Implemented by the European Commission as part of the Copernicus Programme

GloFAS Community Learning Framework

The recent development of large scale probabilistic methods for flood early warning has great potential for improving community resilience to floods.

There is however, a grand challenge in interpreting these forecasts into locally relevant information for improving preparedness for flood disasters.

A solution to this challenge requires an approach that engages and applies the expertise of on-the-ground users in the development of the forecasting system.

The GloFAS Community Learning Framework appoints a two-way practical solutions and learning focus whereby GloFAS forecasters and developers learn how to tailor forecasts and uncertainty information to local users and incorporate local information into the forecasting system, and local users learn how to interpret and use probabilistic early flood information.

For more information on the project please contact the Director: Prof Hannah Cloke,

The first stage of the Learning Framework was developed in a GloFAS Community workshop held May 4th-6th 2016 at the University of Reading.

Further information on this workshop can be found here: the-glofas-community-workshop-supporting-the-integration-of-global-flood-forecasts-locally and here: 16523-newsletter-no148-summer-2016.pdf


The GloFAS Community Learning Framework was initiated by the University of Reading as part of a World Bank GFDRR / DFID Challenge Fund Project with input from ECMWF, the JRC and the Red Cross Red Crescent Climate Centre. Support was also provided by an Impact award by the UK's Natural Environment Research Council, the University of Reading Endowment Fund and the University of Reading's Walker Institute.

Resources from the 1st Community Workshop

1. Keynote talks
  • University of Reading - Cloke
  • DFID
  • JRC
  • RCCC
  • Walker Institute

2. GloFAS exercises
The following exercises were undertaken in the Community workshop.

  • Exercise 1 - plotting

  • Exercise 2 - evaluation

  • Exercise 3 - bias correction