2.1 Floods

2.2 Tropical cyclones

Several studies on tropical cyclone (TC) risk have been carried out at the global scale, using various methods (Table 1). Peduzzi et al. (2009) assess current TC risk hotspots, expressed in terms of expected number of fatalities per year per country. Hazard is represented by computing buffers along individual TC tracks between 1980 and 2000, where wind speed exceeds a threshold of 42.5 m s⁻¹. The tracks are taken from the PreView Global Cyclones Asymmetric Windspeed Profile dataset. The average cyclone frequency per cell is determined by taking the spatial extents of individual cyclones (5 km × 5 km) and averaging the frequency over the entire period. Exposure is represented by population and GDP per capita (5 km × 5 km), taken from GRID and the World Bank, respectively. Vulnerability is represented using a selection of 32 socio-economic and environmental variables. Since the results are only for current conditions, future DRR measures are not accounted for.

Cardona et al. (2014) also assess risk from TCs in the current time period. They express risk in terms of economic damage (average annual loss and probable maximum loss for a fixed return period of 250 years) by combining data on hazard, exposure, and vulnerability within the CAPRA Platform Risk Calculator (https://ecapra.org/, last access: 30 November 2019). Hazard is represented by maps of wind speed for different return periods at a horizontal resolution of 1 km × 1 km. These are derived from a stochastic set of wind fields, calculated using a model forced by historical observations of TCs from the IBTrACS v02r01 dataset (Knapp et al., 2010). Exposure is represented using the common dataset of the GAR 2015, in which it is represented as a group of buildings in each point or cell of analysis with a resolution of 5 km × 5 km. Vulnerability is represented by IDFs relating maximum wind velocity sustained for 5 s gusts at 10 m above ground level (Yamin et al., 2014). Since the results are only for current conditions, future DRR measures are not included. In the paper, rankings of national-level risk are shown.

Fang et al. (2015) assess the global population and GDP at risk from TC winds in the current period. To represent hazard, they develop a 6-hourly TC track database up to 2012 using CMA-track (Ying et al., 2014), HURDAT (Landsea and Franklin, 2013), and IBTrACS (Knapp et al., 2010). To convert the gust wind to sustained wind speed, a gust factor model is applied. Next, a Gumbel distribution is fitted to all grid cells (${\mathrm{30}}^{\prime \prime }\times {\mathrm{30}}^{\prime \prime }$) with more than 20 TC events to calculate wind speeds at several return periods. Exposure is represented using gridded population data at a resolution of ${\mathrm{30}}^{\prime \prime }\times {\mathrm{30}}^{\prime \prime }$ from LandScan 2010 from the Oak Ridge National Laboratory (ORNL) and is represented using gridded GDP data at a resolution of 0.5^∘ × 0.5^∘ from the Greenhouse Gas Initiative (GGI) dataset of the International Institute for Applied Systems Analysis (IIASA) for 2010. Vulnerability is not accounted for, and since the study only examines current risk no future DRR measures are included. They find that China has the highest expected annual affected population and GDP, and the top 10 % of countries are largely in Asia.

Peduzzi et al. (2012) assess risk from TCs over the period 1970–2009, with projections of future risk to 2030 based on projections of increased exposure only. Hazard is represented by maps showing TC frequency and maximum intensity for events between 1970 and 2009 at a horizontal resolution of 2 km × 2 km. The dataset is derived from a TC model of wind speed profiles using a parametric Holland model (Holland, 1980), which is forced using historical observations of TCs from the IBTrACS v02r01 dataset (Knapp et al., 2010). Exposure is represented by gridded maps of population and GDP at a horizontal resolution of ${\mathrm{30}}^{\prime \prime }\times {\mathrm{30}}^{\prime \prime }$. Current population data are taken from LandScan 2008 (LandScan, 2008) and current GDP data are taken from data from the World Bank. They are both extrapolated to each decade from 1970 to 2030, based on UN Environment Programme (UNEP) country data. Vulnerability is represented using different country-level parameters relating to the economy, demography, environment, development, early warnings, governance, health, education, and remoteness. These are used to calculate exposed GDP, affected population, and mortality risk per country for each year between 1970 and 2030. Future DRR measures are not included. The average population exposed to TCs per year is projected to increase by 11.7 % by 2030, with about 90 % of this increase in Asia. In relative terms, the largest increase in risk is in Africa.

Mendelsohn et al. (2012) assess TC risk using future scenarios of hazard, exposure, and vulnerability. Risk is expressed in terms of direct damage, and the damage pertains to storm surge, wind, and freshwater flooding, without distinguishing between the three sources. Hazard is represented by TC landfall locations and intensity from a synthetic dataset of TC tracks, simulated using the model of Emanuel et al. (2008). The TC model is seeded with climate data from four GCMs: CNRM-CM3, ECHAM5, GFDL CM2.0, and MIROC 3.2, for both the current period (1981–2000) and future period (2081–2100) under the SRES A1b emissions scenario; sea level rise is not accounted for. The damage per storm is calculated using a statistical damage function per county in the USA or per country for the rest of the world. The damage function uses population density to represent exposure and income as an indicator of vulnerability. Current exposure and income data are used per county for the USA and per country for other regions. Future projections of population per country are taken from the World Bank (2010), and future projections of GDP per country are based on long-term growth rates for three income groups. Probabilistic risk is expressed as direct damage per year, based on the damages from the stochastic storm tracks. Future DRR measures are not included. The main findings are an increase in global damage of ∼115 % by 2100 due to changes in population and income, with approximately a further doubling due to climate change.

