Operational flash flood forecasting based on grid technology Monitoring and Forecasting Thierion Vincent P.-A. Ayral, V. Angelini, S. Sauvagnargues-Lesage, S. Nativi, and O. Payrastre Laboratoire de Génie de l’Environnement Industriel et des Risques Industriels et Naturels 11 – 15 May, 2009, Toulouse, France CEOS WGISS 27

Prototype Flash floods Grid Context Conclusion - Research issues - French flood crisis management state and requirements - Grid technology potentialities - Existing operational flash flood monitoring and modelling platform - Grid-enabled OWS : an experimental platform - Towards an interoperable and spatial decision-making platform

Forecasting and Monitoring : Hydrological expertise (SPC-GD & SCHAPI)‏ How grid technology capabilities enable the new forecasting mission of Grand Delta flood forecasting service (SPC-GD) ? Hazard : Flash flood Research axes : 1. Improvement of hydrological forecasting capabilities 2. Grid potentialities for operational crisis management VOs ESR & CYCLOPS (EGEE) Prototype Flash floods Grid Context Conclusion ?

According to lessons learnt, flash flood crisis management requires accurate flood hazard evaluation and on-going situation anticipation in order to support civil authorities to : - evaluate hazard intensity - anticipate human and materials damages - organize rescues operations Integration and sharing of sensor observation outputs Geospatial data and processing enhancement Resource availability, short time of response, security and interoperability … Prototype Flash floods Grid Context Conclusion

Existing client - server system provides realistic hydrological monitoring of supervised watersheds BUT SPC-GD computational’s lack limits short-term operational forecasting for the whole of supervised watersheds HPC innovative technologies provide important computational and storage resources to support such requirements Prototype Flash floods Grid Context Conclusion 1.Grid capability provides on-demand remote computational resources to execute in parallel independent simulations 2.Open Geospatial Consortium services (OGC) enable the geospatial data management with high abstraction capability

Distributed computing P2P Super computer LAN VPN WAN Internet - GRID - Grid computing - Metacomputing Distributing computing architecture enabling to share geographically distant resources to obtain important computing and storage capacities (Foster, 2001)  servers farms connection  resources virtualizing  important storage capacities  errors management  secured access system  interoperability Grid access by virtual organization Cyclops based on the middleware gLite (EGEE) Prototype Flash floods Context Grid Conclusion

Prototype Flash floods Context Grid Conclusion SSH HTTP

Efficient flash flood anticipation (few hours) requires more intensive computation and an adapted operational system to allow on-demand forecasting simulations of the whole of SPCGD watersheds Prototype Grid Context Flash floods Conclusion

Prototype Grid Context Flash floods Conclusion

Geographical information system and hydrological simulation Rainfall radar measuring stations Expertise broadcasting Prototype Grid Context Flash floods Conclusion Watersheds data

Geographical information system and hydrological simulation measuring stations Expertise broadcasting Prototype Grid Context Flash floods Conclusion Rainfall radar Watersheds data

Prototype Grid Context Flash floods Conclusion

Prototype Grid Context Flash floods Conclusion Cells independency…

Prototype Grid Context Flash floods Conclusion Cells independency…

Flash floods Grid Context Prototype Conclusion Watersheds data

Flash floods Grid Context Prototype Conclusion

Flash floods Grid Context Prototype Conclusion

Ex: Variations of 50% with a forecasting delay of 3h => 21 ALHTAÏR instances - Local ≈ 200 minutes - Grid ≈ minutes Flash floods Grid Context Prototype Conclusion

–This improved system gives forecasters a wide range of forecasting options on the whole of SPC-GD watersheds –Grid-enabled OWS potentially enhances grid using for inexpert users by its capacity to wrap already prepared grid actions Flash floods Grid Context Conclusion Prototype −Grid-enabled OWS architecture might permit the integration in a rapid way of new operational models in order to:  design a multi-models platform dedicated to the monitoring of the whole of hydrological phenomena occurring in S-E of France  have an on-demand flexible and parametric hydrologic workflow −Meteorological forecasting scenarios have to be more realistic according to well-known past meteorological situations −Simulation processes can easily be manage outside the risk zones

- rivers - Sensors outputs - watersheds characteristics - radar data - Hydrological simulations outputs  hydro-meteorological monitoring  Observed and modelized runoffs  Automatic alert thresholds Query and geoprocessing operations - Stations and rivers search - Parametric meteorological accumulation - Parametric radar animations Raw and modelized data integration Towards a decision support system enabling grid and local processing virtualization and hydro-meteorological data integration Flash floods Grid Context Prospects Prototype

Thank you for your attention ! Special thanks for CEOS WGISS … and for all CYCLOPS and ESR partners for their helps in this research !