Application of auto-regressive (AR) analysis to improve short-term prediction of water levels in the Yangtze estuary

[摘要] Due to the complex interaction between the fluvial and tidal dynamics, estuarine tides are less predictable than ocean tides. Although the non-stationary tidal harmonic analysis (NS_TIDE) model can account for the influence of the river discharge, the predictive accuracy of the water levels in the tide-affected estuaries is yet to be improved. The results from recent studies using the NS_TIDE model in the lower reach of the Yangtze estuary showed the best root-mean-square-error (RMSE) between the predicted and measured water levels being in a range of 0.22 similar to 0.26 m. From the spectral analysis of the predictive errors, it was also found that the inaccurate description of tides in the sub-tidal frequency band was the main cause. This study is to develop a hybrid model in combination of the auto-regressive (AR) analysis and the NS_TIDE model in an attempt to further improve short-term (with time scale of days) water level predictions in the tide-affected estuaries. The results of the application of the hybrid model in the Yangtze estuary show a significant improvement for water level predictions in the estuary with the RMSE of 24 h prediction being reduced to 0.10 similar to 0.13 m.

[发布日期] 2020-11-01

[关键词] Water level prediction;Estuarine tides;Yangtze estuary;NS_TIDE;Auto-regressive model