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A MULTI RISK ASSESSMENT OF DISASTERS RELATED TO CLIMATE CHANGES Paolo Gasparini 1 Warner Marzocchi 2 Amra Scarl, Napoli Dipt. Di Scienze Fisiche, Università di Napoli Federico II Istituto Nazionale di Geofisica e Vulcanologia, Roma . - PowerPoint PPT Presentation
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A MULTI RISK ASSESSMENT OF DISASTERS A MULTI RISK ASSESSMENT OF DISASTERS RELATED TO CLIMATE CHANGESRELATED TO CLIMATE CHANGES
Paolo Gasparini1 Warner Marzocchi2
Amra Scarl, Napoli
1 Dipt. Di Scienze Fisiche, Università di Napoli Federico II2 Istituto Nazionale di Geofisica e Vulcanologia, Roma
ERUPTIONS (tephra fall, pyroclastic flows, …)
SEA LEVEL RISE
EARTHQUAKES (ground shaking)
ANTHROPOGENIC SOURCES
What is the most dangerous hazard for my city??
…mmm…I don’t really know…
How to focus risk mitigation policies?
RAPID MASS MOVEMENTS
FLASH FLOODS
WHY THE MULTI-RISK?WHY THE MULTI-RISK?
What is the most dangerous hazard for my city??
…mmm…I don’t really know…
How to focus risk mitigation policies? WHAT IS NEEDED TO?1. quantitative risk assessment (probability)
needed for decision makers
1. ranking of risks2. interaction among risks
WHAT DO WE HAVE NOW?
risks are considered independently, through inhomogeneous procedures…
…they are not comparable!!!
INTRODUCING MULTI-RISK…
Different approaches to Hazard:
- Geological hazard can be considered constant with time
- Hazard affected by climate change are not constant with time.
Different Time scales
Different Criteria of damage assessment
Specific vs. systemic vulnerability
Different Spatial definition
RISKS ARE NOT COMPARABLE!!!
RISKS ARE TREATED SEPARATELY
starting from the ADVERSE EVENTfrom CLASSICAL RISK APPROACH…
URBAN VULNERABILITY
CLIMATIC CHANGETEMPERATURE
WINDPRECIPITATION
HAZARDPROBABILITYSCENARIOS
RURAL VULNERABILITYILLNESSHUNGER
REFUGEES MULTI RISK ASSESSMENT
PEOPLE PLACES THINGS
COPING CAPACITY•INDIVIDUAL LEVEL •COMMUNITY LEVEL•GOVERNMENT LEVEL
RESILIENCE
CLIMATIC CHANGE
RAINFALLS
URBANCATCHMENTS
DISCHARGES SEWER NETWORK
URBAN FLASH FLOODS
STRUCTURAL AND SOCIAL DAMAGES
STRUCTURAL AND NON STRUCTURAL
MITIGATION OPTIONS
HYDR
OLO
GICA
L RO
UTI
NE
HYDR
AULI
C RO
UTI
NE
VULNERABILITY OF URBAN AREAS
STORAGE FACILITIES
REAL TIME CONTROL
INNOVATIVE LAND USE
MULTI-RISK: assessment of the potential damages caused by all the events threatening an object (industry, city, environment, etc.).
Usually, multi-risk assessment is provided as the “sum” of independent single riskassessment, but:
1) Single risk assessments are not always liable to be summed (i.e., different spatial and temporal resolution, different approaches to vulnerability);
2) Risks are NOT independent: the hazard and vulnerability of one specific event may change significantly if another event occurred. (INTERACTION AND CASCADE EVENTS).
THIS MAY LEAD TO SEVERE UNDERESTIMATION OF THE REAL RISK.
Better consistency using DAMAGE-from-SOURCE.
starting from the TARGET AREA
Comparable Time scales
Same Type of damage
Comparable Spatial definitions
Comparable Approaches to evaluate hazard
Interaction and cascade effects easier to be accounted for
RISKS ARE COMPARABLE!!!
RISKS TREATED COHERENTLY
……to MULTI-RISK APPROACHto MULTI-RISK APPROACH
The sources of Risk are aleatoric events;
The imperfect knowledge of the processes/parameters
introduces epistemic uncertainties;
Bayesian approach allows us to take into account both aleatory
and epistemic uncertainties;
Bayesian approach allows us to merge different types of
information, such as theories, model output, data, and so on.
