Changes in Impacts <strong>of</strong> <strong>Climate</strong> <strong>Extreme</strong>s: Human Systems and EcosystemsChapter 4Table 4-5 | Range <strong>of</strong> regionalized annual costs <strong>of</strong> adaptation for wet and dry scenarios (in 2005 US$ billion). Reflecting the full range <strong>of</strong> estimated costs, the wet scenario costsdo not include benefits from climate change while the dry scenario costs include benefits from climate change within and across countries. Source: World Bank, 2010.Scenario/RegionEast Asia &PacificEurope &Central AsiaLatin America &CaribbeanMiddle East &North AmericaSouth Asiasub-SaharanAfricaTotalWet 25.7 12.6 21.3 3.6 17.1 17.1 97.5Dry 17.7 6.5 14.5 2.4 14.6 13.8 69.6in the study, which were a scenario with the most precipitation (‘wet’)and one with the least precipitation (‘dry’) among all scenarios chosenfor the study, which employ socioeconomic driver information from<strong>IPCC</strong>’s SRES A2 scenario (see Table 4-5).Taking Africa as an example, based on various estimates the potentialadditional costs <strong>of</strong> adaptation investment range from US$ 3 to 10 billionper year by 2030 (UNFCCC, 2007; PACJA, 2009). However, this could bealso an underestimate considering the desirability <strong>of</strong> improving Africa’sresilience to climate extremes as well as the flows <strong>of</strong> internationalhumanitarian aid in the aftermath <strong>of</strong> disasters.4.5.6. Uncertainty in Assessing theEconomic Costs <strong>of</strong> <strong>Extreme</strong>s and DisastersUpon reviewing the estimates to date, the costing <strong>of</strong> weather- andclimate-related disasters and estimating adaptation costs is stillpreliminary, incomplete, and subject to a number <strong>of</strong> assumptions withthe result that there is considerable uncertainty (Agrawala andFankhauser, 2008; Parry et al., 2009). This is largely due to modelinguncertainties in climate change and damage estimates, limited dataavailability, and methodological shortcomings in analyzing disasterdamage statistics. Such costing is further limited by the interactionbetween numerous adaptation options and assumptions about futureexposure and vulnerabilities, social preferences, and technology, as wellas levels <strong>of</strong> resilience in specific societies. Additionally the followingchallenges can be identified.Risk assessment methods: Technical challenges remain in developingrobust risk assessment and damage costing methods. Study results canvary significantly between top-down and bottom-up approaches. Riskbasedapproaches are utilized for assessing and projecting disaster risk(Jones, 2004; Carter et al., 2007), for which input from both climate andsocial scenarios is required. All climatic phenomena are subject to thelimitation that historically based relationships between damages anddisasters cannot be used with confidence to deduce future risk <strong>of</strong>extreme events under changing characteristics <strong>of</strong> frequency and intensity(UNDP, 2004). Yet climate models are today challenged when reproducingspatially explicit climate extremes, due to coarse resolution and physicalunderstanding <strong>of</strong> the relevant process, as well as challenges in modelinglow-probability, high-impact events (see Section 3.2.3). Therefore,projections <strong>of</strong> future extreme event risk involve uncertainties that canlimit understanding <strong>of</strong> sudden onset risk, such as flood risk. Futuresocioeconomic development is also inherently uncertain. A uniform set<strong>of</strong> assumptions can help to provide a coherent global picture andcomparison and extrapolation between regions.Data availability and consistency: Lack <strong>of</strong> data and robust informationincreases the uncertainty <strong>of</strong> costing when scaling up to global levels froma very limited (and <strong>of</strong>ten very local) evidence base. There are doublecountingproblems and issues <strong>of</strong> incompatibility between types <strong>of</strong>impacts in the process <strong>of</strong> multi-sectoral and cross-scale analyses,especially for the efforts to add both market and non-market values (e.g.,ecosystem services) (Downton and Pielke Jr., 2005; Pielke Jr. et al., 2008;Parry et al., 2009). Moreover the full impacts <strong>of</strong> weather- and climaterelatedextremes in developing countries are not fully understood, anda lack <strong>of</strong> comprehensive studies on damage, adaptation, and residualcosts indicates that the full costs are underestimated.Information on future vulnerability: Apart from climate change,vulnerability and exposure will also change over time, and theinteraction <strong>of</strong> these aspects should be considered (see, e.g., Hallegatte,2008; Hochrainer and Mechler, 2011). This has been recognized andassessments <strong>of</strong> climate change impacts, vulnerability, and risk arechanging in focus, leading to more integration across questions. Whileinitial studies focused on an analysis <strong>of</strong> the problem, the field proceededto assess potential impacts and risks, and now more recently started