Understanding and Estimating Disaster Risks Concepts and methods Madhurima Sarkar-Swaisgood ICT & DRR – UNESCAP Expert consultation: Addressing the transboundary dimensions of the 2030 Agenda through Regional Economic Cooperation and Integration in Asia and the Pacific 21-22 November 2019
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Understanding and Estimating Disaster Risks Concepts and methods
Madhurima Sarkar-SwaisgoodICT & DRR – UNESCAP
Expert consultation: Addressing the transboundary dimensions of the 2030 Agenda through Regional Economic Cooperation and Integration in Asia and the Pacific
21-22 November 2019
FORMERLY AN INFRASTRUCTURE INVESTMENT
Estimated global investment in infrastructure 2015 - 2030 approximately USD 90 trillion
Unless this investment is risk informed it represents a major investment in future disasters
Complete/partial destruction of immovable assets and of stock (including final goods, goods in process, raw materials, materials and spare parts).
The flows of goods and services that will not be produced or rendered over a time span that begins after the disaster and may extend throughout the rehabilitation and reconstruction periods
Knock-on impacts on production e.g. through supply chain or medium/long run productivity effects from a natural disaster
Reflect the manner in which disasters modify the performance of the main economic variables of the affected country
Direct
Indirect
Wider
DISASTER AND CLIMATE IMPACTS
Stock
Flow
DIRECT LOSSES
Peru: Coastal ENSO event 20173000 km roads damaged, 323 bridges destroyed. Direct losses approximately USD 7 billion equivalent to 72% of the public investment executed in 2016
WHY DO WE NEED TO ADOPT A PROBABILISTIC APPROACH
PROBABILISTIC RISK MODELS
Probabilistic risk models can provide robust estimates of risk for countries and specific infrastructure sectors
Metrics include Annual Average Loss (AAL) and Probable Maximum Loss (PML)
PROBABILISTIC RISK METRICS
LOSS EXCEEDANCE CURVE (LEC)
[#/a
ño]
Loss
Exce
ed
an
ce r
ate
[#
/year]
AVERAGE ANNUAL LOSS (AAL)
PROBABLE MAXIMUM LOSS (PML)
0.0%
5.0%
10.0%
15.0%
20.0%
25.0%
30.0%
35.0%
40.0%
45.0%
0.00%
0.50%
1.00%
1.50%
2.00%
2.50%
3.00%
3.50%
4.00%
4.50%
0 2344
Pérdidas por evento
Pérdida acumulada
Return period
PM
L
THE GLOBAL RISK MODEL: ANNUAL AVERAGE LOSS (AAL)
Global Annual Average Loss (AAL) = USD 293 billion (2017) in built environment for rapid-onset hazards
Represents 5% of expected annual global investment in infrastructure.
DISASTER LOSSES- MONGOLIA, KAZAKHSTAN, KYRGYZSTAN
Kazakhstan Kyrgyzstan Mongolia
EXTENSIVE RISK INCREASESTHE AAL
Exposed value Retrospective AAL (extensive risk)
Prospective AAL (intensive risk) Hybrid AAL
US$ million US$ million ‰ US$ million ‰ US$ million ‰
As hazard increases in intensity it becomes more difficult and costly to reduce risk.
Defining a reasonable level of resilience
100 year return period or 1% probability per year
[#/a
ño]
Loss
Exce
ed
an
ce r
ate
[#
/year]
Current risk
Loss
Exce
ed
an
ce r
ate
[#
/year]
Resilience target
Risk reduction
Identify risk management alternatives Structural:Adoption of standards for new infrastructure investments
Engineering assessments to retrofit critical infrastructure
Financial:Design of risk transfer instruments
Estimation of costs of risk transfer
Response:Contingency plans
Early warning systems
Reduces Direct Loss
Reduces Net Loss and
accelerates recovery
Improves system performance and reduces the Indirect Loss.
ASSESS COSTS FOR EACH LEVEL OF RESILIENCE
A hypothetical example: flood mitigation infrastructure:
• Currently 1m flood defense wall
• 2m wall – cost $30M reduction in AAL = $10M
• 2.5m wall - cost $60M reduction in AAL = $15M
• 3m wall cost $120M reduction in AAL = $20M
RISK METRICS FOR RESILIENT INFRASTRUCTURE
National multi-hazard risk profile
High resolution infrastructuresector risk model Risk management strategy
LEC/ AAL / PML Extensive riskIdentify financing gapsImplications for sustainable development
Define resilience targetsand strategyIntegrate into public and private investment planning
AAL / PML values for each sector or region (energy, transport etc.)Identification of critical risks in networks and nodesModelling of indirect losses
Identification of risk layers. Define risk appetite
Adoption of standards for new infrastructure investments
Retrofit critical infrastructure
Risk transfer
Contingency plans and early warning systems
Finance and planning ministries
Sector line ministries and sub-national governments
Cost-effective risk management strategies
Climate change scenarios
Utility companies, infrastructure operators
CONTRIBUTION OF AGRICULTURE TO GDP
Value Added Agriculture, Forestry and Fishing –VA AFF
*http://www.fao.org/faostat/en/#data/MK
KAZAKHSTAN KYRGYZSTAN MONGOLIAGDP 184.388 6678,18 11749,6
VALUE ADDED 8.686 939 1569,3VALUE ADDED/GDP (%) 5 14,06 13,35
GFCF/VA (%) 15 2 22
PROBABILISTIC DROUGHT RISK
ASSESSMENT
HAZARD
Simulated weather time seriesSimulated time series are generated stochastically from the historicalinformation.
The objective is not to forecast future weather conditions, but to generatefeasible combinations of drought conditions, such as low precipitation andhigh temperature.
Historical Simulated(not a forecast)
HAZARD
Identification of regional droughts using indexes
DROUGHT HAZARD
Collection of scenariosHazard is represented as a set of stochastic scenarios.
Mutually exclusive
Collectively exhaustive
These scenarios (events) are assumed to be:
They allow probabilistic representation:
Occurrence frequency (temporal probability)
Gridded statistical moments (spatial probability)
Time series, at any location, of weather variables (precipitation and temperature)
EXPOSURE
Location
Crop characteristics
Exposed elements database
Crop valuation
Georeferenced data, area
Geographical distribution
Production cost
Inpu
tsO
utpu
ts
Type and seasonality
VULNERABILITY
Crop development
Crop transpiration
Soil water balance
Biomass production
Yield (Y) from biomass (B)
Decreased hydro-power potential in drought-prone regions
DROUGHT RISK
Relative AAL to agriculture production, PML curves, probability exceedance loss in the next year and next event in a Central American country for current and future climate with different representative concentration pathways (RCP 2.6, 4.5, and 8.5)
Probable Maximum loss
Exceedance loss probability in the next year
Exceedance loss probability in the next event
DROUGHT RISK
Relative Average Annual Loss to the agriculture production by provinces in a Central American country for current and future climate with different representative concentration pathways (RCP 2.6, 4.5, and 8.5)
DATA SOURCES
UN Digital Library
Global Assessment Report on Disaster Risk Reduction (GAR) Risk Atlas, 2015