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ICT for Disaster Risk Management-Managing Disaster Information-Global Risk Identification Programme
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ICT for Disaster Risk Management-Managing Disaster Information-Global Risk Identification Programme


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ICT for Disaster Risk Management-Managing Disaster Information-Global Risk Identification Programme

ICT for Disaster Risk Management-Managing Disaster Information-Global Risk Identification Programme

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  • 1. ICT for Disaster Risk Management – Managing Disaster Information – Global Risk Identification Programme Thematic Platform for the implementation of HFA’s Priority 2: Risk Identification Carlos Villacis, PhD – GRIP Coordinator
  • 2. Overview The problem: Impact on Development Integrating Disaster Information Actual applications in Decision Making The way forward
  • 3. The impact of disasters
    • In 2008,
    • 354 natural disasters were reported
    • 235,000 persons were killed (138,366 by Cyclone Nargis, Myanmar)
    • More than 214 million people were affected (~ 18 times Senegal’s population!)
    • USD 190 billion in economic damages (~ 13 times Honduras’ GDP!)
    • Note: These are only major reported events (≥10 killed, ≥100 affected, state of emergency, call for international assistance)
            • Source: Annual Disaster Statistical Review, CRED, 2009
  • 4. Disaster-related mortality risk Underweight children Epidemic meningitis Epidemic malaria Source: Columbia University Disaster risk and the MDGs: Disaster impacts have become impediments to sustainable development in Africa (Report on Disaster Risk Reduction for Sustainable Development in Africa, 2004)
  • 5. Understanding of the problem Risk Assessment Proper Planning Evidence-based decision making Disaster Risk Reduction + Safe Development Processes Effective Actions The Problem
  • 6.
    • Goal:
      • Promote sustainable development by reducing the impact of natural hazards in high risk areas
    • Specific objectives:
      • To improve risk information
      • To ensure its application in disaster risk management and development processes
    Global Risk Identification Programme - GRIP
  • 7.
      • Learning from the past : to understand their vulnerabilities, high-affected areas and recovery capacities
      • Basic risk information and risk baselines : to set up measurable goals and prepare evidence-based DRR strategies
      • Monitoring and evaluation mechanisms : to measure progress (or lack of it) and evaluate and correct strategies
      • Local Capacity: To produce realistic and locally supported solutions and ensure sustainability
    Identified DRR needs of countries
  • 8.
      • National Disaster Observatories : sustainable institutions for systematic collection, analysis and interpretation of disaster information
      • National Risk Assessments : strategic, multi-hazard risk evaluations to support evidence-based DRM strategies
      • Local Risk Assessments : operational assessments to support urban and contingency planning
      • Capacity Development: all the work is done by local institutions, authorities and experts
    GRIP’s Services to Countries
  • 9. Disaster Information Management
    • Current Situation –
      • No systematic method for collecting information about hazard events and their impacts
      • At the most, scattered information with various agencies without any coherence and coordination
      • No meaningful analysis to understand the trends, spatial and temporal impacts and hence poor understanding of potential risks and their impacts
      • No integration with development programming since no evidence exists
  • 10. National Disaster Observatory
    • Local authorities
    • Sectors
    • Civil society
    Strong institutional coordination
  • 11. Characteristics of Disaster Observatories National Disaster Observatories Common Standards Quality control Compatible national, regional, global Sustainable Institutionalized Applied Based on local capacity
  • 12.
    • Statistical analysis
          • Trends, changes, averages
    • Spatial analysis
      • High risk areas, distribution
    • Sectoral analysis
      • Health, education, agriculture
    • Thematic analysis
      • Gender, poverty
    Analytical functions and knowledge generation Data Information Validation, classification Knowledge Analysis, interpretation
  • 13.
    • Identification of end users
    • Preparation of user-specific packages
    Access and dissemination NDO Central and local authorities Research institutions Various sectors of society General public International community
  • 14. Outcomes & Applications
    • Inputs to the National Disaster Risk Reduction Strategy
      • Better definition of goals, priorities and structure of risk reduction measures
    • Calibration and validation of Risk Assessments
      • Confronting estimated vs. realized losses
    • Assessment of vulnerability and recovery capacity
      • Physical, social, financial, political vulnerabilities
    • Monitoring effectiveness of risk reduction strategies and measures
      • HFA’s goal is reduction of losses
  • 15. National Disaster Observatories
    • Existing (run by Governments, GRIP or Partners)
    • Proposed or underway (countries implementing, interested or having Disaster Database)
    Asia Sri Lanka, Tamil Nadu, Orissa, Indonesia, Iran, Maldives, Thailand, Nepal LAC Mexico, Costa Rica, El Salvador, Colombia, Ecuador, Peru, Bolivia, Venezuela, Argentina, Chile, Paraguay, Panama Asia Armenia, Afghanistan, Bhutan, Cambodia, Laos, PNG*, Vietnam* Africa Mozambique, Malawi, Madagascar LAC Nicaragua*, Guatemala*, Honduras*, Jamaica*, Cuba, Trinidad and Tobago*, Guyana*, Antigua & Barbuda, Uruguay, Organization of Eastern Caribbean States * Have national disaster databases Africa Mozambique, Malawi, Madagascar