tocombine such assessments with the consideration <strong>of</strong> specific riskmanagement methods (Carter et al., 2007).Some studies have suggested incorporating an analysis <strong>of</strong> the ongoingor chronic economic impact <strong>of</strong> disasters into the adaptation planningprocess (Freeman, 2000). A fuller assessment <strong>of</strong> disaster cost at varyingspatial and temporal scales and costs related to impacts on human,social, built, and natural capital, and their associated services at differentlevels can set the stage for comparisons <strong>of</strong> post-disaster developmentstrategies. This would make disaster risk reduction planning andpreparedness investment more cost-effective (Gaddis et al., 2007). Forexample, there is consensus on the important role <strong>of</strong> ecosystems in riskreduction and well-being, which would make the value <strong>of</strong> ecosystemservices an integral part <strong>of</strong> key policy decisions associated with adaptation(Tallis and Kareiva, 2006; Costanza and Farley, 2007).274
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Case StudiesChapter 9Table of Conte
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Case StudiesChapter 99.1. Introduct
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Case StudiesChapter 9••••
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Case StudiesChapter 99.2.1.2.3. Hea
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Case StudiesChapter 9preparedness c
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Case StudiesChapter 99.2.2.5. Outco
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Case StudiesChapter 9practices at b
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Case StudiesChapter 9to implement s
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Case StudiesChapter 9lined by the w
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Case StudiesChapter 94.5 million af
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Case StudiesChapter 9are typically
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Case StudiesChapter 9heightens vuln
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Case StudiesChapter 9multi-hazard r
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Case StudiesChapter 9Bank, 2005b).
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Case StudiesChapter 9Some federal-l
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Case StudiesChapter 9develop in a m
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Case StudiesChapter 9Most states ha
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Case StudiesChapter 9reduction legi
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Case StudiesChapter 9countries. Thr
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Case StudiesChapter 99.2.14. Educat
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Case StudiesChapter 9to be removed
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Case StudiesChapter 9can help to ad
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Case StudiesChapter 9CRED, 2009: EM
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Case StudiesChapter 9Hallegatte, S.
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Case StudiesChapter 9Linnerooth-Bay
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Case StudiesChapter 9O’Neill, M.S
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Case StudiesChapter 9Skaff, M. and
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Case StudiesChapter 9Visser, R. and
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ANNEXI Authors and Expert Reviewers
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Annex IAuthors and Expert Reviewers
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Annex IAuthors and Expert Reviewers
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Annex IAuthors and Expert Reviewers
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Annex IAuthors and Expert Reviewers
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ANNEXIIGlossary of TermsThis annex
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Annex IIGlossary of Termswater vapo
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Annex IIGlossary of Termsdrought, a
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Annex IIGlossary of TermsImpactsEff
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Annex IIGlossary of Termsforcing is
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ANNEXIIIAcronyms565
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Annex IIIAcronymsNAMNAONAPANaTechND
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ANNEXIVList of Major IPCC Reports56
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Annex IVList of Major IPCC ReportsC
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Index573
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Indexresilience building, 378touris
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IndexEM-DAT database, 364Emissions
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Indextransformation and, 324See als
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IndexRisk sharing, 10-11, 397, 523i