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Article

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

1
DIST (Interuniversity Department of Regional and Urban Studies and Planning), Politecnico di Torino & Università di Torino, 10129 Torino, Italy
2
Istituto per la BioEconomia—Consiglio Nazionale delle Ricerche (IBE-CNR), Via Madonna del Piano 10, 50019 Sesto Fiorentino, Italy
3
Swedish Meteorological and Hydrological Institute (SMHI), Folkborgsvagen 17, 601 76 Norrkoping, Sweden
4
AGRHYMET Regional Centre for Agronomy, Hydrology and Meteorology, Niamey P.O. 1011, Niger
5
Direction de l’Hydrologie (DH), Ministère de l’Hydraulique et de l’Assainissement du Niger, Niamey B.P. 257, Niger
6
DIATI (Department of Environment, Land and Infrastructure Engineering), Politecnico di Torino, 10129 Torino, Italy
*
Author to whom correspondence should be addressed.
Water 2020, 12(12), 3504; https://doi.org/10.3390/w12123504
Received: 13 November 2020 / Revised: 11 December 2020 / Accepted: 12 December 2020 / Published: 13 December 2020
In the last decades since the dramatic increase in flood frequency and magnitude, floods have become a crucial problem in West Africa. National and international authorities concentrate efforts on developing early warning systems (EWS) to deliver flood alerts and prevent loss of lives and damages. Usually, regional EWS are based on hydrological modeling, while local EWS adopt field observations. This study aims to integrate outputs from two regional hydrological models—Niger HYPE (NH) and World-Wide HYPE (WWH)—in a local EWS developed for the Sirba River. Sirba is the major tributary of Middle Niger River Basin and is supported by a local EWS since June 2019. Model evaluation indices were computed with 5-day forecasts demonstrating a better performance of NH (Nash–Sutcliffe efficiency NSE = 0.58) than WWH (NSE = 0.10) and the need of output optimization. The optimization conducted with a linear regression post-processing technique improves performance significantly to “very good” for NH (Heidke skill score HSS = 0.53) and “good” for WWH (HSS = 0.28). HYPE outputs allow to extend local EWS warning lead-time up to 10 days. Since the transfer informatic environment is not yet a mature operational system 10–20% of forecasts were unfortunately not produced in 2019, impacting operational availability. View Full-Text
Keywords: Middle Niger River Basin; Sirba River; floods; flood alert; HYPE; model evaluation; hydrological model; optimization; early warning system; SLAPIS Middle Niger River Basin; Sirba River; floods; flood alert; HYPE; model evaluation; hydrological model; optimization; early warning system; SLAPIS
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MDPI and ACS Style

Massazza, G.; Tarchiani, V.; Andersson, J.C.M.; Ali, A.; Ibrahim, M.H.; Pezzoli, A.; De Filippis, T.; Rocchi, L.; Minoungou, B.; Gustafsson, D.; Rosso, M. Downscaling Regional Hydrological Forecast for Operational Use in Local Early Warning: HYPE Models in the Sirba River. Water 2020, 12, 3504. https://doi.org/10.3390/w12123504

AMA Style

Massazza G, Tarchiani V, Andersson JCM, Ali A, Ibrahim MH, Pezzoli A, De Filippis T, Rocchi L, Minoungou B, Gustafsson D, Rosso M. Downscaling Regional Hydrological Forecast for Operational Use in Local Early Warning: HYPE Models in the Sirba River. Water. 2020; 12(12):3504. https://doi.org/10.3390/w12123504

Chicago/Turabian Style

Massazza, Giovanni; Tarchiani, Vieri; Andersson, Jafet C.M.; Ali, Abdou; Ibrahim, Mohamed H.; Pezzoli, Alessandro; De Filippis, Tiziana; Rocchi, Leandro; Minoungou, Bernard; Gustafsson, David; Rosso, Maurizio. 2020. "Downscaling Regional Hydrological Forecast for Operational Use in Local Early Warning: HYPE Models in the Sirba River" Water 12, no. 12: 3504. https://doi.org/10.3390/w12123504

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