- 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

Use of passive microwave radiometry

2

• 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

3

• 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

4

• 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?

5

• 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

6

Nigeria, Niger Onitsha, 2007

Measurement uncertainty can/should be estimated

(1) locally & (2) conditionally (e.g., weather)

7

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)

8

Algorithm filters out flooded fraction “dry land” noise …

… but also filters out lower-level flooding “connected” to the larger event

9

Can a probabilistic framework represent multiple flood types?

Flash / pluvial / fluvial / coastal / etc

Probabilistic [0-1 score] Boolean [flooded/unflooded]

10

Does a flood depth estimate help quantify probability of

correct typing?

Max water depth [m] Max flood extent