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Building Resilience in an Urban World

                                                         Abhas K. Jha
                                                      Program Leader,
                                           Disaster Risk Management
                                                      East Asia Pacific
                                                      The World Bank




       Santa Fe Institute Trustee and Business Network Symposium,
                             November 2, 2012
I am going to talk about 3 things
               today….
1. The rising trend in disasters and
  what are the key drivers behind it.

2. How is the World Bank addressing
  this issue.

3. Some emerging future directions.
3 Main Messages
1. The growth of people and assets in harm’s way, due
   to rapid urbanization will be, by far, the biggest driver
   of disaster risk over the next few decades.

2. The deep uncertainties from climate change implies
   the need for “robust” solutions-that work (“good
   enough”)across a wide range of scenarios.

3. The risks of disasters cannot be completely
   eliminated:
      a)   Preparing for “graceful” failure
      b)   Getting the balance right between structural and non-structural
           measures
1. Trends
Rome Wasn’t Built in Day..




China Does it in Two Weeks!
Where is this urbanization
      happening?
Africa, Indian Sub-Continent and China


                                   15%




                         25%

                  25%
Small and Medium Towns




Growth in population by city scales. Source: based on Population Division of the Department of Economic and Social Affairs of the United
Nations Secretariat, World Population Prospects: The 2008 Revision and World Urbanization Prospects: The 2009 Revision.
Increasing Trend of Disasters




          Source: MunichRe
The Major Takeaway from the IPCC SREX

  “Long-term trends in normalized
  losses have not been attributed to
  natural or anthropogenic climate
  change”

      -IPCC Special Report on Managing the Risks
  of Extreme Events and Disasters to Advance
  Climate Change Adaptation
Source: Pielke and Landsea
http://www.aoml.noaa.gov/hrd/Landsea/lanina/figures.html#fig2
Source: Ranger and Garbett-Shiels, 2011
Cascading failures, fragile networks
“Networks that are efficient
are often not
resilient, because resilient
networks have inefficient
redundancies. Resilience is a
public good, created by the
right kind of redundancy.”
           -Michael Spence
Complex, Cascading, Non-linear
 The major disaster area in          : National population
 Tohoku                                 share 4.5%
 (Iwate, Miyagi and                     National GDP share
 Fukushima)                             4%

Japanese mining and manufacturing production in
March: -15.5% (the biggest drop in history)

Japanese export in April: -12%


                     Source: Prof. Masahisa Fujita
March 11, 2011:
The Headline You Did Not Read



 Prevention pays but design
systems that “fail gracefully”.
Risk Tolerance= F (Affordability)
        ALARP Principle




            ALARP Principle
2. Examples
A Quick Word on the World Bank
Macroeconomic
                                 Planning & Disaster
                                    Risk Financing


                                                            Integration of Climate
Rapid Disaster Impact                                          Change projections
     Estimation                      PACIFIC RISK
                                    INFORMATION
                                       SYSTEM




                                                       Professional and
         Urban Planning and                              Institutional
         Infrastructure Design                             Capacity
                                                        Development




                                       Source SOPAC
Satellite          Administrative   Population        Agricultural
imagery            Boundaries       Census            Census




                   Topographic
Surface Geology                     Surface soil      Bathymetry
                   maps




                    Geodetic and
                                     Infrastructure    References
                    Fault Data
   Source: SOPAC
Creating Robust Risk Information

                                                                                        145° E                        150° E                       155° E

                                                                                                                               0   100 200       400
                                                                                                                                    Kilometers




                                                                                                                                                            5° S
                                                             5° S
                                                                                                        Lae


                                                                                            Port
                                                                                           Moresby




                                                                                                                                                            10° S
                                                         10° S
Hazard                                                              Papua New Guinea
                                                                    AAL / Asset Value

