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Using assessed levels of risk as baselines would allow thecountries to assess the specific hazards they face and totake into account existing DRR measures in place. However,it is to be noted that such an approach would requiresubstantial investments in methodology, data and analysis.While sources for hazard maps include national surveys(e.g. European flood zones), commercial catastrophemodelling companies, international agency initiatives (suchas the Global Earthquake Model (GEM), the Global RiskAssessment, CAPRA) re/insurance companies, independentscientific research and government studies, their coverageis not yet adequate for monitoring purposes particularly inlow- and middle-income countries. Also, the method usesas a variable the effectiveness of a country’s DRR plans, anissue that should be first independently assessed.Proponents of this approach have argued that, wheremethodological gaps exist, the exposure data could still becollected and the 2015 baseline could be then calculatedretrospectively in a few years, when a method would havebeen agreed upon.Probabilistic scenario modelsA third option for baseline setting would be to use a fullmulti-hazard probabilistic scenario model estimate for 2015as a basis for monitoring at both global and national level.Probabilistic risk modelling simulates those future disasterswhich, based on scientific evidence, are likely to occur. As aresult, these risk assessments could resolve the problemposed by the limits of historical data. Probabilistic modelsaim at “completing” historical records by reproducing thephysics of the phenomena and recreating the intensity of alarge number of simulated events.While the scientific data and knowledge used is stillincomplete, provided that their inherent uncertainty isrecognized, these models can provide guidance on thelikely “order of magnitude” of risks. The results ofprobabilistic risk models are usually presented in terms ofmetrics such as average annual loss (AAL), and probablemaximum losses (PML) for various periods. The AAL is theannualized average expected loss annualized over a longtime frame. It represents the amount that countries wouldhave to set aside each year to cover the cost of futuredisasters in the absence of insurance or other disaster riskfinancing mechanisms. PML represents the maximum lossthat could be expected within a given period of time.Typically, PML is relevant to determine the size of reservesthat, for example, insurance companies or a governmentshould have available to buffer losses. In simplified terms,as with the option of assessed risk, the countries could usethe calculated AAL in 2015 as their baseline and aim atbringing the numbers down by reducing their exposure andvulnerability to hazards and increasing their capacities todeal with them.The catastrophe modelling paradigm has principally beenused to help insurance entities quantify financial risk andhence the large majority of catastrophe models have beendeveloped in high- and upper-middle-income countrieswith an active insurance industry. While today a number ofprogrammes aim at expanding the coverage of models,data availability remains a challenge. In addition, a majorityof the models provide information on economic losses butnot for mortality, although examples of national or regionalmortality models can be found for example for Japan andCalifornia 323 .Examples of modelling platforms for disasters includeCAPRA and CRIM. The Comprehensive Approach toProbabilistic Risk Assessment 324 (CAPRA) initiative started inJanuary 2008, as a partnership between the Center forCoordination of Natural Disaster Prevention in CentralAmerica, UNISDR, the Inter-American Development Bank,and the World Bank. It has been used for example to designrisk transfer instruments, and for probabilistic cost-benefitratios of risk mitigation strategies, such as buildingretrofitting. The International Institute for Applied SystemsAnalysis (IIASA) developed the Integrated Catastrophe RiskManagement model (CRIM) 325 . Among the first casestudies, the model was used for designing earthquakeinsurance policies in Russia and Italy by integrating anearthquake hazard module and geographic informationsystem-based maps of seismic intensities andvulnerabilities. The approach has been extended to othertypes of natural and anthropogenic hazards, such as urbanflash floods, windstorms, livestock epidemics, and securitymanagement. The use of assessment models forsustainable development is addressed more in-depth inChapter 2.While an increasing number of risk models are now beingproduced for specific hazards and portfolios of exposedassets, up to now it has been difficult to estimate globaldisaster risk due to major geographical gaps and the factthat global assessments for single hazards use differentdata sets and methodologies. The global AAL for economiclosses has been calculated as part of the new Global RiskAssessment coordinated by UNISDR, the first of its kind toprovide worldwide coverage for multiple hazards. In thebuilt environment alone, global economic losses associatedwith earthquakes, cyclones, tsunamis and floods areestimated at US$ 314 billion 326 . The new global assessment(see Box 4-5) uses the CAPRA modelling platform andenables comparisons of risk levels between countries andregions and across hazard types.80