Coastal flood impacts and lost ecosystem services along Europe’s Outermost Regions and Overseas Countries and Territories

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Vousdoukas, Dominik Patrotny, Lorenzo Mentaschi, Isavela N Monioudi, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5158614/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 07 Jan, 2026 Read the published version in Nature Communications → Version 1 posted You are reading this latest preprint version Abstract Climate change is expected to result in rising seas, exacerbating coastal floods 1 and erosion 2 . Remote islands are projected to be among the most challenged regions, due to their geographic isolation and fragile economies. While, Small Island Developing States have been attracting the attention of scientists and policy makers, Europe’s Outermost Regions (ORs) and Overseas Countries and Territories (OCTs) remain poorly studied in terms of their impacts from Sea Level Rise (SLR). Here we carry out a data-modelling framework to comprehensively study risks of flooding, the submergence of flat regions, and coastal erosion along coastlines of ORs and OCTs. Our study shows that under a high emissions scenario by 2150 annually nearly 3,000 km 2 is expected to be flooded, one third of which by tidal flooding, while 150 km 2 of land will be lost by coastal erosion. This translates into an annual exposure to coastal inundation of up to half a million of people and an economic damage of 5.9 € billion per year - a 40-fold increase from today. Our study shows the increasing benefits in time of stringent climate mitigation, which could nearly halve these impacts in the long run. However, sea levels will continue to rise long after net zero carbon is reached, and so will the consequent impacts, highlighting the critical importance of proactive efforts to increase the resilience of these vulnerable regions against rising seas. Earth and environmental sciences/Natural hazards Earth and environmental sciences/Climate sciences/Climate change Figures Figure 1 Figure 2 Figure 3 Introduction Climate change and consequent sea level rise (SLR) are projected to have a substantial impact on coastal communities through erosion 2 and flooding 3 . Sea levels have been rising with an accelerating pace 4 and even under stringent climate mitigation policies the world is committed to a further increase in Mean Sea Level (MSL) of at least 50 cm by the year 2150 5,6 . While damages from coastal flooding are projected to rise sharply worldwide if current coastal protection is not improved, island states are projected to bear the highest burden of climate change 7 8 . At the same time, some of these remote areas are among the less studied regions. This is the case for Europe’s 9 Outermost Regions (ORs) and 13 Overseas Countries and Territories (OCTs), which are scattered across the Atlantic, Caribbean, Indian Ocean, and Pacific. ORs are an integral part of the EU while OCTs are part of the territory of an EU member state (Denmark, France and the Netherlands), but not of the EU, although they are associated with it. ORs and OCTs are all islands or archipelagos, except for French Guiana that is located on the north Atlantic coast of South America. They are diverse in geography and geology, ranging from volcanic landscapes to coral reefs, and rich in biodiversity and natural resources. Several ORs and OCTs are low-lying atolls e.g. in French Polynesia and the Dutch OCTs (e.g. Aruba and Bonaire) and hence are inherently susceptible to coastal erosion and inundation. Economically, they vary from tourism-driven economies in the Canary Islands and French Polynesia to more traditional fishing and agriculture in others (e.g. Saint Pierre and Miquelon and French Guiana). Geographically, they are dispersed, creating challenges and opportunities in connectivity, climate adaptation, and sustainable development. Despite these differences, their economies rely, to varying degrees, on tourism, agriculture, and fisheries, all sectors profoundly