Integrating social vulnerability into high-resolution global flood risk mapping

Domain: earth_science
Published: 2024-04-11
Editorial outcome: minor_revision

Abstract
High-resolution global flood risk maps are increasingly used to inform disaster risk planning and response, particularly in lower income countries with limited data or capacity. However, current approaches do not adequately account for spatial variation in social vulnerability, which is a key determinant of variation in outcomes for exposed populations. Here we integrate annual average exceedance probability estimates from a high-resolution fluvial flood model with gridded population and poverty data to create a global vulnerability-adjusted risk index for flooding (VARI Flood) at 90-meter resolution. The index provides estimates of relative risk within or between countries and changes how we understand the geography of risk by identifying ‘hotspots’ characterised by high population density and high levels of social vulnerability. This approach, which emphasises risks to human well-being, could be used as a complement to traditional population or asset-centred approaches.

Introduction
Flooding is one of the most common natural hazards associated with climate change globally, with nearly a quarter of the world’s population exposed to a 1-in-100-year event1. Every year, flooding results in thousands of deaths, the displacement of millions, and hundreds of billions of dollars in damage2, and climate change is expected to increase both the frequency and intensity of floods in coming decades3–5. Exposure to flooding is greatest in low- and middle-income countries (LMICs), and this is expected to increase as a consequence of rapid demographic change1. This is of particular concern given that the poorest households and communities have the least coping capacity when confronted with a natural hazard event and suffer the greatest well-being losses2,6–9. Failure to mitigate hazard risks for the most vulnerable contributes to the perpetuation of poverty10 and can exacerbate social inequalities within and between countries11–13.

Within this context, flood risk mapping is an increasingly essential tool for policymakers seeking to mitigate future risk and respond effectively when hazard events occur14,15. In many high-income countries, detailed flood risk maps with multiple indicators of vulnerability have long been used to inform both public policy and private insurance and mitigation16–19. By contrast, high-resolution flood risk mapping has been particularly challenging in lower income countries due to basic data deficits14. However, recent advances in global flood hazard modelling20 and population modelling21,22 have made it possible to produce high-resolution global flood risk maps for all countries, including those with limited data and modelling capacity. These maps are increasingly used to support disaster risk management, prediction and response by national governments and international organisations15.

Yet these global flood risk maps do not adequately account for social vulnerability14, defined broadly as susceptibility to harm from exposure to a hazard event stemming from social factors (e.g. poverty or social status)23. Instead, data from hydrological models are combined with gridded population or income and asset (e.g. buildings) data to estimate the amount of people, income or assets likely to be exposed to future flood events24. The outputs are more accurately characterised as population or wealth exposure maps than risk maps, as they do not incorporate information about the coping capacity of affected populations. As a result, the places that appear to have the highest risk are either the most densely populated areas or those with a high concentration of income or assets (or both). In short, global flood risk mapping approaches generally ignore social vulnerability and emphasise economic risk, rather than human well-being.

Some recent global studies have attempted to incorporate measures of social vulnerability, such as indicators of poverty1 or socio-economic resilience2, but they do so at national scale or for large subnational units (e.g. states, provinces or districts). For example, a recent study estimating the number of low income people exposed to flood hazards globally assumes uniform income across large subnational units1. Another, which provides a nuanced global analysis of how social vulnerability and resilience intersect with natural hazards to shape risk, only offers national-level estimates2. Yet decision makers within local and national governments and international organisations require much higher resolution risk maps to plan for, predict and respond to local flood hazards. The current lack of high-resolution global flood risk maps that account for social vulnerability is therefore an important limitation in current approaches to flood risk analyses that rely on global models.

Here we propose a means of integrating social vulnerability into global fluvial flood risk mapping at 90 m resolution by combining a measure of ‘expected population exposure’ (EPE) with gridded data reflecting relative deprivation (i.e. poverty), which we use as a proxy measure for social vulnerability in the absence of more detailed and globally available social data. EPE was calculated using annual exceedance probability (AEP) data from Version 2 of the University of Bristol/Fathom Global Flood Model and gridded population data from WorldPop (constrained). We use two alternative sources of data as proxies for social vulnerability: gridded gross domestic product (GDP) per capita transformed into a measure of relative deprivation at cell level, and an index of multidimensional relative deprivation (see ‘Methods’). The resulting vulnerability-adjusted risk index (VARI Flood) reflects both the estimated extent of exposure to fluvial flood hazards and the relative vulnerability of potentially affected populations. It can be used to evaluate relative risk at either the global scale (e.g. between countries) or the local scale (e.g. between districts or even neighbourhoods within a district) and can alter how we understand the geography of risk by highlighting areas that are both densely populated and highly vulnerable. Many places that would be considered highly vulnerable due to high population density or high income are found to be relatively less risky when relative vulnerability is considered. The method is particularly useful in national contexts where decision makers faced with scarce resources need to prioritise areas for investment in adaptation measures or response in the aftermath of a hazard event. VARI Flood estimates based on country-standardised measures of relative vulnerability help to reveal ‘hotspots’ of extreme risk for local policy targeting purposes. This approach may therefore serve as a useful complement to traditional population or asset-based measures of risk where decision makers are concerned with mitigating potential well-being losses.

Results
Our sample is comprised of the 175 countries for which gridded GDP and poverty data are available, collectively containing 98.6% of the global population. In Fig. 1, we show results using both the income-based proxy for social vulnerability (Fig. 1b) and the multidimensional relative deprivation proxy (Fig. 1c). Thereafter we only report results using the income-based proxy given its much greater geographic coverage: globally, the poverty data contain only 11% of the cells contained in the GDP data. In Supplementary Information Fig. S2, we provide evidence that GDP per capita is a reasonable substitute for multidimensional relative deprivation and preferable due to much greater geographic coverage. Full technical details for the main results presented here are provided in ‘Methods’.

Global and regional flood risk
In our sample, approximately 2 billion people are estimated to be exposed to a fluvial flood hazard event using a 10 cm depth threshold. However, to identify the most vulnerable areas, we calculate and compare the populations in the top two quintiles of risk using an exposure-only approach and our VARI Flood method. The income-based relative deprivation measure used for the analyses presented in Figs. 1b and 2 are globally standardised—i.e. reflect quintiles of the global income distribution—to facilitate cross-national comparison.

Using this approach, we estimate that 1.23 billion people live in areas in the top two quintiles of EPE, whereas 866 million people live in areas classified in the top two quintiles of our VARI using the income-based relative deprivation measure—our proxy for social vulnerability. Using the alternative multidimensional relative deprivation index yields lower numbers (867 million and 373 million, respectively) due to limitations in geographic coverage. The differences in these estimates are clear in Fig. 1a and b, which report population counts and shares at admin 2 level for all countries in our sample. The admin 2 unit refers to the second geographic tier of political subdivision within countries, just below the admin 1 units of provinces or states. Admin 2 units are generally counties (in the USA), districts, or other local authority areas and are taken from Fieldmaps (Fieldmaps.io). Visual comparison shows that both the magnitude and the geography of risk changes when social vulnerability is factored into the risk assessment. At the global level, many populous areas are classified as high risk when EPE is the sole criterion but register as low risk when vulnerability is considered. Notable examples include large areas of Australia, Europe, North America, and Russia. At this global scale, the highest risk admin 2-unit changes in 75 (43%) of the countries in our sample when we consider the share of the population in VARI Flood categories 4 and 5 versus EPE categories 4 and 5.

Figure 2 illustrates the differences in the global and regional distribution of risk according to each metric. As Fig. 2a shows for the global sample, an exposure-based approach (i.e. not accounting for social vulnerability) shows a monotonic distribution of risk with over half of the population living in areas classified as high (quintile 4) or very high (quintile 5) risk. By contrast, the VARI Flood estimates show a more normal distribution with most people in areas in the lower three risk quintiles. Figure 2b presents the same comparison broken down by major world region. This shift is a result of the weight placed on socially vulnerable areas (as proxied by low areal GDP). It is important to stress that these estimates use globally standardised population and vulnerability quintiles to reflect relative risk between admin 2 units globally. This information is useful for organisations seeking to compare need across major world regions or countries.

Country-level flood risk maps
Yet the primary use case for the VARI Flood method is within-country comparison to support national or local decision makers. For this application, exposure and vulnerability quintiles are standardised against country specific population and income distributions. As with the globally standardised approach, incorporating country standardised relative deprivation measures alters our understanding of the geography of risk: the highest risk admin 2-unit changes in 73 (42%) of countries in our sample. A global map of country standardised EPE at admin 2 scale is available for replication in other country contexts (see ‘Data availability’).

To illustrate, Fig. 3 shows national and subnational results for Nigeria, Pakistan, and Vietnam. These country-level results confirm the global trend: the number and share of people in the highest risk category (5) falls and the geography of risk changes. Comparing the geographic distribution of risk between these approaches, the exposure-only estimates in Nigeria show high and geographically extensive concentrations along the Niger river basin and delta, and in the northern regions—the most densely populated areas of the country. Once social vulnerability is accounted for the broad geographic pattern remains, but there is a reduction of relative risk in the south and increased concentration in the far northeast of the country. In Pakistan the exposure-only and VARI Flood estimates are similar, with a slight increase in the number and share of populations at high risk in southern regions. In Vietnam, both estimates suggest high levels of risk across the country, but the vulnerability-adjusted estimates throw into relief hotspots of particularly high risk (both in absolute and relative terms) in parts of the Mekong River Delta and Central Coast regions.

