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RESPONDING TO NATURAL DISASTERS: THE ROLE OF 
SPACE BASED INFORMATION 
VLADIMIR SMAKHTIN, GIRIRAJ AMARNATH, PAUL PAVELIC, BRINDHA KARTHIKEYAN 
International Water Management Institute 
National workshop on 
โ€œUse of Space based information in Disaster Managementโ€ 
Colombo, Sri Lanka,17 November 2014
WATER-RELATED DISASTERS: THE WORLD 
Average annual characteristics over 1980-2008 
Distribution of Deaths and People Affected 
Source -EM-DAT: The OFDA/CRED International Disaster Database, Brussels 
โ€ข Floods and droughts account for about 90% of all people affected by all natural disasters 
โ€ข Frequency and intensity of these events increases with changing climate 
โ€ข Annual damages may rise to over 400 bill USD globally by 2030
WATER-RELATED DISASTERS: SRI LANKA 
2500000 
2000000 
1500000 
1000000 
500000 
0 
Drought 
1987 
Drought 
1982 
Flood 
1983 
Tsunami 
2004 
Storm 
1978 
Flood 
1969 
Drought 
2001 
Drought 
1988 
Flood 
2003 
Flood 
1989 
300000000 
250000000 
200000000 
150000000 
100000000 
50000000 
0 
Flood 
1992 
Storm 
1978 
Storm 
1964 
Flood 
1989 
Flood 
1991 
Flood 
2003 
Flood 
1969 
Flood 
1966 
Flood 
1967 
People affected โ€“ millions 
Economic damage โ€“ hundreds millions $ 
Source -EM-DAT: The OFDA/CRED International Disaster Database, Brussels
CATASTROPHIC FLOOD HOTSPOTS, ASIA 
โ€ข 6 global data sources, 4000+ floods ,100 km grid 
โ€ข Numbers of floods in each cell over 1900-2011
PPhhoototo :: DDaavviidd BBrraazziieerr//IIWWPPMMhhoIoI ttoo: :DToavmid v Barna Cziaekre/InWbeMrgI he/IWMI 
MAPPING FLOOD EXTENT USING REMOTE SENSING 
Ganges Basin, India 
Sep 22, 2011 
MODIS Satellite Image, 
Estimated inundation extent
SUB-CONTINENTAL FLOOD MAPPING: SOUTH ASIA 
โ€ข 8-days maps of inundation extent 
โ€ข Annual maps of maximum inundation 
โ€ข Inter-annual variation of regional flooding extent 
Flood 
Mixed (Crop) 
Water bodies 
Inter-annual variation (2000 โ€“ 2011) 
Normal River 
2010 
2010
PPhhoototo :: DDaavviidd BBrraazziieerr//IIWWPPMMhhoIoI ttoo: :DToavmid v Barna Cziaekre/InWbeMrgI he/IWMI 
SUB-CONTINENTAL FLOOD MAPPING: SOUTH EAST ASIA 
Flood over land use 
Flood over population 
Flood Frequency 
Regional View; e.g. 13th Oct 2011
SOUTH ASIA DROUGHT MONITORING SYSTEM (DMS) 
๏‚ง Builds on IWMIโ€™s previous experience in on-line drought monitoring in SW Asia 
๏‚ง Near-real time, weekly, 250 m spatial resolution, drought severity maps 
NDVI deviation 
map for a 
District 
Time Series 
graph 
Drought free zone 
Mild Drought zone 
Severe Drought zone
DMS: INTEGRATED 
MONITORING APPROACH Satellite Data 
RS and Meteorological Data 
Biophysical Data 
Data Input variables 
1) NDVI; VCI; Ratio of ET-PET 
2) Percent Annual Seasonal 
Greenness (PASG) 
3) Start of Season Anomaly 
(SOSA) 
1) Standardized 
Precipitation Index 
(SPI) 
2) LST & TCI 
1) Land cover 
2) GIAM 
3) AWC 
4) Ecoregion 
5) Elevation 
Regression 
Tree Model 
using Cubist 
1. Integrated Database Development 
2. Model Development 3. Map Generation 
Drought Severity Index 
4. Online Visualization 
Platform 
Regional to basin level applications 
and decision support products 
SA bi-weekly, 
monthly 
products 
Basin-level high 
resolution 
products 
Country-level, 
1-km, biweekly 
products 
Water 
managers 
Irrigation 
Districts 
State 
Agencies 
Academic 
Institutions 
Federal 
Agencies 
Scientific 
Community 
International 
Agencies 
Disaster Relief 
Organization
2014 DROUGHT: REALITY CHECK OF SPACE BASED DATA 
2014 drought seriously damaged crops in 
Kurunegala, Anuradhapura, Polonaruwa districts 
Water for a food-secure world 
www.iwmi.org
SOUTH ASIA DMS: PLANNED OUTPUTS 
โ€ข Quantification of droughtโ€™s magnitude, spatial extent, and impact โ€“ through a 
combination of climate, vegetation and biophysical indicators 
โ€ข On-line prototype drought monitoring tool โ€“ the basis for coordination of regional 
drought mitigation effort. 
