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Giriraj_Floodactivities_IDRCAug252014
The document discusses the use of satellite data for mapping and assessing catastrophic flood risks, with case studies from Asia and Africa. It highlights the increasing frequency and economic impact of floods due to climate change and urbanization, emphasizing the need for effective flood risk management solutions. Key outcomes include the development of satellite-based flood mapping products and a radar tool for rapid agricultural flood damage assessment.
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Giriraj_Floodactivities_IDRCAug252014
- 1. POTENTIAL OF SATELLITEDATA IN CATASTROPHIC FLOOD RISK MAPPING AND ASSESSMENT: CASE STUDIES FROM ASIA AND AFRICA GIRIRAJ AMARNATH Contributors: Niranga Alahacoon, Ameer Rajah, Yoshiaki Inada, Ryosuke Inoue, Pramod Aggarwal, Vladimir Smakhtin International Water Management Institute (IWMI) Session: Space Observation, GIS and Risk Mapping 5th International Disaster and Risk Conference IDRC 2014 25 August 2014 - Davos Water for a food-secure world www.iwmi.org
- 2. OUTLINE • Synthesisof global flood occurrence and hotspot analysis • Flood Mapping Products using multi-scale satellite data for risk Water for a food-secure world www.iwmi.org assessment – South Asia & South east Asia – Niger – Benue River Basin – Gash catchment in Eastern Sudan • Blending satellite data and RADAR tool for rapid flood damage assessment in Agriculture: A case study in Sri Lanka
- 3. Where we arebased: Tashkent Lahore New Delhi Anand Hyderabad Water for a food-secure world www.iwmi.org Colombo Pretoria Ouagadougou Cairo Addis Ababa Kathmandu Vientiane Washington, DC The Hague
- 4. THE PROBLEM •Floods – primary natural disasters • Precipitation intensity and variability is projected to increase – increasing risks of flooding globally and in Asia • Global flood losses in 2011 >$100 Billion Water for a food-secure world www.iwmi.org o Largest global losses: • Thailand (Jun-Nov) $40-50B • Australia (Jan-Feb) $20-30B • Hurricane Irene (Aug) $5-10B • May rise to over $450B by 2030
- 5. Mapping Flood Hotspotsfor Climatic Change
- 6. CATASTROPHIC FLOODS INASIA: 1900-2011 Water for a food-secure world www.iwmi.org • Collated from 6 global sources • >4000 floods globally • Around 35% - in Asia Annual Flood Occurrence < 10 days < 50 days > 50days Flood Duration Country Flood Occurrence India 237 China P Rep 209 United States 155 Indonesia 142 Philippines 116 Brazil 112 Bangladesh 83 Iran Islam Rep 72 Pakistan 72 Vietnam 67 Source : DFO, SAARC, Sentinel Asia, NDMA etc.
