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In Integrating Glo lobal l EO and Modeli ling Systems to support - - PowerPoint PPT Presentation

Nov 07, 2023 342 likes •727 views

In Integrating Glo lobal l EO and Modeli ling Systems to support Dis isaster Reli lief Agencie ies Albert Kettner 1 Robert Brakenridge 1 Guy Schumann 1,2 Bob Adler 3 Fritz Policelli 4 Dan Slayback 4 Patrick Matgen 5 Michael Souffront 6 1)

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In Integrating Glo lobal l EO and Modeli ling Systems to support Dis isaster Reli lief Agencie ies

Albert Kettner 1

Robert Brakenridge1 Guy Schumann 1,2 Bob Adler 3 Fritz Policelli 4 Dan Slayback 4 Patrick Matgen 5

Michael Souffront 6

1) DFO - Flood Observatory, CSDMS, INSTAAR, University of Colorado 2) Remote Sensing Solutions (RSS) 3) University of Maryland 4) NASA Goddard Space Flight Center 5) LIST, Luxembourg Institute of Science and Technology 6) Aquaveo, Utah

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Guy & Sasha (April 2019) Schumann Bob Brakenridge

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Paul Bates

Awarded the: Commander of the British Empire Recognition for his major contributions towards a better understanding of flood risk management

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In Integrating Glo lobal l EO and Modeli ling Systems to support Dis isaster Reli lief Agencie ies

Albert Kettner 1

Guy Schumann 1,2 Bob Adler 3 Fritz Policelli 4 Dan Slayback 4 Robert Brakenridge1 Patrick Matgen 5

Michael Souffront 6

1) DFO - Flood Observatory, CSDMS, INSTAAR, University of Colorado 2) Remote Sensing Solutions (RSS) 3) University of Maryland 4) NASA Goddard Space Flight Center 5) LIST, Luxembourg Institute of Science and Technology 6) Aquaveo, Utah

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Natural disasters

  • Flooding is the most common

natural hazard worldwide &

  • ften devastating
  • Impacts 21 million people

every year

  • Affects global GDP by ~$100

billion every year

Flooding 43% Storm 28% Earthquake 8% Extreme temperature 6% Landslide 5% Drought 5% Wildfire 3% Volcanic activity 2% Data: 1995-2015 by UN/CRED

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False color Landsat 8 & Sentinel-2. Courtesy of Lauren Dauphin

By y 20 2050 50 for r Eur urope

  • 5 fold increase in economic loss: a) climate

change, b) increasing value of land, c) urban development.

European Environment Agency

By y 20 2030 30

  • 54 million people impacted per year
  • > $400 billion

World Resources Institute

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SLIDE 7
  • August 2018 flooding
  • Heavy monsoon (75% more rainfall)
  • 65% of dams opened to prevent overflowing
  • 501,19 km2 was flooded by 17 August
  • 483 fatalities & ~1million affected

Kerala, In India ia

Source: Twitter - non-profit Stand With Kerala NRSC/ISRO, Hyderabad 15 countries responsible for 80% of the population exposed to river floods Winsemius et al., 2013; Ward et al., 2013

8 countries in Southeast Asia; total 14M people exposed

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DFO - Flo lood Observatory: Archiv ive

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Flo lood products avail ilable in in general - Observations

Flood extent: NRT + historical

Global initiatives

River Watch Gauging site 2029, Mahanadi River

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Flo lood products avail ilable in in general - Observations

Discharge NRT and status

By country

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Flo lood products avail ilable in in general - Sim imulations

Global initiatives

Global Flood Monitoring system (GFMS – UMD; NRT + Forecast) GLOFAS – Global Flood Awareness System NRT + Long term flood forecast JRC & ECMWF Operational since April 2018 at Copernicus Emergency Management Service

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Flo lood products avail ilable in in general - Sim imulations

USA - FEMA: 100 – 500yr return periods

Per country

Hydrograph forecast NOAA - USGS

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Flo lood products avail ilable in in general – New Tech

Social Media Commercial satellites

  • DigitalGlobe
  • SpaceX
  • ……

FloodTags

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In the immediate moments following a disaster event, humanitarian actors need to make rapid decisions on how to prioritize affected areas impacted by the event.

Disa isaster reli lief f agencies When en to res espond?

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What is is mis issin ing?

Time Information

Supply Demand

Information Gap Disaster strikes!

World Food Program (WFP)

  • People affected?
  • Where?
  • Flood frequency?
  • Duration?
  • Where are the most to least

vulnerable?

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“One Stop Shop” for all ll flo flood products

One portal to get to all water related data

  • Global coverage
  • That includes:
  • Simulations (Forecasts + e.g. per return interval)
  • Observations (Extent as well as water discharge - ground and satellite)
  • Near Real Time + Historical data (max flood extent, flood frequency)
  • Keep data at source but connect through API / OGC standards
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Global Runoff Data Center (GRDC, 2010)

Availability of historical discharge data in the GRDC database

  • Countries have only sparse amount of gauging

stations and discharge data gets hardly shared although rivers cross boundaries.

Ground based observ rvatio ions Water dis ischarge

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Global Runoff Data Center (GRDC, 2010)

Availability of historical discharge data in the GRDC database

  • Worldwide, water observation networks are

incomplete to determine water quantity & networks are in jeopardy of further decline.

