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www.unisdr.org

Building capacities for increased public investment in integrated climate change adaptation and disaster risk reduction:

2012 - 2015Regional Workshop on Probabilistic Risk Assessments

Bangkok, October 15-17, 2014

United Nations Development Programme

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Global Assessment Report

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Building capacities for unified climate change adaptation (CCA) and disaster risk reduction (DRR)3-year initiative by UNISDR in partnership with UNDP to strengthen capacities for disaster risk management

Objectives:

-Increasing Loss & Risk Knowledge-Improving decisions to invest scarce resources in DRR and CCA

• Risk-Proofing public investment in DRR and CCA

• Analyzing DRR/CCA investment portfolios and prioritizing actions (Cost Benefit Analysis of risk reduction investments)

• Defining DRM/CCA strategies: how much risk should be reduced, how much retained and how much transferred

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Implemented in 3 independent but tightly interrelated components **

1.Disaster loss and damage accounting:

40 New National Disaster Loss Databases

2. Probabilistic Risk Profiles and Assessments:

30 New Country Profiles

3. Increased risk-sensitive planning and investment:

20-30 Policy dialogs with countries**

** Which should open the door for the implementation of many other risk management tools

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Implemented as parallel process in 6 Regions

• LAC: – Mexico, Panama, Guatemala, Costa Rica, Colombia, Peru (now in C3)

• Caribbean Region: – 7 East Caribbean Countries, Via partnership with UNDP and with GFDRR ACP/EU funding

• Indian Ocean Comission:– 4 member states in the Indian Ocean Commission IOC (Madagascar, Mauritius,

Seychelles, Comoros and Zanzibar), plus Tanzania, Rwanda and/or Malawi

• Asian Region:– Myanmar, Cambodia, Laos, Mongolia, Maldives and Sri Lanka in Asia (UNDP-APCR

partnership) and Pakistan (with WB).

• Pacific Region:– A regional system (20 countries) plus 2 to 4 drill down countries in the Pacific. Fiji,

Solomon, and PNG, are initially identified via partnership with SOPAC/SPC.

• West Africa and IOC:– Niger, Burkina-Faso, Sénégal, Togo, Mali, and Sierra Leone, via partnership with UNDP

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A large number of locally recruited data collectors and specialists• Component 1: disaster loss data collectors,

researchers, environment and IT specialists, DM staff

• Component 2: probabilistic risk specialists, with knowledge in engineering, GIS and possibly hazard/risk modelling)

• Component 3: specialists with knowledge in economics, public finance and public policy

Countries will/have received from UNISDR/UNDP the proposed profiles for the data collectors and specialists.

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Example Timeline of activities: country

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COMPONENT 1

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Typical contents of a DesInventar Disaster database

The actual screen for data capture.

It can be customized by users.

Standard Effects (killed, injured, affected, etc.)

Extension (Sectorial detail information)

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National Disaster databases

Disaster Inventories record and analyse the occurrence and effects of natural disasters

• Disaggregated information is provided in tabular and graphical form (maps and charts)

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Madagascar: Historical Loss Profile

Composition of disasters: frequency by hazard

Data for 1980-2013

Composition of disasters: economic loss of disasters by hazard.

Composition of disasters: mortality by hazard.

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Madagascar: Empirical Loss ProfileSpatial Distribution

Spatial distribution of economic loss from disasters. (1983-2013) Spatial distribution of Mortality from Cyclone disasters.

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MadagascarLoss exceedance curve – Empirical

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• National Workshops Loss data

• Past Disaster Loss Data Collection/Validation

• Analysis/estimation of economic losses

Resources: trainers, data collectors, analysts

Component 1: Disaster loss and damage accounting

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COMPONENT 2

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HAZARD: Constructed from Global model

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Exposure and Vulnerability: enriched and validated with country level data

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Vulnerability functions for all Hazards and selected structural types

Vulnerability is represented using mean damage ratios and variance values at different hazard intensities for each characteristic construction type

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Results: Essential risk metrics: Annual Average Loss, Probable Maximum Loss, Pure Risk Premium…

Loss Exceedance Curve Mauritius (proxy)

   

Hazard AAL (Mill US$)

Wind 182.67

Earthquake -----

Total 182.67

 

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Madagascar: Probabilistic Loss ProfileAAL spatial distribution

Historical economic loss distribution (1983-2013)

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Results: AAL per district Mauritius (proxy)

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Results: PML Curve Mauritius

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Results: Integrated analytical and empirical (“Hybrid”) curves.