Figure 4 drills down further to examine differences at the level of individual human settlements: Amukpe on a tributary of the Benin River in Nigeria, Hyderabad along the Indus River in Pakistan, and Kon Tum on the Dak Bla river in Vietnam. As with both the global and national maps, we observe a noticeable shift in the geography of risk once social vulnerability is factored into our estimates.

These settlement-level risk maps also highlight key limitations of the vulnerability data: it is much lower resolution than the gridded flood model output and population data. While inundation and population can be mapped at high spatial resolution globally (∼90 m), GDP data are only available at 1 km2. This is evident in the clear ‘tiling’ effects, with large squares assigned identical vulnerability values. Although we still capture some variation in risk at the 90 m scale, it may not fully reflect the true variation in risk at this scale given the coarseness of the income data.

Methods
Measuring social vulnerability with GDP and multidimensional poverty data
Social vulnerability is a multifaceted phenomenon, but can be broadly defined as susceptibility to harm from a combination of exposure to stressors and limited capacity to adapt23. There are many factors that affect the vulnerability of individuals, households and communities to natural hazards and these can vary substantially across contexts23,32. For example, poverty, age, gender, race and ethnicity, government capacity, and access to emergency services can all influence the extent to which exposure to a hazard event translates into harm, and the extent of harm. Currently, there is no global geospatial dataset that captures the complexity and local nuances of social vulnerability14, nor variation in the importance of these factors within national and subnational contexts. However, measures of income and poverty are the most consistent predictors of coping capacity over time and space29,33. Simply put, people with lower incomes tend to be more susceptible to harm from stressors than people on higher incomes everywhere in the world. Given the absence of any alternative globally standardised vulnerability data, measures of poverty are the best proxy.

Poverty—or ‘deprivation’ to use the formal terminology—can be measured in both absolute and relative terms34. Absolute deprivation refers to lack of basic needs, such as food, safe drinking water, shelter, etc. This is often measured by using individual or household income as a proxy for the ability to satisfy these basic needs35. For example, a common metric for global comparisons is the World Bank’s ‘$1-a-day’ measure1. However, in recognition of the complex causes of deprivation, multidimensional indices are also common, incorporating measure of health, education, assets, housing conditions, personal security and other ‘goods’35.

These approaches are also applied to measure relative deprivation. Relative deprivation incorporates social context by calibrating measures across populations rather than applying fixed thresholds. For example, one of the key measures of poverty used in the European Union defines a person as relatively deprived if their income is below 60% of national median disposable equivalised income35. Multidimensional approaches can include relative health, education, housing, or asset indicators. Relative deprivation indices are particularly useful for policymakers seeking to make decisions about the allocation of scarce resources by identifying people, households, or areas of least and greatest need.

Here we adopt a relative deprivation approach to mapping poverty. The use of relative deprivation rather than absolute deprivation is motivated by the primary use-case for flood risk maps, which is to inform local, national, and international agencies’ interventions to help communities adapt and mitigate the consequences of flood hazard events. These actors always operate within a context of scarce resources and must therefore prioritise. At the global level, comparisons can be made across countries by using a globally standardised poverty index that ranks areas based upon where they fall in the global distribution of income, as presented in Figs. 1 and 2 above.

The relative deprivation approach adds greater value at the local scale because globally standardised measures of poverty are not well-suited to local prioritisation. For example, decision makers with scarce resources in Nigeria seeking to mitigate the harm from future flood hazard events will be concerned with identifying the most at-risk areas within their jurisdiction. According to the World Bank, Nigeria is a LMIC. Applying the global LMIC Poverty Line of $3.56 (2017 PPP) per day per capita, over 63% of Nigeria’s population lives in poverty36—a figure much too large to facilitate meaningful policy targeting. Conversely, US policymakers at county, state, and federal levels still need measures to identify the most vulnerable places within their jurisdictions, even though less than 1% of the US population lives below the international poverty line. The need for measures that facilitate prioritisation and policy targeting underpins the widespread use by national policy makers of country-specific indicators of relative deprivation2,35. Within the context of flood risk assessment, measures of relative deprivation can be useful in highlighting areas of relative social vulnerability and hence elevated risk at various spatial scales.

However, there are currently few global gridded datasets that can be used to measure relative deprivation at high spatial resolution. Chi et al. provide global gridded estimates of relative poverty and wealth at 2.4 km resolution, which is much coarser than existing population data and flood model outputs37. To achieve the highest possible resolution and greatest coverage we therefore use two alternative sources. Our preferred data are from Chen et al.38 who provide gridded GDP at 1 km2 resolution globally for 175 countries for the years 1992-2019. These areal estimates of income were converted to per capita estimates by dividing cell GDP by cell population from WorldPop (see below).

For the global results presented in Figs. 1 and 2, we divided cell-level GDP per capita estimates into quintiles using the global income distribution and classified those in the bottom two quintiles as relatively poor and hence relatively vulnerable. For the country-specific results presented in Figs. 3 and 4, we divided cell-level GDP per capita into quintiles using country specific income distributions.

Given the multidimensional nature of poverty and vulnerability, which cannot be fully captured by income data, we also conducted analysis with the Global Gridded Relative Deprivation Index from the Center for International Earth Science Observation at Columbia University39. These data are also available globally at 1 km2 resolution but offer less comprehensive geographic coverage, containing just 11% of the number of cells available in the GDP data. As demonstrated in Supplementary Information Fig. S2, there is a close cell-level correspondence between our gridded GDP per capita measure and this multidimensional measure. We therefore report results from the GDP-based measure of relative deprivation in the main body of the paper.

Annual exceedance probability (AEP)
Fluvial flood hazard maps from Version 2 of the University of Bristol/Fathom Global Flood Model (GFM)20 are used in this study. This version uses the same modelling framework as described in Sampson et al., but with improved elevation data from MERIT DEM40, a more accurate river network from MERIT-Hydro41 and an updated method to estimate river conveyance capacity42. Model boundary conditions (i.e. how much water is in the river) is estimated using a global regionalised flood frequency analysis as described by43. All catchments with an upstream area greater than 50 km2 are simulated. Flood depth at 3 arc second spacing (~90 m at the equator) is provided for 10 return periods (5, 10, 20, 50, 75, 100, 200, 250, 500 and 1000). Flood return periods are converted into AEP by first creating a binary flood map where a flood depth > 10 cm is considered flooded, then assigning the flooded pixels the AEP value (i.e. inverse of the flood return period in question). For each pixel, the maximum AEP value (i.e. most frequent flood hazard probability) from the stack of flood hazard probabilities is taken, thus creating a single map of flood hazard probabilities.

The choice of a 10 cm threshold for flooding was chosen based on prior literature25,26. However, this threshold may not be relevant in contexts where people have adapted to regular events of this magnitude27. This low threshold increases uncertainty in our risk estimates as the negative impacts of inundation events becomes increasingly certain as depth increases44. In Supplementary Information Figs. S3–S6, we present sensitivity analysis in our three case study countries using alternative depths of 50 and 100 cm. The higher the threshold, the lower the number of people affected. However, the key conclusion that the distribution of risk changes when vulnerability is accounted for is confirmed. In short, varying our assumptions about the nature of flood hazards does not change our understanding of the nature and geography of risk as much as incorporating a measure of social vulnerability into flood risk assessments.

Gridded population data
Population information is provided by the building constrained version of WorldPop. This version of WorldPop distributes census information to building locations derived from satellite data using a random forest method and various spatial covariates45,46. This data provides population counts at 3-arc second grid spacing for 2020, with estimates adjusted to match United Nations (UN) population estimates.

It is important to note that we chose a ‘constrained’ population dataset, rather than an ‘unconstrained’ version (such as WorldPop Unconstrained, LandScan or GPWv4). Unconstrained population datasets differ in that they do not consider building footprints and thus distribute population to all habitable pixels rather than where people reside. This has important implications when considering flood risk as unconstrained population datasets tend to distribute people across uninhabited land (e.g. frequently flooded floodplains)45,47, whereas in reality people (often) live on the margins of floodplains, out of reach of all but the most extreme floods27. A result of this is that unconstrained population datasets overestimate flood exposure26,48.

Spatial matching of GDP, AEP and population data
The AEP and population data have similar spatial resolutions, 3 arc-seconds at the equator, which is approximately 90 m using WGS1984 Pseudo-Mercator projection system. The spatial resolution of the GDP data, however, is 30 arc-seconds at the equator, approximately 1 km. It is important that the spatial resolutions of the three datasets are consistent prior to combining them to construct VARI Flood. We therefore up-sampled the GDP data for each country to spatially match the resolution of the AEP and population datasets using a ‘nearest neighbour’ technique.

Constructing a VARI Flood
We combine gridded AEP, population data and GDP per capita data to produce a gridded vulnerability-adjusted risk score for all cells with an AEP > 0. This is done in three stages. First, the AEP for every cell is multiplied by the population of the cell to generate a measure of expected population exposure (EPE) across the full range of return periods. Compared to the traditional approach of using a fixed and arbitrary return period threshold such as a 1-in-100-year flood event1,3,49, our approach accounts for the fact that hazard events exist on a spectrum of probabilities that cannot be captured by modelling a single probability of flood event (e.g. 1-in-100-year flood event). Small, relatively frequent flooding (<1-in-50-year flood) can result in severe damage and fatalities, such as 2011 Thailand Floods50. Recent studies at the sub-national level have found that flood risk inequality is especially prevalent at frequent flood probabilities30. On the other end of the spectrum, the likelihood of extreme flood events (>1 in 100) is rising5,51. For example, flood risk from notable recent events such as the July 2021 German floods52,53 and 2017 Hurricane Harvey in the USA54 would be missed from the typical flood risk assessment using the 1-in-100-year flood. To represent a full range of probabilities, we simulate 10 different flood return periods (5, 10, 20, 50, 75, 100, 200, 250, 500, 1000) of fluvial flooding at 90-metre resolution on rivers with catchment areas above 50 km2 and integrate these into a measure of AEP, which is the inverse of return period. This is combined with gridded population data to produce our measure of EPE. The VARI Flood scoring system can be used to evaluate relative risk at multiple scales. For global applications, population and poverty quintiles are drawn from the global distribution of these variables; at the country level the input quintiles are drawn from the country-specific distributions.