- 7. IDENTIFYING FLOOD HOTSPOTS • 100 km grid over the globe; • numbers of floods in each cell over 1900-2011 Water for a food-secure world www.iwmi.org
- 8. CHARACTERISING FLOOD HOTSPOTS Water for a food-secure world www.iwmi.org AGRICULTURE; PAGE (2005) POPULATION CIESIN - 2010 FLOOD EXPOSED GDP WB, 2010
- 9. CHARACHERIZING FLOOD HOTSPOTS • Globally - 90 grid cells with catastrophic Water for a food-secure world www.iwmi.org flood occurrence ≥ 5; • 60% of these cells -in Asia • Estimated total over these hotspots: – annual economic loss due to floods - $20bn – 30 million affected people – 500,000 km2 of affected croplands
- 10. SUB-CONTINENTAL SCALE FLOODMAPPING Examples from SA and SEA – MODIS images Indus Mekong Water for a food-secure world www.iwmi.org August 18, 2009 August 17, 2010 August 26, 2010 August 24, 2011
- 11. SUB-CONTINENTAL SCALE FLOODMAPPING Examples from SA and SEA – MODIS images • 8-days maps of inundation extent • Annual maps of maximum inundation • Inter-annual variation of regional flooding extent Flood Mixed (Crop) Water bodies Inter-annual variation (2000 – 2011) Normal River 2010 2010
- 12. Flood Duration :Indus Basin, Pakistan • Duration of annual flood inundation is defined from the start and end dates of annual flood inundation • Longer flood duration significantly increase the flood risk damage
- 13. Spatial Distribution ofpopulation growth (1980 – 2000) and location of the recent floods mapped using RS data • Sub-continental scale, the dynamics of human settlements ( i.e. population growth between 1980 and 2000) and the location of latest floods in SA using RS data. • Highlights that most of the recent floods (i.e. period 2000 – 2011) have occurred where the population has increased more. • Intensive and unplanned urbanization of flood-prone areas, played a major role in increasing the potential adverse consequences of floods Total Population Affected Population % Bangladesh 152,518,015 99,412,500 65.18 India 1,210,193,422 131,107,100 10.83 Nepal 26,620,080 323,995 1.22 Pakistan 180,440,000 18,933,730 10.49 Sri Lanka 20,277,597 901,151 4.44 Flooding in SA has affected total population = 250million
- 14. Flood Data WebServices !!
- 15. SOUTH EAST ASIA++Flood Mapping August 2011 MODIS Terra Image MODIS 15 tiles covering SE Asia++ Total Images (2000 – 2011) = 7590 MODIS Flood Maps 2011 completed. Validation ongoing Tonle Sap Inundation Map (28th August 2011)
- 16. Flood Mapping Products(Initial Results)
- 17. Flood Impact Assessmentusing RADAR Rapid Agriculture and Disaster Assessment Routine (RADAR)
- 18. Basic concept ofRADAR – Flood Impact Assessment Tool Water for a food-secure world www.iwmi.org Measure an event intensity Event Event Intensity Percentage Loss Damage Flood depth, period Precipitation Model Base or Remote Sensing Knowledge Base Translate from intensity to loss Data Base Calculate the damage from component values
- 19. Basic concept ofRADAR – Flood Impact Assessment Tool Damage is function of three variables Affected area Damage Z (%) Water for a food-secure world www.iwmi.org Value density Percentage Loss X (Rs/ha) Flood Y (ha) Damage (Rs)
- 20. Percentage Loss isexpected value loss when the component is affected by flood event Percentage Loss based on precipitation intensity Intensity Moderate Rain Heavy Rain Torrential Rain Flood Paddy rice 5±2% 30±10% 60±20% 100±10% Livestock 0% 0±1% 15±1% 35±5% Farm Asset 0% 5±1% 15±10% 75±10% Water for a food-secure world www.iwmi.org Percentage Loss Percentage Loss based on inundation intensity Inundation depth (m) 0-0.5 0.5-1.0 1.0-1.5 1.5-2.0 2.0-3.0 3.0- Duration period (days) 1 - 2 3 - 4 5 - 6 7 - 1 - 2 3 - 4 5 - 6 7 - 1 - 2 3 - 4 5 - 6 7 - Paddy rice 21% 30% 35% 50% 25% 45% 50% 70% 35% 55% 65% 75% same as 1.0-1.5 Livestock 10% 20% 60% 100% Farm Asset 15% 25% 30% 65% 70%
- 21. Flood Inundation Extentusing ALOS PALSAR data
- 22. 1. Input beforeFlood Just input affected area from GIS, then You can immediately estimate the damage Water for a food-secure world www.iwmi.org Data Synthesis 2. Input after Flood (Affected area) 3. Run the program and damage will be automatically calculated
