Intake direction is one of the most important factors which have significant effect on vortex formation at intakes. Due to the importance of withdrawal direction to vortex formation, the present study examined the effect of different intake angles on strength of vortices and critical submergence. Experiments were performed on dual and single intakes with three withdrawal directions (with angles of 0°, 45° and 90°). To calculate the strength of formed vortices, the tangential velocity component of vortices was measured using an ADV. Results showed that, by varying the intake angle from vertical to horizontal, strength of vortices decreased by 31% and 35% for single and dual intakes, respectively. An empirical relationship between the circulation number and Froude number for various submergence depths was developed for each withdrawal direction of the dual intakes. Furthermore, the critical submergence of single and dual intakes was investigated and results indicated that the critical submergence is considerably affected by changing the withdrawal directions.

Scores is a Python package containing mathematical functions for the verification, evaluation and optimisation of forecasts, predictions or models. It supports labelled n-dimensional (multidimensional) data, which is used in many scientific fields and in machine learning. At present, scores primarily supports the geoscience communities; in particular, the meteorological, climatological and oceanographic communities.

Tidal estuaries support everyday functions for over 80% of Australia's population living within 50 km of the coastline and thus come under immense pressure of physicochemical changes. Most studies in estuarine applications have used the bed roughness as the single calibration parameter to calibrate hydrodynamic modelling, yet errors in bathymetric data can significantly impose uncertainties into the model outputs. In this study, we evaluated the sensitivity of a hydrodynamic model of a micro-tidal estuary to both the bed roughness and bathymetry offset through comparing observed and modelled water level and velocity. Treating both bathymetry offset and bed roughness as calibration parameters, three calibration scenarios were tested to examine the impact of these parameters. To validate the model, Lagrangian drifter data as a new dataset in shallow estuaries were used. The analysis shows that model outputs are more sensitive to the variation of bathymetry offset than bed roughness. Results show that calibrating the bathymetry offset alone can significantly improve model performance. Simultaneous calibration of both parameters can provide further improvement, particularly for capturing the water level. Drifter and modelled velocities are highly correlated during flood tides, whereas the correlation is low for slack water because of wind-induced current on drifters.