Evaluation of a global ensemble flood prediction system in PeruEvaluation of a global ensemble flood prediction system in Peru Peer-Reviewed Journal Publication Δημοσίευση σε Περιοδικό με Κριτές 2020-05-282019enFlood early warning systems play a more substantial role in risk mitigation than ever before. Hydrological forecasts, which are an essential part of these systems, are used to trigger action against floods around the world. This research presents an evaluation framework, where the skills of the Global Flood Awareness System (GloFAS) are assessed in Peru for the years 2009–2015. Simulated GloFAS discharges are compared against observed ones for 10 river gauges. Forecasts skills are assessed from two perspectives: (i) by calculating verification scores at every river section against simulated discharges and (ii) by comparing the flood signals against reported events. On average, river sections with higher discharges and larger upstream areas perform better. Raw forecasts provide correct flood signals for 82% of the reported floods, but exhibit low verification scores. Post-processing of raw forecasts improves most verification scores, but reduces the percentage of the correctly forecasted reported events to 65%.http://creativecommons.org/licenses/by/4.0/Hydrological Sciences Journal64101171-1189Bischiniotis_et_al_Hydrol Sci J._64(10)_2019.pdfChania [Greece]Library of TUC2020-05-26application/pdf3.8 MBfree Bischiniotis, K Van Den Hurk Bart J.J.M. Zsótér Ervin Coughlan De Perez Erin Gryllakis Emmanouil Γρυλλακης Εμμανουηλ Aerts Jeroen C.J.H. Taylor & Francis Bias-correction Early warning Ensemble streamflow predictions Flood Forecast Risk