                          Exposure/Vulnerability                        0% - 0.05%
                                                                        0.05% - 0.1%
                                                                        0.1% - 0.15%
                                                                                        0.15% - 0.2%
                                                                                        0.2% - 0.3%
                                                                                        0.3% - 0.4%
                                                                                                       0.4% - 0.5%
                                                                                                       0.5% - 1.15%


                                                                                        145° E                        150° E                       155° E




                                                                                                       Risk
Pacific Catastrophe Risk Assessment and Risk Financing in association with SPC/SOPAC and the ADB
Risk Assessment

  Event       Intensity
Generation   Calculation

                                  Damage
                                 Estimation


                                Limit                Loss
                                                  Calculation
               Exposure
             Information   Deductible


                            Mitigation / Policy
                                Policy
                               Conditions
                             Conditions

                                                  Source: SOPAC
Country risk profiles
An Illustration with Solomon Islands
Acting collectively for cost-effective
financial solutions against major disasters


                                                        Adding more countries
                                                        increases risk diversification
                                                        benefits




     Adding more perils increases
     risk diversification benefits




         Note: Impact of risk diversification on 150yr loss
Geonodes: Sharing Information for
           Resilience
Participatory Mapping: OpenStreetMap




DKI Jakarta:

100% coverage

Over 6000
structures
digitized

2,658 RW
mapped
InaSAFE Impact analysis
                          PDF




       Click
Jakarta flood prone areas and hospitals
lo
Exact location of potentially flooded hospitals
Building the Open Source Community
SAFE CodeSprint: Pavia Italy. November 12-16, 2012
Metro Manila and Can Tho:
Getting the Balance Right Between ‘Structural and Non-
structural Measures
Keeping the water away from         Keeping the people away
the people                          from the water
Hard engineered                     Increased preparedness
                                    • Awareness campaigns
• Flood conveyance                  • Urban management
• Flood storage
                                    Flood avoidance
• Urban drainage systems
                                    • Land use planning
• Ground water management
                                    • Resettlement
• Flood resilient building design
                                    Emergency planning & management
• Flood defenses
                                    • Early warning systems and evacuation
Eco-system management               • Critical infrastructure

• Utilizing wetlands                Speeding up recovery
• Creating environmental buffers    • Building back smarter
                                    • Risk insurance
Ho Chi Minh City Developing an Integrated Flood Risk
 Management Strategy
 Urban growth in the periphery of the city had as a result newly-urbanized districts arising
 in sites at flood risk.
 Hard engineering or structural measures to minimize flood risk might be unsustainable
 under large hydrological, land subsidence and urbanization uncertainties.


Components:
• Protection to an appropriate return frequency,
   determined by predictions using historical data
   and non-stationary analysis
• Adaptation to cope with extreme events that
   surpasses design criteria
• Retreat, which means restoring space for water to
   adapt to long-term climate changes.




                                                                                           Ho Long Phi
  Robust Decision Making (RDM) Helps Inform Good
       Decisions Without Reliable Predictions
Robust Decision Making (RDM) Helps Inform Good Decisions
                    Without Reliable Predictions

       RDM follows “Deliberation with Analysis” decision support process

                                                                Participatory Scoping
                                                               1.Define Goals, Uncertainties, and
                                                                 Strategies
Key idea:                                                      2.Choose Candidate Strategy

• Start with strategy
• Use analytics to identify              Tradeoff Analysis                                       Case Generation
  scenarios where strategy fail to    5.Display and Evaluate                                3.Estimate Performance of
  meet its goals                        Tradeoffs Among                                       Strategy in Many Futures
                                        Strateg(ies)
• Use these scenarios to identify
  and evaluate responses
                                                                  Scenario Exploration and
                                                                         Discovery
                                                                4.Characterize Strategy’s
                                                                  Vulnerabilities


