# **1 Introduction**

Natural disasters impose large impacts on society, causing damages and affecting millions of people every year (Winsemius et al., 2016; Hallegatte et al., 2019; Dullaart et al., 2021). The adverse impacts of natural disasters extend beyond physical damages alone. Income and consumption losses, and disruptions to essential services (e.g. electricity, water), can have implications for food security, education and health, further diminishing the wellbeing of affected people (Hallegatte et al., 2019). The poor and most marginalized often experience the largest relative welfare losses, as they live close to subsistence levels, have a more vulnerable asset base (e.g. less diversification), and lack the resources to smooth income shocks, maintain pre-disaster consumption and quickly recover from shocks (Hallegatte and Rozenberg, 2017; Hallegatte et al., 2020).

Most disaster risk analyses, however, focus solely on direct damages to assets, neglecting the wider welfare impacts to households, including identifying those disproportionately hit (Markhvida et al., 2020). As a result, these methods cannot be used to measure the benefits of adaptation options that do not reduce asset damages, such as emergency preparedness (although assets can be moved to higher ground), cash-transfers, and humanitarian relief efforts. More importantly, as asset damages obscure the impacts to the poor, adaptation options are often prioritized in asset rich areas, potentially reducing the level of protection offered to poorer areas. Therefore, accounting for the welfare impacts of disasters can help identify communities that will benefit most from DRR measures for their wider wellbeing (Verschuur et al., 2020).

A growing number of studies have analyzed the _ex-post_ implications of disasters to income, expenditure, malnutrition, health and education, often using (longitudinal) survey data and econometrics (Noy, 2016; Noy and DuPont, 2018). However, studies modeling the welfare implications _ex-ante_ , which would allow them to be embedded in traditional risk assessments, are scarce. As part of the World Bank’s _Unbreakable_ report, Hallegatte et al. (2016) (henceforth H2016) developed a modeling framework to evaluate the impacts of disasters to households’ welfare on a national scale, which over the years has been applied to more detailed case studies in the Philippines (Walsh and Hallegatte, 2019), the San Francisco Bay Area (Markhvida et al., 2020) and Bangladesh (Verschuur et al., 2020). Apart from quantifying the welfare impacts, showing that every 1 USD dollar in asset loss translates into a welfare loss equivalent to 1.6 USD in consumption, the model allows evaluating the wider benefits of both hard (e.g., increased protection) and soft (e.g., cash transfer, access to finance) adaptation options, helping to better target portfolios of interventions to those that benefit most from them in welfare terms.

However, several limitations are inherent to the H2016 modeling framework. First, the use of national survey data, with limited representativeness on a local or regional scale, restricts a more granular analysis of hotspots of welfare impacts within countries. Second, the limited spatial representation of the model prevents us from investigating the drivers of disproportionate welfare impacts. For example, this could be driven by poorer households being relatively more exposed to flooding (e.g., exposure bias) or being dependent on a more vulnerable asset base. Moreover, spatially quantifying the exposure of vulnerable households 2