Abstract
Accurate salinity prediction can support the decision-making of water resources management to mitigate the threat of insufficient freshwater supply in densely populated estuaries. Statistical methods are low-cost and less time-consuming compared with numerical models and physical models for predicting estuarine salinity variations. This study proposes an alternative statistical model that can more accurately predict the salinity series in estuaries. The model incorporates an autoregressive model to characterize the memory effect of salinity and includes the changes in salinity driven by river discharge and tides. Furthermore, the Gamma distribution function was introduced to correct the hysteresis effects of river discharge, tides and salinity. Based on fixed corrections of long-term effects, dynamic corrections of short-term effects were added to weaken the hysteresis effects. Real-world model application to the Pearl River Estuary obtained satisfactory agreement between predicted and measured salinity peaks, indicating the accuracy of salinity forecasting. Cross-validation and weekly salinity prediction under small, medium and large river discharges were also conducted to further test the reliability of the model. The statistical model provides a good reference for predicting salinity variations in estuaries.
Highlights
Saltwater intrusion (SI) has posed a great threat to the freshwater supply for domestic, agricultural and industrial demands in densely populated estuaries
Model Formulation This study introduced an autoregressive model to account for the hysteresis effect of salinity: St = F(St−1, St−2, St−3···)
This study developed a new statistical model that can accurately predict the salinity variations in estuaries
Summary
Saltwater intrusion (SI) has posed a great threat to the freshwater supply for domestic, agricultural and industrial demands in densely populated estuaries. One-dimensional (1D) analytical models have been derived to predict SI length [19,20,21], and various numerical simulations have been carried out to analyze SI intensity using estuarine oceanic hydrodynamic models based on structured or unstructured grids [22,23,24]. A statistical model was developed to predict the salinity in the upper South Branch of the Yangtze River Estuary [31]. This model only requires two variables: runoff at Datong station and lunar calendar date. A mid- to long-term salinity forecasting model can be constructed by analyzing the relationship between main driving forces (river discharge and tidal range) and SI intensity
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