In the second step, the EPE and normalised GDP per capita estimates for all cells are divided into quintiles using the global distribution of these values (for Figs. 1 and 2) and country-specific distributions (for Figs. 3 and 4). We use the inverse of GDP per capita quintiles to reflect social vulnerability, such that the first quintile reflects the richest 20%, and the fifth quintile reflects the poorest 20%. Third, we multiply EPE and vulnerability quintiles, take the square root and round up or down to the nearest integer to get a final risk score (Eq. 1).

VARIFloodScore = \sqrt{EPEquintile * Vulnerabilityquintile} (1)

Values range from 1 to 5, with 1 representing the lowest level of risk (low population and low vulnerability) and 5 representing the highest level of risk (high population and high vulnerability). Figure 5 illustrates how the index is constructed.

This approach differs from past studies by integrating poverty and exposure information to generate a simpler risk scoring system. For example, if population exposure at a single return period were the only factor in evaluating the risk of a location (standard practice), the risk index would be highly correlated with population density in places predicted to flood (i.e. more people=more risk). If GDP were used instead, risk would be highly correlated with income (i.e. more income=more risk). The VARI Flood scoring system offers considerably more nuance by factoring in (a) the full spectrum of flood risk (up to 1-in-1000-year probability), (b) population exposure, and (c) relative vulnerability proxied by measures of poverty.

As Fig. 1 makes clear, factoring relative deprivation into risk calculations in this way can substantially affect our understanding of the distribution of flood risk within a population. For example, a traditional approach to risk mapping would assign the highest score (5) to locations in the highest exposure quintile, yet it is possible for such a location to be assigned a VARI Flood score of just 2 if it falls within the lowest vulnerability quintile. Conversely, it is possible for a location in the lowest exposure quintile to be assigned a VARI Flood score of 2 if it falls within the highest vulnerability quintile. A VARI Flood score of 5 (the highest level of risk) is only assigned to places that fall within the top quintile of both exposure and vulnerability.

