Calibration and Assimilation in Hydrodynamic Model of a Micro-tidal Estuary and Comparison with Lagrangian Drifter Data

Deployment of Lagrangian drifters in water systems can provide a larger spatial coverage and an additional insight into horizontal motion of particles than Eulerian techniques. This feature has provided an opportunity to assimilate Lagrangian data into hydrodynamic models to enhance their accuracies. Numerical models suffer from both systematic and random errors. Conventional data assimilation methods were designed to reduce the stochastic errors, and systematic errors can negatively affect the assimilation systems. Therefore, a calibration process, which is an effective way to reduce systematic errors and consequently biases in the numerical models, is required to be performed before implementation of data assimilation techniques. In this study, D-Flow FM, a hydrodynamic model, was set up for simulating the essential processes in a micro-tidal estuary in Queensland, Australia. To calibrate the model, bathymetry and bed roughness were selected as calibration parameters, while most studies in estuarine application have only considered the bed roughness as the calibration parameter. Evaluation of model performance in terms of correlation and root mean square error between model outputs and observations for both water level and velocity showed that calibration of bathymetry is important. Herein model outputs are validated with Lagrangian drifter velocity data for different environmental conditions. The results showed that calibration with the consideration of bed roughness and bathymetry reduced the systematic errors and increase the correlation between model outputs and Lagrangian drifter data. This is an important step prior to assimilation of Lagrangian data to reduce stochastic errors.