account accurately, thus making flood exposure difficult to compare over time<sup>27,28,29</sup> . Another study based on satellite data from 2000 to 2018 documents increasing flood exposure, but omits at-risk populations who were unaffected during the study period and events that were not detected by satellite observations<sup>29</sup> .

Due to data limitations, past studies also focus on certain types of flood, rather than assessing the combined risks from all flood types (that is, fluvial, pluvial and coastal flooding)<sup>30,31,32,33,34</sup> . Others assess risks for a subset of countries only, falling short of full global coverage<sup>35,36</sup> . Similarly, studies that use relatively coarse spatial resolution flood hazard data tend to inaccurately represent major fluvial floodplains. They do not capture pluvial flood hazards and flooding along secondary rivers, drastically underestimating exposure<sup>35,37,38</sup> . One study using coarse flood data projected that the global number of flood-exposed people will reach 1.3 billion by 2050<sup>3937</sup> but a more recent high-resolution study showed that this threshold has already been exceeded by at least 39% in 2020<sup>39</sup> . This illustrates the importance of using high-resolution data to capture the highly localized nature of flood hazards and people’s tendency to avoid settling in the most hazardous locations<sup>355</sup> .

This study contributes to the literature in several ways: First, by considering flood exposure trends with annual frequency, we demonstrate the value of annual monitoring of evolving flood exposure. The study documents significant variation in year-on-year growth rates, thus highlighting the need for spatial planners to be highly responsive. Second, it explicitly distinguishes the growth dynamics of safe versus flood-exposed spatial development. This allows us to document a divergence in flood exposure – regions that are either increasing or decreasing their flood exposure as they urbanize over time. Third, rather than focusing on a certain flood hazard type, we combine different flood types (fluvial, pluvial, coastal floods) and assess overall flood exposure. Fourth, by using global data sets, we document trends with complete global coverage and within one consistent methodology. Finally, while past studies (both global and country-specific) have documented a general increase in flood exposure, this study corroborates and expands these findings based on data with high spatio-temporal resolution.

# 3. Data and methods

## 3.1. Global flood hazard and settlement data

### Flood hazard data

To obtain complete estimates of urban exposure to flood hazards, this study considers the three most common flood types:

- _Fluvial flooding:_ when intense or excessive precipitation or snow melt causes rivers to overflow

- _Pluvial flooding:_ when surface water builds up beyond the absorptive capacity of soil, due to extended precipitation and insufficient drainage

- _Coastal flooding:_ due to tidal or storm surges, or sea level rise.

Country-level pluvial and fluvial flood data are based on the 2019 global Fathom flood hazard data set<sup>40,41</sup> . These provide gridded information on flood extents and depths at a 3-arcsecond resolution (equivalent to 90 meters at the equator), simulating 5, 20, 50, 100, 250, and 500-year flood events, and are available for all countries. The maps are based on the DEM MERIT elevation model that corrects for multiple errors, including absolute bias, stripe noise, speckle noise, and tree and building height biases<sup>42</sup> . We consider flooding with a 100-year return period.

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