Uncertainty propagation in atmospheric dispersion models for radiological emergencies in the pre- and early release phase: summary of case studies

Abstract

In the framework of the European project CONFIDENCE, Work Package 1 (WP1) focused on the uncertainties in the pre- and early phase of a radiological emergency, when environmental observations are not available and the assessment of the environmental and health impact of the accident largely relies on atmospheric dispersion modelling. The latter is subject to large uncertainties coming from, in particular, meteorological and release data. In WP1, several case studies were identified, including hypothetical accident scenarios in Europe and the Fukushima accident, for which participants propagated input uncertainties through their atmospheric dispersion and subsequent dose models. This resulted in several ensembles of results (consisting of tens to hundreds of simulations) that were compared to each other and to radiological observations (in the Fukushima case). These ensembles were analysed in order to answer questions such as: among meteorology, source term and model-related uncertainties, which are the predominant ones? Are uncertainty assessments very different between the participants and can this inter-ensemble variability be explained? What are the optimal ways of characterizing and presenting the uncertainties? Is the ensemble modelling sufficient to encompass the observations, or are there sources of uncertainty not (sufficiently) taken into account? This paper describes the case studies of WP1 and presents some illustrations of the results, with a summary of the main findings.

Ranking uncertainties in atmospheric dispersion modelling following the accidental release of radioactive material

“…An evaluation of this type was carried out as part of the FASTNET (FAST Nuclear Emergency Tools) project (Chevalier-Jabet, 2019a, 2019b). Results from this project were used in the REM (Radiological Ensemble Modelling) case study (Korsakissok et al, 2020). However, there are uncertainties that cannot be accounted for using this method due to a lack of information or human errors; 2 Coupling dispersion modelling and measurements in the environment.…”

“…Uncertainties in this method can be estimated by considering different measurement data and/or by using ensemble dispersion model output. In Korsakissok et al (2020) nine source terms were selected from the literature to represent uncertainty in the Fukushima accident source term. The source terms were constructed using different meteorological data, different dispersion models and different measurement data.…”

Towards an improved decision-making process to better cope with uncertainties following a nuclear accident

“…Moreover, all participants highlighted the need to reflect uncertainties in supporting information, as clearly as possible. The frequency maps, developed in the framework of V. Durand et al: Radioprotection 2020, 55(HS1), S135-S143 S141 CONFIDENCE WP1 (Korsakissok et al, 2017(Korsakissok et al, , 2020 seek to reflect the various uncertainties associated with the modelling process by delimiting different areas of probability of occurrence of threshold exceedance according to the selected criteria. These maps were designed to be used in the early release phase, when measurement data are not yet available.…”

Evaluation of uncertainties derived from meteorological forecast inputs in plume directions predicted by atmospheric dispersion simulations

“…A similar problem occurs in numerical weather prediction systems, where uncertainties are represented by different elements, such as a probability circle in a typhoon-track forecast. Some recent studies have estimated the uncertainties in ATDM results based on meteorological or multimodel ensemble forecast approaches and have indicated that a variety in the ensembles can be useful to represent the uncertainty [2,23,24]. However, these attempts were only performed for certain periods during the FDNPP accident or during hypothetical scenarios.…”

“…Another useful method to quantify the uncertainty is a probabilistic expression using ensemble simulations (e.g. [2,24]); however, simulations lasting more than one year entail a high computational cost. Therefore, in this study, dispersion simulation results with meteorological analysis inputs are substituted for observational data to statistically evaluate the uncertainty.…”