Hannah et al., 2010

  • Countries have only sparse amount of gauging

stations and discharge data gets hardly shared although rivers cross boundaries.

Ground based observ rvatio ions Water dis ischarge

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Global Runoff Data Center (GRDC, 2010)

Availability of historical discharge data in the GRDC database

  • Countries have only sparse amount of gauging

stations and discharge data gets hardly shared although rivers cross boundaries.

  • Worldwide, water observation networks are

incomplete to determine water quantity & networks are in jeopardy of further decline.

Hannah et al., 2010

So: Societies recognize that measuring river discharge is important from socio-economic or practical view but if already taken, discharge measurements are hardly shared and countries are not enough investing to extend or maintain gauging station networks

Ground based observ rvatio ions Water dis ischarge

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Water dis ischarge from Space

Advantages utilizing satellites

  • Continuous record also in the event of a flood; unlikely gauging

station which could get destroyed during a large event

  • Low maintenance costs
  • Back processing of data once preferable gauging location is set
  • Crossing borders, is applied globally

Disadvantages utilizing satellites

  • Lower temporal resolution (daily not every 5 – 10 minutes)
  • Preferable gauging location is not always an option (steep

canyons, vegetation cover)

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Ground based Gauging station Satellite based Gauging station

DEPTH WIDTH

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Tb dry wet Tb dry wet Dry pixel

Influence of other factors (clouds, ground temperature, etc) is much reduced by comparing dry and wet signal Water has a lower brightness temperature than land

1 2 3 1 2 3 1 2 3

AMSR-E Signal

AMSR SR-E/AMSR-2 Riv iver r disc ischarge Measurement Meth thod

Brakenridge et al, 2005; De Groeve & Riva, 2009

Wet pixel

Measuring temperature change by passive microwave signal

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When rivers rise (discharge, Q, m3/sec, increases), flow width and water surface area also increase. River Watch sites use satellite passive microwave radiometry to sensitively monitor this in-pixel surface temperature change.

Q = Width x Depth x Velocity

Riv iver dis ischarge

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Translate Temperature to Dis ischarge

If possible use Ground gauge data otherwise model

Model-based rating is comparison of WBM modeled monthly mean, maximum, and minimum discharges, 2003-2007, to the satellite-observed, time-equivalent signal

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Cooperative work including EU’s Joint Research Centre (GDACS, Dr. Tom De Groeve) and DFO has resulted in a global network of satellite river gauging sites, with records extending on daily basis from 1998 up to today. Online display (click on dots).

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Flooded area for Normal Flow, Winter (~ 6100 m3/sec, observed February 11-22, 2000)

Brahmaputra, In India

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Flooded area for Moderate Flooding, r = 1.8 yr (37,000 m3/s, observed summer, 2013)

Brahmaputra, In India

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Flooded area for Moderate Flooding, r = 3 yr (44,000 m3/s, observed summer, 2007

Brahmaputra, In India

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We start to have adequate geospatial information on a global basis defining floodplains within the mean annual flood limit, or 25 - 50 - 100 year floodplains.

Floodplain within the alluvial plain of the Waimakariri River, New Zealand.

So combining 2 remote sensing techniques, we can overcome Knowledge gap

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What is is mis issin ing?

Time Information

Supply Demand

Information Gap Disaster strikes!

  • People affected?
  • Where?
  • Flood frequency?
  • Duration?
  • Where are the most to least

vulnerable?

World Food Program (WFP)

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  • Recurrence interval layers (1 in 100 – 500yr)
  • High + low resolution
  • Time machine mode
  • Integrate DFO products with flood forecasts,

e.g. GFMS (UMD), and GLOFAS (JRC)

http://floodobservatory.colorado.edu

Vis ision: One portal, l, all ll flo lood data

Analog to e.g. DarkSky

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Flood layers Add layers

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“Time machine mode” 2019 flo loodin ing part of USA

Mean annual water layer Maximum observed flooding (1993 – now) Flooding in excess of mean annual water layer Dots = Satellite based discharge station low flow Normal flow flooding major flooding

Observing flooding using AQUA/Terra Satellites – MODIS optical data

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Challenges to overcome

  • Global coverage
  • Integrate various temporal + spatial scales
  • Amount of different data sources & formats: observations,

simulations, historical data, discharge data, ……

  • Uncertainties in datasets

Flooding due to Cyclone Idai – Mozambique, Zimbabwe & Malawi

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Thank you!

Albert Kettner kettner@colorado.edu

  • SBIR
  • Applied sciences

http://floodobservatory.Colorado.edu

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Sim imil ilar in init itia iatives

  • NASA disaster portal
  • Multiple disasters
  • Monitoring on event base
  • Pacific Disaster Center (PDC)
  • Multi hazards
  • NRT + forecast, less so historical

events

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Hurricane intensity: The Saffir-Simpson Scale (1971 Herbert Saffir & Robert Simpson) Earthquake intensity:

  • The Moment Magnitude Scale

succeeded in the 70’s Richter scale

  • The Modified Mercalli (MM) Intensity Scale

(1931 Harry Wood and Frank Neumann) Used in the United States.

Flood severity index