The document discusses managing disaster information to support disaster risk reduction efforts. It outlines how establishing national disaster observatories can systematically collect, analyze, and disseminate disaster data to various stakeholders. This information can then be used to inform national disaster risk reduction strategies, risk assessments, and development decisions by providing evidence of hazards, vulnerabilities, and impacts. The document advocates for integrating disaster data into policy and planning to promote more effective disaster risk management.

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 BCPR UNDP (24 Country Offices) WMO UN Habitat UNEP/GRID 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