Abstract

Saltwater intrusion exerts great impact on water supply and water withdrawal from estuarine areas. A chlorinity prediction model based on backpropagation neural network was constructed, calibrated, and validated, considering phase lags, with the Modaomen estuary in the Pearl River Delta (PRD), China as case study. This study aimed to investigate impacts of upstream runoff and tidal level on the changing properties of estuarine chlorinity. Nine boundary conditions – low tide and tidal range both with three different frequencies – were designed to explore the changing process of estuarine chlorinity and obtain the critical upstream runoff for saltwater suppression. Results indicated the model performed efficiently; Nash–Sutcliffe efficiency coefficient and R2 were both 0.91 in training period, 0.76 and 0.82 in testing period, and 0.64 and 0.77 in validation period, respectively, and estuarine chlorinity shows slightly different changing processes of decline rate under the nine boundary conditions when the upstream runoff increases. The higher the designed tidal range and lower daily tides together with the smaller the amount of upstream runoff, the higher the estuarine chlorinity. The critical upstream runoff of the Pinggang pumping station is 2,500 m3/s. These findings provide a foundation for water supply security and upstream reservoir dispatching in estuarine areas in dry periods.

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