气候变化领域集成服务门户(CCPortal): Attribution analysis and forecast of salinity intrusion in the Modaomen estuary of the Pearl River Delta

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DOI	10.3389/fmars.2024.1407690
	Attribution analysis and forecast of salinity intrusion in the Modaomen estuary of the Pearl River Delta
	Tian, Qingqing; Gao, Hang; Tian, Yu; Wang, Qiongyao; Guo, Lei; Chai, Qihui
发表日期	2024
EISSN	2296-7745
起始页码	11
卷号	11
英文摘要	Under the influence of climate change and human activities, the intensification of salinity intrusion in the Modaomen (MDM) estuary poses a significant threat to the water supply security of the Greater Bay Area of Guangdong, Hong Kong, and Macao. Based on the daily exceedance time data from six stations in the MDM waterway for the years 2016-2020, this study conducted Empirical Orthogonal Function (EOF) and decision tree analyses with runoff, maximum tidal range, and wind. It investigated the variation characteristics and key factors influencing salinity intrusion. Additionally, Long Short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNN) were employed to predict the severity of salinity intrusion. The results indicated that: (1) the first mode (PC1) obtained from EOF decomposition explained 89% of the variation in daily chlorine exceedance time, effectively reflecting the temporal changes in salinity intrusion; (2) the largest contributor to salinity intrusion was runoff (40%), followed by maximum tidal range, wind speed, and wind direction, contributing 25%, 20%, and 15%, respectively. Salinity intrusion lagged behind runoff by 1-day, tidal range by 3 days, and wind by 2 days; North Pacific Index (NPI) has the strongest positive correlation with saltwater intrusion among the 9 atmospheric circulation factors. (3) LSTM achieved the highest accuracy with an R 2 of 0.89 for a horizon of 1 day. For horizons of 2 days and 3 days, CNN exhibited the highest accuracy with R 2 values of 0.73 and 0.68, respectively. This study provides theoretical support for basin scheduling and salinity intrusion prediction and serves as a reference for ensuring water supply security in coastal areas.
英文关键词	salinity intrusion; Modaomen estuary; empirical orthogonal function; deep neural network; saltwater forecast
语种	英语
WOS研究方向	Environmental Sciences & Ecology ; Marine & Freshwater Biology
WOS类目	Environmental Sciences ; Marine & Freshwater Biology
WOS记录号	WOS:001232559100001
来源期刊	FRONTIERS IN MARINE SCIENCE
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/300319
作者单位	North China University of Water Resources & Electric Power; China Institute of Water Resources & Hydropower Research
推荐引用方式 GB/T 7714	Tian, Qingqing,Gao, Hang,Tian, Yu,et al. Attribution analysis and forecast of salinity intrusion in the Modaomen estuary of the Pearl River Delta[J],2024,11.
APA	Tian, Qingqing,Gao, Hang,Tian, Yu,Wang, Qiongyao,Guo, Lei,&Chai, Qihui.(2024).Attribution analysis and forecast of salinity intrusion in the Modaomen estuary of the Pearl River Delta.FRONTIERS IN MARINE SCIENCE,11.
MLA	Tian, Qingqing,et al."Attribution analysis and forecast of salinity intrusion in the Modaomen estuary of the Pearl River Delta".FRONTIERS IN MARINE SCIENCE 11(2024).