In summary, several studies have examined the risk from TCs at the global scale. Most have only considered current conditions, except Peduzzi et al. (2009) (change in exposure) and Mendelsohn et al. (2012) (changes in hazard, exposure, vulnerability). A defining aspect of a TC hazard is that it is composed of wind, precipitation, and storm surge, and the impacts result from a combination of these. However, the current studies to date do not explicitly model all of these aspects. At present there are no methods available to parametrically model 2-D precipitation fields from TCs in the same way that wind fields are parametrically modelled using the Holland Model (Holland, 1980). A large range of different approaches have been used for defining and modelling both the hazard and risk. Mendelsohn et al. (2012) and Cardona et al. (2014) express risk in probabilistic terms. Mendelsohn et al. (2012) is the only study to use a synthetic TC dataset, whilst the other studies use historical TC events to construct the hazard.

2.3 Hazards associated with severe convective storms

Perils associated with severe convective storms (SCSs), such as large hail, heavy rainfall, strong wind gusts, or tornadoes, are among the most important perils in several regions of the world (Christian et al., 2003; Virts et al., 2013; Cecil et al., 2014). Of all SCS-related hazards, hail causes the largest economic damage (Kunz and Geissbuehler, 2017). However, the modelling of SCS risk is still in its infancy (Allen et al., 2016; Martius et al., 2018). As a result, there are currently no global risk models available for these SCS events or their sub-perils (hail, wind gusts, tornadoes, heavy rain, lightning), nor are efforts being made in this direction.

However, as insurance companies have to either provide the solvency capital for rare events (e.g. 200-year-return-period event according to the EU Solvency II directive in Europe) or have to reinsure their risk, there is a growing and large demand to better estimate the risk related to the different SCS sub-perils in a scientific manner (Allen et al., 2020). The few existing models owned by the insurance market quantify the risk mainly on a regional (e.g. Schmidberger, 2018), national, or continental level (e.g. Punge et al., 2014). These models are not freely available and the literature is scarce. Initial loss estimates in the insurance industry were based on mathematical analyses of the company's own damage data and portfolio. As more and more data on SCS events and their sub-perils have become available during the last decade, an increasing number of damage and risk models for SCS have been developed either by insurance companies or by companies developing catastrophe models (CAT models). The CAT models consider basic hazard characteristics (e.g. length, width, angle of the footprints of sub-perils) and intensity metrics (e.g. hailstone size, hail kinetic energy, precipitation amount, maximum wind speed) of an underlying event set and quantify the damage for a certain portfolio (exposure data) via fragility curves, a kind of IDF. Due to the restricted record length of SCS hazard event sets, stochastic simulations based on, for example, statistical distributions of the relevant input parameters in combination with Markov or Poisson processes are performed (e.g. Punge et al., 2014; Holmes, 2015; Ritz, 2017; Schmidberger, 2018; Schmidberger et al., 2018). In some cases, the CAT models also consider atmospheric conditions relevant for SCS development. None of the insurance models consider projected changes in the frequency and severity of SCS due to climate change.

2.4 Droughts

A relatively large number of studies have been carried out to assess global risk from droughts. Therefore, these are summarised in this section and in Table 1 and further elaborated upon in the Supplement. Here, we focus specifically on drought risk, rather than water scarcity. Drought itself is a difficult concept to define, and as a result more than 150 indices have been developed for its identification over the past decades. Commonly, drought hazard is defined as a relative concept, mostly as a deficit to normal, e.g. when such drought indices fall below or exceed a given threshold. Nevertheless, since drought is a complex hazard which propagates from a rainfall deficit (meteorological drought) to soil moisture drought to hydrological drought, a variety of corresponding impacts may result with regard to the different types of drought, as well as underlying socio-economic and ecological conditions. Hence, since a universal definition of drought seems impracticable, a paradigm shift towards a definition of drought by its impacts has been recommended (Lloyd-Hughes, 2014).

The multifaceted aspects of drought are reflected in the large range of risk indicators used in the studies described below as well as the very diverse range of approaches and datasets used to represent hazard, exposure, and vulnerability. The studies described below neither explicitly include future DRR measures nor assess risk in a probabilistic sense.

Dilley et al. (2005) are some of the first to conduct a global-scale assessment of drought risk, overlaying layers of hazard (Weighted Anomaly of Standardized Precipitation, WASP), and exposure (population, GDP, road density) information at a relatively coarse resolution of 2.5^∘ × 2.5^∘ for the current time period. They assess risk in terms of affected GDP, population, roads, and infrastructure. Christenson et al. (2014) build on the drought risk assessment of Dilley et al. (2005) by making a further distinction between the type of population exposed (i.e. urban or rural). Neither of the aforementioned studies assess vulnerability. The studies of Yin et al. (2014) and Carrão et al. (2016) do include vulnerability (as well as hazard and exposure) and also calculate risk at a higher spatial resolution of 0.5^∘ × 0.5^∘. Yin et al. (2014) express risk in terms of maize yield, and they represent vulnerability by fitted logistic regressions between historical maize crop loss estimates and simulations of drought stress. Drought hazard is represented as the normalised cumulative water stress index during the growing season, and exposure is represented by a map of fields and maize yield. Carrão et al. (2016) assess risk using a drought index integrating several factors. Vulnerability is represented in the form of proxies of economic, social, and infrastructural vulnerability, at resolutions from ${\mathrm{5}}^{\prime }\times {\mathrm{5}}^{\prime }$ to the country scale. Hazard is represented by WASP, whereby a drought is identified when the monthly precipitation deficit is less than or equal to 50 % of its long-term median value for 3 or more consecutive months. Exposure is represented by gridded maps of agricultural land, population, and livestock. On the basis of their results, Carrão et al. (2016) state that a reduction in drought risk could be rapidly achieved by improved irrigation and water harvesting in regions where infrastructural vulnerability is high.