impacted by rising sea levels. Meanwhile, even seemingly elevated regions like the Canaries are not immune, with coastal infrastructure, settlements, beaches, and agricultural land all under increasing pressure. In the present work, we quantify the socio-economic impacts of rising seas for Europe's OCTs and ORs. To that end, we use state-of-the-art estimates of Extreme Sea Levels (ESLs), produced by a coupled wave-ocean circulation model, and the latest SLR projections from IPCC AR6. We apply a hydrodynamic model to simulate coastal flooding considering the latest available information on the islands’ topography and then combine simulated flooded areas with exposure and vulnerability data to estimate flood economic impacts and population exposure. Finally, we asses coastal erosion and the consequent ecosystem service losses in monetary terms. Direct coastal flood impacts At present, annually 582.29 [370.52 - 900.15] km 2 (values in brackets express the very likely range defined by the 5 th -95 th percentiles) of land along coastlines of ORs and OCTs is expected to be flooded, corresponding to 0.10 ±[0.07 - 0.16%] % of the region's total area. Almost 84% of this total Expected Annual Flooded Area (EAFA) is located in OCTs and 16% in ORs. These flooded areas correspond to 14,366 ±[9,611 - 39,444] people, around 0.24% ±[0.16% -0.67%] ofthe OCTs and ORs population, who are annually exposed to coastal floods, with 88% of Expected Annual Population Exposed (EAPE) living in ORs. The resulting Expected Annual Damage (EAD) of flooding for the OCTs and ORs is 141.9 ±[98.6 - 215.0] € million (Fig. 2a), of which 69% occurs in ORs. OCTs host several uninhabited or sparsely populated islands (e.g. the French Southern and Atlantic Lands), so flooded areas do not alwaystranslate into population exposure and economic impact. On the other hand, certain ORs like the Canary Islands, host urban centers in low lying coastal areas, where extreme events can be more impactful economically. However, when expressed relative to the size of the economy, impacts in the OCTs (EAD of 0.15% ±[0.06% - 0.30%] of GDP) are larger compared to that in the ORs (0.09% ±[0.08% - 0.12%] of GDP) (Fig. 1b). By the year 2050 overall EAFA for all regions increases by more than 70% compared to now and will reach 1,020.00 ±[737.83 - 1,364.38], 1,043.17 ±[763.42 - 1,386.11], 1,057.85 ±[778.02-1,403.60], and 1,085.11 ±[799.04 - 1,440.66] km 2 , under SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5, respectively (Fig. 2c). These values correspond to around 0.18% of the total area. Flooded areas keep growing in time and will reach 1,399.75 ±[767.38 - 2,230.23], 1,575.64 ±[925.34 - 2,472.98], 1,771.39 ±[1,087.30 - 2,744.34], and 1,949.07 ±[1,231.70 - 3,019.80] km 2 by 2100under SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5, respectively. With 0.25%-0.35% of the total land area expected to be flooded, this is 2.5 to 3.5 times more than today. By 2150 the size of flooded areas will amount to 1,757.58 ±[767.38 - 3,129.40], 2,105.84 ±[1009.34 - 3,658.46], 2,527.97 ±[1,305.60 - 4,294.59], and 2,838.58 ±[1,517.26 - 4,886.42] km 2 , hence ranging between 0.31% and 0.5%, or 5 times higher than today in the worst-case scenario. As sea levels rise and flooded areas grow in size, the number of people exposed is projected to increase around 5 times by the year 2050, 10 to 17 times by 2100 and 15 to 28 times by 2150. The resulting 2050 EAPE values are between 0.09 and 0.10 million people, depending on the scenario, and by 2150 they are projected to climb to 0.22 ±[0.04 - 0.47], 0.29 ±[0.09 - 0.58], 0.37 ±[0.14 - 0.69], and 0.42 ±[0.18 - 0.80] million people, under SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5, respectively (Fig. 2b). These values correspond to around 3.77% ±[0.69% - 8.03%], 4.92% ±[1.51% - 9.77%], 6.23% ±[2.42% - 11.74%], and 7.13% ±[3.03% - 13.54%] of the total population by 2150, under SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5, respectively. Direct economic damages increase not only with growing