Attribution analysis and forecast of salinity intrusion in the Modaomen estuary of the Pearl River Delta

Abstract

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.