Iranian researchers improve water quality modelling accuracy by up to 43%
New data assimilation approach improves simulations of dissolved oxygen and temperature in deep reservoirs, supporting more reliable water resource management
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Photo source: megaflopp / iStock
Researchers at the University of Tehran have developed a data-driven approach that significantly improves the accuracy of water quality simulations in deep, stratified reservoirs, reducing modelling errors for dissolved oxygen by 43 per cent and temperature by 16.8 per cent.
The research team from the field of water resources engineering and management combined data assimilation with dynamic model parameter updates. The approach is designed to allow water quality models to adapt as reservoir conditions change over time, as reported by Mehr News Agency, a TV BRICS partner.
Reza Kerachian, Professor at the School of Civil Engineering at the University of Tehran, explained that complex water quality models for deep and stratified reservoirs have to account for parameter uncertainty and variations in physical and chemical processes. Using fixed parameters can therefore limit their ability to accurately reproduce changing reservoir conditions.
“In this research, using a data assimilation approach and the Ensemble Kalman Filter, we developed an automated, adaptive and uncertainty-based framework for the dynamic calibration of reservoir water quality model parameters,” Kerachian said.
He added that the method uses observational data obtained through remote sensing to update model parameters over time, enabling simulations to adjust to environmental changes and processes within a reservoir.
According to the researchers, dynamically updating model parameters can also help account for processes that are missing, simplified or insufficiently represented within a model.
The approach could therefore improve the reliability of water quality simulations and support more informed management and operation of dam reservoirs as environmental and climatic conditions change.
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