Via Nature:

# Projections of global-scale extreme sea levels and resulting episodic coastal flooding over the 21st Century

## Abstract

Global models of tide, storm surge, and wave setup are used to obtain projections of episodic coastal flooding over the coming century. The models are extensively validated against tide gauge data and the impact of uncertainties and assumptions on projections estimated in detail. Global “hotspots” where there is projected to be a significant change in episodic flooding by the end of the century are identified and found to be mostly concentrated in north western Europe and Asia. Results show that for the case of, no coastal protection or adaptation, and a mean RCP8.5 scenario, there will be an increase of 48% of the world’s land area, 52% of the global population and 46% of global assets at risk of flooding by 2100. A total of 68% of the global coastal area flooded will be caused by tide and storm events with 32% due to projected regional sea level rise.

## Discussion

Global model outputs of tide, surge and wave setup have been used to develop historical time series of total sea level around the world’s coasts. These model results were extensively validated against global tide gauge data, showing good agreement. To estimate the extreme sea levels which occur during storm events, an extreme value analysis was applied to both model and tide gauge data. In order to estimate extreme sea levels over the twenty-first century, projected relative sea level rise for IPCC RCP4.5 and 8.5 was added to present day extreme sea levels. These projected extreme sea levels were then used to quantify global episodic coastal flooding in 2050 and 2100.

Results show that for RCP8.5, 0.5–0.7% of the world’s land area will be at risk of episodic coastal flooding by 2100 from a 1 in 100-year return period event (an increase of 48% compared to the present day), impacting 2.5–4.1% of the world’s population (increase of 52%) and threatening assets worth up to 12–20% of global GDP (increase of 46%). Note that these values assume no coastal defences or adaptation measures (see SM5). In many locations, coastal defences are commonly deployed and by 2100, it is expected that adaptation and specifically hard protection will be widespread, hence these estimates need to be seen as illustrations of the scale of adaptation needed to offset risk. Future studies that consider the impact of coastal adaptation and defences could logically build on the present results. As such, we regard the present analysis as a “first-pass” estimate of global impacts of sea level rise.

The analysis shows that tide and storm surge will account for 63% of the global area inundated by 2100, with relative sea level rise accounting for 32% and wave setup accounting for only approximately 5%. Furthermore, projected sea level rise will significantly increase the frequency of coastal flooding by 2100, with results herein showing that for most of the world, flooding associated with a present day 1 in 100-year event could occur as frequently as once in 10 years, primarily as a result of sea level rise. As the episodic events of storm surge and wave setup will, between them, contribute approximately 68% of projected coastal flooding, any climate change driven variations in the frequency and/or severity of storm events could have significant impacts on future episodic coastal flooding.

As noted previously, the present study has a global-scale focus. As such, a number of simplifying assumptions are necessary to render the problem computationally feasible. These simplifications and the resulting implications are discussed in detail in the Supplementary Material (SM5). A summary of these assumptions appears below.

The analysis undertaken is linear in nature. It is assumed that the total sea level (TSL) can be represented by the summation of T + S + WS. This explicitly ignores interactions between these processes. Extreme value analysis is undertaken to determine historic extreme sea levels (ESLH100). The linear assumption is again applied to determine future extreme sea levels (ESLF100) by the linear addition of projected relative sea level rise (RSLR) by the end of 2100. This assumes that changes in wind speed and wave height over the coming century will be small, which is consistent with a number of recent studies38,39,40. SM5 outlines the precedence for such linear superposition approaches for global-scale studies4,7,8,9,10,11 and concludes that the potential errors are relatively small compared to the uncertainties in the extreme value analysis and RSLR projections.

In order to calculate the magnitude of the wave setup (WS) at global scale, it is necessary to use relatively simple models16,18 and to assume a global average bed slope. These assumptions will most likely result in errors at specific locations. However, the analysis ultimately shows that WS is a relatively small component of the ESL, and hence the adopted “first-order” representation of WS appears justified.

To validate the model adopted above, extensive tide gauge data is used (see Fig. S1), as this is the only global water surface elevation data source available. An extensive comparison is undertaken for both ambient and extreme conditions. It should be noted, however, that it is likely that many tide gauges, due to their locations, will not respond to WS. Hence, this validation dataset has its limitations for this application. Although model and tide gauge data agree well at the global scale, there are clear differences at specific locations. For example, in more than 30% of examined locations, the RMSE related to the mean tidal amplitude is greater than 20%. This is largely associated with semi-enclosed basins or regions with wide shelves (e.g. Mediterranean Sea, Baltic Sea, Sea of Japan) and with regions of small tidal range (see Fig. S3, SM1).

The MERIT22 topographic model is used with a “bathtub” flooding model. This assumption is expected to generally overestimate flood extent41,42. Importantly, the analysis also assumes no flood protection is in place, such as dykes or other structures. As a result, the absolute values of flood extent will be over-estimated in many locations. For this reason, we emphasis relative changes in flood extent rather than absolute values.

The above assumptions mean that the present analysis may not model projected flooding at specific sites well. However, results show that, when aggregated to the global scale, the approach adopted here is able to produce first-order estimates of global flooding and its implications. In addition to these simplifying assumptions, both the ESL and RSLR estimates have associated statistical uncertainties. The present study considered these uncertainties in assessing statistical variability associated with estimated flooding extent. The full analysis, given in SM5 and Table S4, indicates the uncertainty associated with projected flooding in 2100 (RCP8.5) is approximately ±16.5%±16.5%.

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