Stefano Salvi Chair of the Supersites Advisory Committee Third UN World Conference on Disaster Risk Reduction, Sendai, March 2015.

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Presentation transcript:

Stefano Salvi Chair of the Supersites Advisory Committee Third UN World Conference on Disaster Risk Reduction, Sendai, March 2015

DRR and satellite EO During the last four decades, satellite imagery has proved to be very well suited for providing important information to support Disaster Risk Reduction, in particular for Geohazards. HOWEVER: Are satellite data fully exploited for DRR? Unfortunately not

Inaccessible data and unexploited knowledge Especially, but not only, in developing countries, data are not easily accessible to map and study geohazards. Sometimes are not even acquired. Where they are acquired and become available there may be a lack of capacity to use them. But even when they are well analysed by the scientific community, the important new knowledge generated is in many cases not efficiently exploited. Restricted data access Research capacity gap Slow knowledge uptake

The Geohazards Supersites concept A voluntary partnership aiming to demonstrate in high risk areas of the world (the Supersites) a streamlined and efficient process by which geohazard research results obtained by the global scientific community can directly benefit local Disaster Prevention and Response activities.

The Geohazard Supersite partnership  The Local Monitoring Agencies  CEOS Space agencies  The Local & Global Scientific Communities  The Local DRM Agencies In situ data Satellite data Knowledge DRR actions

First bulding block: the data CEOS Satellite data In-situ data

Science Team #1 Science Team #2 Science Team #3 Science Product A’ Science Product … Science Product A’’’ Science Product … Science Product A’’ CEOS Satellite data In-situ data The value-added chain

Science Team #1 Science Team #2 Science Team #3 Science Product A’ Collaborative knowledge processing (compare, validate, model, report) Collaborative knowledge processing (compare, validate, model, report) Consensus product generation (hazard model, predictive scenario, etc.) Consensus product generation (hazard model, predictive scenario, etc.) Science Product … Science Product A’’’ Science Product … Science Product A’’ CEOS Satellite data In-situ data … a synthesis is needed … Virtual repository Virtual repository

Science Team #1 Science Team #2 Science Team #3 Science Product A’ Collaborative knowledge processing (compare, validate, model, report) Collaborative knowledge processing (compare, validate, model, report) Consensus product generation (hazard model, predictive scenario, etc.) Consensus product generation (hazard model, predictive scenario, etc.) Science Product … Science Product A’’’ Science Product … Science Product A’’ CEOS Satellite data In-situ data Collaborative process coordinated by local agency Virtual repository Virtual repository … driven by local agencies …

Science Team #1 Science Team #2 Science Team #3 Science Product A’ Collaborative knowledge processing (compare, validate, model, report) Collaborative knowledge processing (compare, validate, model, report) Consensus product generation (hazard model, predictive scenario, etc.) Consensus product generation (hazard model, predictive scenario, etc.) Science Product … Science Product A’’’ Science Product … Science Product A’’ CEOS Satellite data In-situ data Risk products to local DRM users Collaborative process coordinated by local agency Virtual repository Virtual repository … which will provide the information to DRM users

Supersites proposals  They are submitted by national geohazard monitoring agencies, and must be supported by the wider scientific community.  Supersite proposals are evaluated by the Supersite Advisory Committee and by the CEOS.  Resources should be obtained independently by the partners, who could leverage on the Supersite framework.  The proposal must address priorities and requirements from the local DRM users. 

Active geohazard Supersites & Points of Contact 1.Hawaiian volcanoes – PoC is US Geological Survey, USA 2.Icelandic volcanoes – PoC is Univ. of Iceland 3.Etna volcano – PoC is Natl. Inst. Geophysics and Volcanology, Italy 4.Campi Flegrei volcano – PoC is Natl. Inst. Geophysics and Volcanology, Italy 5.Western North Anatolian Fault – PoC is Kandilli Observatory and Earthquake Research Institute, Turkey 6.Taupo Volcano – PoC is GNS Science, New Zealand 7.Tungurahua and Cotopaxi volcanoes – PoC is Instituto Geofísico, Ecuador 

A success story: the Bardabunga eruption 2014 Main volcano is under 800 m thick ice cap. Worst scenario was magma/water mixing, causing strong explosions, 10-km high volcanic ash/gas plume, subglacial floods. Possible very strong impacts on:  Regional air traffic. Eyjafjallajökull eruption, in 2010 caused 100,000 flights to be canceled, for a 5 Bn$ damage.  Global air quality. Laki eruption in 1783 caused global climate effects leading to a number of global casualties in excess of hundreds of thousands.

The Icelandic volcano supersite

January 2015 August 2014 Seismic data show magma migration

Satellite image coverage through the Supersite

InSAR images shows dyke transferring magma for 45 km out of ice area, reducing impact of eruption

GPS and InSAR allowed modeling of the source

Energy Dyke mapping Depth Magma volume

A coordinated effort to support response Analyses of Supersite data by various international research teams, coordinated by University of Iceland and with the local Civil Protection Agency, contributed to provide important information to update the situational awareness, directly driving important decisions by DRM users.

To submit a Supersite proposal Write to: Stefano Salvi: Francesco