Editing IPCC:AR6/SROCC/Chapter-4 (section)

==== 4.3.3.2 Submergence and Flooding of Coastal Areas ====

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Since AR5, a number of continental and global scale coastal exposure studies have accounted for sub-national human dynamics such as coastward migration or coastal urbanisation. These studies project a population increase in the LECZ (coastal areas below 10 m of elevation) by 2100 of 85 to 239 million people as compared to only considering national dynamics (Merkens et al., 2016 <sup>[[#fn:r1176|1176]]</sup> ; Section 4.3.2). Under the five SSPs and without SLR, the population living in the LECZ increases from 640–700 million in 2000 to over one billion in 2050 under all SSPs, and then declines to 500–900 million in 2100 under all SSPs, except for SSP3 (i.e., a world in which countries will increasingly focus on domestic issues, or at best regional ones), for which the coastal population reaches 1.1–1.2 billion (Jones and O’Neill, 2016 <sup>[[#fn:r1177|1177]]</sup> ; Merkens et al., 2016 <sup>[[#fn:r1178|1178]]</sup> ).

The population exposed to mean and ESL events will grow significantly during the 21st century (high confidence) with socioeconomic development and SLR contributing roughly equally (medium confidence). Considering an average relative SLR of 0.7–0.9 m but no population growth, the number of people living below the hundred-year ESL in Latin America and the Caribbean will increase from 7.5 million in 2011 to 9 million by the end of the century (Reguero et al., 2015 <sup>[[#fn:r1179|1179]]</sup> ). Considering population growth and urbanisation, only 21 cm of global mean SLR by 2060 would increase the global population living below the hundred-year ESL from about 189 million in 2000 to 316–411 million in 2060, with the largest absolute changes in South and Southeast Asia and the largest relative changes in Africa (Neumann et al., 2015 <sup>[[#fn:r1180|1180]]</sup> ). Considering population growth, Hauer et al. (2016) <sup>[[#fn:r1181|1181]]</sup> estimate that 4.3 and 13.1 million people in the USA would live below the levels of 0.9 and 1.8 m SLR by 2100.

New coastal flood risk studies conducted since AR4 at global, continental and city scale, reinforce AR5 findings that if coastal societies do not adapt, flood risks will increase by 2–3 orders of magnitude reaching catastrophic levels by the end of the century, even under the lower end SLR expected under RCP2.6 (high confidence; Hinkel et al., 2014 <sup>[[#fn:r1182|1182]]</sup> ; Abadie et al., 2016 <sup>[[#fn:r1183|1183]]</sup> ; Diaz, 2016 <sup>[[#fn:r1184|1184]]</sup> ; Hunter et al., 2017 <sup>[[#fn:r1185|1185]]</sup> ; Lincke and Hinkel, 2018 <sup>[[#fn:r1186|1186]]</sup> ; Abadie, 2018 <sup>[[#fn:r1187|1187]]</sup> ; Brown et al., 2018a <sup>[[#fn:r1188|1188]]</sup> ; Nicholls, 2018 <sup>[[#fn:r1189|1189]]</sup> ). In combination, these studies take into account a SLR scenario range wider than the likely range of AR5 but consistent with the range of projections assessed in this report (Section 4.2.3.2). For example, considering 25–123 cm of SLR in 2100, all SSPs and no adaptation, Hinkel et al. (2014) find that 0.2–4.6% of global population is expected to be flooded annually in 2100, with expected annual damages (EAD) amounting to 0.3–9.3% of global GDP. Assessing 120 cities globally, Abadie (2018) find that under a weighted combination of the probabilistic scenarios, New Orleans and Guangzhou Guangdong rank highest with EAD above 1 trillion USD (not discounted) in each city. For Europe, EAD are expected to rise from 1.25 billion EUR today to 93–960 billion EUR by the end of the century (Vousdoukas et al., 2018b <sup>[[#fn:r1190|1190]]</sup> ). Already today, many small islands face large flood damages relative to their GDP specifically through TCs (Cashman and Nagdee, 2017 <sup>[[#fn:r1191|1191]]</sup> ) and under SLR EAD can reach up to several percent of GDP in 2100, as highlighted in AR5 (Wong et al., 2014 <sup>[[#fn:r1192|1192]]</sup> ). Similar to the exposure studies, estimates of future flood risk without considering adaptation, as presented in this paragraph, do not provide a meaningful characterisation of coastal flood risks, because adaptation and specifically hard protection is expected to be widespread during the 21st century in urban areas and cities (high confidence; Section 4.4.3.2.2). Rather, these estimates need to be seen as illustrations of the scale of adaptation needed to offset risk.

