Preprints
https://doi.org/10.5194/gmd-2022-168
https://doi.org/10.5194/gmd-2022-168
Submitted as: development and technical paper
02 Sep 2022
Submitted as: development and technical paper | 02 Sep 2022
Status: this preprint is currently under review for the journal GMD.

Bayesian transdimensional inverse reconstruction of the 137Cs Fukushima-Daiichi release

Joffrey Dumont Le Brazidec1,2, Marc Bocquet2, Olivier Saunier1, and Yelva Roustan2 Joffrey Dumont Le Brazidec et al.
  • 1IRSN, PSE-SANTE, SESUC, BMCA, Fontenay-aux-Roses, France
  • 2CEREA, École des Ponts and EDF R&D, Île-de-France, France

Abstract. The accident at the Fukushima-Daiichi nuclear power plant yielded massive and rapidly varying atmospheric radionuclide releases. The assessment of these releases and of the corresponding uncertainties can be performed using inverse modelling methods that combine an atmospheric transport model with a set of observations and have proven to be very effective for this type of problem. In the case of Fukushima-Daiichi, a Bayesian inversion is particularly suitable because it allows errors to be modelled rigorously and a large amount of observations of different natures to be assimilated at the same time. More specifically, one of the major sources of uncertainty in the source assessment of the Fukushima-Daiichi releases stems from the temporal representation of the source. To obtain a well time-resolved estimate, we implement a MCMC sampling algorithm within a Bayesian framework, the Reversible-Jump MCMC, in order to retrieve the distributions of the magnitude of the Fukushima-Daiichi 137Cs source as well as its temporal discretisation. In addition, we develop Bayesian methods allowing to combine air concentration and deposition measurements, as well as to assess the spatio-temporal information of the air concentration observations in the definition of the observation error matrix. These methods are applied to the reconstruction of the posterior distributions of the magnitude and temporal evolution of the 137Cs release. They yield a source estimate between 11 and 24 March, as well as an assessment of the uncertainties associated with the observations, the model and the source estimate. The total released reconstructed activity is estimated to be between 10 and 20 PBq, although it increases when taking into account the deposition measurements. Finally, the variable discretisation of the source term yields an almost hourly profile over certain intervals of high temporal variability, signaling identifiable portions of the source term.

Joffrey Dumont Le Brazidec et al.

Status: open (until 28 Oct 2022)

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Joffrey Dumont Le Brazidec et al.

Joffrey Dumont Le Brazidec et al.

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Short summary
When radionuclides are released into the atmosphere, the assessment of the consequences depends on the evaluation of the magnitude and temporal evolution of the release, which can be highly variable as in the case of Fukushima-Daiichi. In this paper, we propose Bayesian inverse modelling methods and the Reversible-Jump Markov Chain Monte Carlo technique, which allows to evaluate the temporal variability of the release and to integrate different types of information in the source reconstruction.