Estimation of the caesium-137 source term from the Fukushima Daiichi nuclear power plant using a consistent joint assimilation of air concentration and deposition observations

Abstract

International audienceInverse modelling techniques can be used to estimate the amount of radionuclides and the temporal profile of the source term released in the atmosphere during the accident of the Fukushima Daiichi nuclear power plant in March 2011. In Winiarek et al. (2012b), the lower bounds of the caesium-137 and iodine-131 source terms were estimated with such techniques, using activity concentration measurements. The importance of an objective assessment of prior errors (the observation errors and the background errors) was emphasised for a reliable inversion. In such critical context where the meteorological conditions can make the source term partly unobservable and where only a few observations are available, such prior estimation techniques are mandatory, the retrieved source term being very sensitive to this estimation. We propose to extend the use of these techniques to the estimation of prior errors when assimilating observations from several data sets. The aim is to compute an estimate of the caesium-137 source term jointly using all available data about this radionuclide, such as activity concentrations in the air, but also daily fallout measurements and total cumulated fallout measurements. It is crucial to properly and simultaneously estimate the background errors and the prior errors relative to each data set. A proper estimation of prior errors is also a necessary condition to reliably estimate the a posteriori uncertainty of the estimated source term. Using such techniques, we retrieve a total released quantity of caesium-137 in the interval 11.6 − 19.3 PBq with an estimated standard deviation range of 15 − 20% depending on the method and the data sets. The "blind" time intervals of the source term have also been strongly mitigated compared to the first estimations with only activity concentration data

Long-term assessment of airborne radiocesium after the Fukushima nuclear accident: re-suspension from bare soil and forest ecosystems

“…Since then, a number of studies have been conducted, particularly during the months that followed the accident. These assessments include primary emission estimations (Chino et al, 2011;Danielache et al, 2012;Stohl et al, 2012;Terada et al, 2012;Katata et al, 2012aKatata et al, , b, 2015Winiarek et al, 2012Winiarek et al, , 2014Hirao et al, 2013;Saunier et al, 2013;Yumimoto et al, 2016;Danielache et al, 2016), field observations (ground aerosol sampling: Masson et al, 2011Masson et al, , 2013Kaneyasu et al, 2012;Adachi et al, 2013;Tsuruta et al, 2014;Igarashi et al, 2015;Oura et al, 2015;aircraft measurements: NRA, 2012; and measurements carried out on foot: Hososhima and Kaneyasu, 2015), and numerical simulations of transport and depositions (deterministic simulation: Chino et al, 2011;Morino et al, 2011;Yasunari et al, 2011;Stohl et al, 2012;Terada et al, 2012;Katata et al, 2012a, b;Winiarek et al, 2012Winiarek et al, , 2014Hirao et al, 2013;Saunier et al, 2013;Katata et al, 2015;Yumimoto et al, 2016;Danielache et al, 2016; deterministic simulation with sensitivity runs: Morino et al, 2013;Adachi et al, 2013;Groëll et al, 2014;Saito et al, 2015;…”

Predicting and Controlling Nuclear Accident Hazards: Issues and Challenges

“…Not only is the radioactivity discharge constantly evolving, but so are the meteorological conditions impacting the dispersion and deposition of radioactivity (Angevine et al, 2014;Arnold et al, 2014). Inverse modeling approaches that combine environmental measurements and atmospheric dispersion models are efficient in reducing uncertainty and improving assessment of atmospheric releases, and therefore are commonly used for source determination (Stohl et al, 2012a;Saunier et al, 2013;Eslinger et al, 2014;Hamburger et al, 2014;Winiarek et al, 2014). Other source estimates of airborne radionuclides from the FD-NPP reactors apply Lagrangian particle dispersion (e.g., FLEXPART model; Stohl et al, 2005) and a different calculation algorithm (Stohl et al, 2012a;Benmergui, 2013;Eslinger et al, 2014).…”

“…A data assimilation algorithm based on inverse modeling methods with an observation targeting strategy has been proposed for the sequential reconstruction of the nuclide plume (Abida et al, 2009). In Winiarek et al (2012Winiarek et al ( , 2014, techniques were proposed to estimate prior errors for performing inverse modeling using air concentration and deposition observations. Chai et al (2015) developed the emission inversion method based on a Lagrangian model and a cost function using the transfer coefficient matrix, and conducted sensitivity analysis for the inverse estimation by using the original concentration c and ln(c) differences between model and observations in the cost function.…”

Weak-constraint inverse modeling using HYSPLIT Lagrangian dispersion model and Cross Appalachian Tracer Experiment (CAPTEX) observations – Effect of including model uncertainties on source term estimation

“…Chino et al, 2011;Katata et al, 2012;Terada et al, 2012;Hirao et al, 2013;Kobayashi et al, 2013;Oza et al, 2013; 15 Katata et al, 2015;Achim et al, 2014) to those sophisticated ones using various dispersion models and inverse modeling schemes (e.g. Stohl et al, 2012;Winiarek et al, 2012;Saunier et al, 2013;Winiarek et al, 2014;Chai et al, 2015). Another active field for STE applications is the estimation of the volcanic ash emissions.…”