Several studies assess future drought risk, as a result of either hazard or hazard and exposure. The forward-looking studies below all use a horizontal resolution of 0.5^∘ × 0.5^∘, except for that of Smirnov et al. (2016), where a resolution of 2^∘ × 2^∘ is used. The majority of these studies do not include vulnerability (Arnell et al., 2013, 2018; Smirnov et al., 2016; Liu et al., 2018), whilst Li et al. (2009) and Guo et al. (2016) do include vulnerability, but only under current conditions.

Li et al. (2009), Arnell et al. (2013), and Guo et al. (2016) perform future simulations by projecting changes in hazard only for different time slices up to the end of the 21st century, as a result of climate change. As with the current drought risk studies, the risk, hazard, exposure, and vulnerability are represented using very diverse metrics. All assess risk in terms of agricultural impacts, although the metric used is different in all cases. Hazard is represented by the Palmer Drought Severity Index (PDSI), Standardized Precipitation Index (SPI), namely SPI-12, and normalised cumulative water stress index, whereby different thresholds are used to identify hazardous drought conditions. Representation of exposure is diverse but shows some similarities across the studies, represented by data on crop yields, cropland/field areas, or a combination of both. Vulnerability is included in very different ways: the proportion of area equipped for irrigation per country in Li et al. (2009) and proxies of economic, social, and infrastructural vulnerability in Guo et al. (2016).

Smirnov et al. (2016), Arnell et al. (2018), and Liu et al. (2018) add a layer of complexity in their future drought risk assessments by including projections of change in exposure as well as hazard, although vulnerability is not included in these studies. Risk is expressed in terms of the population affected in all of the studies, as well as cropland area affected by Arnell et al. (2018). Again, the indicators used to represent hazard are very diverse: Standardized Precipitation Evapotranspiration Index (SPEI), namely SPEI-24; Standardised Runoff Index (SRI); and PDSI respectively. Since they all assess the population affected by drought, they all use gridded population projections for the current and future time period, with Arnell et al. (2018) additionally using crop data.

As a result of the wide range of risk indicators and approaches used, estimates of global drought risk vary significantly from study to study. Nevertheless, all studies find a robust increase in future drought risk due to changes in both climate and socio-economic conditions.

2.5 Wildfire

Wildfire is increasingly understood as being an inherently socio-natural phenomenon with feedbacks between society, vegetation, fire weather, and climate (Riley et al., 2019). However, global wildfire risk is a particularly understudied area of disaster risk assessment. This may be due to a focus on global burnt area products to provide input to global climate modelling as a significant source of emissions (Giglio et al., 2009; GCOS, 2011; Chuvieco et al., 2016) rather than a disaster risk emphasis. It is also due to the large degree of complexity with interactions between natural and human processes driving occurrence and intensity of wildfires as well as exposure and vulnerability to them.

In the studies reviewed below, DRR measures are not explicitly accounted for in the modelling frameworks. This is largely due to uncertainty in human actions – continued ability to manage fuel and suppress fires under different climatic conditions and increased sprawl into wildland–urban interface (WUI) areas – and due to regime changes in weather and vegetation making previously non-hazardous vegetation areas susceptible to fire, especially with increased logging and fragmentation, particularly in tropical areas (Laurance and Williamson, 2001; Flannigan et al., 2009; Corlett, 2011; Jolly et al., 2015). Significant steps forward could be made by examining interactions between vegetation, weather, climate, and human activities that cause wildfires.

Meng et al. (2015) map forest wildfire risk globally under current conditions only. Risk is expressed in terms of forest area burnt for different return periods at a resolution of 0.1^∘ × 0.1^∘ but is not integrated into estimates of probabilistic risk. Hazard is represented as annual forest wildfire occurrence based on historical data from MODIS satellite imagery (0.1^∘ × 0.1^∘). The short historical time series of wildfire occurrence (12 years) required the use of fuzzy mathematics to allow for the calculation of different return periods of forest wildfire (Huang, 1997, 2012). Exposure is represented by the area of forest (representing economic value), using land cover data at 0.1^∘ × 0.1^∘. Vulnerability is modelled as a function derived from fire occurrence versus burnt area, whereby a cell with high vulnerability indicates that a small number of fires can cause a large amount of burnt area, with the inverse being true for low vulnerability. The results show the highest risk in central Africa, central South America, northwestern Southeast Asia, middle-eastern Siberia, and the northern regions of North America. High risk can also be seen in eastern Australia, Laos, Cambodia, Thailand, Bangladesh, eastern Russia, and the borders of Zambia, Angola, and the Democratic Republic of Congo. Risk results from Meng et al. (2015) are used by Shi and Kasperson (2015) as part of a multi-hazard global risk assessment. In this study, impacts are integrated across return periods to estimate probabilistic risk. However, the results are aggregated across 11 different hazards, so the risk attributed to wildland fire cannot be identified.

