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Extreme weather events such as Cyclone Pam, which devastated the Republic of Vanuatu in March 2015, and the prolonged drought now affecting the western US highlight two issues: the increased risk from extreme events due to climate change, and our insufficient monitoring and understanding of their societal impacts. Fatality estimates for Cyclone Pam range between 11–24 deaths and the estimates for the storm's economic impact fluctuate between US$300–570 million. For the drought-stricken state of California, analysts estimate direct agricultural impacts in the billions of US dollars, although an exact figure is unavailable — despite the fact that the drought is entering its fourth year.

These examples underscore the uncertainties surrounding loss estimates for climate-sensitive hazards and other disasters such as earthquakes. Although there have been advances in observing and documenting the physical parameters of extreme events (wind speed, rainfall amounts and so on), the quantification of socioeconomic impacts is often lacking. Impact figures vary depending on when and where the event took place and the source of the information. The disparities in death toll reports are particularly disconcerting given that death is a readily defined measure of human losses and often far easier to determine than economic losses. These examples serve as a backdrop for understanding what the recently adopted Sendai targets on disaster risk reduction face in their implementation: measuring and monitoring economic and human impacts of disasters and extreme events in the absence of reliable loss data.

The Sendai Framework for Disaster Risk Reduction (SFDRR) was adopted on 18 March 2015 by 187 UN member states. Recognizing that disasters continue to exact a heavy toll despite the efforts of the Hyogo Framework for Action (2005–2015), the SFDRR lays out policies and goals for risk reduction for the next fifteen years and stresses the importance of disaster risk reduction measures for adapting to climate change and sustainable development¹. The difference between the SFDRR and its predecessors is the inclusion of specific measurable targets, which will be difficult to quantify given that empirical data are either missing or severely skewed, as the examples above illustrate.

To achieve global progress in risk reduction, seven targets were agreed upon (Box 1), but the SFDRR has not yet specified how these targets will be measured or which data sources will be used. However, each target is to be measured at the global scale, not at the individual country level, preventing comparisons on the relative achievement among nations. Further, only two of the targets have a referent baseline (2005–2015) and none are measured in absolute terms, for example, reduce disaster mortality by a certain percentage. The referent baseline for the targets (2005–2015) is good, but will that be based on the 2005 figure, the 2015 figure, and average over the period of time, or best available data for the entire time period? For the other targets, there is no baseline year or period, so it is difficult to determine precisely what substantially increase or decrease by 2020 or 2030 actually means — relative to what starting point?

Creating systems for socioeconomic impact accounting to provide and share national loss statistics and making them compatible at the global scale is a major challenge. It is not simply about whether such systems exist. Far greater challenges include what the input data should be (the quality, accuracy, and spatiotemporal coverage of loss estimates), and how they compare across systems⁹. To establish a sound knowledge and empirical basis for the Sendai targets, loss accounting requires consistent and reliable impact estimates, preferably georeferenced to the local (or at least the subnational scale)¹⁰. It also requires comprehensive coverage of all hazards, because it does not take an extreme event to produce an extreme impact.

In the case of the US, for instance, there is no governmental all-hazards loss database. The only comprehensive source for loss information is the university-based SHELDUS database¹¹. SHELDUS is a compilation of loss statistics from several US government agencies at the county scale from 1960 to the present. Similar to the primary data sources in EM-DAT, the loss data in SHELDUS, despite coming from US governmental agencies, are also based on third-party accounts and are neither complete nor of reliable validity. Good-quality disaster loss information is hard to come by, even in seemingly data-rich countries.

Given the paucity of high-quality loss statistics to support the Sendai targets, what is needed is a collection of the 'everyday' extreme events — every day, everywhere, and in every country. To accomplish this, a network of loss data collectors, and a system to validate, document, store and share the information, must be established. For example, when is a loss a direct or indirect loss? Who determines the cause of death for a disaster victim and how will it be coded in the International Classification of Diseases¹²? Is a landslide a geophysical or hydrometeorological event? These may seem like minor questions but they are essential in establishing an evidentiary basis for the impacts from disasters.

