Evaluation of a global ensemble flood prediction system in Peru

Abstract

Flood 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%.

Downscaling Regional Hydrological Forecast for Operational Use in Local Early Warning: HYPE Models in the Sirba River

“…TS and HSS jointly consider the POD and FAR emphasizing the best performances of NH compared to WWH. In wider terms, from both continuous and categorical points of view, optimization process allows to significantly reduce the gap between forecasts and observations confirming previous results reached in the literature [30,57].…”

Reply on RC1

“…La Niña conditions are weakly associated with increased precipitation in the western Amazon basin (Garreaud et al, 2009). (Bischiniotis et al, 2019), there is an opportunity to leverage complementary prediction frameworks to improve forecast performance. Similarly, an EAP is in development for the Piura basin in coastal northwest Peru to address extreme precipitation and flooding.…”

Leveraging multi-model season-ahead streamflow forecasts to trigger advanced flood preparedness in Peru

“…Early actions, which involve the prepositioning of supplies and release of funds, are triggered when 75 % of GloFAS ensemble members forecast streamflow above the 80th percentile (IFRC, 2019) at a 45 d lead time. Because GloFAS exhibits only modest forecast skill in Peru when detecting floods at short lead times (Bischiniotis et al, 2019), there is an opportunity to leverage complementary prediction frameworks to improve forecast performance. Similarly, an EAP is in development for the Piura basin in coastal northwest Peru to address extreme precipitation and flooding.…”

“…Statistical (also called empirical or datadriven) models forgo the parameterization of complex physical processes in favor of understanding the lagged relationships between precipitation or streamflow and antecedent land, atmosphere, and ocean conditions. Statistical and physical models have been successfully applied to seasonal prediction of hydrologic variables including precipitation and streamflow (e.g., Badr et al, 2013;Block and Rajagopalan, 2009). Both frameworks have their own set of advantages and disadvantages with prediction skill varying according to season and location (Infanti and Kirtman, 2014).…”