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Catchment summary

This table provides global summary statistics, where the performance measures have been calculated for each simulation based on stacking the data from all sites together and then the median value of the performance measure across the simulations has been calculated.

Site summary statistics

This table provides site-specific summary statistics, where mean and median have been taken across the simulations.

GLUE analysis

KS test

This project has received funding from the European Union's Seventh Programme for Research, Technological Development and Demonstration under grant agreement No. 603587.

Catchment costs

Sampling cost

Use the table to update the sampling costs; changes will be reflected in the catchment analysis.

User mitigation

Select data representing your own mitigation strategy.


Select sites you want to optimise performance at. Model runs which produce optimal performance will be shown on the plots in red.

Optimal model runs

What is UNCOVER?

UNCOVER is a tool to help explore uncertainty and trade-offs within ensemble model simulations.

Trade-off analysis

UNCOVER implements a search procedure to discover ensemble member simulations that maximise performance against a set of one or more preferences. The resulting set, called a Pareto set, contains the model output ensemble members for which your data contains no better-performing simulation when considering all of the preferences.

The Pareto set is found using a search algorithm based on block-nested-loop and lattice algorithms.

Uncertainty analysis

While trade-off analysis is useful to understand possible strategies to balance competing goals or preferences, UNCOVER also provides a way to explore the uncertainty in model predictions using the GLUE (Generalized Likelihood Uncertainty Estimation) methodology first developed by Beven and Binley.

The method weights each of an ensemble of behavioural (i.e. potentially acceptable) models by a measure of its performance over a calibration period. The performance metrics are interpreted analagously to likelihoods. The resulting set of predictions are used to construct a weighted cumulative distribution function (CDF) as an expression of the uncertainty for any predicted variable of interest. The method offers flexibility to assess uncertainty conditionally on a range of measures, although this flexibility can lead to ambiguity without a clear account of the judgements made by the analyst in a particular application. Future revisions of UNCOVER will offer tools to expose and record these judgements.

Release notes

This web page is currently a demonstration prototype.