  • 16. Actual applications
  • 17. Applications to National DRM Strategies The Districts of Kalutara , Ratnapura, Puttalam, Kurunegala, Anuradhapura
  • 18. Impact on Housing (Damaged and Destroyed)
    • Floods have caused the greatest damage and destruction to housing, followed by cyclones, gale force winds, storms, landslides and urban floods.
    The table in this slide depicts the damage and destruction caused to housing from the occurrence of the 12 most frequently occurring hazards from 1974-2006. The six most frequently reported hazards represented in the pie chart above Event No of damaged & destroyed houses Flood 139849 Cyclone 129769 Gale 62701 Storm 19120 Landslide 5743 Urban flood 4632 Coastline 2727 Animal attack 2201 Surge 1017 Tornado 1013 Tidal wave 350 Flash flood 300
  • 19. Detection of anomalies in risk models
  • 20. Applications in Decision making started Location of UNDP Early Recovery Advisors
  • 21. Before Nargis After Nargis From Loss assessment to needs assessment
    • Shift in Agriculture as Income Source
    • Change in percentage of population for which agriculture is a main source of income:
    • Decrease in agriculture as main source from 34% to 22% of population
    • Decrease in Fisheries from 16% to 8%
    • Shift to other sources of income (from 10% to 29%)
    Source: Village Tract Assessment (VTA) component of the PONJA (Post Nargis Joint Assessment) project developed jointly by the ASEAN, the Government of Myanmar, and the UN.
  • 22. Before Nargis After Nargis From loss assessment to needs assessment
    • Shift in Fishery as Income Source
    • Change in percentage of population for which fishery is a main source of income:
    • Decrease in Fisheries from 16% to 8%
    • Shift to other sources of income (from 10% to 29%)
    • Decrease is very dramatic in the coastal area
    Source: Village Tract Assessment (VTA) component of the PONJA (Post Nargis Joint Assessment) project developed jointly by the ASEAN, the Government of Myanmar, and the UN.
  • 23. The way forward
  • 24. Global integration: Database Catalog
  • 25. Simple analyses readily available
    • For about 30 countries
      • Real time access – reflect updates
      • Event occurrence
      • Human impact – Deaths and affected people
      • Impact to Housing Sector – Damaged and destroyed
    • Simple analyses
      • Causality
      • Time analysis – trends
      • Geographic distribution – mapping applications
    • Results presentation
      • Graphics
      • Downloadable, exportable tables
      • HTML and PDF versions
  • 26. Example for Nepal – Causality
  • 27. Example for Nepal – Time analysis
  • 28. Example for Nepal – Spatial distribution
  • 29. Expert working groups
    • Disaster loss data
      • CRED, Munich-Re, ADRC, La Red, GDACS,UNDP
    Munich – Feb 08
  • 30. Implementing partners
    • Governments:
    • Centro Nacional de Prevención de Desastres (CENAPRED)
    • CENOE, Moz
    • INGC, Moz
    • National Disaster Management Coordinating Board of Indonesia
    • National Institute of Disaster Management, India
    • Orissa State Disaster Management Authority, India
    • Government of Uttar Pradesh
    • State of Tamil Nadu, Government of India
    • Department of Urban Development and Building Construction, Government of Nepal
    • Regional Government of Arequipa
    • State Government of Baja California, Mexico
    • Government of Tijuana, Mexico
    • Government of Chile
    • Government of Myanmar
    • Government of Republic of Dominicana
    • Ministry of Civil Defense of Guyana
    • Ministry of Defence and Finance, Government of Maldives
    • Ministry of Interior, Government of Iran
    • Municipality of Illam
    • Municipality of Kathmandu
    • Municipality of Maputo
    • Municipality of Panauti
    • Government of Mexicali, Mexico
    • Government of Rosarito, Mexico
    • Government of Tecate, Mexico
    • Government of Ensenada, Mexico
    • Municipality of Iquique, Chile
    • Municipality of Arica, Chile
    • Municipality of Antofagasta, Chile
    • Municipality of Mejillones, Chile
    • Municipality of Taltal, Chile
    • Regional Government of Antofagasta, Chile
    • Regional Government of Tarapaca, Chile
    • Municipalidad de Pampacolca, Peru
    • Municipalidad de Viraco, Peru
    • Municipalidad de Machahuay, Peru
    • SENAMHI, Peru
    • INRENA
    • NGOs/INGOs:
    • OSSO
    • NSET
    • COPASA
    • La Red
    • RADIUS Working Group, Mexico
    • IFRC
    • ADRC
    • NGI
    • OYO International
    • ProVention Consortium
    • ISDR
    • Academia:
    • NASA Socioeconomic Data and Applications Center (operated by CIESIN)
    • University of California at San Diego  (UCSD)
    • Universidad Autónoma de Baja California (UABC)
    • Instituto Tecnológico de Tijuana (ITT)
    • Dartmouth Flood Observatory
    • Universidad Catholica del Norte, Chile
    • University Eduardo Mondlane, Mozambique
    • University of Florida
    • University of West Indies
    • CETYS Universidad
    • CRED
    • Columbia University
    • CICESE, Mexico
    • Colegio de la Frontera Norte (COLEF)
    • Instituto Geofísico de la UNSA , Peru
    • UN system:
    • UNDP (24 Country Offices)
    • WMO
    • UN Habitat
    • UNESCO
  • 31. Steering Committee
  • 32. Key messages
    • Information is useful only when it becomes knowledge that then supports effective actions
    • Actions will become solutions only when based on sound information and evidence-based strategies