Transcript Identifying new data needs and sources Linking DRR and Adaption: Disaster Inventories Data on impacts and vulnerability Global Assessment Report Team GAR United Nations International.

Identifying new data needs
and sources
Linking DRR and Adaption: Disaster Inventories
Data on impacts and vulnerability
Global Assessment Report Team GAR
United Nations International Strategy for Disaster Reduction UNISDR
Geneva, February 2-4, 2011
HYOGO FRAMEWORK FOR ACTION
 In January 2005, 168 Governments adopted a 10-year
plan to make the world safer from natural hazards at
the World Conference on Disaster Reduction, held in
Kobe, Hyogo, Japan.
 Its goal is to substantially reduce disaster losses in lives,
and in the social, economic, and environmental assets
of communities and countries.
 The Hyogo Framework offers guiding principles
summarized in 5 priorities for action
Geneva, February 2-4, 2011
HFA 5 PRIORITIES FOR ACTION
 Ensure that disaster risk reduction is a national and a local
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priority with a strong institutional basis for implementation.
Identify, assess and monitor disaster risks and enhance early
warning.
Use knowledge, innovation and education to build a culture of
safety and resilience at all levels.
Reduce the underlying risk factors.
Strengthen disaster preparedness for effective response at all
levels.
Geneva, February 2-4, 2011
HYOGO FRAMEWORK FOR ACTION A2
 Develop, update periodically and widely disseminate risk
maps and related information to decision-makers, the general
public and communities at risk
 Develop systems of indicators of disaster risk and vulnerability
at national and sub-national scales
 Record, analyse, summarize and disseminate statistical
information on disaster occurrence, impacts and losses, on a
regular bases through international, regional, national and
local mechanisms.
Geneva, February 2-4, 2011
GEO/GEOSS goals
GEOSS will yield a broad range of societal benefits, notably:
 Reducing loss of life and property from natural and human-induced
disasters
 Understanding environmental factors affecting human health and wellbeing
 Improving the management of energy resources
 Understanding, assessing, predicting, mitigating, and adapting to
climate variability and change
 Improving water resource management through better understanding
of the water cycle
 Improving weather information, forecasting and warning
 AND OTHERS....
Geneva, February 2-4, 2011
Current data for monitoring
disaster risk
• Hyogo Framework for Action implementation
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Monitoring of levels of risk to disasters
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Monitoring levels of losses
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Progress in measures to reduce risk
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Global Assessment Report (Biennial)
• Special Report of IPCC (SREX)
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First IPCC review of what constitutes effective measures to reduce
risk to extreme events
Geneva, February 2-4, 2011
Typical contents of a Disaster database
• Simple, low technology
• Non expensive
• High impact, ROI
The actual screen
for data capture.
Customizable by users.
Standard Effects
(killed, injured, affected,
etc.)
Extension (Sectorial
detail information)
Geneva, February 2-4, 2011
What are National Disaster Inventories?
•Disaster Inventories record and analyse the
occurrence and effects of natural disasters
• Disaggregated information is provided in tabular
and graphical form (maps and charts)
• Richer than global data: Events of all scales, more
indicators, closer (local) level of observation
Geneva, February 2-4, 2011
Temporal Analysis (Trends): distribution of losses over time
Behaviour of disaster losses is key in understanding trends and essential for monitoring the effectiveness of DRR
Seasonal distribution of floods in Mexico
Number of reports of floods and
people killed by epidemics in Orissa,
India 11 years, showing a high
correlation between floods and
epidemics.
Ovals show non-related epidemic
events.
Geneva, February 2-4, 2011
Spatial Analysis (patterns): distribution of losses over space
The Municipalities located over the Andes mountain area are the most prone to landslide disasters
Spatial distribution of landslides
in Colombia
Geneva, February 2-4, 2011
Usage of Disaster loss data in Risk Assessments.
1,000
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Economic loss [mill. $USD]
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Typical analytical loss exceedance curve, Colombia
0.00001
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Economic loss [Mill. $USD]
Events (w/o Other events)
The hybrid loss exceedance curve, Colombia
Geneva, February 2-4, 2011
Fiscal
Hybrid
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Return period [years]
1
Loss exceedance rate [1/year]
10
Return period [years]
Loss exceedance rate [1/year]
100
Impact and extent of (possibly) Climate Change related events
Temporal distribution of Surge reports, PERU
Mortality due to Surge , PERU
Spatial distribution of Surge reports, PERU
Damage to housing sector – due to Surge , PERU
Geneva, February 2-4, 2011
Impact and extent of (possibly) Climate Change related events
Frequency of extreme precipitation-related events , 8 South American countries
Mortality due to extreme precipitation events
Geneva, February 2-4, 2011
Usage of Historical Loss Data in DRRM
• Modeling probable maximum losses up to a return period
of approximately 30 – 50 years.
• Provide historical vulnerability indexes/functions
• Allow monitoring of DRR measures
• Historical data can help validating Risk Assessments
• Provide a dynamic vision of risk evolution over time
• Provide evidence-based support to decision makers
• Generate proxy indicators of Risk (for hard-to-model risks or when no data is available)
…
• Climate Change
Adaptation?
Geneva, February
2-4, 2011
UN sponsored Disaster Inventories
Asia/Pacific
Sri Lanka, Indonesia, Iran, Maldives, Nepal, India (Tamil Nadu,
Orissa, Andra Pradesh, Uttranchal, Delhi), Jordan, Syria, Vietnam,
Laos*, Vanuatu*, Solomon*, SOPAC, East Timor, Philippines
LAC
Mexico, Costa Rica, El Salvador, Panama, Colombia, Ecuador,
Peru, Bolivia, Venezuela, Argentina, Chile, Paraguay, Panama,
Guatemala, Jamaica, Trinidad & Tobago, Guyana, Antigua
Africa
Egypt, Morocco, Yemen, Mozambique, Mali, Djibouti *
Many other countries (USA, Australia, etc.) have independently
build datasets. A total of about 60 datasets identified.
Geneva, February 2-4, 2011
Potential Usage of Historical Loss Data in CC
• Provide measures of historical/current impact ?
• Historical data to be input layer for Impact Assessments
• Permit finer grain impact analysis (compared to global datasets)
• Validate hypothesis of realized change?
• Allow monitoring of Climate Change impact ?
o Frequency
o Severity
o Location
•Other?
Geneva, February 2-4, 2011
Global Assessment Report on Disaster Risk
GAR
United Nations International Strategy for Disaster
Reduction UN-ISDR www.unisdr.org
IEH International Environment House
7-9 Chemin de Balextert, 4th floor
Julio Serje
John Harding
Justin Ginnetti
[email protected]
[email protected]
[email protected]
Geneva, February 2-4, 2011