Ensemble and parameter uncertainty
Models are run across parameter sets and forcing ensembles; the spread is reported as the range, not collapsed to a mean.
The Global Centre for Risk and Innovation (GCRI)
Uncertainty quantification is what makes the evidence honest: every modelled value on this platform carries a range, a label and the assumptions behind it, so a decision-maker can see how much weight it bears.
Water NexusWireframe of the working environment, not a live screen. Names and values are placeholders.
Uncertainty quantification and model validation characterise how uncertainty in inputs, parameters and model structure affects predictions. They establish how much confidence a model’s outputs deserve. In water, climate and hazards and critical infrastructure, uncertainty quantification shows where risk estimates are robust and where they are fragile. It guides where better data or models would change decisions. The canonical description is on the STEM Commons.
Models are run across parameter sets and forcing ensembles; the spread is reported as the range, not collapsed to a mean.
Uncertainty in inflow is carried into storage, generation, treatment and health effects, so the final range reflects every step.
Each value is labelled observed, official, modelled, estimated, forecast or unknown, and the label travels with the value into every figure and table.
Where this page’s subject sits in the world: boundaries and context now, the field layers when the interactive map loads.
MapWorld map in the Equal Earth projection, land and boundaries from Natural Earth (public domain). The interactive map loads through the Nexus Foundry plugin (globe or flat view).
Data from the source register feeds the methods; calibration and an independent test gate the estimate and its limits; the decision stays with the competent authority.
Fig. 01Nodes carry their type, metrics and state; connectors show what feeds what; the gates are the validation steps; the timeline is one application. Design of the method’s use, not a record of a run.
Datasets and records used by uncertainty quantification and model validation on this platform, with the role each plays and the label its values carry. Sources are listed in the source register.
| Data | Role | Label |
|---|---|---|
| Model ensembles | Spread and sensitivity | MODELLED |
| Observation error estimates | Input uncertainty | ESTIMATED |
| Expert elicitation | Where no data exist | ESTIMATED |
Predictive distributions are tested for calibration and sharpness against held-out observations. Verification confirms that code solves the intended equations.
| Check | How it is done |
|---|---|
| Sensitivity analysis | The inputs that drive most of the output range are identified and reported |
| Calibration of probabilities | Stated probabilities are checked against outcomes where a record exists |
| Label audit | A sample of published values is traced back to the evidence behind its label |
Structural uncertainty and unknown unknowns are difficult to represent. Computational cost limits exploration of high-dimensional models.
Systems Science and Modelling stewards uncertainty quantification and model validation for the Consortium and reviews how it is applied across platforms.
| Link | Relationship | Why |
|---|---|---|
| Uncertainty quantification and model validation | Canonical method page | Platform application links to the canonical method page. |
| Uncertainty quantification and model validation in systems science and modelling | Steward platform | Systems Science and Modelling stewards this method. |
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