Cao et al. (2015) performed a similar study to Meng et al. (2015), but they present results for grassland wildfire risk at a resolution of 1 km × 1 km. The analysis is carried out for current conditions, expressing risk as average impacts per year over 2000–2010. Hazard is calculated based on probability of ignition, slope, and vegetation properties (calculated from MODIS data) (1 km × 1 km). A logistic regression model is developed using these properties and historical burnt area records to model the probability of grassland burning. Exposure is based on the assumption that the primary impact of grassland fires is on the stock industry, which is dependent on available biomass from grassland. Therefore global net primary product (NPP) (1 km × 1 km) is used as the proxy for exposed value for grassland wildfire. Vulnerability is the probability of fire spread or propagation and contributes to the calculation of probability of grassland burning. The key results show that the areas of highest risk are in Australia, Brazil, Mozambique, Madagascar, the United States of America, Russia, Kazakhstan, China, Tanzania, Canada, Angola, South Africa, Venezuela, Argentina, Nigeria, Sudan, and Colombia. Again, the results are used in Shi and Kasperson (2015) as part of a multi-hazard global risk assessment.

Knorr et al. (2016) present wildfire risk for 1901 to 2005 and then to 2100 using Representative Concentration Pathways (RCPs) and Shared Socioeconomic Pathways (SSPs) to project hazard and exposure. Risk is expressed in terms of affected people and burnt area per year at a resolution of 1^∘ × 1^∘. Hazard is modelled by coupling a semi-empirical fire model, SIMFIRE (Knorr et al., 2014), with a global dynamic land ecosystem and biogeochemical model LPJ-GUESS (Ahlström et al., 2012). This produces hazard metrics of fractional burnt area per year. The coupling with LPJ-GUESS allows vegetation and weather factors to update, with SIMFIRE providing annual updates on fire frequency per grid cell. The hazard modelling is driven using RCP4.5 and RCP8.5 data for monthly mean precipitation, temperature, and radiation from CMIP5. Exposure is represented by population under SSP2, SSP3, and SSP5, although this does not include changing rates of population splits between urban and rural areas, which is an important factor for future interactions with wildfire. Vulnerability and DRR measures are not explicitly considered, although population density is a factor within SIMFIRE, accounting for the idea that generally increasing population will suppress wildfires. Under the future scenarios, there is a significant increase in the number of people in fire-prone areas between 1971–2000 and 2071–2100: between 23 % and 56 % for RCP4.5 and between 25 % and 73 % for RCP8.5, averaged across SSPs.

2.6 Earthquakes

A relatively large number of studies have been carried out to assess global risk from earthquakes. Therefore, these are summarised in this section and in Table 1 and elaborated on in the Supplement. Several global assessments have used index-based methods or overlays of a single hazard map with exposure data (e.g. population, GDP) to assess global exposure to earthquake hazard, and they are not explicitly discussed in this review. All of the studies reviewed in Table 1 explicitly include hazard, exposure, and vulnerability, although none of them carry out future projections of risk.

One of the first global earthquake risk models to go beyond this approach is that of Chan et al. (1998), who examine direct damage via macroeconomic indicators to derive global seismic loss at the relatively coarse scale of 0.5^∘ × 0.5^∘. The study of Dilley et al. (2005) also uses hazard data based on past events (based on the Richter scale), but in addition it uses 50-year-return-period hazard maps from the Global Seismic Hazard Assessment Program (GSHAP). The resolution of the risk analysis is higher, at ${\mathrm{2.5}}^{\prime }\times {\mathrm{2.5}}^{\prime }$, and a wider range of impact indicators are used (affected population, affected GDP, affected road and rail infrastructure, and fatalities). Global studies at a higher resolution of 1 km × 1 km were performed by Jaiswal and Wald (2010, 2011). These two studies essentially use the same approach, but the former assesses risk in terms of affected people and fatalities, whilst the latter also assesses risk in terms of direct economic damage and affected GDP. Hazard is represented by ShakeMaps of intensity from past events at a resolution of 1 km × 1 km.

Daniell (2014) and Daniell and Wenzel (2014) also assess global earthquake risk at 1 km × 1 km, whereby risk is expressed in terms of direct and indirect damage, fatalities, affected people, and affected GDP, at a resolution of 1 km × 1 km. In this case, hazard is represented by the spectral acceleration and/or macroseismic intensity (MMI) at each point, which is then rasterised on a 1 km × 1 km grid for each event. None of the aforementioned studies assess risk probabilistically, instead calculating impacts for past events or for a given return period.

Li et al. (2015) describe a global earthquake risk model using a probabilistic approach, in which the impacts are integrated over several exceedance probabilities. Hazard is represented by peak ground acceleration (PGA) at 0.1^∘ × 0.1^∘ with conversion to macroseismic intensity. Risk is expressed in terms of direct damage, fatalities, affected people, and affected GDP, at a resolution of 0.5^∘ × 0.5^∘ for mortality and 0.1^∘ × 0.1^∘ for economic–social wealth.

From the GAR2013 onwards (UNDRR, 2013, 2015a, 2017), a probabilistic approach using stochastic hazard modelling has been used in the GARs. Risk, in terms of direct damages, is calculated stochastically at a country resolution and expressed at the national scale in terms of probable maximum loss and annual average losses. Hazard is represented by spectral accelerations at a resolution of 5 km × 5 km, using a stochastic event set of earthquakes around the world. The Global Earthquake Model (GEM) (Silva et al., 2018) also uses stochastic event sets to produce probabilistic risk estimates in terms of economic damage to buildings (1 km × 1 km).

