Operationalising an ensemble approach in the description of uncertainty in atmospheric dispersion modelling and an emergency response

Bedwell, Peter; Korsakissok, Irène; Leadbetter, Susan; Périllat, Raphaël; Rudas, Csilla; Tomas, Jasper; Wellings, Joseph; Geertsema, Gertie; Vries, H. de

doi:10.1051/radiopro/2020015

Cited by 3 publications

(1 citation statement)

References 7 publications

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“…A range of different representations of the uncertainties has been used to analyze the case studies; the need for various, complementary outputs, including graphical representations and statistical indicators (Bedwell et al, 2020), has been highlighted.…”

Section: Summary and Findingsmentioning

confidence: 99%

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

et al. 2020

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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.

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Section: Summary and Findingsmentioning

confidence: 99%

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

et al. 2020

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Editorial: the main results of the European CONFIDENCE project

Raskob

Duranova

2020

Radioprotection

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In nuclear emergency management and long-term rehabilitation, dealing with uncertain information on the current situation, or predicted evolution of the situation, is an intrinsic problem for decision making. Uncertain information related to, for instance, incomplete information on the source term and the prevailing weather can result in dose assessments that differ dramatically from reality. Uncertainty is also an intrinsic part of model parameters. In the presence of uncertainty, ineffective decisions are often taken (e.g. too conservative or optimistic predictions, inadequately accounting of non-radiological risks), which may result in more overall harm than good due to secondary causalities as observed following the Chernobyl and Fukushima accidents. Therefore, the reduction of uncertainty, and how to deal with uncertain information, is essential to improve decision making for the protection of the affected population and to minimise disruption of normal living conditions.Decisions on early countermeasures (e.g. evacuation, sheltering, and provision of iodine tablets) are often taken based on deterministic calculations considering the best available source term information and the state of the art numerical weather prediction data as inputs. The source term uncertainty is large and quantification difficult. Uncertainty in meteorological forecasts is routinely quantified by "ensemble predictions". However, these predictions are not used in emergency management. One reason for this is the effort and time required to process 30 to 100 different weather sequences within atmospheric dispersion models.At present, decision making in the early phase is mainly based on modelling followed by a period where monitoring has made enough progress to provide a robust contamination and dose map. However, in between some monitoring data will be available; however, no operational tool exists to combine simulated dose estimations with monitoring data to obtain a more consolidated picture of the radiological situation.The key endpoint for all decision making is the dose and associated risk to affected population groups. However, in the case of the Fukushima accident, only order-of-magnitude dose bands were estimated from (local) contamination and model predictions, with no accounting for individual human behaviour patterns or location. Individual dose measurements could greatly reduce this uncertainty but are limited by capacity.Completely missing from emergency management is the assessments of risk in the early and the transition phases. However, this is important for decision making on the need for medical surveys and long-term support of the affected population.Operational decisions concerning land and food chain management rely on radioecological models that are mostly based on simple, but highly uncertain, transfer ratios to predict contamination in foodstuffs (though such data are few for some climatic areas, (e.g. Mediterranean) and foodstuff-radionuclide combinations). Process-based models might provide an approach to red...

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Operationalising an ensemble approach in the description of uncertainty in atmospheric dispersion modelling and an emergency response

et al. 2020

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Uncertainties were propagated through the chain of atmospheric dispersion and radiological assessment models based on an ensemble approach for a range of scenarios. It was apparent that the time taken to complete model runs, ranging from several hours to a few tens of hours, was not appropriate for an emergency response. Thus, for an operational method, there was a requirement to reduce the number of ensemble members and/or reduce model run time for a single ensemble member, such that a measure of uncertainty may be obtained within the timeframe of one hour, but without significant detriment to the model endpoints derived, the uncertainty estimated and the radiation protection advice inferred. This study proposes recommendations for operationalising an ensemble approach used in the description of uncertainty in atmospheric dispersion modelling and an emergency response.

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Operationalising an ensemble approach in the description of uncertainty in atmospheric dispersion modelling and an emergency response

Cited by 3 publications

References 7 publications

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

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

Editorial: the main results of the European CONFIDENCE project

Operationalising an ensemble approach in the description of uncertainty in atmospheric dispersion modelling and an emergency response

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