Oxygen data assimilation for estimating micro-organism communities’ parameters in river systems

Autor: Shuaitao Wang, Thomas Romary, Nicolas Flipo
Přispěvatelé: Centre de Géosciences (GEOSCIENCES), MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)
Jazyk: angličtina
Rok vydání: 2019
Předmět:
Environmental Engineering
0208 environmental biotechnology
[SDU.STU]Sciences of the Universe [physics]/Earth Sciences
Soil science
02 engineering and technology
010501 environmental sciences
01 natural sciences
Algal bloom
Data assimilation
Hydrology (agriculture)
Rivers
[MATH.MATH-MP]Mathematics [math]/Mathematical Physics [math-ph]
Water Quality
Phytoplankton
[MATH.MATH-AP]Mathematics [math]/Analysis of PDEs [math.AP]
Sensitivity (control systems)
[SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces
environment
Waste Management and Disposal
Ecosystem
ComputingMilieux_MISCELLANEOUS
0105 earth and related environmental sciences
Water Science and Technology
Civil and Structural Engineering
Trophic level
Estimation theory
[SDE.IE]Environmental Sciences/Environmental Engineering
Ecological Modeling
Pollution
6. Clean water
020801 environmental engineering
Oxygen
Environmental science
Water quality
Environmental Monitoring
Zdroj: Water Research
Water Research, IWA Publishing, 2019, 165, pp.115021. ⟨10.1016/j.watres.2019.115021⟩
ISSN: 0043-1354
DOI: 10.1016/j.watres.2019.115021⟩
Popis: 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.
Databáze: OpenAIRE
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