LAUSR

The coupling of high frequency data of water quality with physically based models of river systems is of great interest for the management of urban socio-ecosystems. One approach to exploit high frequency data is data assimilation which has received an increasing attention in the field of hydrology, but not for water quality modeling so far. We present here a first implementation of a particle filtering algorithm into a community-centered hydro-biogeochemical model to assimilate high frequency dissolved oxygen data and to estimate metabolism parameters in the Seine River system. The procedure is designed based on the results of a former sensitivity analysis of the model (Wang et al., 2018) that allows for the identification of the twelve most sensible parameters all over the year. Those parameters are both physical and related to micro-organisms (reaeration coefficient, photosynthetic parameters, growth rates, respiration rates and optimal temperature). The performances of the approach are assessed on a synthetic case study that mimics 66 km of the Seine River. Virtual dissolved oxygen data are generated using time varying parameters. This paper aims at retrieving the predefined parameters by assimilating those data. The simulated dissolved oxygen concentrations match the reference concentrations. The identification of the parameters depends on the hydrological and trophic contexts and more surprisingly on the thermal state of the river. The physical, bacterial and phytoplanktonic parameters can be retrieved properly, leading to the differentiation of two successive algal blooms by comparing the estimated posterior distribution of the optimal temperature for phytoplankton growth. Finally, photosynthetic parameters' distributions following circadian cycles during algal blooms are discussed.