provide here an indication of the benefits from these tools, making it possible to compare them with implementation costs and alternative approaches such as land‐use planning or building retrofitting.

Regarding poverty, these results indicate that there is no trade‐off between poverty reduction and risk management. Even if poverty is reduced without improving the exposure or asset vulnerability to floods, thus increasing asset losses when disasters hit (by 1.1% in our model), the net effect on welfare is positive (losses are reduced by 3.2%) due to the increased ability of poor people to cope with asset losses. This is an important finding, for example for city managers who may worry that better living standards and higher‐value buildings increase asset risks: while they do, wellbeing risks are ultimately reduced.

To account for the uncertainty in the exposure to the flood and its consequences and in socioeconomic characteristics (e.g., diversification), we perform a systematic sensitivity analysis by varying all uncertain parameters 33% above and below their central value, and measure the robustness of our findings (see the Online Technical Note for full details). Error bars in Figure 4 report the resulting interquartile uncertainty on the impacts of the various policies on asset and welfare losses. It shows our results to be robust. We find also that the relative ranking of the policies is stable and not sensitive to the uncertainties. Additionally, we explore changes in the elasticity of the marginal utility of consumption, which is a normative choice, not an uncertainty. We find that it affects only the magnitude of the benefits from reducing poverty or the exposure bias, but it does not affect our qualitative results and the ranking of policy options.

# Improving socioeconomic resilience to floods in 90 countries

We use this model to quantify socioeconomic resilience in 90 countries and assess how different policy options can improve resilience and reduce the welfare impacts of river floods. Since we cannot rely on ex‐ post observation and household surveys to calibrate the model, we use proxies and national‐level data. Also, we do not calculate the resilience for one event – like for Mumbai in 2005 – but the resilience to many possible flood events. We calculate asset and welfare losses for several return periods (between 5 and 1,000 years) and estimate the socioeconomic resilience as the ratio of expected asset losses to expected welfare losses. A complete description of the model is available in the Online Technical Note.

Hazard is represented by a flood’s probability of occurrence, above the level of protection provided by embankments and artificial dikes. In each country, we set to zero the exposure to all events with a return period equal or lower than the protection level. We calibrate the protection level by country using the global open and collaborative database FLOPROS (Scussolini et al., 2015). FLORPOS provides estimates at the sub‐national level based on expert estimates of _de facto_ protection if available, _de jure_ legislation and performance standards otherwise, or simple economic modeling if both de facto and de jure data are lacking.

Above the protection level, population and asset exposure to floods is estimated using GLOFRIS, a global river flood model (Jongman et al., 2015, Jongman et al., 2012). This model provides gridded flood inundation estimates at the 1km<sup>2</sup> resolution for 9 return periods (from 5 to 1000 years), all countries, and in all large river basins. The model does not include all floods: coastal floods are not taken into account, 9