Hydrological Model Application in the Sirba River: Early Warning System and GloFAS Improvements

Abstract

In the last decades, the Sahelian area was hit by an increase of flood events, both in frequency and in magnitude. In order to prevent damages, an early warning system (EWS) has been planned for the Sirba River, the major tributary of the Middle Niger River Basin. The EWS uses the prior notification of Global Flood Awareness System (GloFAS) to realize adaptive measures in the exposed villages. This study analyzed the performances of GloFAS 1.0 and 2.0 at Garbey Kourou. The model verification was performed using continuous and categorical indices computed according to the historical flow series and the flow hazard thresholds. The unsatisfactory reliability of the original forecasts suggested the performing of an optimization to improve the model performances. Therefore, datasets were divided into two periods, 5 years for training and 5 years for validation, and an optimization was conducted applying a linear regression throughout the homogeneous periods of the wet season. The results show that the optimization improved the performances of GloFAS 1.0 and decreased the forecast deficit of GloFAS 2.0. Moreover, it highlighted the fundamental role played by the hazard thresholds in the model evaluation. The optimized GloFAS 2.0 demonstrated performance acceptable in order to be applied in an EWS.

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].…”

“…The methodology applied in this study consists of three steps: (1) an evaluation of the original forecast performances, (2) an optimization of model outputs to improve forecast reliability and 3an evaluation of optimized forecasts to quantify the improvements reached in the post processing procedures [30].…”

“…GloFAS version 2.1 has been optimized on the Sirba River applying correction factors to model outputs because of the poor reliability of the original forecasts on local application. The optimization ensured a substantial improvement in forecast accuracy (particularly the shape of the forecasted hydrograph rather than its intensity) [30] allowing optimized GloFAS 2.1 to be used in the EWS platform for the Sirba River as pre-warning, while warnings will only be sent using the in-situ measurements.…”

“…In such a context of poorly gauged basins, where hydrological models cannot be adequately calibrated, hydrological forecasts are nevertheless useful to timely activate the pre-warning tasks while operational warnings were delivered in the case of threshold overpassing in the upstream field observations linked to flood scenarios developed with ad-hoc hydraulic models [29]. Nevertheless, the longer lead times ensured by the hydrological forecasts application underline the importance of downscaling and optimizing models' outputs to improve the performances and ensure a higher reliability of the local early warning systems [30].…”

Community and Impact Based Early Warning System for Flood Risk Preparedness: The Experience of the Sirba River in Niger

“…The optimization process was conducted through the linear regression method over homogeneous periods of the rainy season and was based on 10 years of simulations (2008-2018). The optimization allows quality improvement with an increase in Root Mean Square Error (RMSE) and the Probability of Detection (POD) of extreme events and, at the same time, reduces the False Alarm Rate (FAR), as described by Passerotti et al [51]. A further improvement in the forecasting system is foreseen with the integration of a second model, Niger-HYPE [23].…”