Exposure data used in global earthquake models have generally increased in resolution from ∼0.5^∘ to ∼1 km, usually using datasets such as gridded population and GDP. A defining feature of more recent global studies has been the use of capital stock data (e.g. Daniell, 2014; Daniell and Wenzel, 2014; Silva et al., 2018). Vulnerability is represented in various ways, ranging from empirical loss and fatality functions based on reported losses and fatalities to empirical fatality and loss ratios and IDFs based on different building types.

2.7 Tsunamis

As with other natural hazards, tsunami risk can be broken down into hazard, vulnerability, and exposure. However, limited empirical data on vulnerability and the computational burden of tsunami wave propagation simulations mean that only a limited number of studies have so far been developed, and most of them have either limited scope or resolution. Early risk assessments at the local scale from Berryman (2005) and Grezio et al. (2012) use methods to estimate the inundation at low resolution. The highest spatial resolution simulations have been carried out by Wiebe and Cox (2014) for parts of Oregon, Jelínek et al. (2012) for Cádiz in Spain, and De Risi and Goda (2017) for the Miyagi prefecture in Japan. Some models, such those of Tinti et al. (2008), or Okumura et al. (2017), have assessed risk in terms of fatalities.

Løvholt et al. (2015) assess global risk in terms of direct damage, affected people, and fatalities at a resolution of 1 km × 1 km. Risk is calculated probabilistically using stochastic event sets and a probable maximum loss curve at 100, 500, and 1500 years, as well as average annual losses. Risk is only assessed for current conditions, and therefore no DRR measures are included. Hazard is represented in terms of inundation depth at a resolution of 90 m × 90 m. This is simulated using a 25 km propagation model, followed by a 2-D model at a resolution of ${\mathrm{1}}^{\prime }\times {\mathrm{1}}^{\prime }$ and is then further downscaled using a 90 m DEM. Exposure is represented by capital stock and population at 1 km × 1 km, taken from the coastal database of De Bono and Chatenoux (2015) used as part of GAR2015. Vulnerability is represented by IDFs for different building types derived from expert workshops (Maqsood et al., 2014) for the Asia Pacific. The Suppasri et al. (2013) functions are used for the rest of the world. They find that Japan and the Philippines, among other island nations, have the highest relative risk.

Schäfer (2018) introduce a generic tsunami risk assessment framework that could be applied for global risk modelling, and they apply it to various regions around the world. Risk is expressed in terms of direct damage, affected GDP, affected population, and fatalities for current conditions not accounting for DRR measures. It is calculated at 90 m × 90 m resolution for various return periods, with expected annual impacts being assessed by integrating across different exceedance probabilities. Hazard is represented by maps of inundation depths (90 m × 90 m), based on numerical simulations using shallow water wave equations and machine learning. Exposure is derived using a population-based capital stock model (90 m × 90 m) built from the Global Human Settlement Layer (Pesaresi et al., 2016). Vulnerability is represented by depth–damage IDFs that are resolved for three building classes (light, moderate, and massive buildings) and a fatality function considering both water depth and arrival time. The study is tested in various regions, including Japan, Chile, and the Caribbean, and derives average annual losses and probable maximum loss curves.

2.8 Volcanoes

Volcanoes can produce a variety of hazards, including pyroclastic density currents (pyroclastic flows, surges, and blasts), tsunamis, lahars, tephra (including volcanic ash and ballistics), debris avalanches (sector collapse), gases and aerosols, lava flows and domes, and lightning. Not all volcanic hazards are produced by every volcano or eruption, and some can occur without an eruption, for instance volcanic gas. Whilst many hazards might be triggered by the volcano directly, the occurrence or distribution of others can be influenced by hydrometeorological factors, for instance, rainfall-triggered lahars and landslides or the influence of wind on the distribution of volcanic ash. Comprehensive assessments of volcanic risk at the global scale do not exist. Quantitative risk assessments at the local scale (where they exist) rarely include the effect of DRR measures, many of which are not measurable in a quantitative sense (e.g. engagement between scientists and civil protection, policymaking, preparedness, and planning). The field of volcanic hazard and risk assessment can therefore appear less well developed compared to other natural hazard fields of study, with the effect that assessments combining multiple natural hazards typically underestimate the threat from volcanic activity. There are at least three key factors that limit our ability to assess volcanic risk at the global scale: the multi-hazard, time-varying, and complex nature of volcanic events; a large discrepancy in the quality and quantity of data required to inform global volcanic hazard assessments between regions; and limitations of data to inform global volcanic risk assessment, especially because large, damaging eruptions impacting populated areas are relatively infrequent and impacted zones can be dangerous and sometimes inaccessible for long periods. International collaborations have now been established to facilitate the production of systematic evidence, data, and analysis of volcanic hazards and risk from local to global scales (e.g. Loughlin et al., 2015; Newhall et al., 2017; Bonadonna et al., 2018).

Past approaches to assessing global volcano risk, described below, have mainly aimed to identify those volcanoes or cells that pose the greatest relative danger, in order to inform subsequent in-depth investigations using local data and knowledge. The first assessment of the hazard threat posed by volcanoes globally is Yokoyama et al. (1984). They use an index-based approach to identify “high-risk” volcanoes. Binary indices are used to score 10 hazard and five exposure components, describing the frequency of recent explosive activity and hazards, and the size of the population within a certain radius of the volcano, respectively. Two quasi-vulnerability binary scores are used: one for if the volcano had produced historical fatalities and one for if evacuations had resulted from historical eruptions. Scores for each volcano are summed, with high-risk volcanoes defined as those with a score ≥10. This approach results in notable volcanoes that have produced some of the worst volcanic catastrophes of the 20th century being considered not high risk.

