Particle filter for high frequency oxygen data assimilation in river systems. (May 2022) - Wang, Shuaitao; Flipo, Nicolas; Romary, Thomas; Hasanyar, Masihullah
Abstract: High frequency water quality data measured by in-situ sensors allowed the development of auto-calibration methods for water quality simulation programs. However, these methods consider static parameter values, which may be unrealistic for microorganism activities. An alternative technique is to use data assimilation methods. This paper presents a first application of a particle filter that assimilates dissolved oxygen (DO) data into the hydro-ecological model ProSe. It demonstrates the capability of the approach for simulating DO concentrations and characterizing time-varying physiological properties of living communities in contrasted trophic contexts. DO concentrations are better estimated, especially during algal bloom when phytoplankton physiological parameters match the ones reported in the literature. Despite the simulation capabilities related to phytoplankton, further improvements related to low flow periods can still be achieved, especially concerning the heterotrophic bacteria properties as well as a finer description of the biodegradable component of the organic matter flux at the system's boundaries. Highlights: A particle filtering method is applied for the first time in the Seine River system. The filter improves significantly the simulation of oxygen and algae dynamics. The filter can capture time-varying phytoplanktonic parameters during algal blooms. The description of BDOM data need to be further investigated for low flow periods.