โ€ข Identified hot-spot areas โ€“ where droughts are more intense and frequent โ€“ to plan 
preventive measures 
โ€ข National capacity in drought monitoring in all participating countries 
โ€ข Regional sharing and dissemination of operational drought information
MANAGING FLOODS AND DROUGHTS TOGETHER: 
Underground Taming of Floods for Irrigation (UTFI)
MANAGING FLOODS AND DROUGHTS TOGETHER: 
Underground Taming of Floods for Irrigation (UTFI)
MANAGING FLOODS AND DROUGHTS TOGETHER: 
Underground Taming of Floods for Irrigation (UTFI)
MANAGING FLOODS AND DROUGHTS TOGETHER: 
Underground Taming of Floods for Irrigation (UTFI)
UTFI SYSTEM DESIGN 
rice paddies 
Infiltration ponds 
canals 
recharge 
structure 
well 
irrigation 
+ + + 
+ + 
city 
+ 
+ 
+ 
+ 
+ 
+ 
+ 
flood 
prone 
sea 
+ 
dam 
diversion 
Photos: IWMI
UTFI โ€“ ONE SOLUTION TO MULTIPLE PROBLEMS 
Rural 
Farmers 
Surface Water 
Urban Flood 
Managers 
Opportunities 
Impacts 
Upstream 
Downstream 
Groundwater
THE ROLE OF SPACE INFORMATION: 
Input to UTFI suitability analysis 
โ€ข Around 30 indicators reflecting various aspects of suitability (geohydrology, land 
use, topography, flood risks, population, etc). Many indicator are derived from RS

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Responding to Natural Disasters: The Role of Space Based Information

  • 1. RESPONDING TO NATURAL DISASTERS: THE ROLE OF SPACE BASED INFORMATION VLADIMIR SMAKHTIN, GIRIRAJ AMARNATH, PAUL PAVELIC, BRINDHA KARTHIKEYAN International Water Management Institute National workshop on โ€œUse of Space based information in Disaster Managementโ€ Colombo, Sri Lanka,17 November 2014
  • 2. WATER-RELATED DISASTERS: THE WORLD Average annual characteristics over 1980-2008 Distribution of Deaths and People Affected Source -EM-DAT: The OFDA/CRED International Disaster Database, Brussels โ€ข Floods and droughts account for about 90% of all people affected by all natural disasters โ€ข Frequency and intensity of these events increases with changing climate โ€ข Annual damages may rise to over 400 bill USD globally by 2030
  • 3. WATER-RELATED DISASTERS: SRI LANKA 2500000 2000000 1500000 1000000 500000 0 Drought 1987 Drought 1982 Flood 1983 Tsunami 2004 Storm 1978 Flood 1969 Drought 2001 Drought 1988 Flood 2003 Flood 1989 300000000 250000000 200000000 150000000 100000000 50000000 0 Flood 1992 Storm 1978 Storm 1964 Flood 1989 Flood 1991 Flood 2003 Flood 1969 Flood 1966 Flood 1967 People affected โ€“ millions Economic damage โ€“ hundreds millions $ Source -EM-DAT: The OFDA/CRED International Disaster Database, Brussels
  • 4. CATASTROPHIC FLOOD HOTSPOTS, ASIA โ€ข 6 global data sources, 4000+ floods ,100 km grid โ€ข Numbers of floods in each cell over 1900-2011
  • 5. PPhhoototo :: DDaavviidd BBrraazziieerr//IIWWPPMMhhoIoI ttoo: :DToavmid v Barna Cziaekre/InWbeMrgI he/IWMI MAPPING FLOOD EXTENT USING REMOTE SENSING Ganges Basin, India Sep 22, 2011 MODIS Satellite Image, Estimated inundation extent
  • 6. SUB-CONTINENTAL FLOOD MAPPING: SOUTH ASIA โ€ข 8-days maps of inundation extent โ€ข Annual maps of maximum inundation โ€ข Inter-annual variation of regional flooding extent Flood Mixed (Crop) Water bodies Inter-annual variation (2000 โ€“ 2011) Normal River 2010 2010
  • 7. PPhhoototo :: DDaavviidd BBrraazziieerr//IIWWPPMMhhoIoI ttoo: :DToavmid v Barna Cziaekre/InWbeMrgI he/IWMI SUB-CONTINENTAL FLOOD MAPPING: SOUTH EAST ASIA Flood over land use Flood over population Flood Frequency Regional View; e.g. 13th Oct 2011
