Arcorace, M.

Bjorgo, E.

Boni, G.

Dell\u2019Oro, L.

Rudari, R.

Silvestro, F.

English

M. Arcorace

F. Silvestro

R. Rudari

G. Boni

L. Dell’Oro

E. Bjorgo

Archivio istituzionale della ricerca - Università di Genova

Geophysical Research AbstractsVol. 18, EGU2016-8770, 2016EGU General Assembly 2016© Author(s) 2016. CC Attribution 3.0 License.Forecast-based Integrated Flood Detection System for EmergencyResponse and Disaster Risk Reduction (Flood-FINDER)Mauro Arcorace (1), Francesco Silvestro (2), Roberto Rudari (2), Giorgio Boni (2,3), Luca Dell’Oro (1), andEinar Bjorgo (1)(1) UNITAR-UNOSAT, Geneva, Switzerland, (2) CIMA Research Foundation, Savona, Italy, (3) DIBRIS, University ofGenoa, Genoa, ItalyMost flood prone areas in the globe are mainly located in developing countries where making communities moreflood resilient is a priority. Despite different flood forecasting initiatives are now available from academia andresearch centers, what is often missing is the connection between the timely hazard detection and the communityresponse to warnings. In order to bridge the gap between science and decision makers, UN agencies play a keyrole on the dissemination of information in the field and on capacity-building to local governments. In this context,having a reliable global early warning system in the UN would concretely improve existing in house capacities forHumanitarian Response and the Disaster Risk Reduction.For those reasons, UNITAR-UNOSAT has developed together with USGS and CIMA Foundation a Global FloodEWS called “Flood-FINDER”. The Flood-FINDER system is a modelling chain which includes meteorological,hydrological and hydraulic models that are accurately linked to enable the production of warnings and forecastinundation scenarios up to three weeks in advance. The system is forced with global satellite derived precipitationproducts and Numerical Weather Prediction outputs. The modelling chain is based on the “Continuum” hydrolog-ical model and risk assessments produced for GAR2015. In combination with existing hydraulically reconditionedSRTM data and 1D hydraulic models, flood scenarios are derived at multiple scales and resolutions. Climate andflood data are shared through a Web GIS integrated platform. First validation of the modelling chain has been con-ducted through a flood hindcasting test case, over the Chao Phraya river basin in Thailand, using multi temporalsatellite-based analysis derived for the exceptional flood event of 2011.In terms of humanitarian relief operations, the EO-based services of flood mapping in rush mode generally sufferfrom delays caused by the time required for their activation, programming, acquisitions and image processing.Flood-FINDER aims to pre-empt this process and to provide preliminary analyses where no field data is available.In the early 2015, the Flood-FINDER’s forecast along the Shire River has been used to guide the rapid mappingactivities in Southern Malawi and Northern Mozambique. It proved efficient support providing timely informationabout the evolution of the flood event over an area lacking of field data. Regarding in-country capacity building,Flood-FINDER allowed UNOSAT to set up in middle 2015 a flood early warning system in Chad along the ChariRiver basin with the collaboration of Chadian Ministry of hydraulics and livestock. Weekly flood bulletins havebeen shared with local authorities and UN agencies over the entire rainy season. Finally, an experimental versionof the global web alerting platform has been recently developed for supporting the El Nino flood preparedness inthe Horn of Africa.Flood-FINDER′s mission is to support decision makers throughout all the disaster management cycle with floodalerts, modelled scenarios, EO-based impact assessments and with direct support at country level to implement dis-aster mitigation strategies. The aim for the future is to seek funding for having the global system fully operationalusing CERN’s supercomputing facilities and to establish new in-country projects with local authorities.

Forecast-based Integrated Flood Detection System for Emergency Response and Disaster Risk Reduction (Flood-FINDER)

https://iris.unige.it/bitstream/11567/935447/1/EGU2016-8770.pdf

Forecast-based Integrated Flood Detection System for Emergency Response and Disaster Risk Reduction (Flood-FINDER)

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Archivio istituzionale della ricerca - Università di Genova