Global satellite-based flood monitoring workshop: Building a … · 2018. 7. 5. · - Use of...
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- Use of microwave radiometry - Merging data for “best estimate” flood maps - Potential for automated operations Global Flood Partnership Conference 25-27 June 2018 1 Global satellite-based flood monitoring workshop: Building a Global Flood Observatory
Transcript of Global satellite-based flood monitoring workshop: Building a … · 2018. 7. 5. · - Use of...
- Use of microwave radiometry
- Merging data for “best estimate” flood maps
- Potential for automated operations
Global Flood Partnership Conference
25-27 June 2018
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Global satellite-based flood monitoring workshop:
Building a Global Flood Observatory
Use of passive microwave radiometry
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• Types of near-real-time monitoring
available today
– Virtual river gauge height (DFO
River Watch, global selected
sites)
– Low-resolution (>10 km) flooded
fraction (various sources, coast-
to-coast)
– High-resolution (90 m) flood
depiction via downscaling (AER
FloodScan)
Example
coverage
Use of passive microwave radiometry
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• Types of near-real-time monitoring
available today
– Virtual river gauge height (DFO
Riverwatch, global selected sites)
– Low-resolution (>10 km) flooded
fraction (various sources, coast-
to-coast)
– High-resolution (90 m) flood
depiction via downscaling (AER
FloodScan)
Uganda
18 Sept 2007
Use of passive microwave radiometry
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• Types of near-real-time monitoring
available today
– Virtual river gauge height (DFO
Riverwatch, global selected
sites)
– Low-resolution (>10 km) flooded
fraction (various sources, coast-
to-coast)
– High-resolution (90 m) flood
depiction via downscaling (AER
FloodScan)
Uganda, 18 Sept 2007
Flooded fraction Downscaled 90-m flood map
What’s needed to automatically merge flood maps?
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• Simultaneous metadata
– Known blind spots (trees!)
– Degree of ambiguity – whether mapped as flooded or not
– Error estimate (+/- X%)
– Quality control flags (categorical conditions)
– Effective observation timing
• Data merging frameworks
– Probabilistic (probably!)
– Temporally & spatially connective, e.g., through hydrology, topography, etc.
– Categorized by flood type, e.g., pluvial, fluvial, flash, coastal, compound
– ¿ What else ?
FloodScan MODIS
Estimating uncertainty:
Flooded fraction time series analysis
Yearly seasonal flood
Irregular yearly floods
Chad, Lake Chad, north lobe, 2007
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Nigeria, Niger Onitsha, 2007
Measurement uncertainty can/should be estimated
(1) locally & (2) conditionally (e.g., weather)
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Small detectable flood Noise or undetectable floods
Burkina Faso, Plateau-Central, 2009Uganda, Okok and Okere Rivers, 2007
Measurement uncertainty can/should be estimated
(3) spatially & (4) temporally (e.g., through a model)
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Algorithm filters out flooded fraction “dry land” noise …
… but also filters out lower-level flooding “connected” to the larger event
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Can a probabilistic framework represent multiple flood types?
Flash / pluvial / fluvial / coastal / etc
Probabilistic [0-1 score] Boolean [flooded/unflooded]
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Does a flood depth estimate help quantify probability of
correct typing?
Max water depth [m] Max flood extent