Small and Naumann (2001) and Freire et al. (2019) rank volcanoes globally according to the population exposed within radii of 200 and 10–100 km from a volcano, respectively. Hence, hazard and vulnerability are not included. Exposure is represented by gridded population data at a resolution of ${\mathrm{2.5}}^{\prime }\times {\mathrm{2.5}}^{\prime }$ (Small and Naumann, 2001) and 250 m (Freire et al., 2019). For the GAR2015, an index-based approach to assessing volcanic hazard and risk is also used, combining data and the approaches of Auker et al. (2015) and Brown et al. (2015a, b). Hazard is represented by Auker et al. (2015) using an index method to represent the hazard level of each volcano according to the frequency and intensity of past eruptions, while accounting for record incompleteness. Exposure is represented using the method of Brown et al. (2015a), an index of population exposure weighted to historical data of fatalities (Auker et al., 2013) with distance, and vulnerability is expressed in terms of affected population and fatalities for past events. Combining the hazard index with fatality-weighted population counts gives estimates of individual volcanic threat. Not all volcanic hazards are considered in the weighting; the weights are sourced from volcanic flow fatality data only, as pyroclastic density currents and lahars are the source of the majority of direct historical fatalities (Auker et al., 2013). Brown et al. (2015b) further aggregate the volcanoes to the national scale to provide country-level estimates of volcanic threat. Whilst this approach represents our most recent attempt at considering volcanic hazard and risk at the global scale, it still assumes concentric radii around each volcano.

Grid-based global assessments of volcanic risk have been carried out by Dilley et al. (2005) and Pan et al. (2015). Dilley et al. (2005) include volcanoes as part of their multi-hazard hotspot analysis, whereby risk is expressed in terms of affected GDP and population and fatalities (${\mathrm{2.5}}^{\prime }\times {\mathrm{2.5}}^{\prime }$). Hazard is expressed in terms of the count of volcanic activity, which is gridded to 2.5^∘ × 2.5^∘, between 79 and 2000 CE (i.e. no consideration of the size of eruptions or their spatial extent). Exposure is represented by gridded population from the Gridded Population of the World 3 (GPWv3) dataset from CIESIN; GDP per capita at the national scale from the World Bank; and transportation lengths from the VMAP datasets, all at ${\mathrm{2.5}}^{\prime }\times {\mathrm{2.5}}^{\prime }$. Vulnerability is represented by two quasi-vulnerability values, for mortality and economic loss rates globally as a result of volcanic activity between 1981 and 2000 based on the EM-DAT database, aggregated to country and regional levels. A major limitation of this approach, as recognised by the authors themselves, is that the hazard records are too short to capture the larger, typically more damaging events and are biased towards those volcanoes for which we have good and recent records of past activity. Also, no attempt was made to account for far-reaching volcanic hazards like ash. Pan et al. (2015) built on this approach to assess fatalities, by extending the length of the fatality database used to 1600 CE. They also add relationships between eruption Volcanic Explosivity Index (VEI) and frequency and hazard extent, although concentric circles are still assumed. Hazard is defined here as the frequency of each VEI eruption, using the method of Jenkins et al. (2012). Exposure is represented using gridded population data (${\mathrm{30}}^{\prime \prime }\times {\mathrm{30}}^{\prime \prime }$) of 2010 from the Oak Ridge National Laboratory (ORNL) (Bright et al., 2011). Vulnerability is represented by fatality curves fitted to the historical average fatality of each VEI provided by the National Oceanic and Atmospheric Administration (NOAA).

2.9 Landslides

Assessing the risk associated with landslides at a global scale is challenging for several reasons. Firstly, the spatial extent of individual landslides is typically small, limiting the effectiveness of routinely monitoring these events at a global scale. Further, the diversity of parameters influencing the hazard susceptibility (e.g. elevation, lithology), preconditioning (e.g. soil moisture, seismicity), and triggering (e.g. extreme rainfall, earthquakes) make it difficult to physically model these processes using uniform approaches. While the spatial extent of landslide source regions is generally small, the downstream hazards such as debris flows – which are often associated with the greatest damage (Badoux et al., 2014) – can be more widely distributed, meaning risk assessments must consider locations both close to and far from the source areas.

Efforts have been made to catalogue landslides and their impacts, based on a range of data sources including media reports, government statistics and other written sources, remote sensing, and citizen science (e.g. Guzzetti et al., 1994; Kirschbaum et al., 2010; Petley, 2012; Tanyas et al., 2017; Froude and Petley, 2018; Juang et al., 2019). In addition, several studies have tried to model global landslide hazard (e.g. Stanley and Kirschbaum, 2017; Kirschbaum and Stanley, 2018). In the following paragraphs, those studies that have explicitly assessed global risk are described and summarised in Table 1.

Nadim et al. (2004, and updated in 2006) are among the first to assess risk associated with landslides at the global scale. Risk is expressed in terms of the number of fatalities per year at a resolution of ${\mathrm{30}}^{\prime \prime }\times {\mathrm{30}}^{\prime \prime }$, over the period 1980–2000, associated with landslides and avalanches. Hazard is estimated using a range of global datasets based on both susceptibility (factors such as slope, lithology, and soil moisture) and triggering factors (rainfall and seismicity). A similar approach is used to define the hazard associated with avalanches. Exposure is represented by the Global Population of the World v4 (GPWv4) dataset (${\mathrm{30}}^{\prime \prime }\times {\mathrm{30}}^{\prime \prime }$) (CIESIN, 2016). Vulnerability is estimated using empirical data on loss of life from landslides in a number of countries, using the EM-DAT database. Future projections and therefore future DRR measures are not included in the study. They find that hotspots for landslide fatalities are the Himalayas, Taiwan, the Philippines, Central America, northwestern South America, the Caucasus, Indonesia, Italy, and Japan, with smaller proportional impacts in other countries and regions.

