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

Chen, Yongping, Gan, Min, Pan, Shunqi ORCID: https://orcid.org/0000-0001-8252-5991, 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 Advanced Research Computing @ Cardiff (ARCCA)
Publisher:	Elsevier
ISSN:	0022-1694
Date of First Compliant Deposit:	16 August 2020
Date of Acceptance:	3 August 2020
Last Modified:	23 Nov 2024 11:15
URI:	https://orca.cardiff.ac.uk/id/eprint/134234

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