Flood risk studies that have included adaptation find that hard coastal protection is generally very effective in reducing flood risks during the 21st century even under high SLR scenarios (high confidence; Hinkel et al., 2014 <sup>[[#fn:r1193|1193]]</sup> ; Diaz, 2016 <sup>[[#fn:r1194|1194]]</sup> ; Brown et al., 2018a <sup>[[#fn:r1195|1195]]</sup> ; Hinkel et al., 2018 <sup>[[#fn:r1196|1196]]</sup> ; Lincke and Hinkel, 2018 <sup>[[#fn:r1197|1197]]</sup> ; Tamura et al., 2019 <sup>[[#fn:r1198|1198]]</sup> ) (Section 4.4.2.2.2). For example, Hinkel et al. (2014) find that under 25–123 cm of SLR in 2100 and all SSPs, hard coastal protection reduces the annual number of people affected by coastal floods and EAD by 2–3 orders of magnitude. Under high-end SLR and beyond the 21st century, effectiveness of coastal adaptation is expected to decline rapidly, but there is a lack of studies addressing this issue. Furthermore, there is a lack of studies taking into account responses beyond hard protection such as ecosystem-based adaptation, accommodation, advance and retreat (Sections 4.4.2).

Studies also confirm AR5 findings that the relative costs and benefits of coastal adaptation are distributed unequally across countries and regions (high confidence; Wong et al., 2014 <sup>[[#fn:r1199|1199]]</sup> ; Diaz, 2016 <sup>[[#fn:r1200|1200]]</sup> ; Lincke and Hinkel, 2018 <sup>[[#fn:r1201|1201]]</sup> ; Tamura et al., 2019 <sup>[[#fn:r1202|1202]]</sup> ). For example, while the median cost of protection and retreat under RCP8.5 in 2050 has been estimated to be under 0.09% of national GDP, large relative costs are found for small island states such as the Marshall Islands (7.6%), the Maldives (7.5%), Tuvalu (4.6%) and Kiribati (4.1%; Diaz, 2016 <sup>[[#fn:r1203|1203]]</sup> ). Furthermore, on a global average and for urban and densely populated regions, hard protection is highly cost efficient with benefit-cost ratios up to 104, but for poorer and less densely populated areas benefit-cost ratios are generally smaller than one (Lincke and Hinkel, 2018 <sup>[[#fn:r1204|1204]]</sup> ). Hence, without substantial transfer payments supporting poor areas, coastal flood risks will evolve unequally during this century, with richer and densely populated areas well protected behind hard structures and poorer less densely populated areas suffering losses and damages, and eventually retreating from the coast.

While continental to global scale flood exposure and risk studies have also explored a wider range of uncertainty as compared to AR5, much remains to be done. All of these studies rely on global elevation data, but few studies have explored the underlying bias. For example, for the Po delta in Italy, it was found that elevation data based on the widely used Shuttle Radar Topography Mission (SRTM), Reuter et al. (2007) overestimates the 100-year floodplain by about 50% as compared to local Lidar data (Wolff et al., 2016 <sup>[[#fn:r1205|1205]]</sup> ), while in the Ria Formosa region in Portugal SRTM underestimates EAD by up to 50% depending on the resampled resolution of the Lidar data (Vousdoukas et al., 2018a <sup>[[#fn:r1206|1206]]</sup> ). For the USA, SRTM data systemically underestimates population exposure below 3 m by more than 60% as compared to coastal Lidar data (Kulp and Strauss, 2016 <sup>[[#fn:r1207|1207]]</sup> ). A global scale comparison of major contributors to flood risk uncertainty finds that uncertainty in digital elevation data is roughly at equal footing with uncertainties in socioeconomic development, emission scenarios, and SLR in determining the magnitude of flood risks in the 21st century (Hinkel et al., 2014 <sup>[[#fn:r1208|1208]]</sup> ). At a European level, the number of people living in the 100-year coastal floodplain can vary between 20–70% depending on the different inundation models used and the inclusion or exclusion of wave set up (Vousdoukas, 2016 <sup>[[#fn:r1209|1209]]</sup> ). Comparing damage functions attained in different studies for European cities, Prahl et al. (2018 <sup>[[#fn:r1210|1210]]</sup> ) find up to four-fold differences in damages for floods above 3 m. Another major source of uncertainty relates to uncertainties in present-day ESL events due to the application of different extreme value methods (Wahl et al., 2017 <sup>[[#fn:r1211|1211]]</sup> ; Section 4.2.3.4). While all of the uncertainties reported above affected the actual size of exposure and flood risk figures, they do not affect the overall conclusions drawn here.

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