Why Bayesian Methods?
The Bayesian approach is particularly useful in practical problems characterized by few data and scarce theoretical knowledge.
The Bayesian approach implies that the probability is not a single value but it is a probability distribution.
The probability distribution has an average (the best guess of the probability) and a standard deviation.
These two parameters estimates the aleatoric and epistemic uncertainties.
Why Bayesian Methods?
Prior (e.g. given by models)
Likelihood (e.g.. DATA)
POSTERIOR PDF
(no epistemic uncertainty)
Bayes theoremBayes theorem
+
Risks are NOT independent: the hazard and vulnerability of one specific event may change significantly if another event occurred.
Example: Risk for one event E1 that depends on a second one E2
The yellowyellow box is the hazard. The blueblue box is the damage.
R1 p(E1 | E2)p(E2) p(E1 | E 2)p(E 2)
p[Ck1 | (E1,E 2)]p(E 2) p[Ck1 | (E1,E2)]p(E2) k Lk
Risks are NOT independent: the hazard and vulnerability of one specific event may change significantly if another event occurred.
Let us consider only one hazard (due to the event E1 depending on the event E2)
- Usually, long-term H1 is determined by databases. If p(E2) is not changed across the time covered by the database (i.e., the boundary conditions are the same), the database provides directly an unbiased estimation of H1.
- If p(E2) varies with time (e.g., global warming), the database provides a biased estimation. In this case, we need to estimate p(E2), p(E1 | E2) and p(E1| NOT E2).
- In the short-term hazard assessment, we may be interested in estimating p(E1 | E2) instead of H1, because we know that E2 is already occurred (cascade effects).
H1 p(E1 | E2)p(E2) p(E1 | E 2)p(E 2)
Risks are NOT independent: the hazard and vulnerability of one specific event may change significantly if another event occurred.
Let us consider one hazard (E1) due to the occurrence of intensive rainfall (E2; here for simplicity E2 is dichotomic: 0 – no intensive rainfall; 1 – intensive rainfall, e.g. rainfall over a given threshold):
- if no heavy rainfall occurred in the past, from the database we can estimate a biased value of H1 that is given by p(E1 | NOT E2) (being p(NOT E2)=1). Then, p(E2) is the probability to have a rainfall over the given threshold. p(E1 | E2) is the probability that we can estimate from a scenario: the probability to have E1 given a rainfall over the given threshold (INTERACTION).
H1 p(E1 | E2)p(E2) p(E1 | E 2)p(E 2)
Naples case Annual risks for human life:
•R seis = 0.0017
•R vulc = 1.37
•R flood = 4.2 10-5
•R land = 6 10-7
•R ind = 1.83 10-6 < IR < 1.83 10-8
•R env = 0.0125
Multi risk annual probabilities
Industrial accident (Toxic emission): 3.6 x 10-3
NAPLES CASE: SCENARIOS OF IN TOWN LANSLIDE TRIGGERED BY INTENSIVE PRECIPITATION NAPLES CASE: SCENARIOS OF IN TOWN LANSLIDE TRIGGERED BY INTENSIVE PRECIPITATION Heavy rainfall
Slow landslide Fast landslide No landslide
No failure of infrastructure
Failure of infrastructures
Over threshold
GPL
Fire Toxic release Explosion
NW
People (residents, workers,..)
Loss of containment
W SW Air, soil, subsoil, superficial water,
groundwater
Below threshold
…
Clone Clone
No loss of containment
Heavy rainfall
Flash floodsFast landslides
Damage to tanks of water supply network
Failure of sewer network
Damage to building and infrastructures
Over the threshold Below the threshold
Fast landslides
Damage to building and infrastructures
Damage to tanks of water supply network
Damage to building and infrastructures
Rio Yanuncay, Cuenca, Ecuador
Cuenca Project (supporting agencies: BID, ETAPA)
EC FP7 CLUVA CLUVA
Climate Change and Urban Vulnerability in Africa
Studied Cities:Douala, CamerounSaint Louis, SenegalOuagadougou, Burkina FasoAddis Ababa, EthiopiaDar Es Salaam, Tanzania