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
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.
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
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”.
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
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
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)
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.
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.
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.
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
Major effort in assembling, processing and organizing one of the largest collection of geo-referenced datasets in the region
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.
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.
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.
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.