At the global level, the index can be calibrated to assess relative risk across countries and regions by taking EPE and GDP per capita quintiles from the global distribution (Figs. 1 and 2). However, the same approach can by applied to assess relative risk within countries by using country-specific population and vulnerability quintiles (Figs. 3 and 4). When constructed in this way, the index can be used to support decision makers working within their national context.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "I first would like to congratulate the authors for having conducted an extensive analytical work that\ncombines the more traditional flood risk analysis (i.e., solely using population and/or asset exposure)\nwith social vulnerability considerations, at global level. The topic of and the key message from the\nresearch are timely and relevant. Further, the proposed VARI (vulnerability-adjusted risk index)\napproach is elegant – it is simple, easy to understand, and at the same time have deep meaning and\nimplications.\nDespite the innovative work, I still have some reservations for getting this work published due to a\nnumber of reasons:\n1. Inconsistencies between the main message and the proposed method. To my understanding, the\nauthors’ main message is how the geography of flood risk would change at national level when we\naccount for social vulnerability. However, I find the proposed approach of using and quantizing globally\ngridded GDP can be flawed because of four reasons.\n1.1. First, as the authors acknowledge that social vulnerability is more than just income, why not\nsimply use local statistics that often contain much richer set of variables that can be used to picture\nsocial vulnerability? Bangladesh has district (adm-2) statistics reports that are publicly available.\nIndonesia has statistics up to adm-4 level (SMERU Poverty Map). I know that this will not be global –\nhowever, with a number of country case studies, the authors will be able to convey the same message\nas exemplified in the abstract but with a much more robust methodology. The author would still be\nable to produce something like Figure 4 that shows how the geography of flood risks would change.\n1.2. Second, if the goal is to quantify globally how the number of people in the top two quintiles would\nchange, then the approach of classifying GDP at country level to measure social vulnerability is clearly\nflawed. This simply assumes that the bottom 20% in the USA is just as vulnerable as the bottom 20%\nin Somalia, which is clearly wrong. Having said that, I don’t think that the author should emphasize\nthe global numbers in the paper. For such global numbers, the approach of Rentschler et al 2022 (first\nreference in the paper), for instance, is better in the sense that the authors used an internationally\nstandardized measure of social vulnerability (i.e., poverty line).\n1.3. Third, if the goal is to look at how the geography of flood risk would change at a more local level,\nthen as the author mentioned on line 206-209 and then 235-242, the proposed approach of using\nglobally gridded GDP is not at all justifiable. This is especially true in urban settings where the poor\nand the vulnerable often live literally side by side with the rich, in which case, not only the 1km grid is\ninsufficient, but also the 90m flood map is too coarse. Having said this, as the author mentioned that\nurbanization is one key driver of global flood risk and as urban poor becomes a growing concern, I'm\nwondering on the applicability of the overall approach proposed by the author for capturing social\nvulnerability in global scale flood risk assessment (as more and more poor and vulnerable people live\nin urban settings).\n1.4. Fourth, I assume that there might be correlations between the globally gridded GDP with\npopulation density – as more population tends to result in higher GDP. Except if the population in all\ngrids in a country is the same (which is unrealistic), GDP is a very weak proxy of social vulnerability.\nLow GDP can simply be a factor of less densely populated areas. GDP per capita would be a somewhat\nbetter proxy for social vulnerability, as we then have normalized GDP with the population headcount of\nthe grid.\n2. The authors need to justify the use of 10cm as a flood depth threshold for population exposure,\nwhich seems to be arbitrary for now. I would advise the authors to perform a quick sensitivity analysis\nwith varying the flood depth thresholds.\n3. I'm not too convinced with the robustness of the current operationalization of VARI. For example, the\nrounded down squared root implies that EPE=3 and vulnerability=3 would have the same VARI as\nEPE=5 and vulnerability=3 or EPE=3 and vulnerability=5. In reality, government would definitely\nprioritize EPE=5 and vulnerability=3 rather than EPE=3 and vulnerability=3. Having said that, I would\nsuggest authors to also perform a sensitivity analysis on the operationalization of VARI. One scenario\ncould be not rounding down the squared root value, but rounding up/down to the nearest integer\ninstead (more than 0.5 -> round up, less than 0.5 -> round down). Another way is to treat social\nvulnerability as a multiplication factor of flood risk, so that VARI = EPE * (1+Vulnerability) where\nVulnerability is between 0-1.", "reviewer_2": "• What are the noteworthy results?\no When accounting for social vulnerability in terms of GDP, a large portion of the population exposed\nto high risk appears to have a much lower risk. This analysis identifies the most at-risk individuals\nwith lower coping capacity.\n• Will the work be of significance to the field and related fields? How does it compare to the\nestablished literature? If the work is not original, please provide relevant references.\no The global scale analysis is a significant contribution.\n• Does the work support the conclusions and claims, or is additional evidence needed?\no The claim that GPD is a drastically different measure than previous methods\nneeds stronger justification. Also, see next response.\n• Are there any flaws in the data analysis, interpretation and conclusions? Do these prohibit\npublication or require revision?\no There are a couple of methodological concerns that in my opinion require revision and, at a\nminimum, some sensitivity analysis. First, this study only considers fluvial flooding. Inclusion of pluvial\nand coastal flooding, as well as the compound effects of all three types flooding would likely result in\nsubstantially different patterns in VARI. Further, the VARI is only based on the most frequent flood\nevent that inundates a particular location. This assumption, like the previous assumption, may\nsignificantly underestimate exposure and vulnerability. For example, imagine that two pixels with the\nsame population and GDP characteristics are both inundated 10 cm deep by the 50% annual\nexceedence probability event; however, the 1% AEP event inundates one of the pixels 20 cm deep and\nthe other 200 cm deep due to differences in hydro-meteorological processes and topographic factors.\nBased on the methods described in the manuscript, the VARI would be based solely on the 50% event\nand they would have the same VARI value despite marked differences in the effects of less frequent\nevents. A more comprehensive estimate of VARI would be based on integration of flood intensity\nacross all the events that inundate a particular pixel. I recommend that, at a minimum, the authors\nperform some sensitivity analysis to examine how much the results change if all inundation events and\ntheir frequencies are incorporated in calculating VARI. I also recommend, at a minimum, more\ndiscussion of the implications of only considering fluvial flooding.\n• Is the methodology sound? Does the work meet the expected standards in your field?\no The general approach of multiplying an integrated exposure value times a social vulnerability score\nto assess risk is standard. See concerns above.\n• Is there enough detail provided in the methods for the work to be reproduced?\no Yes.\nGeneral comments:\nassessments: Economic assets and income are used interchangeably when the authors describe how\nprevious work has assessed flood risk (lines 112, 223). GDP is used as the only indicator to measure\nsocial vulnerability. GDP seems like a similar economic measure, so I think an explanation of what\nGDP is, how it is measured, and what makes it different from income or other economic asset\nmeasures is needed.\nresearch:\nThe authors provided justification for including only GDP as the sole measure of social vulnerability\ndue to data limitations and previous work that state measures of income and poverty are consistent\nmeasures of coping capacity. The difficulty of capturing the variation in significance of vulnerability\nfactors depending on context in lines 279-281 is an important part of the justification of only using\nGDP. This paper demonstrates the challenges of developing a generalizable set of social vulnerability\nindicators which fits into the greater body of social vulnerability to natural hazards literature. However,\nthe authors don’t express how this challenge fits into the greater body of literature.\nOne of the results was that the risk map loses utility at the local scale because the coarse resolution\nof the data does not capture local nuances in social vulnerability. This is a commonly seen issue in\nsocial vulnerability assessments, and it could be beneficial to mention other work that had the same\nfindings to demonstrate how this work fits into the larger body of social vulnerability work.\ncomparison: It was made clear that the output of the study is an analysis of relative risk within\ncountries, but it was not clear why the relative risk approach was taken. A comparable cross-country\nanalysis seems possible with the available data, and it would seems to provide revealing comparison\nof risk levels across countries. Comparing the results from both approaches could also provide\nvaluable insight.\ntransparently discuss key uncertainties their implications for their findings. For example, where is\nuncertainty in relative VARI expected to be greatest? Perhaps in relatively flat areas affected by\ncompound flooding with high spatial heterogeneity in social characteristics?\nLine specific comments:\nLine 9 –10: Specify that “these estimates” refers to global flood risk estimates as there are many\nstudies that incorporate social vulnerability in flood risk estimates at national and subnational scales.\nLine 23: Add “a” before “1-in-100 year event.”\nLine 29: Consider replacing “against this backdrop” with “Within this context”\nLine 30: Consider splitting this sentence into two – Add period after “occur” and replace “but” with\n“This”\nLine 34: Same comment as line 9-10; specify global flood mapping approaches\nLine 35: the word \"data\" is switching between plural and singular\nLine 36: List full phrase the first time an abbreviation is used for GDP\nLine 40: add “economic” before “assets”\nLine 56: suggest not assigning an approximate return period to Harvey, probably much less than 1000\nyr\nLines 59-60: Probability expressions are easier for non-experts to interpret than what? Research\nshows that probability of flooding is also poorly understood by the public\nLine 67: Consider replacing “and these” with “that” to make sentence more clear\nLine 70: Consider explicitly stating that “relative risk within countries” is not a cross county\ncomparison of flood risk. This is mentioned in line 165, but I think having it explicitly stated earlier in\nthe paper will make the rest of the paper clearer to the reader.\nLines 99 –100: Mention implicit assumption of previous mapping exercises. Are there citations for\nthese studies?\nLine 102: The phrase “perceived” flood risk is used several times in the manuscript, but it is unclear\nwhat this is referring to. Perhaps “understood flood risk” is a different option? (Also in lines 110, 171,\n179, 193, 221)\nLine 112: Insert “be” in between “to” and “relatively.”\nLine 124: Insert period after “methods”\nLine 133: The phrase “admin 2 level” is used several times, and it is unclear what this is referring to.\n(Also in lines 138)\nLine 134: It is clear how the magnitude of risk changes when considering social vulnerability, but it is\nless clear what the authors mean by the geography of risk changing. Consider elaborating further.\nLine 143: Why does much of India appear to have the same VARI, why does VARI appear to be\ninsensitive to within county differences across a billion people? Is this a concern?\nLines 151 – 152: Looking at Figure 3a, this approach is able to identify people who have high\nexposure, but low coping capacity from those who have high exposure and high coping capacity.