Probabilistic scenarios

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The text on this page is taken from an equivalent page of the IEHIAS-project.

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The future can rarely be seen with any degree of certainty. Prognostic assessments are therefore based not on precise specifications of what will happen, but on pictures of what might happen in the future. Despite this, most scenarios are, within themselves, unequivocal: they give fixed projections of the future. Uncertaintities are usually recognised only by providing alternative fixed projections (such as so-called best and worst case scenarios). Probabilistic scenarios attempt to be more realistic about the uncertainties in our projections of the future, by incorporating them into the scenario.

They usually do so in the form of distributions: i.e. by representing future elements of the system as a range of values, weighted by their likelihood. In an assessment of the impact of future climate change on water quality and health, for example, we can define temperatures not as a predefined average (e.g. present temperature plus 2 degrees) but as a distribution with a mean of, say, 2 degrees above present, but a standard deviation of 0.5 degres around that. In this way, a range of futures can be explored, of varying likelihood, and from these the statistical distribution of potential health effects can be estimated.

This approach is clearly more realistic and informative than approaches based on other types of scenario. It nevertheless comes at a substantial cost in terms of the complexity of the analysis, for when using probabilistic scenarios we have to model a large range of alternatives, each of which may have to be pursued right through the causal chain to final impacts. This is usually done using some form of Monte Carlo simulation. With large data sets, and a number of complex and interlinked models, the computing requirements rapidly escalate. Inevitably, therefore, this approach tends to be restricted to major assessments carried out by well-resourced research teams, and with the support of large government or commercial organuisations.


See also

Integrated Environmental Health Impact Assessment System
IEHIAS is a website developed by two large EU-funded projects Intarese and Heimtsa. The content from the original website was moved to Opasnet.
Topic Pages

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Examples and case studies Defining question: Agriculture Waste Water · Defining stakeholders: Agriculture Waste Water · Engaging stakeholders: Water · Scenarios: Agriculture Crop CAP Crop allocation Energy crop · Scenario examples: Transport Waste SRES-population UVR and Cancer
Models and methods Ind. select · Mindmap · Diagr. tools · Scen. constr. · Focal sum · Land use · Visual. toolbox · SIENA: Simulator Data Description · Mass balance · Matrix · Princ. comp. · ADMS · CAR · CHIMERE · EcoSenseWeb · H2O Quality · EMF loss · Geomorf · UVR models · INDEX · RISK IAQ · CalTOX · PANGEA · dynamiCROP · IndusChemFate · Transport · PBPK Cd · PBTK dioxin · Exp. Response · Impact calc. · Aguila · Protocol elic. · Info value · DST metadata · E & H: Monitoring Frameworks · Integrated monitoring: Concepts Framework Methods Needs
Listings Health impacts of agricultural land use change · Health impacts of regulative policies on use of DBP in consumer products
Guidance System
The concept
Issue framing Formulating scenarios · Scenarios: Prescriptive Descriptive Predictive Probabilistic · Scoping · Building a conceptual model · Causal chain · Other frameworks · Selecting indicators
Design Learning · Accuracy · Complex exposures · Matching exposure and health · Info needs · Vulnerable groups · Values · Variation · Location · Resolution · Zone design · Timeframes · Justice · Screening · Estimation · Elicitation · Delphi · Extrapolation · Transferring results · Temporal extrapolation · Spatial extrapolation · Triangulation · Rapid modelling · Intake fraction · iF reading · Piloting · Example · Piloting data · Protocol development
Execution Causal chain · Contaminant sources · Disaggregation · Contaminant release · Transport and fate · Source attribution · Multimedia models · Exposure · Exposure modelling · Intake fraction · Exposure-to-intake · Internal dose · Exposure-response · Impact analysis · Monetisation · Monetary values · Uncertainty