Impact of upstream runoff and tidal level on the chlorinity of an estuary in a river network: a case study of Modaomen estuary in the Pearl River Delta, China

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.

Salinity prediction in Qiantang Estuary based on Improved LSTM model

“…The hyperparameters of the LSTM model optimized by ISSA included the number of neurons in the first hidden layer (L1), the number of neurons in the second hidden layer (L2), the branching factor (B), the number of iterations (K), and the base learning rate (lr). The ranges of five hyperparameters were [1,100], [1,100], [16,64], [10,100] and [0.001,0.01].…”

“…In recent years, with the rapid development of machine learning technology, data-driven models have been widely applied in water quality prediction [9][10][11][12][13] , with neural network-based deep learning models being the primary representatives. Compared to numerical models, these models can establish relationships between parameters and avoid the constraints imposed by complex boundaries or initial conditions 14 .…”

Inland Reservoir Water Quality Inversion and Eutrophication Evaluation Using BP Neural Network and Remote Sensing Imagery: A Case Study of Dashahe Reservoir

“…, q), rendering the network output as close as possible to the target output and minimizing the sum of the squared errors from the output layer. In order to gradually approximate the target output, it is desirable to continuously calculate the change in the network weight and the deviation in the direction of the decreasing slope relative to the function error, among which the change in the weight and deviation forms the positive correlation with the network error and transmits to each layer with the form of back propagation [65]. In the current study, the BP neural network was implemented using the newff function of the MATLAB software(Ver.…”

“…As a supervised learn network, the logic of the BP neural network is that, for the input learning samples P i ( 1,2, … , q), and their corresponding output samples T i (𝑖 = 1,2, … , q), the purpose learning is to correct the weights via the errors between the network output A i ( 1,2, … , q) and the target vectors T i (𝑖 = 1,2, … , q), rendering the network output as cl as possible to the target output and minimizing the sum of the squared errors from output layer. In order to gradually approximate the target output, it is desirable to c tinuously calculate the change in the network weight and the deviation in the directio the decreasing slope relative to the function error, among which the change in the wei and deviation forms the positive correlation with the network error and transmits to e layer with the form of back propagation [65]. In the current study, the BP neural netw was implemented using the newff function of the MATLAB software(Ver.…”

Spatial pattern and driving forces of regional water use efficiency: From spatial spillover and heterogeneity perspective

“…Over the past 32 years, it has experienced rapid and significant regional socioeconomic development imbalances (He et al, 2020). With a critical shortage of available freshwater resources per capita (less than a quarter of the world average), severe water quality pollution, uneven spatial and temporal distribution (densely distributed in the mid‐Guangdong region), significant inter‐annual variability in water resources (Xian et al, 2022), and the water supply security of the lower reaches of the Pearl River Delta is threatened by upstream runoff decrease and downstream saltwater intrusion (He et al, 2018), the province is now faced with a huge pressure on water supply security, both to ensure high‐quality economic and social development and to protect the water environment. Improving WUE contributes to alleviate regional water shortages, to reduce water pollution, and to make the province's development more sustainable.…”