# **3.1. Flooding hazard data**

Floods can be caused by a variety of factors. The most common types are considered in this analysis:

**Fluvial flooding** occurs when intense precipitation or snow melt causes rivers to overflow. **Pluvial flooding** occurs when rainwater builds up beyond the absorptive capacity of soil. Country-level pluvial and fluvial flood maps, developed by Fathom Ltd, are used in this analysis (Smith et al. 2015). The data sets provide information on flood extents and flood depth at an approximate spatial resolution of 90 meters and are available for all countries. The maps are based on a global hydrological and topographic model. The model simulates flood events with return periods of 5, 20, 50, 100, 250, and 500 years. This study considers flooding with a 100-year return period. A 1-in-100-year event, for example, is expected to occur once every 100 years on average (i.e. it has a probability of 1% of occurring in any given year). As with all global flood maps, the effects of artificial flood protection structures like dikes are not incorporated.

Two editions of the Fathom global flood maps are available; 2016 and 2019. Wherever available, the newer version has been used. The 2019 version differs from the 2016 version in the resolution of the digital elevation model that has been used to simulate event scenarios. The 2019 edition uses the newest DEM MERIT elevation model that corrects for multiple errors, including tree and building height adjustments.

**Coastal flooding** is caused by storm surges and high tides in coastal areas. A commonly used global coastal flood risk map is used in this study: the Global Tide and Surge Reanalysis (GTSR) data set by Muis et al. 2016. As it uses a digital terrain model that does not account for common errors such as stripe noise and tree heights (unlike for instance DTM MERIT), this map is expected to provide a lower bound or conservative estimate of global coastal flood risk. In particular, resulting from coarse resolution in modeling elevation, bathymetry and meteorological forcing, the data set underestimates extreme sea levels. In addition, the largest tropical cyclone-induced surges are not all included in the available observations after which storm surges were modeled due to the limited period of the available records (Muis et al. 2016). However, no global coastal flood maps are readily available at this point to address these shortcomings (specifically, no flood maps that offer different return periods based on modeled storm surges).

**Interpreting return periods: classifying the probability and intensity of natural shocks:** Natural hazard data typically distinguish _return periods_ to describe the spatial distribution and intensity of natural shocks. A return period describes how much time is expected to pass before a natural shock of the same intensity occurs again. Using historical data and based on the statistical frequency of a shock of a certain intensity, it describes the probability of such an event.

Figure 1 illustrates the extent of floods with two return periods: one with a 25-year return period (or a 1- in-25-year flood) and one with a 100-year return period (or a 1-in-100-year flood). The 1-in-25-year flood has a 1/25 or 0.04 annual probability of occurring. In other words, each year there is a 4 percent chance of such an event occurring, regardless of when the last such event took place. The probabilistic nature of return periods means that there is a 63.9 percent probability that a flood of at least this intensity will occur once within a 25-year period. But this also leaves the possibility for this event to not occur at all, or to occur several times. In comparison, a 1-in-100-year flood is a more extreme event with a lower probability but higher intensity — that is, it affects a wider area and has a greater depth.

As time passes, more climatic data become available, which will update the empirical probabilities associated with certain natural shocks. As the impacts of climate change increase, intense shocks will probably become more frequent. This means that there is significant uncertainty around the probability 5