\nConsider elaborating on this finding and its application to flood management in the discussion.\nFigure 2: Unclear what unit “Population” is measured in\nFigure 3a: Consider including labels of quintiles with the level of risk\nFigure 3b: the authors repeatedly underscore that this approach is for relative comparisons within\ncountries, but this table lumps countries together. Why is it okay to do this?\nLine 216: The in-text citation is in a different format than the others and not included in the reference\nlist.\nLine 236: List full phrase the first time an abbreviation is used for LMIC\nLine 237: List full phrase the first time an abbreviation is used for UN\nLine 239: Delete “a” before “particularly”\nLine 251: The reference being referred to is missing at the end of sentence.\nLine 251: What is the reason that only catchments with an upstream area greater than 50 km2 are\nsimulated?\nLine 252: Insert space after “simulated.”\nLine 254: Why were depths less than 10 cm not considered flooded?\nLine 267: Delete dash before rather\nLine 267: Replace “why” with “where”\nLines 282 – 283: Consider changing word choice for “poor” and “rich”\nLine 290: List full phrase the first time an abbreviation is used for PPP; List full phrase the first time an\nabbreviation is used for HDI\nLine 297: Insert “that” between “important” and “the”\nLines 309 – 310: Why was the square root taken?", "reviewer_3": "The authors tackle an important question, considering flood exposure in the context of people’s social\nvulnerability, i.e. coping capacity. The authors correctly identify a gap in the literature – some existing\nstudies have considered flood exposure along with poverty (but only focused on 1-in-100 year floods),\nwhile others have conducted flood exposure assessments without accounting for socio-economic\ncharacteristics at all.\nWhile the study has several strengths (e.g. it is well written, concise, and the analysis of 10 different\nreturn periods is a useful extension of past studies), unfortunately it does not quite meet the\nstandards of this journal.\nThe study relies on a somewhat outdated gridded GDP dataset (Kummu et al 2018). There are newer\ndatasets (e.g. Chen et al 2022) which claim higher accuracy, which the authors do not mention. The\nchoice of GDP dataset is crucial for the intended purpose of this study. For instance, the Kummu et al\ndataset does reasonably well in capturing areas with dense urbanization and economic activity – yet it\noften fails to detect poor and more sparsely populated areas, which is problematic for a study that\ntries to detect poor (flood exposed) areas. The manuscript offers no critical analysis or discussion of\nthe adequacy of the Kummu et al dataset – especially a robustness check would be needed to check\nhow the Kummu dataset performs at the low end of the income distribution and capturing poverty\n(since this was not an objective of the Kummu et al paper).\nThe study simply assumes that income is a proxy for vulnerability, but in reality a wide range of\nfactors (apart from income) determine people’s vulnerability to disasters. For instance, social\ngroupings, ethnicity, gender, religion, rural vs urban location, access to credit, the quality of\ninfrastructure, and the quality of government support programs (e.g. social insurance schemes) –\nthese are all crucial to reducing vulnerability, yet none of them are reflected in the analysis. While the\nauthors acknowledge that they do not account for these factors, this does not resolve the\nshortcoming. It would be more accurate to describe the analysis in this study as “income-adjusted”\nrather than “vulnerability-adjusted”. Past studies – e.g. Hallegatte et al 2017, cited in the manuscript –\nhave actually developed a quantitative model to incorporate such diverse factors of vulnerability with\nglobal coverage. In short, the study is not robustly capturing vulnerability, which is its key proposition.\nThe study appears to have a significant technical flaw in defining poverty – unfortunately this alone\ndisqualifies it. It’s objective is to identify areas with high vulnerability, proxied by low incomes (i.e.\nhigh poverty). It assumes that the bottom 40 percent (bottom 2 quintiles) of the income distribution\nin each country are vulnerable. This forces the arbitrary result that 40% of people in every country are\nvulnerable because of their social status. This makes little sense in high-income countries where even\nthose in the bottom 40% have relatively high incomes by international standards (i.e. are not\nvulnerable according to the author’s assumption), as well as having access to high quality social and\nphysical infrastructure. But the assumption also doesn’t make sense for the lowest income countries,\nwhere much more than 40% of the population live in poverty (e.g. in Malawi, Niger, DRC, Madagascar,\nSouth Sudan, Burundi, and Mozambique respectively around 80-90% of the population live in poverty\naccording to the World Development Indicators) – but only 40% of them are classified as vulnerable in\nthis study because poverty is defined based on income quintiles rather than levels. As a consequence,\nthe artificially imposed income quintile definition in this study massively overestimates vulnerability in\nhigh-income countries, and massively underestimates vulnerability in poor countries – i.e. the\nopposite of the study’s stated motivation. This is illustrated by Figure 2b, which shows virtually zero\nflood-vulnerability hotspots in Africa (a continent with major flood and poverty challenges).\nMinor points:\n- The study claims that it has a spatial resolution of 90 m, but the effective resolution of analysis is\n1km (which is the resolution of the Kummu et al 2018 data used in this study). The color blocks in\nFigure 5, second column, show that the GDP data does not have 90m resolution.\n- GDP is not a measure of wealth or assets (which are stock measures), but of a measure of economic\nactivity (which is a flow measure). This is misstated throughout the manuscript.\nChen, J., Gao, M., Cheng, S. et al. Global 1km×1km gridded revised real gross domestic product and\nelectricity consumption during 1992–2019 based on calibrated nighttime light data. Sci Data 9, 202\n(2022). https://doi.org/10.1038/s41597-022-01322-5"}, "author_response": "Response to reviewers\nReviewer 1\nI first would like to congratulate the authors for having conducted an extensive\nanalytical work that combines the more traditional flood risk analysis (i.e., solely\nusing population and/or asset exposure) with social vulnerability considerations, at\nglobal level. The topic of and the key message from the research are timely and\nrelevant. Further, the proposed VARI (vulnerability-adjusted risk index)\napproach is elegant – it is simple, easy to understand, and at the same\ntime have deep meaning and implications.\n(1) Inconsistencies between the main message and the proposed method.\nTo my understanding, the authors’ main message is how the geography of flood\nrisk would change at national level when we account for social vulnerability.\nHowever, I find the proposed approach of using and quantizing globally gridded\nGDP can be flawed because of four reasons.\n1.1. First, as the authors acknowledge that social vulnerability is more than just\nincome, why not simply use local statistics that often contain much\nricher set of variables that can be used to picture social vulnerability?\nBangladesh has district (adm-2) statistics reports that are publicly available.\nIndonesia has statistics up to adm-4 level (SMERU Poverty Map). I know that\nthis will not be global – however, with a number of country case studies, the\nauthors will be able to convey the same message as exemplified in the abstract\nbut with a much more robust methodology. The author would still be able to\nproduce something like Figure 4 that shows how the geography of flood risks\nwould change.\nThe reviewer is correct that for some national and local studies, richer data are\navailable. However, the primary aim is to generate consistent global estimates of\nflood risk at high spatial resolution, particularly in places that do not have\nany/reliable local statistics. A key application of global flood hazard models is risk\nassessment in places with significant geospatial data deficits, including many low-\nand middle-income countries 1–3. At present, these assessments do not factor in\nsocial vulnerability. Introducing a means of doing so at 90m resolution represents\na significant step forward in terms of accounting for social vulnerability in flood\nrisk assessments in data poor contexts. We have made this point much clearer in the\nAbstract and Introduction with additional references.\n1.2. Second, if the goal is to quantify globally how the number of people in\nthe top two quintiles would change, then the approach of classifying\nGDP at country level to measure social vulnerability is clearly flawed.\nThis simply assumes that the bottom 20% in the USA is just as vulnerable as the\nbottom 20% in Somalia, which is clearly wrong. Having said that, I don’t think\nthat the author should emphasize the global numbers in the paper. For such global\nnumbers, the approach of Rentschler et al 2022 (first reference in the paper), for\ninstance, is better in the sense that the authors used an internationally\nstandardized measure of social vulnerability (i.e., poverty line).\nOur primary goal was not to quantify the change in the top 2 quintiles globally and\nrecognize our presentation was confusing. We have removed it from the paper.\nHowever, given the feedback, we have now computed globally standardized\nestimates suitable for cross-country comparison and have added this new exhibit.\nThis analysis is now similar to Rentschler et al 2022, but instead of using a single\nabsolute poverty threshold estimated at subnational administrative unit level, we\nuse a new gridded relative poverty product to build up estimates based upon much\nfiner geographic resolution.\n1.3. Third, if the goal is to look at how the geography of flood risk would\nchange at a more local level, then as the author mentioned on line 206-209\nand then 235-242, the proposed approach of using globally gridded GDP\nis not at all justifiable. This is especially true in urban settings where the poor\nand the vulnerable often live literally side by side with the rich, in which case, not\nonly the 1km grid is insufficient, but also the 90m flood map is too\ncoarse. Having said this, as the author mentioned that urbanization is one key\ndriver of global flood risk and as urban poor becomes a growing concern, I'm\nwondering on the applicability of the overall approach proposed by the author for\ncapturing social vulnerability in global scale flood risk assessment (as more and\nmore poor and vulnerable people live in urban settings).\nSee reply above to 1.1. Our primary aim is to introduce social vulnerability into\nglobal flood risk maps, which are already used to inform local/regional decision-\nmaking in data sparse contexts for hazard preparedness and response. We agree\nthat the resolution is sub-optimal, but it is currently the highest possible resolution\nachievable for global flood risk assessment with publicly available data. Even\nwithin large cities, our approach offers improved spatial nuance over\nadministrative units in most contexts. We note in the conclusion that an important\ngoal for next generation global flood risk modelling is increasing the quality and\nresolution of social vulnerability maps.\n1.4. Fourth, I assume that there might be correlations between the globally\ngridded GDP with population density – as more population tends to result in\nhigher GDP. Except if the population in all grids in a country is the same (which is\nunrealistic), GDP is a very weak proxy of social vulnerability. Low GDP can simply\nbe a factor of less densely populated areas. GDP per capita would be a\nsomewhat better proxy for social vulnerability, as we then have normalized\nGDP with the population headcount of the grid.\nThis assumption is correct. Our analysis uses GDP per capita. We have made this\nclearer in the revised manuscript where we present GDP estimates.\n2. The authors need to justify the use of 10cm as a flood depth threshold for\npopulation exposure, which seems to be arbitrary for now. I would advise the\nauthors to perform a quick sensitivity analysis with varying the flood depth\nthresholds.