Fig. 11: Relationship between road network density and percentage failed routes grouped by countries and regions with different development stages. a-g. Scatter plots combined with kernel density estimation plots of road network density and percentage failed routes under five selected flood return periods (5-year, 50-year, 100-year, 500-year, and 1000-year) and categorized by economies’ development levels. The R-squared (R<sup>2</sup> ) value the dependent variable– percentage failed routes and the independent variable–road network density for each subplot is labeled on the top right corner. h. World map of the distribution of 247 countries and territories color coded by their development stages.

In addition, the flood exposure pattern varies across different geographies as well. When overlaying flood hazard maps with different road network topologies, the direct and indirect impacts are a function of both the infrastructure layer and the hazard layer. To account for both flood exposure patterns and network density, we perform a log-transformed linear regression of direct flood exposure percentage and network density for 10 flood return periods under the =30 cm inundation threshold (Supplementary Table 4). The results of the analysis show a positive relationship between percentage failed routes and direct flood cc exposure and a negative relationship between percentage failed routes and network density. Such pattern γγ remains consistent across all 10 flood return periods considered in the analysis. Assuming a significance threshold of =0.05, both direct flood exposure percentage and network density is statistically significant ( _P_ value=0.000; Supplementary Table 4). The proportion of the variance for percentage failed routes explained by the independent variables—flood exposure and network density, increase with flood return αα period. As is shown in Supplementary Table 4, flood scenarios with higher return periods exhibit higher values ( =0.677 for 1000-year flood scenario) than lower return periods ( =0.221 for 5-year flood scenario). Furthermore, we observe that network density and flood exposure influence network resilience RRin different ways. As is shown in Supplementary Table 4, the absolute values of network density coefficients<sup>2</sup> RR<sup>2</sup> RR<sup>2</sup> decrease with flood return period. That is, the change (negative) in average percentage of failed routes decreases given a one-unit increase in network density. This suggests that road density, as an indicator of network redundancy, contributes positively to overall network resilience yet the level of contribution decreases with higher flood intensity. As flood return period increases, the absolute values of the flood exposure coefficients first increase and then decrease with the 75-year flood as the inflection point. This agrees with the exposure pattern observed in Fig. 7a and suggests the diminishing impact of flood exposure on network resilience.

# **Discussion**

This study provides the first global evaluation of both direct (hazard exposure) and indirect (mobility and travel behavior) impact of flood hazards on road transportation networks. We define settlement cluster boundaries around the world based on global settlement distribution instead of administrative or selfdefined boundaries. We use these urban clusters as spatial masks to extract meaningful road networks from OSM. In addition, we leverage the latest global high-resolution flood maps which detail the extent and depth of pluvial and fluvial floods for 10 return periods. Combining these data sets with exposure mapping, travel simulation, and vulnerability analysis, we are able to better understand the far-reaching impacts of floods on road network resilience to flood hazards.

The impact of flood hazards on road networks in terms of both infrastructure exposure and mobility disruption (measured as the percentage of failed routes, travel delay, and travel distance increase) is heterogeneous across space. Specifically, the road networks in Africa, South Asia, East Asia, Southeast Asia, Central America, and South America are less resilient to pluvial and fluvial floods. Flood disruptions on road networks are costly for local residents and households as they give rise to longer travel times, wasted fuel, and missed work opportunities<sup>25</sup> . Compared with direct flood exposure, the indirect impact of 21