To advance the state-of–the-art in operational hydro-meteorological forecasting, a newly funded project called IMPREX has recently been launched. IMPREX stands for: IMproving PRedictions and management of hydrological EXtremes. The project, awarded €7.9 million over 4 years by the European Commission, aims to improve society’s ability to anticipate and respond to future hydrological extreme events in Europe.
The project kicked off at the premises of the coordinating organisation, the Royal Netherlands Meteorological Institute (KNMI), in De Bilt, Netherlands on 30 November 2015. The 3-day kick-off meeting allowed representatives from the 23 consortium members to build a strong team spirit and set the foundations for the work ahead. Johannes Hunink and Sergio Contreras represented FutureWater at this meeting.
IMPREX works across time-scales by focusing on both the quality of short-to-medium term predictions as well as the reliability of future climate projections. It does this by improving the representation of key processes in the current state-of-the-art forecasting systems. The application-oriented approach of the project hopes to improve the uptake of climate information in strategic economic sectors and contribute to risk management strategies across Europe. As a key outreach product, a periodic hydrological risk outlook for Europe will be produced.
IMPREX is built upon a strong team of experts from public and private sectors as well as universities and research institutes with complementary skills and experiences. The direct involvement of a broad range of users from key economic sectors will ensure the relevance of the project outputs.
FutureWater coordinates the “Drought and Agriculture” work package of IMPREX. Johannes Hunink outlined the objectives for the first year of the project, introduced the GEISEQ-Infosequía toolbox developed by FutureWater, and made a brief description of the Segura River Basin which is one of pilot watersheds selected in Europe for including seasonal weather forecasts and climate predictions in decision support systems for drought management in agriculture.
