A Reduced‐Complexity Model to Predict Seasonal Variation in Estuarine Salinity

Abstract Accurate prediction of seasonal variations in salinity is essential for assessing the health of estuarine environments. Traditional estuarine salinity models face challenges such as high computational demands and extensive data requirements. Here, we introduce a novel, reduced‐complexity model that computes seasonal variations in estuarine salinity based on three key inputs: river discharge, tidal water levels, and marine salinity. The model predicts the seasonal (monthly moving average) salinity at a given location using a single dimensionless variable that represents the ratio of freshwater discharge to tidally driven discharge. The model is validated using data from 11 estuaries globally, showing strong predictive performance for seasonal salinity time series in each estuary, with mean absolute errors (MAEs) of 2.5 ± 1.3 psu across all estuaries. Moreover, we also show that our reduced‐complexity model predicts seasonal estuarine salinity with comparable accuracy as a fully three‐dimensional Delft3D simulation in one estuary.