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Ensemble flood forecasting based on operational forecasts of the regional Eta EPS in the Taquari-Antas basin

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posted on 26.12.2018, 02:46 by Vinícius Alencar Siqueira, Walter Collischonn, Fernando Mainardi Fan, Sin Chan Chou

ABSTRACT Hydrological Ensemble Prediction Systems (HEPS) play an important role on operational flood forecasting. Unlike in deterministic approach, which relies on a single prediction of future river flows, these systems can represent the forecast uncertainty and provide a better detection of extreme hydro-meteorological events. In this context, the present study aimed to assess both the quality of ensemble flood forecasts on Taquari-Antas basin and its potential to provide additional information to a local Flood Alert System. The hydrological model MGB-IPH was coupled to the high-resolution meteorological EPS Eta model with five members of different parameterization schemes and boundary conditions, as well as to the deterministic version of Eta regional model. On a single event evaluation, the peak discharge was reasonable well predicted by at least one ensemble member, in nearly all forecasts, with a good prediction of the flood timing for the considered lead times. In a comparison with deterministic forecasts, the ensemble ones showed higher accuracy and higher probability of detection (POD) for the reference thresholds, preserving false alarm rates at reasonably low levels. An overall tendency of underestimation was also identified, with most observations falling between the higher ranks of the ensemble. Furthermore, the combination of previous forecasts (t-12h) with the recent ones leads to a slight increase of ensemble spread and POD, despite the performance reduction in terms of accuracy and bias for the ensemble mean. Results suggest that there is a benefit in having hydrological ensemble forecasts obtained from the high resolution EPS Eta model, which can be used as a complementary information to a local Flood Alert System supporting pre-alert issues and Civil Defense internal planning actions.