flooded areas, but also with higher inundation depths, so the relative rise in projected EAD is even more pronounced compared to EAFE and EAPE. By 2050, EAD increases at least 7 times compared to present and will reach 1.18 ±[0.54 - 2.09], 1.24 ±[0.58 - 2.15], 1.28 ±[0.61 - 2.19], and 1.35 ±[0.64 - 2.28] € billion under SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5, respectively (Fig. 2a). These impacts remain below 1% of GDP, but by the end of the century they will rise 14 - 24 times compared to present to reach 1.60 ±[0.42% - 3.23%], 1.92% ±[0.65% - 3.66%], 2.31% ±[0.98% - 4.20%], and 2.66% ±[1.26% - 4.74%] of the GDP under SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5, respectively (Fig. 1d). The corresponding EAD values are 2.15 ±[0.56 - 4.33], 2.58 ±[0.87 - 4.92], 3.10 ±[1.31 - 5.64], and 3.58 ±[1.70 - 6.36] € billion, respectively. By 2150, projected EAD reaches 3.1 to 5.9 € billion, corresponding to 2.3-4.4% of the OCT/ORs GDP, or a 21 to 40 times increase compared to the present-day levels (Fig. 2a). Overall, OCTs contribute at least 73% of the EAFA by 2050, a number which is reduced to 60.75% - 66.10% by the end of the century and to 56.52% - 62.12% by 2150. As several of the OCTs are very sparsely populated, ORs contribute to at least 88% of the total EAPE, a number which rises to 93% by the year 2150. The same year, the corresponding median EAPE values are 208,198 – 395,892, 14,524 – 25,396 people, for ORs and OCTs, respectively, with range expressing the scenario uncertainty. Similarly, ORs contribute to most of the 2150 EAD which is around 2.43 - 4.77 € billion, compared to 0.64 - 1.12 € billion for the OCTs, accounting for 2.32% - 4.55% and 2.16% - 3.79%, of the GDP respectively (Fig. 3c). Presently, the regions with the highest flooded area as percentage of the total are Aruba (1.09% ±[0.01% - 2.54%]), followed by Bonaire, Sint Eustatius and Saba (1.04% ±[0.53% - 5.88%]), French Polynesia (0.94% ±[0.47% - 1.39%]), Guadeloupe (0.54% ±[0.35% - 1.90%]) and New Caledonia (0.52% ±[0.16% - 1.03%]). These values correspond to area equal to 1.97 ±[0.02 - 4.62], 3.31 ±[1.70 - 18.76], 33.50 ±[16.89 - 49.53], 9.04 ±[5.76 - 31.55] and 96.16 ±[29.47 - 190.48] km 2 , respectively (Fig. 3c). At least 60% of the flooded area is found in the French Southern and Antarctic Lands and another 30% is distributed along New Caledonia, French Guiana and French Polynesia. As for the present-day, also by the end of the century, most of the EAFA is from the French Southern and Antarctic Lands (36.18% - 38.87%, depending on the scenario), followed by the French Guiana (24.1% - 28.7%) and New Caledonia (17.0% - 18.9%). The corresponding median EAFA values are 544.1 - 705.1, 336.8 - 559.1 and 265.1 - 331.2 km 2 , respectively and climb to 651.4 - 964.3, 484.9 - 916.9 and 305.1 - 440.3 km 2 , respectively, by 2150. By the same year, all regions are projected to experience an increase in EAFA between 1 and 40 times compared to the present, depending on the area and scenario. Some smaller islands are projected to have at least 4% of their area annually flooded; e.g. Bonaire, Sint Eustatius and Saba (11.0% - 16.6%), Aruba (8.8% - 15.5%), Guadeloupe (5.0% - 8.6%) and Saint-Martin (4.5% - 6.7%). The corresponding median EAFA values are 35.1 - 53.1, 15.9 - 28.3, 82.5 - 143.6 and 2.3 - 3.4 km 2 , respectively (Fig. 3c). For the above regions the projected increase in EAFA exceeds 7 and can reach up to 15 times compared to the present. But the highest relative increase in EAFA is projected in Reunion (19.3 - 40.0 times compared to the present), Bonaire, Sint Eustatius and Saba (9.6 - 15.0), Martinique (9.4 - 16.7), Saint-Martin (9.3 - 14.4) and Guadeloupe (8.1 - 14.9). In terms of the present-day EAPE, the region with the highest EAPE as percentage of the total population is Mayotte (0.52% ±[0.38% - 0.67%]), followed by Wallis and Futuna (0.52% ±[0.38% - 0.65%]), French Guiana (0.45% ±[0.33% - 1.10%]), Guadeloupe (0.34% ±[0.22% - 1.41%]) and Reunion (0.30% ±[0.23% - 0.52%]). Such values