Development of a set of standardized protocols for constructing disaster loss databases at the national scale is a step in the right direction. Such protocols could provide guidance on which parameters are to be collected (such as deaths, injuries, economic loss, people affected). Protocols (or ontologies) for hazard and impact definitions also are needed to ensure compatible loss attribution, particularly for complex events consisting of multiple hazard types.

Furthermore, to foster trust, transparency and accountability, the information must be publicly available and curated by accepted bodies, which may (or may not) be meteorological agencies, statistical bureaus, or universities. Focusing on the impacts of the last major tropical cyclone or winter storm should not overshadow the chronic impacts from prolonged droughts or routine flooding. The livelihoods of people are not only affected by catastrophic events, but the everyday hazards that adversely affect their resilience.

International research and infrastructure initiatives such as the Integrated Research on Disaster Risk (IRDR) can support this process of developing protocols for managing loss databases, which greatly increases transparency and data compatibility. There are numerous examples of these science-based inputs. For example, the Joint Research Centre of the European Commission is developing frameworks for loss in European nations⁷. Science-based expert guidance on peril harmonization and hazard terminology is available¹³ and is being implemented in many of the existing global (NatCatSERVICE, sigma, EM-DAT) and national (DesInventar, SHELDUS) loss databases. Similarly, a framework for defining and measuring human and economic disaster loss indicators was recently produced¹⁴.

There is no need to establish new and separate bureaucratic structures for disaster loss accounting. Instead, it should be part of existing statistical accounting efforts within countries, whether in the development agencies, environmental departments, or censuses. Such accounting also means the impacts of climate-induced events such as sea-level rise, coastal erosion and flooding, and saltwater intrusion should be included. Establishing separate databases on climate change impacts will only further the fragmentation of loss accounting without resolving issues of data quality, terminology, data coverage and loss quantification.

What does this mean for the Sendai targets? We have well-established indicators for measuring development such as poverty, literacy, or gross domestic product. Why not the same for disaster and extreme event losses? One could argue that disasters impede development, so disaster risk reduction should become the foundation for sustainable development as well as a pillar of climate change adaptation. Without knowing the true impact of small- to large-scale events, planners, government officials and stakeholders are not held accountable for placing people and infrastructure into harm's way along coastlines or on floodplains. Waiting for the human and socioeconomic loss escalator to go up rather than trying to reverse its course has become a global pastime. It's now time to take loss reduction seriously, beginning with national loss inventories as the foundation for risk reduction and for meeting the challenges of the Sendai targets.

1. Sendai Framework for Disaster Risk Reduction 2015–2030 (UNISDR, 2015).
2. Wirtz, A., Kron, W., Löw, P. & Steuer, M. Nat. Hazards 70, 135–157 (2014).
   • Article
3. Guha-Sapir, D., D'Aoust, O., Vos, F. & Hoyois, P. in The Economic Impacts of Natural Disasters (eds Guha-Sapir, D. & Santos, I.) 7–27 (Oxford Univ. Press, 2013).
4. Gall, M. Int. J. Glob. Warm. (in the press).
5. The Global Assessment Report on Disaster Risk Reduction (UNISDR, 2015).
6. A Comparative Review of Country-Level and Regional Disaster Loss and Damage Databases (UNDP/BCPR, 2013).
7. De Groeve, T., Poljansek, K. & Ehrlich, D. Recording Disaster Losses (European Commission, 2013).
8. Otto, I. M. et al. Nature Clim. Change 5, 503–506 (2015).
   • Article
9. Gall, M., Borden, K. A. & Cutter, S. L. Bull. Am. Meteorol. Soc. 90, 799–809 (2009).
   • Article
10. Kron, W., Steuer, M., Löw, P. & Wirtz, A. Nat. Haz. Earth Syst. Sci. 12, 535–550 (2012).
    • Article
11. The Spatial Hazards Events and Losses Database for the United States Version 13.1 (HVRI, accessed 1 September 2014); www.sheldus.org
12. International Statistical Classification of Diseases and Related Health Problems (ICD-10) 4th edn (World Health Organization, 2010).
13. Peril Classification and Hazard Glossary (IRDR, 2014).
14. Guidelines on Measuring Losses from Disasters: Human and Economic Impact Indicators (IRDR, 2015).

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