                                                                                                               Deliberation

                                                                                                               Analysis
                                     Robust Strategy                 Vulnerabilities
      Source: Rob Lempert, RAND                                                                                Deliberation with
                                                                                                               Analysis
Factors Potentially Considered in Our Analysis
X: Exogenous uncertainties                     L: Policy levers
• Extreme precipitation (X mm/3 hrs)           • System described in 2001 JICA Master Plan
• Mean sea level                               • Adaptation options include:
• Subsidence rate                                  • Elevating buildings
• Infrastructure performance                       • Small scale pumps
• Delays in implementing flood control plans       • Public awareness
• Rate and patterns of economic and            • Retreat options include:
  population growth                                • Restrictions/appropriate land use
• Effectiveness of policies                    • Adaptive decision strategies
• Costs of implementing policies                   • Signposts
                                                   • Responses

R: Relationships                               M: Measures of merit
• SWMM Model                                   • RI: Risk exposure (population/housing)
• ArcGIS for calculating RI and DI             • DI: Damage exposure (economic)


                      Source: Rob Lempert, RAND
Risk Layering: A Balance-Sheet Approach to Risk
                        Finance
  • Catastrophe Bonds
100 Parametric insurance
  •
  • Traditional
                Insurance
                                                Probable
                                                Maximum
 90                                             Loss

 80
 70                                                        Transfer
 60Contingent lines of credit
  •
  • Loans (Standard or Emergency)
 50Budget reallocations
  •
 40
                                                           Retention
 30
• Reserves/Calamity funds
 20
 10
   0
   1980           1985            1990   1995   2000           2005
3. Emerging Directions
Emerging Directions
1. Recognizing, measuring and responding to
   complexity (CCN for networks?).
2. Cognitive limitations, Communicating
   uncertainty
3. Beyond cost-benefit (fat tails), evidence based
   DRM, Value-for-Money
4. The economics (and politics!) of open data for
   resilience.
5. Decision-making in data-scarce
   environments, Big data, Simulations, Serious
   Gaming
Ultimately it boils down to….
• INVESTMENTS: What concrete actions can we
  take to build resilience into our program?
• INSTRUMENTS: What instruments do we need
  to support our clients to mainstream
  resilience ? (Data, metrics, analytical work
  etc.)
• INCENTIVES: Institutionally why is this not
  happening, even for events that we know that
  are bound to take place? (Changing
  incentives)
EAP DRM KNOWLEDGE PRODUCTS
Thank you!


Abhas K. Jha
www.worldbank.
org/eapdisasters
@abhaskjha

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Building Resilience in an Urban World

