Downscaling Regional Hydrological Forecast for...
Giovanni Massazza, Vieri Tarchiani, Jafet C. M. Andersson, Abdou Ali, Mohamed Housseini Ibrahim, Alessandro Pezzoli, Tiziana De Filippis, Leandro Rocchi, Bernard Minoungou, David Gustafsson and Maurizio Rosso. Water 2020, 12, 3504; doi:10.3390/w12123504 http://www.mdpi.com/journal/water
Abstract: In the last decades since the dramatic increase in flood frequency and magnitude, floods have become a crucial problem inWest Africa. National and international authorities concentrate efforts ondeveloping 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 effciency NSE = 0.58) thanWWH(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” forNH (Heidke skill score HSS = 0.53) and “good” forWWH(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.
Keywords: Middle Niger River Basin; Sirba River; floods; flood alert; HYPE; model evaluation; hydrological model; optimization; early warning system; SLAPIS
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Información adicional
| Campo | Valor |
|---|---|
| Última actualización de los datos | 15 de diciembre de 2020 |
| Última actualización de los metadatos | 15 de diciembre de 2020 |
| Creado | 15 de diciembre de 2020 |
| Formato | |
| Licencia | Creative Commons Attribution |
| Datastore active | False |
| Has views | False |
| Id | 0fb862f2-9b1b-4056-87fc-820bf3bd2216 |
| Mimetype | application/pdf |
| Package id | c5af00bc-9e72-42f2-bd13-5f0296fbc05a |
| Position | 10 |
| Size | 3,7 MiB |
| State | active |
| Url type | upload |