- 23. Results (in BatticaloaDistrict – Sri Lanka) Estimated Damage Paddy Livestock Farm asset Comparison with Direct measurement Water for a food-secure world www.iwmi.org 887 mil.Rs 762 mil.Rs 276 mil.Rs 2.43 billion Rs. 1.93 billion Rs. RADAR Estimate Reported Estimate* *source: District secretariat – Batticaloa and Disaster Management Centre
- 24. THANK YOU “Let not a single drop of water received from rains go waste into the sea without benefiting the man and the beast ” Water for a food-secure world King Parakramabahu (1153-1186 AD) www.iwmi.org
Editor's Notes
- #3 GHF – Global Humanitarian Forum total economic losses; not only insured losses
- #5 GHF – Global Humanitarian Forum
- #7 6 sources include: CRED-EMDAT (Centre for Research on the Epidemiology of Disasters (CRED) has been maintaining an Emergency Events Database) GLIDE - The Global Disaster Identifier Number Humanitarian Aid and Civil Protection (ECHO) at the European Commission United Nations Office for the Coordination of Humanitarian Affairs (OCHA) DFO -Dartmouth Flood Observatory SAARC - South Asian Association for Regional Cooperation IWMI work… Ctastrophic -Catastrophic floods is an extremely large-scale disaster. These floods are caused by some significant and unexpected events, for instance dam breakages, million affected and high economic losses resulted from this event Average duration in Asia – 3-5 days, but many floods in excess of 50 days (second map)
- #8 Flood hotspots are areas that are highly flood prone having frequent flood events; causing several damages both in livelihood and infrastructure / crop loss PAGE? - Pilot Analysis of Global Ecosystems (PAGE) Giriraj, Amarnath; Ameer, Mohamed; Aggarwal, Pramod; Smakhtin, Vladimir. 2012. Detecting spatio-temporal changes in the extent of seasonal and annual flooding in South Asia using multi-resolution satellite data. In Civco, D. L.; Ehlers, M.; Habib, S.; Maltese, A.; Messinger, D.; Michel, U.; Nikolakopoulos, K. G.; Schulz, K. (Eds.). Earth resources and environmental remote sensing/GIS applications III: proceedings of the International Society for Optics and Photonics (SPIE), Vol.8538, Amsterdam, Netherland, 1-6 July 2012. Bellingham, WA, USA: International Society for Optics and Photonics (SPIE). 11p.
- #9 Flood hotspots are areas that are highly flood prone having frequent flood events; causing several damages both in livelihood and infrastructure / crop loss PAGE? - Pilot Analysis of Global Ecosystems (PAGE) Giriraj, Amarnath; Ameer, Mohamed; Aggarwal, Pramod; Smakhtin, Vladimir. 2012. Detecting spatio-temporal changes in the extent of seasonal and annual flooding in South Asia using multi-resolution satellite data. In Civco, D. L.; Ehlers, M.; Habib, S.; Maltese, A.; Messinger, D.; Michel, U.; Nikolakopoulos, K. G.; Schulz, K. (Eds.). Earth resources and environmental remote sensing/GIS applications III: proceedings of the International Society for Optics and Photonics (SPIE), Vol.8538, Amsterdam, Netherland, 1-6 July 2012. Bellingham, WA, USA: International Society for Optics and Photonics (SPIE). 11p.
- #10 The plot is global -not Asia How many (58%) of these 90 grids are in Asia / SA , SEA Other bulleted numbers are for the globe (90 cells)
- #11 Major catastrophic floods in Pakistan along the Indus River – Top image Aug. 2009 and bottom image Aug 17, 2010 Flooding along the Mekong river – In August 2011, floods extended from Cambodia’s Tônlé Sab (Tonle Sap) southward past Phnum Penh (Top image Aug 26, 2010 and bottom image Aug 24, 2011) Message: this snapshot from remote sensing data offers mapping inundation over large areas. In the following slides methods and tools were developed to map the extent more precisely and accurately
- #12 The left map is for 2010 year example shows different classes of “water” “Mixed crops” class refer to areas that are inundated in cropland, meaning reflectance value has mixed values from vegetation and water content
- #25 http://www.eomd.esa.int/news.php?id=214 http://www.eomd.esa.int/projects/eomd_project.php?id=149 http://www.eomd.esa.int/stories.php?id=429 http://www.eomd.esa.int/stories.php?id=223