Living in a modelled world The volume of risk information produced has

in-creased significantly over time and is accompa-nied by associated communities of practice, in-creasing data availability and scientific and technical capacities to transform that data into risk information.

In parallel with the growth of the disaster risk management sector as a whole, there has been equally exponential growth in the number of people and institutions involved in risk assess-ment. At all levels, the volume of risk information produced has increased significantly, and this development has been accompanied by a com-mensurate increase in the size of the associated community of practice, in the data available and in the scientific and technical capacities to trans-form that data into risk intrans-formation. The series of Understanding Risk meetings organized by the World Bank since 2010⁵ highlights the emergence of an increasingly large and dedicated commu-nity of practice in this area. It would appear that disaster risk management is increasingly taking place in a modelled world.

Until the early 1990s, much of the insurance industry based its business decisions on actuar-ial approaches using historical data. The use of catastrophe risk models in this industry grew dra-matically after Hurricane Andrew, which struck

Florida in 1992 and gave rise to insured loss-es far greater than those expected on the basis of historical experience (GFDRR, 2014a). Shortly after the hurricane made landfall, the risk-mod-elling company AIR modelled and estimated the insured losses; these were far larger than any experienced in the past and closer to the actual insured losses from the hurricane.

The arrival of powerful personal computers in the early 1990s ushered in the era of mathemat-ical modelling of disaster risks using probabilis-tic approaches. Following Hurricane Andrew, the insurance industry began to invest heavily in risk modelling in order to set premiums appropriately and to protect itself against insolvency.

With a few notable exceptions,⁶ probabilistic risk modelling was largely confined to the insurance industry and to specialized risk modelling com-panies until the advent of the HFA. While many national and local governments, internation-al organizations and non-governmentinternation-al organi-zations carried out risk assessments, these were generally qualitative in character (e.g. highlight-ing areas of low, medium or high risk) or deter-ministic (calculating risk quantitatively for one particular hazard scenario) and limited by the availability of historical data.

The HFA gave detailed guidance on key activities in risk assessment under Priorities for Action 2 and 3 (Box 7.6).

Box 7.6 Key activities related to risk assessment in the HFA

National and local risk assessments

(a) Develop, update periodically and widely disseminate risk maps and related information to decision-makers, the general public and communities at risk in an appropriate format.

(b) Develop systems of indicators of disaster risk and vulnerability at national and sub-national scales that will enable decision-makers to assess the impact of disasters on social, economic and environmental conditions and disseminate the results to decision makers, the public and populations at risk.

(c) Record, analyse, summarize and disseminate statistical information on disaster occurrence, impacts and losses on a regular basis through international, regional, national and local mechanisms.

Capacity

(i) Support the development and sustainability of the infrastructure and scientific, technological, technical and institutional capacities needed to research, observe, analyse, map and where possible forecast natural and related hazards, vulnerabilities and disaster impacts.

(j) Support the development and improvement of relevant databases and the promotion of full and open exchange and dissemination of data for assessment, monitoring and early warning purposes, as appropriate, at international, regional, national and local levels.

(k) Support the improvement of scientific and technical methods and capacities for risk assessment, monitoring and early warning, through research, partnerships, training and technical capacity- building. Promote the application of in situ and space-based earth observations, space technologies, remote sensing, geographic information systems, hazard modelling and prediction, weather and climate modelling and forecasting, com-munication tools and studies of the costs and benefits of risk assessment and early warning.

(l) Establish and strengthen the capacity to record, analyze, summarize, disseminate, and exchange statistical information and data on hazards mapping, disaster risks, impacts, and losses; support the development of com-mon methodologies for risk assessment and com-monitoring.

Regional and transboundary risks

(m) Compile and standardize, as appropriate, statistical information and data on regional disaster risks, impacts and losses.

(n) Cooperate regionally and internationally, as appropriate, to assess and monitor regional and trans-bound-ary hazards, and exchange information and provide early warnings through appropriate arrangements, such as, inter alia, those relating to the management of river basins.

