models, data pre-processing is essential prior to DA [15]. In addition, a key facet of the Lagrangian DA process is the difference between the observational (Lagrangian) and numerical model (Eulerian) frameworks; this requires extracting Eulerian velocity from Lagrangian data [16]. Methods introduced for DA fall into two general categories: (i) variational methods, including 3D-VAR and 4D-VAR; (ii) sequential methods, including Kalman filtering and its variants and particle filtering. Sequential methods are robust techniques for DA in a broad range of applications [19]. Different DA schemes have been used to assimilate Lagrangian data into hydrodynamic models; among them, the Ensemble Kalman Filter (EnKF) algorithms are gaining more attention in water systems due to ease of implementation in addition to applicability for non-linear systems [16; 19; 20]. Using the EnKF algorithm, we can assess the effects of several elements, including the number of ensembles, frequency of assimilation, and observation density (number of drifters), to obtain an effective DA system [21]. The two main reasons that constrain the application of Lagrangian DA in estuarine dynamics studies are the programming effort required and the underestimation of the value of such assimilation. Herein, we present a flexible and easy-to-implement framework in which we use OpenDA; an open-source DA software for Lagrangian data assimilation. OpenDA has been widely used for DA and calibration purposes in lakes, rivers, and estuaries; however, cost-effective GPS-tracked Lagrangian drifters have not previously been used in an estuary. Sediment transport processes in tidal channels usually result in erosion and deposition [22]. Determination of water circulation patterns in estuaries is a key element to support the sustainable management of these systems [23]. Tidal systems with asymmetric flow properties induce residual currents that cause net flow migration in either the ebb or flood direction [24]. It is also evident that, in tidal systems, residual currents are dominant in one direction; opposite residual currents can occur locally and change the direction of flow [25]. Residual currents control the exchange of sediments, including adsorbed nutrients and contaminants with the adjacent coastal zones and consequently impact geomorphic processes (e.g., stream meandering, bank erosion) and the overall health of estuarine ecosystems. The direction of residual currents (non-tidal currents) is important in sediment transport and bank erosion studies in estuaries because residual circulation and flood/ebb tidal asymmetry are the major mechanisms controlling the transport of fine suspended sediment [26]. In Currimumudi Lake, with an asymmetric behaviour, in addition to the longshore transport of sand resulting in the migration of the inlet channel, the net flow direction has a pivotal impact on sand redirection causing bank erosion and entrance channel migration. Thus, tidal hydrodynamics and entrance behaviour studies of the lake play a key role in the effective management of the problems that this estuary encounters.