2015/2016 El Nino: Methodologies for Loss Assessment

Transcription

1 2015/2016 El Nino: Methodologies for Loss Assessment Regional Consultative Workshop on El Niño in Asia-Pacific 7-9 June 2016 VIE Hotel Bangkok, Thailand

2 Damage and Loss Assessment: Concepts Close to 50 percent of disaster impacts are not accounted for Global Assessment Report 2013

3 Damage and Loss: Typical Hazard Context Earthquake Higher value of damage Floods and Cyclone Higher value of damage and losses Drought High value of losses 75% Damage 50% Damage 10% Damage

4 Damage and loss Three key questions - How much is at risk? - How much was lost? - How much likely to be lost in the future? PRE-DISASTER RISK ASSESSMENT: Hazard, vulnerability, Exposure - Geospatial approach - Probabilistic Approach DISASTER LOSSES (PAST EVENTS) Loss Accounting - Recording impacts (damage and loss) - Measuring Trends DISASTER LOSSES (FUTURE RISK) - Probability of losses / Average Annual Loss - Downscaling climate scenarios HOW MUCH IS AT RISK? HOW MUCH WAS LOST? HOW MUCH IS LIKELY TO BE LOST IN THE FUTURE?

5 PRE-DISASTER RISK ASSESSMENT: Hazard, vulnerability, Exposure Geospatial approach Probabilistic Approach

6 Geospatial Approach Data intensive Hazard Exposure Vulnerability Impact/Risk Cartographic, Geological, Hydro-meteorological.. Geospatial Data Vector and Raster Value at Risk Statistical - census and survey data GIS/Geospatial Infrastructure, settlements, land use.. Source: Modified from Francis Ghesquiere, The Word Bank

7 Understanding vulnerability and exposure for loss assessment Higher exposure corresponds to greater losses in Samoa Source: Olivier Mahul, (World Bank, 2010)

8 Probabilistic Approach: Average Annual Loss (AAL) AAL refers to the long-term expected losses per year, averaged over many years and linked with the approximate return periods of a specific hazard with significant intensity events. The general procedure of calculating AAL consists on an individual evaluation of losses for each hazard scenario, and a subsequent probabilistic integration of these results, using the frequency of occurrence of each scenario EXPOSURE HAZARD RISK VULNERABILITY

9 Loss (US$) Average Annual Loss (AAL): Probable Maximum Loss (PML) Area under Loss Exceedance curve = Average Annual Losses (AAL) El Nino amplifies AAL by a factor related to specific hazards cyclone, floods, drought,heat waves Source:

10 Average Annual Loss (AAL): Hazard Specific Assessment of Hazard: Historical occurrence and impact data provide an initial idea of the destructive potential of the phenomena and can help establish the approximate return periods of the most significant intensity events. Hazard is measured using the frequency of occurrence and severity of events, characterized using some parameter of intensity of the danger at a specific geographical location. Threat Effect Parameter of intensity Earthquakes Landslides Acceleration, velocity and maximum land movement, and spectral values for various structural periods Earthquakes Tsunami Depth and area on impact Hurricane Hurricane force winds Distribution of peak wind velocities for gusts of 3 seconds Hurricane Storm surge Depth and area of flooding Flooding Flooding Depth and extension of the area of flooding Back

11 Average Annual Loss (AAL): Methodological Approach: Loss Exceedance Curve The relationship between loss estimation and risk is the Loss Exceedance Curve, which represents the relationship between frequency and severity. Different disaster events can be characterized by different Loss Exceedance Curves, for example, infrequent but high impact events (like earthquakes) will have lower and flatter curves to reflect this AAL = probability that event occurs * loss associated with event Nepal case based on UNDP Disaster loss database

12 Average Annual Loss (AAL): Summary of Asia-Pacific AAL composition in Asia-Pacific by type (%) 100% AAL for Asia-Pacific total US$ 166 billion In Asia-Pacific, floods are the largest contributor to AAL In this figure, cyclones account for US$ 36 billion Floods have AAL of US$ 59 billion 75% 50% 25% 0% East and North-East Asia North and Central Asia Pacific South and South-West Asia South-East Asia Asia-Pacific Total Floods Storm Surge Earthquake Tsunami Wind AAL for storm surge in Asia-Pacific (USD Millions) AAL for floods in Asia-Pacific (USD Millions) 1, ,662 East and North-East Asia North and Central Asia Pacific 11,529 25,476 East and North-East Asia North and Central Asia Pacific 31,235 South and South-West Asia South-East Asia 12,067 4,698 5,700 South and South-West Asia South-East Asia

13 Group Exercise: Probabilistic models for loss assessment

14 Average Annual Loss (AAL): Country example for GAR website Detailed information on country level AAL and PML can be found in the link: Users can find sector and hazard-wise AAL estimates for countries across the world

15 Average Annual Loss (AAL): Country example: Fiji AAL by disaster type (US$ million) Earthquake 1.5 Storm Surge 85.1 Cyclonic Wind 45.2 Government 5.3 AAL by sectors (US$ million) Education 12.8 Residential 19.8 Services 8.8

16 Average Annual Loss (AAL): Country example: Papua New Guinea AAL by hazard type (million US$) AAL by sectors (million US$) Education 7.9 Health 0.2 Flood 94.2 Storm Surge 0.6 Earthquake 73.6 Cyclonic Wind 0.9 Tsunami 0.6 Services 36.6 Residential 26.8

17 El Nino Loss Assessment: [AAL*Amplification Factor] El Nino can lead to amplifications of the potential impacts of weather scenarios The El Nino amplification factor is a rough-and-ready way of calculating the potential additional losses in the case of El Nino event in a country It is found using the ratio of country-level climatological parameters in the no-el Nino case and El Nino case For example, if 3 storms are expected in no-el Nino event and 5 storms are expected in El Nino event scenario, the amplification factor will be 5/3 = 1.67

18 El Nino Potential Economic Impacts: Pacific Island Countries North Pacific Central Pacific Southern Pacific z z Country AAL (Million USD) Micronesia 9.8 El Nino associated amplification factor (Cyclone) Potential losses (Million USD) Marshall Islands Palau 2.8 Kiribati Papua New Guinea Tuvalu Cook Islands Fiji Niue Samoa Tonga Vanuatu

19 El Nino - Impact-based Forecasting An illustration on how El Nino information can be translated to response actions Relevant information from El Nino information is extracted and placed into the situation context to produce impact estimations; With potential impact information available, response scenarios can be set-up Source: Baode Chen and Xu Tang (2014) Translating weather forecasts into impact-relevant information

20 Thank you Sanjay K Srivastava Chief, Disaster Risk Reduction Section United Nations Economic and Social Commission for Asia and the Pacific Bangkok srivastavas@un.org