  • 1. Building Resilience in an Urban World Abhas K. Jha Program Leader, Disaster Risk Management East Asia Pacific The World Bank Santa Fe Institute Trustee and Business Network Symposium, November 2, 2012
  • 2. I am going to talk about 3 things today…. 1. The rising trend in disasters and what are the key drivers behind it. 2. How is the World Bank addressing this issue. 3. Some emerging future directions.
  • 3. 3 Main Messages 1. The growth of people and assets in harm’s way, due to rapid urbanization will be, by far, the biggest driver of disaster risk over the next few decades. 2. The deep uncertainties from climate change implies the need for “robust” solutions-that work (“good enough”)across a wide range of scenarios. 3. The risks of disasters cannot be completely eliminated: a) Preparing for “graceful” failure b) Getting the balance right between structural and non-structural measures
  • 5. Rome Wasn’t Built in Day.. China Does it in Two Weeks!
  • 6. Where is this urbanization happening?
  • 7. Africa, Indian Sub-Continent and China 15% 25% 25%
  • 8. Small and Medium Towns Growth in population by city scales. Source: based on Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat, World Population Prospects: The 2008 Revision and World Urbanization Prospects: The 2009 Revision.
  • 9. Increasing Trend of Disasters Source: MunichRe
  • 10. The Major Takeaway from the IPCC SREX “Long-term trends in normalized losses have not been attributed to natural or anthropogenic climate change” -IPCC Special Report on Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation
  • 11. Source: Pielke and Landsea http://www.aoml.noaa.gov/hrd/Landsea/lanina/figures.html#fig2
  • 12. Source: Ranger and Garbett-Shiels, 2011
  • 13. Cascading failures, fragile networks “Networks that are efficient are often not resilient, because resilient networks have inefficient redundancies. Resilience is a public good, created by the right kind of redundancy.” -Michael Spence
  • 14. Complex, Cascading, Non-linear The major disaster area in : National population Tohoku share 4.5% (Iwate, Miyagi and National GDP share Fukushima) 4% Japanese mining and manufacturing production in March: -15.5% (the biggest drop in history) Japanese export in April: -12% Source: Prof. Masahisa Fujita
  • 15. March 11, 2011: The Headline You Did Not Read Prevention pays but design systems that “fail gracefully”.
  • 16. Risk Tolerance= F (Affordability) ALARP Principle ALARP Principle
  • 18. A Quick Word on the World Bank
  • 19. Macroeconomic Planning & Disaster Risk Financing Integration of Climate Rapid Disaster Impact Change projections Estimation PACIFIC RISK INFORMATION SYSTEM Professional and Urban Planning and Institutional Infrastructure Design Capacity Development Source SOPAC
  • 20. Satellite Administrative Population Agricultural imagery Boundaries Census Census Topographic Surface Geology Surface soil Bathymetry maps Geodetic and Infrastructure References Fault Data Source: SOPAC
  • 21. Creating Robust Risk Information 145° E 150° E 155° E 0 100 200 400 Kilometers 5° S 5° S Lae Port Moresby 10° S 10° S Hazard Papua New Guinea AAL / Asset Value Exposure/Vulnerability 0% - 0.05% 0.05% - 0.1% 0.1% - 0.15% 0.15% - 0.2% 0.2% - 0.3% 0.3% - 0.4% 0.4% - 0.5% 0.5% - 1.15% 145° E 150° E 155° E Risk Pacific Catastrophe Risk Assessment and Risk Financing in association with SPC/SOPAC and the ADB
  • 22. Risk Assessment Event Intensity Generation Calculation Damage Estimation Limit Loss Calculation Exposure Information Deductible Mitigation / Policy Policy Conditions Conditions Source: SOPAC
  • 23. Country risk profiles An Illustration with Solomon Islands
  • 24. Acting collectively for cost-effective financial solutions against major disasters Adding more countries increases risk diversification benefits Adding more perils increases risk diversification benefits Note: Impact of risk diversification on 150yr loss
  • 26. Participatory Mapping: OpenStreetMap DKI Jakarta: 100% coverage Over 6000 structures digitized 2,658 RW mapped
  • 28. Jakarta flood prone areas and hospitals lo
  • 29. Exact location of potentially flooded hospitals
  • 30. Building the Open Source Community SAFE CodeSprint: Pavia Italy. November 12-16, 2012