\nThe 10cm threshold was based on prior literature4,5. In the UK at least this\nthreshold came about because it was similar to the errors in the best available\ntopography data and insurance companies regarded this as the typical front\ndoorstep height for residential buildings. However, the reviewer is correct in that\nit’s otherwise somewhat arbitrary because the depth at which flooding becomes a\nproblem is exceedingly variable and complex (this issue applies to essentially all\nflood risk studies from global to local). We agree that the most suitable way to\nreflect this issue is to consider the sensitivity of our results to the assumption,\ntherefore we have now incorporated estimates across three thresholds (10, 50 and\n100cm) as a sensitivity analysis in an appendix and a discussion explaining the\nchoice of the threshold for the main results presented in the paper.\n3. I'm not too convinced with the robustness of the current operationalization of\nVARI. For example, the rounded down squared root implies that EPE=3 and\nvulnerability=3 would have the same VARI as EPE=5 and vulnerability=3 or\nEPE=3 and vulnerability=5. In reality, government would definitely prioritize\nEPE=5 and vulnerability=3 rather than EPE=3 and vulnerability=3. Having said\nthat, I would suggest authors to also perform a sensitivity analysis on the\noperationalization of VARI. One scenario could be not rounding down the\nsquared root value, but rounding up/down to the nearest integer\ninstead (more than 0.5 -> round up, less than 0.5 -> round down).\nWe thank the reviewer for this comment. We have revised the rounding procedure\nto the nearest integer. We have also added discussion around the normative\nimplications of incorporating VARI Flood estimates into decision making processes\n(i.e. trade-offs between targeting areas of greatest exposure vs greatest\nvulnerability). We note that the new procedure changes the results, but not\ndrastically. Our headline findings regarding how this approach changes our\nunderstanding of the distribution and geography of risk remain unchanged. In\nsum, our method is sensitive to the issue highlighted, but not sufficiently so to alter\nour key conclusions, which we believe to be robust.\nReviewer 2\n“The global scale analysis is a significant contribution.”\n(1) The claim that GDP is a drastically different measure than previous methods\nneeds stronger justification. Also, see next response.\nWe do not believe we have made this claim in the paper. Our key contribution is\nusing the data in a novel way—essentially turning it upside down to draw\nattention to vulnerability as opposed to potentially exposed wealth.\n(2)There are a couple of methodological concerns that in my opinion require\nrevision and, at a minimum, some sensitivity analysis. First, this study only\nconsiders fluvial flooding. Inclusion of pluvial and coastal flooding, as well as the\ncompound effects of all three types flooding would likely result in substantially\ndifferent patterns in VARI.\nWe thank the reviewer for highlighting the importance of other forms of flooding.\nWe have noted this limitation in the revised manuscript, but we have not\nundertaken a full multi-hazard assessment at global scale in the revision for\nseveral reasons.\nFirst,, it is worth noting that our definition of a fluvial floodplain, extending to\nrivers with only 50K km2 of upstream area, captures rivers an order of magnitude\nsmaller than those of typical global flood modelling studies (e.g. those undertaken\nby JRC and University of Tokyo). It’s also worth noting that there is no clear\ndefinition of what constitutes pluvial and fluvial flooding and making comparisons\nbetween estimates from different models is complicated by different minimum\ncatchment sizes. True off floodplain pluvial flooding would be significantly more\nlocalised than the smallest rivers included in our fluvial domain, and critically\nwould be mostly too localised to be resolved in the latest global scale DEMs.\nTherefore, a “pluvial” flood model in this global scale context would cover only\nflooding from rivers smaller than 50k km2. Simulating very localised flooding in a\nglobal flood modelling context is difficult given the quality of global rainfall and\nelevation data and is also very difficult to validate as relevant observations are\nlimited. This results in very high sensitivity to threshold choice6. We therefore\nprefer to take an approach where we include in our domain the hazards we have\nbeen able to simulate and evaluate over providing complete coverage of all\nhazards (see for example discussion of pluvial and fluvial hazards in Hawker et al.\n(2023)7. This way we can also make a clear statement regarding the types of\nflooding we have included.\nWe would make a similar statement regarding coastal flooding. At the time of\nwriting there is no global hydrodynamic coastal hazard model. The existing\nmodels all take a much simpler bathtub type approach to simulating the\ninundation that will significantly overestimate hazard and cannot simulate\ncompounding effects. We are aware the upcoming generation of Fathom’s global\nflood model will include coastal flooding, this will obviously increase exposure at\nthe coasts, however a global compound hydrodynamic model for all flood types is\nnot currently available.\nClearly from a wider disaster risk reduction perspective it would be beneficial to\ninclude all types of flooding (coastal, groundwater, dam break) in our assessment,\nand indeed it would be worth considering other hazards such as earthquake, wind\nand fire. However, in this paper we were keen to investigate the novelty of\nconsidering social vulnerability rather than undertake a multi-hazard assessment\nthat would generate a number of additional research questions.\n(3)Further, the VARI is only based on the most frequent flood event that inundates\na particular location. This assumption, like the previous assumption, may\nsignificantly underestimate exposure and vulnerability. For example, imagine that\ntwo pixels with the same population and GDP characteristics are both\ninundated 10 cm deep by the 50% annual exceedence probability event; however,\nthe 1% AEP event inundates one of the pixels 20 cm deep and the other 200 cm\ndeep due to differences in hydro-meteorological processes and topographic\nfactors. Based on the methods described in the manuscript, the VARI would be\nbased solely on the 50% event and they would have the same VARI value despite\nmarked differences in the effects of less frequent events. A more comprehensive\nestimate of VARI would be based on integration of flood intensity across all the\nevents that inundate a particular pixel. I recommend that, at a minimum,\nthe authors perform some sensitivity analysis to examine how much\nthe results change if all inundation events and their frequencies are\nincorporated in calculating VARI.\nThe reviewer is correct that there are more sophisticated methods for\ncharacterising the hazard and exposure. An event set modelling approach where\n10,000 of years of synthetic events are simulated would allow us to properly\ncharacterize the exceedance probability. Previously we have implemented such\nmethods in the US and UK8. However, we are unaware of any such data set with\nglobal coverage that is in the public domain. We believe the reviewer is suggesting\na more limited solution based on integrating flood impacts across the multiple\nreturn periods to produce an approximation of annual expected impacts, an\nexample of this in the global flood risk context is provided by Ward et al 20134 and\nwe copy the methods figure form this paper below. This method is useful for\nestimating damage or loss, however it requires a relationship to be defined between\nflood magnitude (e.g. depth) and the impact (e.g. financial damage). Defining\nglobal depth damage relationships is difficult to do and most relationships are\nheavily biased towards data from developed nations. We do not believe it is possible\nat this time to derive a dept-impact relationship with reference to social\nvulnerability that would improve upon what we have done currently, which\nessentially assumes all flooding above a particular threshold is a problem.\n(4)I also recommend, at a minimum, more discussion of the implications of only\nconsidering fluvial flooding.\nSee response to previous reviewers question and new text in the main body of the\npaper.\n(5)Further explanation for how GDP is a different measure than income/economic\nassets in previous assessments: Economic assets and income are used\ninterchangeably when the authors describe how previous work has assessed flood\nrisk (lines 112, 223). GDP is used as the only indicator to measure social\nvulnerability. GDP seems like a similar economic measure, so I think an\nexplanation of what GDP is, how it is measured, and what makes it different from\nincome or other economic asset measures is needed.\nWe have now incorporated an alternative measure of multidimensional relative\ndeprivation in a new appendix and provide evidence that GDP per capita is a\nreasonable spatial proxy for this alternative measure (and by extension social\nvulnerability). We explain in the revised paper that GDP is a measure of income,\nwhich is widely used in poverty measurement around the world.\n(6)The authors provided justification for including only GDP as the sole measure of\nsocial vulnerability due to data limitations and previous work that state measures\nof income and poverty are consistent measures of coping capacity. The difficulty\nof capturing the variation in significance of vulnerability factors depending on\ncontext in lines 279-281 is an important part of the justification of only using\nGDP. This paper demonstrates the challenges of developing a generalizable set of\nsocial vulnerability indicators which fits into the greater body of social\nvulnerability to natural hazards literature. However, the authors don’t express\nhow this challenge fits into the greater body of literature.\nWe have now provided this context in the introduction with reference to recent\nliterature.\n(7)One of the results was that the risk map loses utility at the local scale because the\ncoarse resolution of the data does not capture local nuances in social\nvulnerability. This is a commonly seen issue in social vulnerability assessments,\nand it could be beneficial to mention other work that had the same findings to\ndemonstrate how this work fits into the larger body of social vulnerability work.\nWe have added a comment and key reference on this point in the Discussion.\n(8)Provide reasoning for assessing relative risk within countries instead of doing a\ncross-country comparison: It was made clear that the output of the study is an\nanalysis of relative risk within countries, but it was not clear why the\nrelative risk approach was taken. A comparable cross-country analysis\nseems possible with the available data, and it would seems to provide revealing\ncomparison of risk levels across countries. Comparing the results from both\napproaches could also provide valuable insight.\nWe have incorporated a new cross-country analysis with globally-standardized\npoverty estimates and significantly expanded our justification for also presenting\nthe country-standardized relative estimates in both the Introduction and Methods\nsections.\n(9)There is no mention of uncertainty and modeling error. At a minimum, the\nauthors should briefly and transparently discuss key uncertainties their\nimplications for their findings. For example, where is uncertainty in relative\nVARI expected to be greatest? Perhaps in relatively flat areas affected by\ncompound flooding with high spatial heterogeneity in social characteristics?\nThe reviewer makes a good point, and have added a brief discussion of\nuncertainties in the Discussion. Regarding the hazard model the largest error\nsources will be the discharge frequency relationship used in the model9,10 followed\nby the assumptions around the river channel convenance and the DEM. These\nerrors can cause serval meters of vertical error in the simulated water sources and\ntypical fits to remotely sensed imagery and local modelling (neither of which are\nperfect) are around 0.6 (Critical success index). However, although local errors can\nbe quite high these tend to average over larger domains and its typically\nrecommended to conduce the analysis at the pixel scale and then aggregate the\nfindings to reduce these errors. For example, Sampson et al., 201511 found that the\nglobal flood model error in flooded areas compared to UK government flood maps\nwas unbiased when aggregated to 1 km resolution.\nRegarding compound flooding, this is not one of the first order errors sources for\nthis type of model in our experience. The fluvial model will account for\ncompounding effects between river tributaries in the hydraulic calculations. We do\nnot simulate compound flooding from coastal/fluvial processes beyond considering\nthe average sea levels. This will underestimate the flood hazard close to the coast,\nhowever it’s worth noting that compounding extreme between coastal and fluvial\nprocesses are unusual. For example, for a tropical cyclone the storm surge will\ntypically pass before runoff from the land reaches the coast for most storm tracks.