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Oxygen data assimilation for estimating micro-organism communities' parameters in river systems
Shuaitao Wang1, Nicolas Flipo1, Thomas Romary1
1Geosciences and Geoengineering Department, MINES ParisTech, PSL University, 35 Rue Saint-Honoré, 77300, Fontainebleau, France.
Water Research
|September 3, 2019
Summary
This study introduces particle filtering for assimilating high-frequency dissolved oxygen data in river models. It successfully estimates key hydro-biogeochemical parameters, improving water quality management in urban river systems.
Area of Science:
- Environmental Science
- Hydrology
- Biogeochemistry
Background:
- High-frequency water quality data is crucial for managing urban river systems.
- Data assimilation is gaining traction in hydrology but is underutilized for water quality modeling.
Purpose of the Study:
- Implement a particle filtering algorithm for data assimilation in a hydro-biogeochemical model.
- Assimilate high-frequency dissolved oxygen data to estimate river metabolism parameters.
- Apply the method to the Seine River system.
Main Methods:
- Utilized a community-centered hydro-biogeochemical model.
- Integrated a particle filtering algorithm to assimilate dissolved oxygen data.
- Focused on twelve key parameters identified through sensitivity analysis, including physical and microbial factors.
Main Results:
- Successfully assimilated virtual dissolved oxygen data, matching reference concentrations.
- Parameter identification was influenced by hydrological, trophic, and thermal river conditions.
- Effectively retrieved physical, bacterial, and phytoplanktonic parameters, distinguishing two algal blooms.
Conclusions:
- The particle filtering approach is effective for water quality modeling and parameter estimation in rivers.
- River thermal state significantly impacts parameter identification accuracy.
- The method allows for detailed analysis of phytoplankton dynamics, including circadian cycles.