Fei Kai, Du Haoxuan, Gao Liang
State Key Laboratory of Internet of Things for Smart City and Department of Ocean Science and Technology, University of Macau, Macao; Center for Ocean Research in Hong Kong and Macau (CORE), Macao.
State Key Laboratory of Internet of Things for Smart City and Department of Ocean Science and Technology, University of Macau, Macao; Center for Ocean Research in Hong Kong and Macau (CORE), Macao.
Sci Total Environ. 2023 Nov 15;899:165592. doi: 10.1016/j.scitotenv.2023.165592. Epub 2023 Jul 17.
Due to the interaction between upstream discharge and astronomical tides in tidal reaches, the typhoon-induced storm surge processes are quite different from that in other coastal regions. Investigating the contributions of driving factors is essential to deepen the understanding of storm surges in tidal reaches. In this study, a coupled hydrological-hydrodynamic storm surge model is first developed to explore the main driving factors of storm surges in Makou-Dahengqin tidal reach during the three most influential typhoon events (Hagupit, Hato and Mangkhut). After that, the machine learning method is integrated to assess the water level in response to storm surges. The driving factors of storm surge are decomposed into remote forcing (upstream discharge, astronomical tide) and direct local forcing (wind stress, atmospheric pressure). The relative contributions of remote forcing are the highest near the estuary mouth. The relative contributions of local forcing to water levels are higher in the sections 40-80 km away from the estuary mouth. The most impacting period of the local forcing is about 48 h, while the relative contributions of remote forcing increase before and after the period. The local forcing-induced surges are highest at the upper reach during Hagupit, while it causes extreme surges at the estuary mouth during more powerful typhoons (Hato, Mangkhut). The maximum water levels and remote forcing-induced maximum surges invariably appear at the upper reach. However, when local and remote forcings are in the same phase, the maximum storm surge appears in the lower reaches during Hato. If local and remote forcings are in the same phase, the peak water levels would be amplified by up to 15.04 %, 36.23 % and 40.68 % during Hagupit, Hato and Mangkhut, respectively. Moreover, Remote forcing contributes more to the amplification of peak water levels than local forcing does, accounting for 68.5 % to 100 %.
由于潮汐河段上游流量与天文潮汐之间的相互作用，台风引发的风暴潮过程与其他沿海地区有很大不同。研究驱动因素的贡献对于深化对潮汐河段风暴潮的理解至关重要。在本研究中，首先建立了一个水文 - 水动力耦合风暴潮模型，以探究马口水道 - 大横琴潮汐河段在三次最具影响力的台风事件（黑格比、哈托和山竹）期间风暴潮的主要驱动因素。之后，集成机器学习方法来评估风暴潮响应下的水位。风暴潮的驱动因素被分解为远程强迫（上游流量、天文潮汐）和直接局地强迫（风应力、大气压力）。远程强迫的相对贡献在河口附近最高。局地强迫对水位的相对贡献在距河口40 - 80公里的断面较高。局地强迫的最显著影响期约为48小时，而在此期间前后远程强迫的相对贡献增加。黑格比期间局地强迫引发的涌浪在上游最高，而在更强的台风（哈托、山竹）期间则在河口引发极端涌浪。最大水位和远程强迫引发的最大涌浪总是出现在上游。然而，当局地和远程强迫同相时，哈托期间最大风暴潮出现在下游。如果局地和远程强迫同相，在黑格比、哈托和山竹期间，峰值水位将分别放大高达15.04%、36.23%和40.68%。此外，远程强迫对峰值水位放大的贡献比局地强迫更大，占68.5%至100%。