  • 31. Metro Manila and Can Tho: Getting the Balance Right Between ‘Structural and Non- structural Measures Keeping the water away from Keeping the people away the people from the water Hard engineered Increased preparedness • Awareness campaigns • Flood conveyance • Urban management • Flood storage Flood avoidance • Urban drainage systems • Land use planning • Ground water management • Resettlement • Flood resilient building design Emergency planning & management • Flood defenses • Early warning systems and evacuation Eco-system management • Critical infrastructure • Utilizing wetlands Speeding up recovery • Creating environmental buffers • Building back smarter • Risk insurance
  • 32. Ho Chi Minh City Developing an Integrated Flood Risk Management Strategy Urban growth in the periphery of the city had as a result newly-urbanized districts arising in sites at flood risk. Hard engineering or structural measures to minimize flood risk might be unsustainable under large hydrological, land subsidence and urbanization uncertainties. Components: • Protection to an appropriate return frequency, determined by predictions using historical data and non-stationary analysis • Adaptation to cope with extreme events that surpasses design criteria • Retreat, which means restoring space for water to adapt to long-term climate changes. Ho Long Phi Robust Decision Making (RDM) Helps Inform Good Decisions Without Reliable Predictions
  • 33. Robust Decision Making (RDM) Helps Inform Good Decisions Without Reliable Predictions RDM follows “Deliberation with Analysis” decision support process Participatory Scoping 1.Define Goals, Uncertainties, and Strategies Key idea: 2.Choose Candidate Strategy • Start with strategy • Use analytics to identify Tradeoff Analysis Case Generation scenarios where strategy fail to 5.Display and Evaluate 3.Estimate Performance of meet its goals Tradeoffs Among Strategy in Many Futures Strateg(ies) • Use these scenarios to identify and evaluate responses Scenario Exploration and Discovery 4.Characterize Strategy’s Vulnerabilities Deliberation Analysis Robust Strategy Vulnerabilities Source: Rob Lempert, RAND Deliberation with Analysis
  • 34. Factors Potentially Considered in Our Analysis X: Exogenous uncertainties L: Policy levers • Extreme precipitation (X mm/3 hrs) • System described in 2001 JICA Master Plan • Mean sea level • Adaptation options include: • Subsidence rate • Elevating buildings • Infrastructure performance • Small scale pumps • Delays in implementing flood control plans • Public awareness • Rate and patterns of economic and • Retreat options include: population growth • Restrictions/appropriate land use • Effectiveness of policies • Adaptive decision strategies • Costs of implementing policies • Signposts • Responses R: Relationships M: Measures of merit • SWMM Model • RI: Risk exposure (population/housing) • ArcGIS for calculating RI and DI • DI: Damage exposure (economic) Source: Rob Lempert, RAND
  • 35. Risk Layering: A Balance-Sheet Approach to Risk Finance • Catastrophe Bonds 100 Parametric insurance • • Traditional Insurance Probable Maximum 90 Loss 80 70 Transfer 60Contingent lines of credit • • Loans (Standard or Emergency) 50Budget reallocations • 40 Retention 30 • Reserves/Calamity funds 20 10 0 1980 1985 1990 1995 2000 2005
  • 37. Emerging Directions 1. Recognizing, measuring and responding to complexity (CCN for networks?). 2. Cognitive limitations, Communicating uncertainty 3. Beyond cost-benefit (fat tails), evidence based DRM, Value-for-Money 4. The economics (and politics!) of open data for resilience. 5. Decision-making in data-scarce environments, Big data, Simulations, Serious Gaming
  • 38. Ultimately it boils down to…. • INVESTMENTS: What concrete actions can we take to build resilience into our program? • INSTRUMENTS: What instruments do we need to support our clients to mainstream resilience ? (Data, metrics, analytical work etc.) • INCENTIVES: Institutionally why is this not happening, even for events that we know that are bound to take place? (Changing incentives)
  • 39. EAP DRM KNOWLEDGE PRODUCTS
  • 40. Thank you! Abhas K. Jha www.worldbank. org/eapdisasters @abhaskjha

