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A MULTI RISK ASSESSMENT OF DISASTERS RELATED TO CLIMATE CHANGES

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 .

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A MULTI RISK ASSESSMENT OF DISASTERS RELATED TO CLIMATE CHANGES

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  1. A MULTI RISK ASSESSMENT OF DISASTERS RELATED TO CLIMATE CHANGES • Paolo Gasparini1 Warner Marzocchi2 • Amra Scarl, Napoli • Dipt. Di Scienze Fisiche, Università di Napoli Federico II • Istituto Nazionale di Geofisica e Vulcanologia, Roma

  2. How to focus risk mitigation policies? ANTHROPOGENIC SOURCES FLASH FLOODS EARTHQUAKES (ground shaking) ERUPTIONS (tephra fall, pyroclastic flows, …) SEA LEVEL RISE RAPID MASS MOVEMENTS What is the most dangerous hazard for my city?? WHY THE MULTI-RISK? …mmm… I don’t really know…

  3. What is the most dangerous hazard for my city?? How to focus risk mitigation policies? INTRODUCING MULTI-RISK… …mmm… I don’t really know… • WHAT IS NEEDED TO? • quantitative risk assessment (probability) needed for decision makers • ranking of risks • interaction among risks WHAT DO WE HAVE NOW? risks are considered independently, through inhomogeneous procedures… …they are not comparable!!!

  4. RISKS ARE TREATED SEPARATELY from CLASSICAL RISK APPROACH… starting from the ADVERSE EVENT 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!!!

  5. TEMPERATURE WIND PRECIPITATION CLIMATIC CHANGE PROBABILITY SCENARIOS HAZARD ILLNESS HUNGER RURAL VULNERABILITY URBAN VULNERABILITY MULTI RISK ASSESSMENT REFUGEES PLACES THINGS PEOPLE • INDIVIDUAL LEVEL • COMMUNITY LEVEL • GOVERNMENT LEVEL COPING CAPACITY RESILIENCE

  6. CLIMATIC CHANGE VULNERABILITY OF URBAN AREAS RAINFALLS HYDROLOGICAL ROUTINE URBAN CATCHMENTS SEWER NETWORK DISCHARGES HYDRAULIC ROUTINE URBAN FLASH FLOODS STRUCTURAL AND SOCIAL DAMAGES STORAGE FACILITIES STRUCTURAL AND NON STRUCTURAL MITIGATION OPTIONS REAL TIME CONTROL INNOVATIVE LAND USE

  7. 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 risk • assessment, but: • 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.

  8. RISKS TREATED COHERENTLY …to MULTI-RISK APPROACH starting from the TARGET AREA Better consistency using DAMAGE-from-SOURCE. 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!!!

  9. Why Bayesian Methods? • 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.

  10. 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.

  11. Accounting forepistemic and aleatoryuncertainty Eachprobabilitymayberepresentedby a single value or, more appropriately, by a distributionwhosecentralvaluerepresent the “best guess”, and the spreadingmimics the epistemicuncertainty on the best guess. POSTERIOR PDF (no epistemic uncertainty) Likelihood (e.g.. DATA) Bayes theorem + Prior (e.g. given by models)

  12. 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 yellow box is the hazard. The blue box is the damage.

  13. 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).

  14. 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).

  15. 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

  16. NAPLES CASE: SCENARIOS OF IN TOWN LANSLIDE TRIGGERED BY INTENSIVE PRECIPITATION Heavy rainfall Over threshold Below threshold No landslide Slow landslide Fast landslide Failure of infrastructures No failure of infrastructure Loss of containment No loss of containment GPL Toxic release Fire Explosion NW W SW … Air, soil, subsoil, superficial water, groundwater People (residents, workers,..) Clone Clone

  17. Cuenca Project (supporting agencies: BID, ETAPA) Heavy rainfall Over the threshold Below the threshold Fast landslides Flash floods Rio Yanuncay, Cuenca, Ecuador Failure of sewer network Damage to tanks of water supply network Damage to building and infrastructures Fast landslides Damage to building and infrastructures Damage to tanks of water supply network Damage to building and infrastructures

  18. EC FP7 CLUVA Climate Change and Urban Vulnerability in Africa Studied Cities: Douala, Cameroun Saint Louis, Senegal Ouagadougou, Burkina Faso Addis Ababa, Ethiopia Dar Es Salaam, Tanzania

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