Ex Ante Tool for Risk Sensitive Development Planning: Probabilistic Catastrophic Hazard Risk Assessment

Transcription

1 Enhancing Knowledge and capacity for the management of disaster risks for a resilient future in Asia and the Pacific Ex Ante Tool for Risk Sensitive Development Planning: Probabilistic Catastrophic Hazard Risk Assessment Regional Conference on Strategies and Tools for Integrating Disaster Risk Reduction into Development Planning and Financing February 2015, Bangkok, Thailand

2 Probabilistic hazard risk assessments for risk sensitive development planning Target 1. Identifying the potential economic losses, despite insufficient historical data for analyzing Target 2. The ex-ante assessment tool to support the analysis of the future impact from the natural hazards Target 3. Creating powerful incentives for countries to develop planning options and tools to cope with risk Target 4. Contributing well-structured resource allocations to reduce those potential damage and safeguard development Target 5. Achieving mainstreaming Disaster Risk Reduction

3 Ex-Ante: Probabilistic Risk Assessment Perspectives in the modelling Vulnerability data Hazard data Risk data AND exposure data Hazards, Vulnerability, Exposure and Risk Data Reference: Piers Blaikie, Terry Cannon, Ian Davis, Ben Wisner, 2011, At Risk

4 Ex-Ante: Probabilistic Risk Assessment Principle modules in probabilistic earthquake risk model Hazard Module Exposure Module Vulnerability Module Set of Scenarios Damage and Loss Module Risk Transfer and Retention Module Cost-Benefit Analysis (CBA) Module Reference: (Cardona et al., 2008) Statistics Risk curve: Expected loss or loss occurrence probability

5 A concept of Probable Maximum Losses AEL: Annual Expected Losses = Single loss expectancy annual rate of occurrence PML: Probable Maximum Losses Estimates the maximum potential damage the value of the largest loss that could result from a disaster Losses Reference and source: European Environment Agency (2008),

6 Millions US$ 18,000 16,000 14,000 12,000 10,000 8,000 6,000 4,000 2,000 - Probable Maximum Losses ( yrs) in South East Asia EQ and Typhoon Probabilistic Models Phillippines Indonesia Vietnam Thailand Malaysia Cambodia Lao PDR Singapore % of GDP years 200 years Phillippines Indonesia Vietnam Thailand Malaysia Cambodia Lao PDR Singapore 100 years 200 years Reference and source: World Bank, GFDRR, ASEAN and UNISDR (2011), Advancing Disaster Risk Financing and Insurance un ASEAN Countries

7 Introduction of Probabilistic Catastrophic Hazard Risk Assessment for Development Planners Case Study : Nepal

8 Probabilistic risk assessment model - Development Planners Perspectives Perspective 1. Identifying insufficient data and capacity for each country for conducting probabilistic risk assessment Perspective 2. Easy to access and manage with insufficient data in the analysis Perspective 3. Developing less complicated and easyunderstanding applications Perspective 4. More concentrates on Macro-level economic losses: Using GDP and actively working population Perspective 5. Approach to sectorial analysis

9 A comparative study of probabilistic risk assessment models CAPRA OpenSHA OpenQuake Hazus MH 2.1 Selena 2.0 B E N E F I T S Multi-hazard risk assessment tool Detailed process and well-structured theoretical framework Supported by World Bank, UNISDR and the Inter-American Development Bank Being developed by CIMNE in Polytechnic University of Catalonia Specialized to earthquake Graph-based outcome are supported Collecting all possible ruptures and provide more accurate probability of occurrence Open Source risk assessment package Verified testing with same results Outcome calculate automatically Working with GIS application Well-structured application theoretical framework and manual Various base data for vulnerability and exposure analysis Open Source risk assessment package All theoretical framework from Hazus MH 2.1 Understandable manual Easy to customize: depends on types of data and disasters User-friendly interface L I M I T A T I O N S Numerous data are complementally required Need several trainings to run the application, data sorting and coding Annual average loss and other losses are manually calculated Do not have loss and damage function Only used for creating a set of scenarios and finding a probability of occurrence Should work with Hazus MH 2.1 or other applications those have loss and damage function Hard to understand the manual and need to know computer languages to run the application Only for an earthquake risk assessment The application should be worked with ArcGIS (ArcGIS 10 with SP2) Open Source package, but it is hard to customize Only US based data can be used Still not open to the rest of World Only for an earthquake risk assessment Not GIS based application Numerical base outcome will be shown in Excel file: not support for graphs and sorted table

10 Probabilistic Catastrophic seismic risk assessment proto-type application A modified SELENA 2.0 is being developed by UNESCAP (SEismic Loss EstimatioN uisng a logic tree Approach: SELENA) Developed by NORSAR and Universidad de Alicante Hazard data: Location (longitude, latitude, magnitudes of earthquakes, PGA (Peak Ground Acceleration), types of soil and Depth Based on these data, using random variables and creating scenarios (Ground shaking or shaking map) Vulnerability data: types of building, type of occupants, length of road Exposure data: GDP per Capita, Population and Economically active population These data will be applied in to a logic tree scheme which is the most critical part of SELENA 2.0. For the macro level analysis, the modified logic tree scheme was implemented.