Research

(n) Develop improved methods for predictive multi-risk assessments and socioeconomic cost–benefit analysis of risk reduction actions at all levels; incorporate these methods into decision-making processes at regional, national and local levels.

(o) Strengthen the technical and scientific capacity to develop and apply methodologies, studies and models to assess vulnerabilities to and the impact of geological, weather, water and climate-related hazards, including the improvement of regional monitoring capacities and assessments

Since the adoption of the HFA, there has been explosive growth in the production of risk infor-mation by public-sector scientific and techni-cal institutions, universities and international organizations at all scales (GFDRR, 2014a). This growth has been facilitated by the increasing availability of remotely sensed information, open source software and platforms, social media, crowdsourcing and mobile phones, and by expo-nential increases in computing power. At the same time, there has been growing convergence between risk assessment efforts in the insurance and catastrophe modelling industries and those in the public and academic sectors. As a result,

significant progress has been made in each crit-ical element of the risk assessment process (Fig-ure 7. 4).

There has been substantial progress towards cre-ating and providing open access to many global and national data sets critical to understanding hazard. The greater availability of global data sets on population, building types, satellite imag-ery, and so on is opening up significant oppor-tunities to model global exposure at higher and higher resolutions. At the national and sub-national levels, data and information from gov-ernment ministries (such as statistics authorities,

transportation and infrastructure departments, and education and health departments) are increasingly being liberated⁷ and merged in order to understand community, city and national exposure. At the city and community levels, the growing popularity of crowdsourcing has also enabled the collection of exposure data as well as the survey and mapping of disaster impacts in real time, for example after the 2010 Haiti earth-quake and Pakistan floods (Degrossi et al., 2014;

Chohan et al., 2011). Applications such as Open-StreetMap (Box 7.7) are helping to make this infor-mation increasingly pervasive.

At the same time, tools and models for identify-ing, analysidentify-ing, and managing risk have grown in

number and utility; and risk data and tools are increasingly being made freely available to users as part of a larger global trend towards open data.

More than 80 freely available software packages, many of which are open source, are now avail-able for flood, tsunami, cyclone (wind and storm surge) and earthquake risk assessment, with at least 30 of them in widespread use (GFDRR, 2014a). Significant progress has also been made in improving open source geospatial tools such as QGIS, GeoNode and PREVIEW, which are low-ering the financial barriers to risk information at the national and sub-national levels.

Since the publication of an initial global multi-hazard risk analysis by UNDP in 2004 (UNDP, 2004), models designed to provide insight into global and regional trends in disaster risk have also become more sophisticated and, as high-lighted in Chapter 3 of this report, have now adopted probabilistic approaches.

Disaster loss data has also significantly improved.

As mentioned in Chapter 4, over 85 nationally owned databases of disaster loss and damage now exist, compared to only 12 in 2005. Similar-ly, efforts have been undertaken to improve the interoperability of disaster loss data from nation-al and globnation-al databases through the develop-ment of common data standards⁸ and unique identifiers such as GLIDE.⁹ In a number of coun-tries the compilation of national disaster loss data has opened the doors to a broader approach

HFA Priority for Action 2: Identify, assess and monitor disaster risks and enhance early warning.

(Source: UNISDR with data from the HFA Monitor.)

Figure 7.4 Progress in risk identification and assessment

Box 7.7 OpenStreetMap

OpenStreetMap is an online geospatial database and a global community of over 1.5 million contributors engaged in building a free and open map of the world. The principle that anyone can contribute to the data-base and that it can be used in any tool or analysis is the reason why it is also known as the “the Wikipedia of maps”. Established in the United Kingdom in 2004 in reaction to restrictions around the use and/or availabil-ity of geospatial data across the world, OpenStreetMap is a confederation of organizations and technologies that seek to improve public understanding of natural hazard risks and climate change impacts.

to disaster risk management including probabi-listic risk assessment and dialogue with finance and planning ministries (Box 7.8).

7.8