Seychelles

Seychelles

0,01

0,1

1

10

100

1.000

10.000

100.0000,00001

0,0001

0,001

0,01

0,1

1

10

100

0,001 0,01 0,1 1 10 100 1.000 10.000 100.000

Retu

rn p

erio

d [y

ears

]

Loss

exc

eeda

nce

rate

[1/y

ear]

Economic loss [Mill. $USD]

CatastrophicRetrospectiveHybrid

Hazard AAL (Mill US$)

Wind 0.59

Earthquake 0

Total 0.59

Empirical AAL: 0.448

(independent set or hazards, not cyclone/earthquake)

TOTAL AAL : 1.2 Million USD(aprox)

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• Hazard Training Regional workshop

• Hazard data collection/processing

• Exposure/Vulnerability Regional workshop

• Exposure/Vulnerability data collection/processing

• Risk model and follow-up Regional workshop

• Generation of hybrid loss exceedance curves/model

Component 2: Probabilistic Risk Profiles and Assessments

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COMPONENT 3

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From Components 1 and 2: What has been lost & how much can be lost

Component 3: Mainstreaming Risk Knowledge into Public Investment and Planning.

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Working with Finance and Planning entities on

•Determining impact of Disasters and DRR on Economy

•Identifying impact of DRR investments (Cost Benefit

analysis)

•Looking at optimal use of resources and tools (Risk

Reduction, Retention, Transfer, etc.)

•Use Cases: risk-proofing public investment

Component 3: Mainstreaming Risk Knowledge into Public Investment and Planning.

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Extensive

Intensive

Risk curve gives insights on DRM strategy…

Extensive Risk Layer:Risk reduction

Middle Risk Layer:Risk avoidance

Contingency budget & credit

HighRisk Layer:Risk Transfer

ResidualRisk

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DRR makes risk curve move inward…

Scenario 1(strong DRR)

Scenario 2(New risk generation)

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Impact of DRR on Macro-Economy

JICA DR2AD Model: Pakistan GDP growth estimate

0

2,000,000

4,000,000

6,000,000

8,000,000

10,000,000

12,000,000Real GDP （Average）

without disaster

Average (with disaster, with IDRR)

Average (with disaster, without IDRR)

million ＄

Source: provided by JICA

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Cost benefit analysis of DRR policy

Benefit = avoided lossCost = investment cost + maintenance costDiscount rate = interest rate, social discount rate

2 3

B/C = Sum of benefits in all project periods/ Sum of costs in all project periods

Benefit cost ratios

Source: Wethli 2013 cited by the World Bank

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Budget Analysis in Latin American Countries

Country Reportyear

DRR and prevention

Relief and reconstruction

Total(%)

Bolivia 2011     0.150Chile 2011     0.030Chile 2013     0.040Colombia 2011     0.115Colombia 2013     0.520Dominican Republic

2013 0.050 1.000 1.050

Ecuador 2013 0.300 1.600 1.900El Salvador

2011     0.001

Mexico 2013 0.100 3.100 3.200Peru 2011     0.180Peru 2012     1.100

Source: HFA Progress Report

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Summary of Good Practices in Latin American Countries, 2014

Source: UNISDR survey (2014)

COL CRI GTM MEX PAN PER

Risk analysis implemented before public investment

Y Y Y N Y Y

Manuals and trainings Y Y Y N Y Y

DRR Budget tracking Y N Y N Y Y

Risk financing instruments (e.g. insurance)

Y N N Y N N

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• 1st Regional Workshop on theory

• Socio-economic-financial data collection and

implementing economic and financial analysis

• 2nd Regional Workshop on practice

• National workshop for mobilizing all stakeholders and

raising awareness on the importance of DRR investment

• Regional event for dissemination of all results of

DEVCO initiatives

Component 3: Increased risk-sensitive planning and investment

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THANK YOUhttp://www.preventionweb.net/gar

http://www.unisdr.org

http://www.desinventar.net