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 was 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.

中文翻译：

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微潮汐河口水动力建模和模型对河床粗糙度和水深偏移的敏感性

潮汐河口支持居住在距海岸线 50 公里范围内的澳大利亚 80% 以上人口的日常活动，因此承受着巨大的物理化学变化压力。河口应用中的大多数研究都使用河床粗糙度作为单一校准参数来校准水动力模型，但测深数据中的误差会显着增加模型输出的不确定性。在这项研究中，我们通过比较观测和模拟的水位和速度，评估了微潮汐河口水动力模型对河床粗糙度和水深偏移的敏感性。将测深偏移和床层粗糙度作为校准参数，测试了三种校准场景以检查这些参数的影响。为了验证模型，使用拉格朗日漂流数据作为浅河口的新数据集。分析表明，模型输出对水深偏移的变化比床粗糙度更敏感。结果表明，单独校准测深偏移可以显着提高模型性能。两个参数的同时校准可以提供进一步的改进，特别是在捕获水位方面。在涨潮期间，漂流器和模拟速度高度相关，而由于漂流器上的风诱导电流，对于松散的水，相关性很低。两个参数的同时校准可以提供进一步的改进，特别是在捕获水位方面。在涨潮期间，漂流器和模拟速度高度相关，而由于漂流器上的风诱导电流，对于松散的水，相关性很低。两个参数的同时校准可以提供进一步的改进，特别是在捕获水位方面。在涨潮期间，漂流器和模拟速度高度相关，而由于漂流器上的风诱导电流，对于松散的水，相关性很低。