11 Probabilistic Catastrophic seismic risk assessment prototype application A modified SELENA Input interfaces

12 Nepal Accumulated Seismic Risk The entire country lies in a high earthquake hazard area and thus the entire country is prone to earthquake hazards (GRIP and NSET, 2011). Between 1970 and 2007, Far Western and Eastern Regions recorded the highest levels of accumulated losses having faced the occurrence of a significant number of earthquakes Earthquake-prone areas in Nepal Reference: GRIP and NSET, 2011

13 Case study: Nepal 8 Earthquake histocial data in Nepal Total earthquake occurrences: 94 events recorded in DesInventar Total number of death: Over 9,000 reported in EM-DAT Total number of affect people: Around 730,000 recorder in EM-DAT Total damages and losses in US$: Over 300 million US$ recorded in EM-DAT

14 Historical data for earthquakes in Nepal ( Near Kathmandu Date Date Time Time M Focal Depth Location Latitute Longitude Type of Magnitude Aug 21-Aug 23:09 6: km Lahan mb Sep 19-Sep 12:40 19: km Gangtok, Sikkim, India mb Aug 25-Aug 13:21 20: km Baglung mb Mar 20-Mar 14:51 21: km Khandbari mb Jul 29-Jul 14:58 21: km Pithoragarh, Uttarakhand, India mb Apr 8-Apr 20:04 3: km Baglung mb Mar 24-Mar 14:16 21: km Banepa mb Jul 11-Jul 23:08:00 6: km Baglung mb Nov 20-Nov 19:00 2: km Gangtok, Sikkim, India mb Sep 27-Sep 5:26 12: km Kathmandu mb Sep 4-Sep 18:15 1: km Khandbari mb Nov 3-Nov 2:29 9: km Kathmandu mb Feb 26-Feb 4:42 11: km Khandbari mb Oct 29-Oct 9:10 16: km Banepa mb Jan 31-Jan 12:38 19: km Bharatpur mb Apr 20-Apr 6:40 1: km Lahan mb Jan 10-Jan 3:46 10: km Banepa mb Nov 8-Nov 20:08 3: km Kathmandu mb Jan 1-Feb 20:02 3: km Kathmandu mb Oct 4-Oct 13:53 20: km Banepa mb Near Xizang Nepal Border Date Date Time Time M Focal Depth Location Latitute Longitude Sep 27-Sep 5:26 12: km Kathmandu mb Aug 10-Aug 21:15 4: km Baglung mb Nov 3-Nov 2:29 9: km Kathmandu mb Jan 31-Jan 12:38 7: km Bharatpur mb Nov 19-Nov 22:04 5: km Pokhara mb Feb 11-Feb 17:29 12: km Bharatpur mb Aug 3-Aug 5:57 12: km Kathmandu mb Jan 19-Jan 7:46 2: km Baglung mb Near Nepal India Border Date Date Time Time M Focal Depth Location Latitute Longitude Jul 29-Jul 12:23 19: km Tikapur mb May 18-May 4:28 11: km Tikapur mb Nov 27-Nov 7:31 2: km Tikapur mb Nov 27-Nov 8:53 3: km Tikapur mb Dec 9-Dec 1:02 8: km Tikapur mb Apr 4-Apr 11:31 18: km Pithoragarh, Uttarakhand, India mb May 11-May 18:43 1: km Tikapur mb Mar 21-Mar 21:26 4: km Khandbari mb Jun 3-Jun 22:07 5: km Tikapur mb

15 Probabilistic Catastrophic seismic risk assessment Proto-type application for Nepal Input data (Nepal Planning Commission 2011 Census, Output data Set of scenarios Macro level Outcome: damage in GDP per capita Macro level data: GDP per capita Hazard data (Historic) Vulnerability data: Micro level NPC 2011 Census data Exposure data Causalities output Loss and Damage in Physical assets or causalities (Micro level outcomes)

16 Probabilistic Catastrophic seismic risk assessment proto-type application Period Probable Maximum Losses >8 magnitude (4-5) magnitude GDP per Capita Losses Period GDP per Capita Losses 10 years years years years years years years years years years 0

17 Far western Mountain Far western Hill Mid western Mountain Probabilistic risk assessment outcomes: *Magnitude 5-4 and within 10 years* Far western Tarai Mid western Hill Western Mountain Mid western Tarai Western Hill Western Tarai Central Hill Central Mountain Eastern Mountain Region Exposed Damage in GDP per Capita Central Hills Eastern Mountain Central mountain Eastern Hills Far Western Tarai Central Tarai Eastern Hill Eastern Tarai Nepal Rupee (GDP per Capita), 2011 Census data in Nepal

18 Far western Mountain Far western Hill Mid western Mountain Probabilistic risk assessment outcomes: *Magnitude greater than 8 and within 200 years* Far western Tarai Mid western Hill Western Mountain Mid western Tarai Western Hill Western Tarai Central Hill Central Mountain Eastern Mountain Region Exposed Damage in GDP per Capita Central Hills Eastern Mountain Central mountain Eastern Hills Far Western Tarai Central Tarai Eastern Hill Eastern Tarai Nepal Rupee (GDP per Capita), 2011 Census data in Nepal

19 Probabilistic Catastrophic seismic risk assessment A proto-type Ex Ante Tool for Risk Sensitive Development Planning 4 Take away messages It s easy to customize for development planner, easy to adapt It s based on macro level data that is easier to access and collect than micro level data on vulnerability and exposure May be adapted for provincial or district level analysis for land management and land use planning, regional development planning or large engineering projects It may be used for ex-ante analysis for risk assessment before conducting more detailed analysis

20 Enhancing Knowledge and capacity for the management of disaster risks for a resilient future in Asia and the Pacific Contributors Jonghyo Nam, Economist ESCAP Consultant D Das, Analyst, RIMES Indrajit Pal, Seismologist, AIT Sung Eun, Economist ESCAP Staff Member Sanjay Srivastava, Chief Disaster Risk Reduction Section, ESCAP