Global Flood Partnership Annual Conference, Guanzhou, China 11-13 - PowerPoint PPT Presentation

Global Flood Partnership Annual Conference, Guanzhou, China 11-13 June 2019 Risk financing against floods: an application leveraging on global models and EO data Roberto Rudari , CIMA Research Foundation Joost Beckers, Deltares Patrick Matgen, LIST

The Application • Immediate support and relief to population in SEA • Risk financing options: Parametric insurance

The Application Access to Risk Profile NRT Trigger Information • Hydrologic/hy • Model Based • Platform draulic estimates implementatio scenarios n • EO based modelling estimates • Operations • EO based maintenance • Merged Validation estimates • Impact scenarios modelling

The Application

The Interface

The Interface

Risk Profile Hazard maps and scenarios GLOFFIS for riverine flood hazard GLOSSIS for coastal flood hazard

Risk Profile Unosat image 2015 50 yr flood map 100 yr flood map Hazard maps Validation

Risk Profile Event maps validation

Risk Profile Impact Scenarios validation

NRT Flood Analyzer Best Flood Map Best Match Algorithm Global models BM … 2 1 Satellite Telemetry imagery

Best Match: Map selection Flood extent (km 2 ) Flood extent from EO image … T=10 T=5 T=2 Model result Gauge reading T=1.5 Water level (masl) S2 GA = (h floodmap - h gauge - h bias ) 2 /stdev(h floodmap ) 2 Gauge level similarity: • GLOFFIS level similarity: S2 GL = (h floodmap - h GLOFFIS - h bias ) 2 /stdev(h floodmap ) 2 • S EO = (N pospos +N negneg ) / N total EO map similarity: • N total = N pospos +N negneg +N posneg +N negpos • Total similarity = S2 GA + S2 GL + (1-S EO ) 2 • Weights can be added (but not used so far): • Total similarity = W GA S2 GA + W GL S2 GL + W EO (1-S EO ) 2 •

NRT Flood Analyzer Population Layers WPOP Best Flood Map NRT Affected People Estimates GHS-JRC BM GUF

The need After 1 year of pre-operation: • Strengthen the EO component • Concentrating on SAR data processing Challenges: • Operation in NRT • Geographical scope (full country coverage) • Reliability (compared with the 7-day window) • Urban areas

e-DRIFT Virtual Platform: • Directly based on the DIAS • Accessible through Machine-to-machine protocols • Providing added value EO services

Back Office

The flood Archive Algorithm

The flood Archive Algorithm Permanent water Reference grid Blind spots layer bodies (i.e. Tiles) WASDI Systematic Processing Change Detection (per tile) query to identify and 1. Clip Input Raster based on retrieve 100 km x 100 km Sentinel-1 reference grid Water bodies mapping imagery ∆WB - 2. Compute difference i-1:i (per tile) over tiles image S1 i -S1 i-1 3. Mask blind spots 1. Compute WB i 4. Parameterize distribution 2. Subtract permanent functions S1 i Mosaicking water layer 5. Thresholding & region growing to generate ∆WB + i-1:i maps of positive/negative S1 i-1 changes Sentinel-1 data hub Reference WB i-1 WB i image Output User selection Archiving water bodies maps / floodwater maps Flood record

Methodology: flood mapping in Myanmar ∆WB + i-1:i S1 i-1 ∆WB - i-1:i S1 i WB i-1 WB i

Methodology: flood mapping in Myanmar «Blind spots» Permanent Water Floodwater

The SEA DRIF eDRIFT Integration External data sources Hydrological Operational EXTERNAL EO Flood Maps data Models SOURCES SEA DRIF External Trigger / Synthetic COMPONENTS Manual Flood Authomatic Flood Man made scenario Flood Scenarios Matching Scenarios Matching detection Scenarios eDrift SERVICES Man Made Scenarios Selected flood scenarios EO Augmented Impact assessment Historical Scenario Population Enhanced affected/ Population Economic impact Historic Flood Density Layers estimates Scenarios Selection Historical Scenarios Selection

Combined use of Model and EO data Rapid assessment by matching of EO and model simulation E.g. 2015 monsoon floods, Myanmar Sentinel-1, August 11, 2015 T=20 modelled flood map

Flood impact NRT estimation Built-up area: World Settlement Footprint based on Landsat and Sentinel1 Critical infrastructure from police station various sources: • Airports • Seaports • Railways village • Roads • Hospitals railway • Fire stations station flooded road • Police stations • Schools • Government buildings flooded railroad

GRAZIE