Editor's Notes

  1. We are experiencing the largest wave ofurbanization ever seen in history. In 2008, half of the world’s population lived in urban areas, with two-thirds of this in low-income and middle-income nations. This is estimated to rise to 60 percent in 2030, and 70 percent in 2050. Urbanization itself is not the problem, but the combination of rapid and unplanned urbanization, makes urban flooding more dangerous and more costly to manage in the short-medium term – because of the sheer size of the population exposed within urban settlements. The figure on the slide depicts the growth in population by city size. The red line represents the growth of cities with fewer than 500.000 people – steepest. Peri-urban, small and middle sized cities are expected to grow most – these are the cities that lack infrastructure, services and the capacities to prepare for their rapid growth. This puts their dwellers, especially the urban poor, socially disadvantaged, and informal settlers at risk. Climate changecan have a compounding effect on existing flood risk, for example, by augmenting sea level rise, changing rainfall patters, and an increase in storm surges. The science between the linkages between CC and extreme weather events is still not conclusive. Research by Roger Pielkeof Colorado, Kerry Emanuel of MIT and many others have shown that, if you normalize for asset and population growth, there is absolutely no climate change signal in damages from extreme weather events for the foreseeable future. PCC SREX Report "long-term trends in normalized losses have not been attributed to natural or anthropogenic climate change" ttp://ipcc-wg2.gov/SREX/images/uploads/SREX-All_FINAL.pdf p.26. The IPCC SREX report reflected  the scientific literature on the state of attribution with respect to extreme events --for events such as floods, hurricanes, tornadoes, bushfires and on other topics there remain enormous uncertainties.  A new paper is forthcoming in the journal Climatic Change in 2012 helps to shed some additional light on such claims. The new paper -- titled  "A Trend Analysis of Normalized Insured Damage from Natural Disasters" by Fabian Barthel and Eric Neumayer of the London School of Economics concludes based on its examination of weather-related losses from the Munich Re global dataset  from 1980 to 2008 (emphasis added): [At a global scale] no significant trend is discernible. Similarly, we do not find a significant trend if we constrain our analysis to non-geophysical disasters in developed countries . This implies that people and asset growth in harm's way will be, by far, the biggest driver of deaths and damages from extreme weather events for the foreseeable future.Growing population and prosperity means that more people and wealth are exposed to risks and larger losses.
  2. Climate changecan have a compounding effect on existing flood risk, for example, by augmenting sea level rise, changing rainfall patters, and an increase in storm surges. The science between the linkages between CC and extreme weather events is still not conclusive. Research by Roger Pielkeof Colorado, Kerry Emanuel of MIT and many others have shown that, if you normalize for asset and population growth, there is absolutely no climate change signal in damages from extreme weather events for the foreseeable future. PCC SREX Report "long-term trends in normalized losses have not been attributed to natural or anthropogenic climate change" ttp://ipcc-wg2.gov/SREX/images/uploads/SREX-All_FINAL.pdf p.26. The IPCC SREX report reflected  the scientific literature on the state of attribution with respect to extreme events --for events such as floods, hurricanes, tornadoes, bushfires and on other topics there remain enormous uncertainties.  A new paper is forthcoming in the journal Climatic Change in 2012 helps to shed some additional light on such claims. The new paper -- titled  "A Trend Analysis of Normalized Insured Damage from Natural Disasters" by Fabian Barthel and Eric Neumayer of the London School of Economics concludes based on its examination of weather-related losses from the Munich Re global dataset  from 1980 to 2008 (emphasis added): [At a global scale] no significant trend is discernible. Similarly, we do not find a significant trend if we constrain our analysis to non-geophysical disasters in developed countries . This implies that people and asset growth in harm's way will be, by far, the biggest driver of deaths and damages from extreme weather events for the foreseeable future.Growing population and prosperity means that more people and wealth are exposed to risks and larger losses.
  3. Forget about optimal design and anticipating all risksInstead focus on “robust” design, simple rules of thumbConsider the consequences of failure in designInvest in data, emergency preparedness and response.
  4. Where to from here. In Phase 3 the focus is on Applications development ie demonstrating the use of the information. Strengthening of Pacific Risk Information SystemIntegration of climate change scienceExtend hazard models (e.g. local flood models)Extend and update risk exposureDevelopment of ApplicationsDisaster risk financing /macro-economic planningRapid disaster impact estimationIntegration of risk information in urban/infrastructure planningIntegration of community vulnerability/resource mapping
  5. Major effort in assembling, processing and organizing one of the largest collection of geo-referenced datasets in the region