Dilley et al. (2005) estimate the risk associated with landslides, expressed in terms of direct damage and affected GDP and population at ${\mathrm{2.5}}^{\prime }\times {\mathrm{2.5}}^{\prime }$, for current conditions. Hazard is represented using the data of Nadim et al. (2004) at ${\mathrm{30}}^{\prime \prime }\times {\mathrm{30}}^{\prime \prime }$. Exposure is represented by data at ${\mathrm{2.5}}^{\prime }\times {\mathrm{2.5}}^{\prime }$ constructed from Gridded Population of the World (GPW) population data, road density data from VMAP datasets developed by the United States National Imagery and Mapping Agency, and gridded economic and agricultural activity from the World Bank and based on Sachs et al. (2001). Vulnerability is represented by empirical loss rates based on the EM-DAT database. Future projections and therefore future DRR measures are not included in the study. Their primary output is in map form, with the most elevated impacts found in many of the same locations as in the study of Nadim et al. (2004, 2006), although they find higher impacts in terms of total GDP in China.

Yang et al. (2015) assess risk in terms of fatalities for the current period at a resolution of 0.25^∘ × 0.25^∘. Hazard is represented based on the method of Nadim et al. (2006), using TRMM satellite rainfall data to estimate the number of landslide events and filling in gaps in the data using information diffusion theory. Exposure is represented by population data from LandScan, resampled to 0.25^∘ × 0.25^∘. Vulnerability is based on empirical mortality rates per country calibrated using a dataset of global landslides causing fatalities from Kirschbaum et al. (2010). Future projections and therefore future DRR measures are not included in the study. They find similar patterns to Dilley et al. (2005) and Nadim et al. (2006), but they additionally find many areas that have elevated risk of mortality compared to those prior studies, including large parts of China and sub-Saharan Africa.

Nowicki Jessee et al. (2018) present a global earthquake-induced landslide hazard model, which is implemented within the USGS Ground Failure hazard and risk model. Risk is expressed in terms of exposed population in near-real time for each earthquake event that triggers landsliding. Hazard is calculated by leveraging the earthquake-triggered database from Tanyas et al. (2017), based on various sources of information describing factors controlling susceptibility (such as slope and lithology) and earthquake parameters, such as shaking intensity, to estimate the relative density of landsliding within an area impacted by a major earthquake. The model landslide density estimates are not dependent on resolution. Exposure is represented by LandScan population maps, with a resolution of ${\mathrm{30}}^{\prime \prime }\times {\mathrm{30}}^{\prime \prime }$ (Bright et al., 2017). No vulnerability data are incorporated. Future projections and therefore future DRR measures are not included in the study. For each earthquake, an estimate of population exposed to landsliding and liquefaction is presented.

3.1 (Dynamics of) risk elements

As our review focuses on global-scale natural hazard risk assessments, we have not included studies that only examine the hazard. All but two of the studies have an explicit representation of hazard intensity and/or probability and of exposure. The only exceptions are the volcano studies of Small and Naumann (2001) and Freire et al. (2019), in which the population living within a set radius of volcanoes is estimated, without an explicit representation of the hazard. About two-thirds of the reviewed studies include a specific representation of vulnerability. Across the various hazards, there is no clear difference in the proportion of studies including vulnerability as we move towards the most recent publications. It is noteworthy that all of the earthquake and tsunami studies reviewed include a specific representation of vulnerability. For pluvial flooding and SCS, there are currently no global-scale risk models, with the local scale of the hazard and impact of these events making their large-scale modelling difficult.

In terms of the inclusion of dynamic risk drivers, there is a clear difference between studies focusing on hydrological, climatological, and meteorological hazards and those focusing on geological hazards. For geological hazards, none of the reviewed studies include future projections, whilst for hydrological, climatological, and meteorological hazards, around two-thirds of the studies include projections of at least one of the risk drivers. The difference between the studies of hydrological, climatological, and meteorological hazards, compared to those of geological hazards in terms of projections, may be due to the climate-change-related focus of many studies in the former group. This provides a policy context for carrying out forward-looking hazard projections to examine the influence of climate change on risk. In total, there are 32 reviewed studies that include future projections: 19 include projections of hazard and exposure, eight include projections of hazard only, and three include projections of exposure only. The remaining two studies include projections of vulnerability (as well as hazard and exposure): one study for river flooding and one for TCs. Time horizons used for the forward-looking studies tend to be towards the middle and late 21st century. For river flooding and drought, several recent studies have examined future warming levels, rather than future time slices. It is interesting to note that projections of the other risk drivers have not yet been examined for the geological hazards at the global scale, despite their importance for other policy contexts, such as the Sendai Framework and SDGs, although definitions used for monitoring of these frameworks explicitly state disaster risk assessment as “evaluating existing conditions of exposure and vulnerability” without considering future change (UNGA, 2016). The global-scale geological risk studies could apply some of the forward-looking models of exposure in their analysis to estimate future risk, which is of importance for designing prospective risk management strategies and measures.

