Geospatial data for disaster risk reduction and response

Keiko Saito, PhD

East Asia and Pacific DRM

GSURR, The World Bank October 26, 2017

UNOOSA Conference 23 October 2017

Outline Integrating In-situ data for risk modelling Ex-post applications of space data and in-situ data integration Improving in-situ data availability Remote Assessments Conclusion

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One of the main use of data for Disaster Risk Management

= Risk Modelling

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1. MODEL outputs no calibration using in-situ data

2. MODEL outputs calibration using in-situ data

3. In-situ data only (more for ex-post applications)

Hazard modelling Exposure modelling

Vulnerability modelling

Risk (final output, loss to assets estimated)

or

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1. MODEL outputs no calibration using in-situ data

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1. MODEL outputs no calibration using in-situ data

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2. MODEL outputs calibration using in-situ data (met office rain gauge, discharge data)

Fiji flood modelling example

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• Global hazard models calibrated using local in-situ data allow high level assessment of risk

• Data sharing policy should be in place

• For detailed engineering level risk modelling, local in-situ data is necessary, especially topographic data (DEM)

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Presentation Title 9

Ex-post applications of space data and in-situ data integration

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Table of Contents

Main Title •Subtitle

12

Presentation Title 13

Used UAV imagery to confirm the wind speed boundaries from model

Improving in-situ data availability

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OPEN SOURCE

Improving hydromet stations

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Establishing and maintaining Vertical Reference frame in Tonga

Sometimes, only remote assessment is possible

Syria Damage and Needs Assessment

Information from Social media and other news outlets were combined with analysis of high-resolution satellite images + sector specialist knowledge to generate the full picture of the baseline + impact from the war

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+ =

Availability of in-situ data helps calibrate global models Data sharing policy must be in place New technology and tools becoming available for in-situ data collection

Conclusions

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Use of Space technology for SDG monitoring