  • 8. SOUTH ASIA DROUGHT MONITORING SYSTEM (DMS) ๏‚ง Builds on IWMIโ€™s previous experience in on-line drought monitoring in SW Asia ๏‚ง Near-real time, weekly, 250 m spatial resolution, drought severity maps NDVI deviation map for a District Time Series graph Drought free zone Mild Drought zone Severe Drought zone
  • 9. DMS: INTEGRATED MONITORING APPROACH Satellite Data RS and Meteorological Data Biophysical Data Data Input variables 1) NDVI; VCI; Ratio of ET-PET 2) Percent Annual Seasonal Greenness (PASG) 3) Start of Season Anomaly (SOSA) 1) Standardized Precipitation Index (SPI) 2) LST & TCI 1) Land cover 2) GIAM 3) AWC 4) Ecoregion 5) Elevation Regression Tree Model using Cubist 1. Integrated Database Development 2. Model Development 3. Map Generation Drought Severity Index 4. Online Visualization Platform Regional to basin level applications and decision support products SA bi-weekly, monthly products Basin-level high resolution products Country-level, 1-km, biweekly products Water managers Irrigation Districts State Agencies Academic Institutions Federal Agencies Scientific Community International Agencies Disaster Relief Organization
  • 10. 2014 DROUGHT: REALITY CHECK OF SPACE BASED DATA 2014 drought seriously damaged crops in Kurunegala, Anuradhapura, Polonaruwa districts Water for a food-secure world www.iwmi.org
  • 11. SOUTH ASIA DMS: PLANNED OUTPUTS โ€ข Quantification of droughtโ€™s magnitude, spatial extent, and impact โ€“ through a combination of climate, vegetation and biophysical indicators โ€ข On-line prototype drought monitoring tool โ€“ the basis for coordination of regional drought mitigation effort. โ€ข Identified hot-spot areas โ€“ where droughts are more intense and frequent โ€“ to plan preventive measures โ€ข National capacity in drought monitoring in all participating countries โ€ข Regional sharing and dissemination of operational drought information
  • 12. MANAGING FLOODS AND DROUGHTS TOGETHER: Underground Taming of Floods for Irrigation (UTFI)
  • 13. MANAGING FLOODS AND DROUGHTS TOGETHER: Underground Taming of Floods for Irrigation (UTFI)
  • 14. MANAGING FLOODS AND DROUGHTS TOGETHER: Underground Taming of Floods for Irrigation (UTFI)
  • 15. MANAGING FLOODS AND DROUGHTS TOGETHER: Underground Taming of Floods for Irrigation (UTFI)
  • 16. UTFI SYSTEM DESIGN rice paddies Infiltration ponds canals recharge structure well irrigation + + + + + city + + + + + + + flood prone sea + dam diversion Photos: IWMI
  • 17. UTFI โ€“ ONE SOLUTION TO MULTIPLE PROBLEMS Rural Farmers Surface Water Urban Flood Managers Opportunities Impacts Upstream Downstream Groundwater
  • 18. THE ROLE OF SPACE INFORMATION: Input to UTFI suitability analysis โ€ข Around 30 indicators reflecting various aspects of suitability (geohydrology, land use, topography, flood risks, population, etc). Many indicator are derived from RS

Hinweis der Redaktion

  1. This is the processed result example of the previous slide.
  2. The left map is for 2010 year example shows different classes of โ€œwaterโ€ โ€œMixed cropsโ€ class refer to areas that are inundated in cropland, meaning reflectance value has mixed values from vegetation and water content
  3. Show ghe drought in SL โ€“ latest TS graph then go into images of drought
  4. ONE OF OUR FOCAL AREAS IS IN THE GANGES BASIN. BUT THE BASIN IS VAST โ€“ COVERS 3 COUNTRIES AND HOUSES >200m PEOPLE. WHERE IS THE EFFORT MOST NEEDED? WE DEVELOPED A METHOS FOR ANALYSING SUITABILITY TAKING INTO ACCOUT OF FLOOD, RECHARGE, STORAGE, REUSE