3.2 Resolution and type of input data

The list of studies in Table 1 shows a general tendency towards higher-resolution risk analyses as we move towards more recent studies, though this is not the case for all studies. Many of the early studies are at resolutions from several degrees to 0.5^∘, and for many hazards there has been a progression towards higher resolutions of 30^′′ or 1 km, and even up to 90 m per point value in some cases. We do see differences between the various hazards. Most of the early coastal flood risk studies examine risk using the coastal segments from the DIVA database, although recent studies have also moved towards 30^′′ or 1 km. For drought, the resolution tends to be much lower, from around 0.5 to 2.0^∘ in general. This is in line with the difference in resolution at which drought impacts are felt (Stahl and Hisdal, 2004), compared to many of the other hazards for which the direct impacts are felt more locally. For a similar reason, TC studies tend to show a much higher resolution in their analysis, which reflects the fact that TC impacts for an individual event are felt over smaller areas. Most global volcano risk studies to date examine risk at the resolution of an individual volcano, rather than on a raster grid. Several recent studies on earthquakes (Silva et al., 2018) and earthquake-induced landslides (Nowicki Jessee et al., 2017) use model frameworks that can use variable scales.

In most cases, the resolution of the risk analysis follows the resolution of the hazard and exposure datasets used as input. In most cases these have the same resolution, and where this is not the case the tendency is to either resample the lower-resolution datasets to the higher resolution or vice versa. Therefore, the resolution of the input hazard and exposure databases tends to show the same overall patterns as those discussed above for the risk calculations. The most commonly used datasets used to represent exposure are gridded datasets of population and GDP. Direct economic damage is further assessed using land use data, whilst in recent studies related to earthquakes and tsunamis the use of capital stock estimates (based on building typologies) has become more common. Given the importance of agricultural impacts for drought, exposure is also represented using datasets such as gridded agricultural area, cropland area, planting area, and so forth. For wildfires, areas of forest and grassland are also used. Nevertheless, Kreibich et al. (2019) highlighted that, especially for drought, data on losses and impacts that are directly attributed to the hazard of drought are lacking.

Methods and datasets used to represent vulnerability are highly diverse. Within the flooding community, the most common approach is to use intensity–damage functions (IDFs). For flooding, the IDF takes the form of a depth–damage function. In most studies, one global IDF is used (especially for coastal flooding), whilst for river flooding several studies have also used regional or country-level IDFs. Jongman et al. (2015) also use regional ratios of affected GDP to report losses and affected population to report fatalities; the latter is also used by Dottori et al. (2018). Global IDFs are also used in studies that examine wildfires. Some earthquake and tsunami studies have also used IDFs. They either use IDFs per income class or region (i.e. not a global function), or in more recent years there has been a tendency to use IDFs related to building types. Another approach is to use empirically derived regressions between reported impacts and a given level of hazard and exposure to derive empirical regression equations. In volcanology the limited amount of impact data has meant that only a few vulnerability and fragility functions for physical vulnerability have been developed (Blong, 2003; Jenkins et al., 2014; Maqsood et al., 2014; Wilson et al., 2014). Global TC and volcano studies tend to use various socio-economic variables at the country (or state) level as a proxy of vulnerability. Therefore, the spatial representation of vulnerability is much coarser and does not use gridded datasets in the same way as hazard or exposure.

It is clear that the different hazard communities use some common datasets to represent exposure, for example relating to population and GDP. However, there are also differences and opportunities for interaction, sharing, and knowledge exchange. For example, the use of building stock data based on building typologies in earthquake and tsunami studies has provided opportunities for improving the assessment of other hazards (e.g. volcanic tephra). The same can be said for the other hazards, where advances in IDFs related to building type offer opportunities outside the seismic community. On the other hand, approaches that have been developed to project future exposure in the flood risk community could be harnessed by the other hazard communities.

3.3 Risk indicators

The most commonly used risk indicator is the number of affected people, which is used in 59 % of the reviewed studies. A large number of studies also use some indicator of direct economic damage (44 %), with fatalities (26 %) and affected GDP (24 %) having also been used in many studies. Fatalities have been much less commonly assessed in flood and drought risk studies than in studies of the other hazards, offering potential for cross hazard knowledge exchange on methods for fatality assessment.

3.4 Future DRR measures

To date, future DRR measures have only been explicitly included in several studies, all of which are related to flooding. Global-scale assessments in coastal flooding have been forward looking from the outset, since the original studies were developed to assist climate adaptation studies. As such, they also include a wider range of DRR measures (both structural and nature-based) than the few river flood studies that have explicitly included DRR measures in recent years. The costs of DRR measures have only been explicitly assessed in a couple of studies, for coastal or river flood risk. Hence, in this regard the global flood modelling community has a lot of knowledge and examples that can be used to begin to include DRR measures in global-scale assessments of other hazards. To date, no global studies have assessed the influence that human behaviour and perception can have on the effectiveness of DRR measures.

3.5 Type of analysis

In Table 1, we have classed studies as either non-probabilistic (NP) or probabilistic (P), whereby probabilistic refers to studies that assess expected annual impacts by integrating across return periods based on a probabilistic stochastic event set. For droughts, volcanoes, and landslides, studies to date have used non-probabilistic approaches only. Studies on floods and earthquakes have seen a move towards more probabilistic studies in more recent years, and the two studies reviewed for tsunamis also use a probabilistic approach. For wildfires and TCs, both approaches are used, with too few studies to be able to see any particular change in focus through time. A major difference between the studies of earthquakes and tsunamis, in comparison to the other hazards, is the extensive use of stochastic event sets in the former. Stochastic modelling could also be beneficial for the assessment of several other hazards, as discussed in Sect. 4.