\nSo although a combination of extreme river flows and coastal surge might produce\nsignificant compound flooding the probability of this happening is low.\nLine specific comments:\nLine 9 –10: Specify that “these estimates” refers to global flood risk estimates as\nthere are many studies that incorporate social vulnerability in flood risk estimates at\nnational and subnational scales. Reworded to clarify.\nLine 23: Add “a” before “1-in-100 year event.” Done\nLine 29: Consider replacing “against this backdrop” with “Within this context” Done.\nLine 30: Consider splitting this sentence into two – Add period after “occur” and\nreplace “but” with “This” Done\nLine 34: Same comment as line 9-10; specify global flood mapping approaches Done.\nLine 35: the word \"data\" is switching between plural and singular Fixed.\nLine 36: List full phrase the first time an abbreviation is used for GDP Have\nsimplified here with “income”\nLine 40: add “economic” before “assets” Done\nLine 56: suggest not assigning an approximate return period to Harvey, probably\nmuch less than 1000 yr. Agreed and removed.\nLines 59-60: Probability expressions are easier for non-experts to interpret than\nwhat? Research shows that probability of flooding is also poorly understood by the\npublic We have removed this sentence.\nLine 67: Consider replacing “and these” with “that” to make sentence more clear . We\nhave reworded this sentence to make it clearer.\nLine 70: Consider explicitly stating that “relative risk within countries” is not a cross\ncounty comparison of flood risk. This is mentioned in line 165, but I think having it\nexplicitly stated earlier in the paper will make the rest of the paper clearer to the\nreader. The revised text and exhibits have resolved this issue.\nLines 99 –100: Mention implicit assumption of previous mapping exercises. Are\nthere citations for these studies? This has been removed in the course of revision.\nLine 102: The phrase “perceived” flood risk is used several times in the manuscript,\nbut it is unclear what this is referring to. Perhaps “understood flood risk” is a\ndifferent option? (Also in lines 110, 171, 179, 193, 221). Thanks for this suggestion.\nWe have changed throughout.\nLine 112: Insert “be” in between “to” and “relatively.” Done\nLine 124: Insert period after “methods” Done.\nLine 133: The phrase “admin 2 level” is used several times, and it is unclear what this\nis referring to. (Also in lines 138) This is explained in the revised text.\nLine 134: It is clear how the magnitude of risk changes when considering social\nvulnerability, but it is less clear what the authors mean by the geography of risk\nchanging. Consider elaborating further. This is illustrated in the country case\nstudies.\nLine 143: Why does much of India appear to have the same VARI, why does VARI\nappear to be insensitive to within county differences across a billion people? Is this a\nconcern? This figure was confusing to interpret as presented and has been removed\nand replaced with a globally standardised analysis.\nLines 151 – 152: Looking at Figure 3a, this approach is able to identify people who\nhave high exposure, but low coping capacity from those who have high exposure and\nhigh coping capacity. Consider elaborating on this finding and its application to flood\nmanagement in the discussion. This comment is unclear, but we hope it has been\naddressed in our extensive revisions.\nFigure 3b: the authors repeatedly underscore that this approach is for relative\ncomparisons within countries, but this table lumps countries together. Why is it okay\nto do this? We’ve replaced this map with new globally standardised estimates.\nLine 216: The in-text citation is in a different format than the others and not included\nin the reference list. Fixed.\nLine 236: List full phrase the first time an abbreviation is used for LMIC Done.\nLine 237: List full phrase the first time an abbreviation is used for UN Done.\nLine 239: Delete “a” before “particularly” Done.\nLine 251: What is the reason that only catchments with an upstream area greater\nthan 50 km2 are simulated? We need some minimum threshold. This was seen as a\nsmallest catchment that could be reliably simulated given the quality of the DEM.\nBelow this size the floodplains tend to be smaller than the cell resolution and would\nnot be resolved in the model. Note that this threshold is much smaller than any\nother global flood model.\nLine 252: Insert space after “simulated.” Done\nLine 254: Why were depths less than 10 cm not considered flooded? We chose a\ncommon lower threshold. Ultimately, there is no universal definition of a ‘flood’. We\nnote, however, that a recent paper in Nature Comms that estimates the number of\npeople in poverty exposed to floods uses 15cm as the lowest threshold6.\nLine 267: Replace “why” with “where” Done.\nLine 297: Insert “that” between “important” and “the” Done.\nLines 309 – 310: Why was the square root taken? This is a simple and transparent\npart of the dimensionality reduction to transform a matrix comprised of three\ninputs into an index with just five values for our final risk index.\nReviewer 3\nThe authors tackle an important question, considering flood exposure in the\ncontext of people’s social vulnerability, i.e. coping capacity. The authors correctly\nidentify a gap in the literature – some existing studies have considered flood\nexposure along with poverty (but only focused on 1-in-100 year floods), while others\nhave conducted flood exposure assessments without accounting for socio-economic\ncharacteristics at all.\n(1) The study relies on a somewhat outdated gridded GDP dataset (Kummu et al\n2018). There are newer datasets (e.g. Chen et al 2022) which claim higher\naccuracy, which the authors do not mention. The choice of GDP dataset is\ncrucial for the intended purpose of this study. For instance, the Kummu et al\ndataset does reasonably well in capturing areas with dense urbanization and\neconomic activity – yet it often fails to detect poor and more sparsely\npopulated areas, which is problematic for a study that tries to detect poor\n(flood exposed) areas. The manuscript offers no critical analysis or discussion\nof the adequacy of the Kummu et al dataset – especially a robustness check\nwould be needed to check how the Kummu dataset performs at the low end of\nthe income distribution and capturing poverty (since this was not an objective\nof the Kummu et al paper).\nWe thank the reviewer for this suggestion. We have incorporated the newer GDP\ndata, which offers much better spatial coverage. This affects our overall population\nexposure estimates (e.g. total population exposed to potential event rises from 1.45\nbillion to 2 billion at 10cm).\n(2)The study simply assumes that income is a proxy for vulnerability, but in\nreality a wide range of factors (apart from income) determine people’s\nvulnerability to disasters. For instance, social groupings, ethnicity, gender,\nreligion, rural vs urban location, access to credit, the quality of infrastructure,\nand the quality of government support programs (e.g. social insurance\nschemes) – these are all crucial to reducing vulnerability, yet none of them are\nreflected in the analysis. While the authors acknowledge that they do not\naccount for these factors, this does not resolve the shortcoming.\nWe agree. However, at present there is no other dataset that could be used to\nmeasure the multidimensional nature of social vulnerability with the geographic\ncoverage and spatial resolution of the GDP per capita data. We also believe that our\nassumption that poorer people are, on balance, relatively more vulnerable than\nrich people is both theoretically and empirically justified. According to Hallegate et\nal (2017, pp 2-3) “poor people are exposed to hazards more often, lose more as a\nshare of their wealth when hit, and receive less support from family and friends,\nfinancial systems, and governments.” The authors also show strong correlations\nbetween GDP per capita and (a) socioeconomic resilience, and (b) risk to well-being\n(pp. 9).\nWe have also conducted further analysis with a new gridded measure of\nmultidimensional relative deprivation12. Estimates using these data are reported in\na new appendix because they have much less comprehensive geographic coverage\nin many flood-prone areas. This results in generally lower estimates across most\nrisk quintiles. However, the broad findings related to changes in our understanding\nof the distribution of risk hold.\n(3)It would be more accurate to describe the analysis in this study as “income-\nadjusted” rather than “vulnerability-adjusted”.\nWe prefer “vulnerability adjusted” to “income adjusted” to draw attention to the\nway we are using GDP per capital data in a new way. Previous studies have used\nGDP per capita as a proxy for income or economic assets at risk (i.e. higher = more\nrisk) while we are inverting that assumption (higher = lower risk).\n(4)Past studies – e.g. Hallegatte et al 2017, cited in the manuscript – have\nactually developed a quantitative model to incorporate such diverse factors of\nvulnerability with global coverage. In short, the study is not robustly capturing\nvulnerability, which is its key proposition.\nThe Hallegatte et al 2017 report does not provide results disaggregated sub-\nnationally. Where such analyses are available, they are at the admin 1 or 2 level.\nOur approach allows for much higher-resolution risk mapping.\n(5)The study appears to have a significant technical flaw in defining poverty –\nunfortunately this alone disqualifies it. It’s objective is to identify areas with\nhigh vulnerability, proxied by low incomes (i.e. high poverty). It assumes that\nthe bottom 40 percent (bottom 2 quintiles) of the income distribution in each\ncountry are vulnerable. This forces the arbitrary result that 40% of people in\nevery country are vulnerable because of their social status. This makes little\nsense in high-income countries where even those in the bottom 40% have\nrelatively high incomes by international standards (i.e. are not vulnerable\naccording to the author’s assumption), as well as having access to high quality\nsocial and physical infrastructure. But the assumption also doesn’t make sense\nfor the lowest income countries, where much more than 40% of the\npopulation live in poverty (e.g. in Malawi, Niger, DRC, Madagascar, South\nSudan, Burundi, and Mozambique respectively around 80-90% of the\npopulation live in poverty according to the World Development Indicators) –\nbut only 40% of them are classified as vulnerable in this study because poverty\nis defined based on income quintiles rather than levels.\nWe respectfully disagree that there is a technical flaw in our approach to defining\npoverty, but found this challenge useful in bringing our approach more tightly into\nfocus. We have revised the manuscript to clarify that we are using a ‘relative\ndeprivation’ approach to defining and measuring poverty. We refer the reviewer to\nthe UN Guide to Poverty Measurement, which outlines various approaches,\nincluding the relative deprivation approach13.\nThis is a standard approach globally. For example, in Europe people with less than\n60% of median income (country-standardized) are classified as ‘relatively poor’13.\nUnited Nations SDG indicators 1.2.1 and 1.2.2, which relate to Goal 1: “End poverty\nin all its forms everywhere,” specifically refer to such national definitions.\nHallegate et al (2017) break down the impacts of hazards on the poor and non-\npoor, “defined as the bottom 20 percent and the top 80 percent in terms of\nconsumption in each country” respectively (pp. 6)14.\nThe benefit of this ‘relative deprivation’ approach is its direct relevance to local\ndecision makers. For example, policy makers, planners or disaster response teams\nin Nigeria have little use for national or district level comparisons with India. They\ncare about assessing relative vulnerability within and across districts in Nigeria,\nwhich is what our country-standardized VARI scores at cell level provide.\n(6)As a consequence, the artificially imposed income quintile definition in this\nstudy massively overestimates vulnerability in high-income countries, and\nmassively underestimates vulnerability in poor countries – i.e. the opposite of\nthe study’s stated motivation. This is illustrated by Figure 2b, which shows\nvirtually zero flood-vulnerability hotspots in Africa (a continent with major\nflood and poverty challenges).\nOur measures based on country-standardized risk estimates cannot be directly\ncompared cross-nationally. As such, Figure 2b was a confusing presentation and\nhas been removed. The fact that there appeared to be so few hotspots in Africa was\na function of data presentation rather than underlying analysis or results (as the\nNigeria example showed). In the revised manuscript we have also included new\nglobally standardized estimates of relative poverty to facilitate direct cross-\nnational comparison.