and flash floods and small river basins have not been represented in a global model at this stage. These flood maps are overlaid with a global population density data set, Landscan (Geographic Information Science and Technology, 2015) to estimate the percentage of the population exposed to floods in each country, river basin, and for each return period.

An important complication is the difference between the exposure to a flood in a basin for a given return period (say, the 100‐year flood), and the number of people that are affected in the country _at once_ : while 2.1 percent of Brazil’s population is exposed to a 100‐year event in our simulations, this population is distributed in many river basins that will not flood at the same time. Here, we make the simplifying assumption that river basins in each country are independent, neglecting cross‐basin correlation (Jongman et al., 2014), and we perform a Monte Carlo analysis to derive exposure in a country at a given return period by aggregating simulations at the basin level.

We use estimations of the exposure bias published by the World Bank (Hallegatte et al., 2016a). The World Bank study overlays the same GLOFRIS flood maps with geo‐localized household surveys (from the Demographic and Health Survey) to assess the exposure of poor people relative to the exposure of non‐ poor people within 52 countries. Countries where data are not available are attributed the average exposure bias.

We proxy asset vulnerability and the asset‐vulnerability bias using a global data set of building types (USGS PAGER). We classify buildings in three categories (low, medium, and high quality), which we match to simple damage‐depth functions (Hallegatte et al., 2013), and we assume richer households live in and use higher quality assets. What fraction of assets is lost to a disaster also depends on softer measures, such as the existence of early warning systems. In addition to being an effective way of reducing casualties and fatalities, early warning allows households to plan for a disaster, and move some of their assets outside (or above) the affected zone, thereby reducing asset losses (Hallegatte, 2012; Kreibich et al., 2005) **.** Based on previous case studies, we assume that asset losses are reduced by 20 percent when people have access to early warnings, and we use data from the Hyogo Framework for Action (HFA) monitoring system on early warning to estimate the fraction of population with such access.

For income diversification, social protection and financial inclusion, we build on global databases such as the Atlas for Social Protection – Indicators of Resilience and Equity (ASPIRE) and Global Financial Inclusion (FINDEX). Parameters related to the socioeconomic characteristics that modify risks of irreversible human capital losses – employment, healthcare, and education – are from the World Development Indicators (WDI).

Adaptive social protection, where social protection benefits and/or beneficiaries is expanded automatically in the aftermath of a disaster, is an efficient tool to reduce the welfare impact of disasters (Davies et al., 2013; Hallegatte et al., 2016c). It is impossible to predict the support that will be provided after a disaster, so we assume a willingness to share the losses, and proxy for the ability to provide such support, depending on institutional capacity and public financial management. To measure countries’ ability to manage public finance and reallocate resources in times of crisis, we use sovereign credit ratings. We use data from the HFA on contingent finance, the existence of plans for emergency response and 10