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 R2 of 0.89 for a horizon of 1 day. For horizons of 2 days and 3 days, CNN exhibited the highest accuracy with R2 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.

中文翻译：

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珠江三角洲磨刀门河口盐度入侵归因分析及预测

受气候变化和人类活动影响，磨刀门河口盐度入侵加剧，对粤港澳大湾区供水安全构成重大威胁。本研究基于2016-2020年MDM航道六个站点的每日超标时间数据，对径流、最大潮差和风力进行了经验正交函数（EOF）和决策树分析。研究了盐度入侵的变化特征及影响关键因素。此外，还采用长短期记忆（LSTM）网络和卷积神经网络（CNN）来预测盐度入侵的严重程度。结果表明：（1）EOF分解得到的第一模态（PC1）解释了日氯超标时间变化的89%，有效反映了盐度入侵的时间变化； (2)对盐分入侵贡献最大的是径流(40%),其次是最大潮差、风速和风向,分别贡献25%、20%和15%。盐度入侵滞后于径流1天，潮差滞后3天，风滞后2天； 9个大气环流因子中，北太平洋指数（NPI）与盐水入侵的正相关性最强。 (3) LSTM 取得了最高的准确率右21 天的范围内为 0.89。对于 2 天和 3 天的时间范围，CNN 表现出最高的准确率右2值分别为 0.73 和 0.68。该研究为流域调度和盐度入侵预测提供理论支撑，为保障沿海地区供水安全提供参考。