Community and Impact Based Early Warning...
URL: https://www.mdpi.com/2071-1050/12/5/1802
Vieri Tarchiani, Giovanni Massazza, Maurizio Rosso, Maurizio Tiepolo, Alessandro Pezzoli, Mohamed Housseini Ibrahim, Gaptia Lawan Katiellou, Paolo Tamagnone, Tiziana De Filippis, Leandro Rocchi, Valentina Marchi and Elena Rapisardi Published in: Sustainability Date: February 28, 2020 Abstract Floods have recently become a major hazard in West Africa (WA) in terms of both their magnitude and frequency. They affect livelihoods, infrastructure and production systems, hence impacting on Sustainable Development (SD). Early Warning Systems (EWS) for floods that properly address all four EWS components, while also being community and impact‐based, do not yet exist in WA. Existing systems address only the main rivers, are conceived in a top‐down manner and are hazard‐centered. This study on the Sirba river in Niger aims to demonstrate that an operational community and impact‐based EWS for floods can be set up by leveraging the existing tools, local stakeholders and knowledge. The main finding of the study is that bridging the gap between topdown and bottom‐up approaches is possible by directly connecting the available technical capabilities with the local level through a participatory approach. This allows the beneficiaries to define the rules that will develop the whole system, strengthening their ability to understand the information and take action. Moreover, the integration of hydrological forecasts and observations with the community monitoring and preparedness system provides a lead time suitable for operational decision‐making at national and local levels. The study points out the need for the commitment of governments to the transboundary sharing of flood information for EWS and SD.
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Informations additionnelles
| Champ | Valeur |
|---|---|
| Dernière modification de la donnée | 4 décembre 2020 |
| Dernière modification de la métadonnée | 4 décembre 2020 |
| Créé le | 4 décembre 2020 |
| Format | |
| Licence | Creative Commons Attribution |
| Id | 928490ab-a65b-4f4e-bb88-16b2d4bc81cc |
| Package id | c5af00bc-9e72-42f2-bd13-5f0296fbc05a |
| Position | 8 |
| State | active |