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

Chen, Yongping, Gan, Min, Pan, Shunqi, Pan, Haidong, Zhu, Xian and Tao, Zhengjin 2020. Application of auto-regressive (AR) analysis to improve short-term prediction of water levels in the Yangtze estuary. Journal of Hydrology 590 , 125386. 10.1016/j.jhydrol.2020.125386
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Abstract

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 ~ 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 ~ 0.13 m.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Engineering
Publisher: Elsevier
ISSN: 0022-1694
Date of First Compliant Deposit: 16 August 2020
Date of Acceptance: 3 August 2020
Last Modified: 18 Aug 2020 13:15
URI: http://orca.cf.ac.uk/id/eprint/134234

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