  6. The key technical ingredients to understanding risk and building resilience are hazard, exposure with vulnerability. The images from the Pacific Catastrophe Risk Assessment and Risk Financing Initiative (PCRAFI) display (1) 100 year mean return period seismic hazard, (2) detailed exposure for 45 unique classes of infrastructure including estimated replacement cost values paired in the analysis with detailed vulnerability functions, (3) Risk in terms of the average annual loss at the district level normalized by total replacement value.The PCRAFI is an extensive study has been conducted to analyze the risk from tropical cyclones, earthquakes, and tsunamis with quantitative impacts to population, economic losses to buildings, infrastructure, and crops. This included the generation of detailed exposure information to locate and characterize over 3.5 million buildings and infrastructure in 15 Pacific Island Countries(PICs). This information was gathered from a combination of sources including extensive survey (over 80,000 locations validated in the field), remote analysis from satellite imagery, review of available national/regional databases.
  7. A customized GeoNode is being deployed in Fiji at SPC-SOPAC to serve the terabytes of data associated with the PCRAFIGeoNode is a open source, web-based data sharing platform– it is one of the tools that enables the principles of the Open Data for Resilience Initiative Information Communication Technology (ICT) experts at SPC-SOPAC fully engaged in development of the platform In the image a custom feature allows the detailed field survey data and photos to be displayed in the interacting web mapping interface.The vision at SOPAC-SPC: “Different projects, different data, different requirements, different access levels – One Platform, the GeoNode”There are over 30 planned or ongoing GeoNode based data sharing project across most of the Bank’s regions.
  8. Some 60 percent of Ho Chi Minh City (HCMC) is comprised of lowland areas subject to tidal effects. Examining flooding in HCMC is complicated as it isaffected by upstream, downstream and local impacts. Despite an increase in heavy rainfall events, an upgrade to the drainage system in the central districts of Ho Chi Minh City has reduced flood risk. Nevertheless,urban growth in the periphery of the city had as a result newly- urbanized districts arising in sites at flood risk.To protect the city from sea level rise, a dike and tide gate system is planned. The total cost for the construction of 12 large gates and 170 kilometers of dike couldreach US$ 2 billion. The Tide Control Project uses large polders but, although approved, it remains controversial as saline intrusion has been more serious than was initially expected. The construction of a sea dike is also being considered.Hard engineering or structural measures to minimize flood risk might be unsustainable under large hydrological, land subsidence and urbanization uncertainties. The Steering Center for Urban Flood Control in HCMC pointsout that an integrated flood management strategy (IFMS) is most likely to be successful in reducing flood risk. Components of an IFMS include:–– Protection to an appropriate return frequency, determined by predictions using historical data and non-stationary analysis–– Adaptation to cope with extreme events that surpasses design criteria–– Retreat, which means restoring space for water to adapt to long-term climate changes.The dynamic balance among the three components may vary, depending on location and timing. As the Steering Center for Urban Flood Control suggests, it should be decided via a robust Decision Support System (DSS). This case demonstrates that urban flood risk management cannot be associated solely with hard-engineered measures, but rather with an integrated and flexible approach in order to respond to future climate and socio-economic uncertainties.
  9. Comprehensive framework of ex-ante and ex-post risk management measures to mainstream disaster risk management into their development plans and processes.The World Bank supports countries around the world in mainstreaming a holistic approach to disaster risk management into development.With its overarching mission to fight poverty, World Bank’s disaster risk management efforts aim to build resilient communities. Between fiscal years 2006 and 2012, IBRD and IDA committed an estimated $11.7 billion to projects related to disaster risk management (GFDRR, 2012). In East Asia and the Pacific, the World Bank supports a range of low- to upper-high-income countries in developing effective ex-ante and ex-post risk management measures. Paying close attention to countries singular context, the World Bank provides analytical and advisory services, helps to build climate and disaster resilience into core investments across sectors, and offers unique financial solutions to better manage the contingent fiscal risks from disasters. The estimated active DRM portfolio in the region is USD$ XX (tbc). World Bank disaster risk management activities are part of a comprehensive frameworkfocusing on a number of core areas: risk identification, risk reduction, emergency preparedness, financial protection, and sustainable recovery and reconstruction. Systematically addressing each core area, this Report takes stock of the current situation of EAP countries, identifies the key challenges, as well as outlines priorities for policy makers to reduce risks and build resilience in the short-, medium-, and long-run.