\n(6) Minor points:\n- The study claims that it has a spatial resolution of 90 m, but the effective resolution\nof analysis is 1km (which is the resolution of the Kummu et al 2018 data used in\nthis study). The color blocks in Figure 5, second column, show that the GDP data\ndoes not have 90m resolution.\nThe effective resolution of the exposure estimates is 90m, while the effective\nresolution of the social vulnerability estimates is 1km. As a result, our risk estimates\ndo vary spatially at 90m. While this is a very significant improvement on previous\napproaches, we note in the manuscript that the 1km resolution is not ideal for social\nvulnerability assessment. Future research that upscales these estimates could be\neasily incorporated into revised global VARI maps.\n- GDP is not a measure of wealth or assets (which are stock measures), but of a\nmeasure of economic activity (which is a flow measure). This is misstated throughout\nthe manuscript.\nThis is corrected in the revised manuscript.\nREFERENCES\n1. Wardrop, N. A. et al. Spatially disaggregated population estimates in the absence\nof national population and housing census data. Proc. Natl. Acad. Sci.115, 3529–\n3537 (2018).\n2. Carr-Hill, R. Missing Millions and Measuring Development Progress. World Dev.\n46, 30–44 (2013).\n3. Kuffer, M., Owusu, M., Oliveira, L., Sliuzas, R. & van Rijn, F. The Missing\nMillions in Maps: Exploring Causes of Uncertainties in Global Gridded\nPopulation Datasets. ISPRS Int. J. Geo-Inf.11, 403 (2022).\n4. Ward, P. J. et al. Assessing flood risk at the global scale: model setup, results, and\nsensitivity. Environ. Res. Lett.8, 044019 (2013).\n5. Smith, A. et al. New estimates of flood exposure in developing countries using\nhigh-resolution population data. Nat. Commun.10, 1814 (2019).\n6. Rentschler, J., Salhab, M. & Jafino, B. A. Flood exposure and poverty in 188\ncountries. Nat. Commun.13, 3527 (2022).\n7. Hawker, L. et al. Assessing the next generation of Global Flood Models in the\nCentral Highlands of Vietnam. Nat. Hazards Earth Syst. Sci. Discuss. 1–42\n(2023) doi:10.5194/nhess-2023-93.\n8. Bates, P. D. et al. A climate-conditioned catastrophe risk model for UK flooding.\nNat. Hazards Earth Syst. Sci.23, 891–908 (2023).\n9. Smith, A., Sampson, C. & Bates, P. Regional flood frequency analysis at the global\nscale. Water Resour. Res.51, 539–553 (2015).\n10.Devitt, L., Neal, J., Wagener, T. & Coxon, G. Uncertainty in the extreme flood\nmagnitude estimates of large-scale flood hazard models. Environ. Res. Lett.16,\n064013 (2021).\n11. Sampson, C. C. et al. A high-resolution global flood hazard model. Water Resour.\nRes.51, 7358–7381 (2015).\n12. Center for International Earth Science Information Network - CIESIN - Columbia\nUniversity. Global Gridded Relative Deprivation Index (GRDI), v1 (2010–2020).\n(2022) doi:10.7927/3xxe-ap97.\n13. United Nations. Guide on Poverty Measurement. (United Nations Economic\nCommission for Europe, 2017).\n14. Hallegatte, S., Vogt-Schilb, A., Bangalore, M. & Rozenberg, J. Unbreakable:\nBuilding the Resilience of the Poor in the Face of Natural Disasters. (World\nBank, 2017). doi:10.1596/978-1-4648-1003-9."}, {"round": 2, "reviewer_comments": {"reviewer_2": "REVIEWERS' COMMENTS\nReviewer #2 (Remarks to the Author):\nI am grateful to the authors for their concerted effort to address my comments and concerns. They\nhave by and large satisfactorily addressed them all with the exception of two minor revisions that I\nrecommend because the authors continue to downplay the potential effects of pluvial and compound\nflooding. They state in their response:\n\"there is no clear\ndefinition of what constitutes pluvial and fluvial flooding and making comparisons\nbetween estimates from different models is complicated by different minimum\ncatchment sizes. True off floodplain pluvial flooding would be significantly more\nlocalised than the smallest rivers included in our fluvial domain, and critically\nwould be mostly too localised to be resolved in the latest global scale DEMs.\"\n\"a combination of extreme river flows and coastal surge might produce\nsignificant compound flooding the probability of this happening is low.\"\nFor the record, I would argue that pluvial and fluvial flooding are sufficiently defined to an extent that\nthey can be operationalized in compound flood hazard assessments. This study is valuable even\nthough the models used cannot resolve pluvial and compound flooding; however, I think it is important\nto acknowledge: 1) that while compound flooding may have a low probability (so does their 1000 year\nfluvial flood!) compound flooding risks are also increasing and can have severe nonlinear\nconsequences; and 2) the majority of streams / watersheds in the world have drainage areas less than\nthe 50 km^2 threshold used in this study (see for example basic fluvial geomorphology texts like\nLeopold, Wolman and Miller (1964)). An implication of these points is that the cumulative effects of\npluvial flooding, fluvial flooding in small watersheds <50km^2, and compound flooding could be\nsubstantial. As one example, many of the streams around Houston TX have small watersheds, but\nwhen it rains about 1.5 meters as it did with Harvey, extensive compound flooding happens in these\nsmall watersheds.\nTo summarize, my recommendation is accept after the authors add a few sentences to acknowledge\nthis context for the study.\nCongratulations to the authors on a valuable contribution.", "reviewer_4": "Reviewer #4 (Remarks to the Author):\nSee comments to Editors below.", "reviewer_5": "Reviewer #5 (Remarks to the Author):\nI think you addressed all the issues raised in the first revision round.\nIn particular:\n1.1) you explained why you cannot use local statistics for social vulnerability, and other abstracts and\nintroduction are now clearer\n1.2) you re-computed the globally standardized estimates of social vulnerability, and now results are\nsuitable for cross-country comparison\n1.3) you justified why you cannot use subgrid information for GDP\n1.4) you clarified that the analysis already uses GDP per capita\n2) you performed a sensitivity analysis of the minimum water depth threshold\n3) you updated your rounding method of VARI and showed that the results did not change so to affect\nthe key conclusions\nThe paper can be published as it is."}, "author_response": "Response to reviewers\nReviewer 2\nI am grateful to the authors for their concerted effort to address my comments and\nconcerns. They have by and large satisfactorily addressed them all with the exception\nof two minor revisions that I recommend because the authors continue to downplay\nthe potential effects of pluvial and compound flooding. They state in their response:\n\"there is no clear definition of what constitutes pluvial and fluvial flooding and making\ncomparisons between estimates from different models is complicated by different\nminimum catchment sizes. True off floodplain pluvial flooding would be significantly\nmore localised than the smallest rivers included in our fluvial domain, and critically\nwould be mostly too localised to be resolved in the latest global scale DEMs.\"\n\"a combination of extreme river flows and coastal surge might produce significant\ncompound flooding the probability of this happening is low.\"\nFor the record, I would argue that pluvial and fluvial flooding are sufficiently defined\nto an extent that they can be operationalized in compound flood hazard assessments.\nThis study is valuable even though the models used cannot resolve pluvial and\ncompound flooding; however, I think it is important to acknowledge: 1) that while\ncompound flooding may have a low probability (so does their 1000 year fluvial flood!)\ncompound flooding risks are also increasing and can have severe nonlinear\nconsequences; and 2) the majority of streams / watersheds in the world have drainage\nareas less than the 50 km^2 threshold used in this study (see for example basic fluvial\ngeomorphology texts like Leopold, Wolman and Miller (1964)). An implication of these\npoints is that the cumulative effects of pluvial flooding, fluvial flooding in small\nwatersheds <50km^2, and compound flooding could be substantial. As one example,\nmany of the streams around Houston TX have small watersheds, but when it rains\nabout 1.5 meters as it did with Harvey, extensive compound flooding happens in these\nsmall watersheds.\nWe would like to thank the reviewer for their support for the manuscript. We\nabsolutely agree with the key point that flood risk will be substantial for catchments\nbelow 50 km2and that compounding will in places add to this risk. We certainly\nwould not want to downplay this in the manuscript, but equally, as the reviewer\nnotes, we don’t believe it detracts significantly from the novelty or value of the work\nnot to include these additional hazards. To address the reviewer concern that we\nshould not downplay the importance of these other flood processes we adjust the\nmanuscript in ways.\nFirst, we have changed “flood risk” to the more specific “fluvial flood risk” in the\nabstract, introduction, results, and methods sections. Second, we have noted the\nriver size threshold we have used such that this is clear to the reader “on rivers with\ncatchment areas above 50 km2“ (lines 322 & 327 in Methods). Finally, we have\nexpanded comment in the discussion (lines 215-219) to highlight the additional risk\nwe know is missing from a fluvial only analysis: “ Further, we have only estimated\nfluvial hazards. Incorporating pluvial, small river (<50km2) and coastal flood\nexposure, as well as compounding effects between these hazards, would change our\nrisk estimates. However, this would also introduce considerable uncertainty,\nparticularly with regards to pluvial flood models, which are highly sensitive to\ndepth thresholds changes1 and local topographic features.”\nOur statement regarding the challenge of defining pluvial verses fluvial risk is\npredominantly aimed at the global hazard models available to characterise this,\nwhich use all use different thresholds for defining pluvial and fluvial components of\nthe hazard, or simply restrict their analysis to very large rivers. In fact, our hazard\nmodel includes smaller river systems than any other published global flood model\nto our knowledge. From a more local operational perspective, the definitions, as the\nreviewer suggest, are likely sufficient for many practical tasks. Nevertheless, we\nbelieve we can address the reviewer main concerns with the adjustment to the text\nabove.\nReviewer 4\nThe authors suggest that the main aim is to identify, within countries, areas of the\ngreatest relative vulnerability to flooding, for example to help inform allocation of\nscarce resources in humanitarian and disaster response. Given this aim, the\napproach that has been adopted seems reasonable.\nHowever, the article still contains efforts to make global comparisons. The critique\nmade by R3 is then very relevant. I think it’s the case that the authors are (now)\nusing a separate relative deprivation measure (based on global income quintiles?) for\nthe purpose of these global comparisons. But I felt this needed to be discussed a lot\nmore prominently – for example the section (starting on page 4 of the manuscript)\nwhere relative and absolute deprivation measures are discussed should include some\nexplicit discussion of the two different approaches taken (within country and\nbetween country) to measuring relative deprivation.\nWe have made multiple changes to ensure clarity in the final manuscript. These\ninclude:\nLines 100-103 at the beginning of the Results section: “The income-based relative\ndeprivation measure used for the analyses presented in Figure 1b and Figure 2 are\nglobally standardized—i.e. reflect quintiles of the global income distribution—to\nfacilitate cross-national comparison.”\nLines 131-133: “It is important to stress that these estimates use globally\nstandardized population and vulnerability quintiles to reflect relative risk between\nadmin 2 units globally.”\nLines 141-143: “Yet the primary use case for the VARI Flood method is within-\ncountry comparison to support national or local decision makers. For this\napplication, exposure and vulnerability quintiles are standardized against country\nspecific population and income distributions.”\nWe have also changed the figure titles and captions for figures 2, 3 & 4 to better\ncommunicate the distinction between the globally standardized and country\nstandardized estimates.\nWe hope these changes are sufficient to ensure clarity in the final manuscript."}]

Decision letter