EufoRiA: A new multi-constituent nutrient and harmful algal blooms model for river networks with online data assimilation

Abstract

Surface water quality impairment is an increasing challenge for water managers in the face of urbanization and climate change. While contaminant fate and transport models are essential for addressing water quality threats like harmful algal blooms, there is a lack of models designed for real-time simulation and decision support in large, regulated river basins. We propose EufoRiA, a new water quality model for river networks incorporating unsteady hydraulics, contaminant transport, reaction kinetics for 23 eutrophication-related constituents, and an online data assimilation scheme using Kalman Filtering that integrates real-time observations to improve model performance. Validating against long-term data from South Korea’s Nakdong River, EufoRiA offers competitive performance with existing models in predicting constituents like nitrogen, phosphorus, and algae. Moreover, data assimilation significantly improves water quality constituent estimation compared to model-only approaches, particularly at ungaged locations. EufoRiA will enable enhanced decision-making for public safety and health against increasing water quality threats.