correspond to EAPE equal to 1,737, 60, 1,332, 1,327 and 2,588 people, respectively. At least one third of the people affected are found in the Canary Islands (4797 people), 18% is in Reunion (2588) and the remaining 31% in distributed along Guadeloupe (1327), Mayotte (1737) and French Guiana (1332) (Fig. 3b). In terms of EAD, about half of the total present-day damages are estimated in New Caledonia and the Canary Islands (24 ±[7.5 - 50.9] and 40 ±[34.4 - 46.5] € million, respectively) and one third in French Guiana, Reunion, and French Polynesia (20 ±[16.9 - 22.9], 17 ±[14.4 - 19.4], and 15 ±[7.7 - 23.3] € million, respectively) (Fig. 3a). Some of the above countries feature also among the ones with the highest EAD as percentage of the GDP: French Guiana (0.50% ±[0.43% - 0.58%]), followed by New Caledonia (0.24% ±[ 0.07% - 0.50%]), Mayotte (0.23% ±[0.20% - 0.27%]), Saint Pierre and Miquelon (0.22% ±[0.19% - 0.25%), and French Polynesia (0.20% ±[0.10% - 0.30%]) (Fig. 3a). By the end of the century, most of the EAPE comes from the Canary Islands (34.53% - 37.09% of the total, depending on the scenario), followed by the Reunion (28.81% - 32.01%). The corresponding median EAPE values are 57,857 – 88,908 and 44,946 – 82,405 people, respectively. The same values increase further by 2150 to reach 78,537 – 138,868 and 69,894 – 142,677 people, respectively; representing a 15 - 28 and 26 - 54 times increase from the present. While the above values express the highest absolute EAPE, the highest EAPE as percentage of the population in 2150 are estimated for Reunion (8% - 17%), French Guiana (7% - 15%), St Barthelemy (5% - 12%), Guadeloupe (4% - 8%) and Bonaire, Sint Eustatius and Saba (4% - 7%), with corresponding median EAPE values being 69,894 – 142,677, 21,130 – 43,167, 478 – 1,125, 16,865 – 30,497 and 1,068 – 1,794 people, respectively (Fig. 3b). The highest relative increase in EAPE comes from St Barthelemy (42 – 100 times from the present), Reunion (26 - 54), Saint Pierre and Miquelon (25 - 49), and Canary Islands (15 - 28). Guadeloupe accounts for most of the total OCT/ORs EAD by the year 2050 (i.e. around 19%), but after that year it is replaced by Reunion, i.e. 20.00% - 23.69% and 23.07% - 26.36%, by 2100 and 2150, respectively (range expresses the different emission scenarios). Other countries with high contributions to the total EAD (by 2100 and 2150) are Guadeloupe (14.9% - 16.5%), Canary Islands (14.4% - 15.8%), French Guiana (12.2% - 13.3%), and New Caledonia (8.6% - 9.8%). The corresponding 2150 median EAD values are 0.7 - 1.5, 0.5 - 1.0, 0.5 - 0.9, 0.4 - 0.8 and 0.3 - 0.5 € billion, respectively (Fig. 3a). These values correspond to a 41 - 89, 139 - 263, 11 - 20, 18 – 37 and 11 - 21 times increase from the present, respectively. By the end of the century, the highest EAD as percentage is projected in French Guiana (6.0% - 11.1%), Guadeloupe (4.5% - 7.1%), Bonaire, Sint Eustatius and Saba (4.4% - 6.4%), Saint Pierre and Miquelon (2.9% - 4.3%), and Saint-Martin (2.5% - 3.5%) (e.g. Fig. 1c), corresponding to a 11.1 - 21.2, 111.3 – 176.3, 47.6 – 69.9, 12.1 - 18.5, and 83.7 – 116.7 times increase from the present, respectively. Guadeloupe and Saint-Martin are also the countries where the highest increase is projected, followed by Martinique (85.0 – 133.5), Aruba (71.0 – 109.7), and Curacao (49 – 70.5). Overall, the above rankings extend also to 2150. Anticipated lost coastal ecosystem services The current coastal ecosystem services in OCT/ORs are estimated around 147.9 € billion annually, half of which come from New Caledonia and 25% from French Guiana and French Polynesia. More than 90% of the ecosystem services are from tropical tree cover (Fig. 1e). Rising seas are projected to drive shoreline retreat, which will deplete a part of such ecosystem services. An estimated 198.35 ±[20.24 - 435.04], 214.49 ±[33.92 - 451.33], 222.41 ±[37.21- 461.86], and 245.24 ±[47.57 - 492.98] € million of such ecosystem services are projected to be lost by the year 2050, under SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5, respectively (Fig. 2d). The same estimates climb to 474.80 ±[38.77 - 1,053.49], 615.64 ±[164.19 - 1,250.29], 748.72 ±[268.68 - 1,434.20], and 867.60 ±[366.63 - 1,626.72] € million, respectively, by the end of the century. The above estimates imply emission mitigation benefits of at least 38.18% for the entire most likely range of the projections. By the year 2150, ecosystem services of 736.41 ±[20.58 - 1702.10], 1020.10 ±[243.47 - 2131.25], 1314.44 ±[445.63 - 2574.21], and 1499.61 ±[571.23 - 2970.29] € million are projected to be lost, under SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5, respectively (Fig. 2d). By the years 2100 and 2150, the highest contributions come from the Outermost Regions (64.90% - 67.58%, depending on the scenario), followed by the Overseas countries and territories (32.42% - 35.10%). By the end of the century the corresponding median values are 308.15 – 578.47, and 166.65 – 289.13 € million, respectively (Fig. 1f) and by the year 2150 these values climb to 488.20 – 1013.47, and 248.21 – 486.14 € million, respectively. The country with the most losses in coastal ecosystem services by the end of the century is projected to be French Guiana (45.85% - 47.82% of the total, range expresses SSP uncertainty), characterized by a long, low-lying coastline (Fig. 3d). It is followed by the New Caledonia (17.1% - 18.4%), Martinique (8.1% - 8.2%), French Polynesia (7.8% - 7.9%), and French Southern and Antarctic Lands (4.2% - 4.5%), respectively. The corresponding median lost ecosystem services values are 217.70 – 414.87 € million, 87.17 – 148.72 € million, 38.47 – 70.98 € million, 37.33 – 67.37 € million, and 21.35 – 36.83 € million, respectively (Fig. 1f). The corresponding 2150 median values are 349.02 – 732.13 € million, 128.16 – 247.75 € million, 60.13 – 123.29 € million, 57.34 – 115.71 € million, and 31.66 – 61.74 € million, respectively. Discussion Our results show a strong increase in impacts from rising seas for Europe’s OCTs and ORs. In the short term, the effect of climate mitigation is limited and impacts of high emissions in 2050 are marginally worse compared to implementing stringent climate mitigation policies. The benefits of mitigation become more substantial in time. By 2100, our findings indicate an additional 139 km 2 of land lost to erosion, 102,774 more people exposed to floods and 1,542 € billion of additional direct flood losses for the highest emissions scenario compared to the lowest emissions scenario. Such, deviations are projected to keep increasing beyond the present century as sea levels will keep rising long after emission’s have been reduced. Therefore, even under strong mitigation pathways such as SSP1-1.9, for which the maximum global average temperature is expected to occur before 2100 9 , coastal impacts of SLR will continue to grow in time (at least until 2150 according to our analysis). For the lowest emission scenario (SSP1-1.9), and assuming present socioeconomic exposure in our analysis, impacts by 2150 will be 40% larger compared to impacts in 2100. Impacts of rising sea levels will vary strongly among OCTs and ORs. Even for the lowest emissions scenario and as early as 2050, several OCTs and ORs are projected to experience expected annual direct economic impacts that exceed 1% of their GDP (e.g. French Guiana, Saint Pierre and Miquelon, Aruba, Guadeloupe, Martinique and New Caledonia). As time proceeds and under higher emissions scenarios, direct economic impacts could rapidly grow to several percentage points (more than 10% in the extreme case of French Guiana). These values reflect longer term average impacts, while actual extreme flood events and their impacts can constitute substantial shocks for any economy, without even considering indirect impacts, like business interruption and other spill-over effects 10 . The most affected areas in OCTs and ORs are flatter low-lying coastal parts characterized by high population density. This includes, for example, the cities of Las Palmas de Gran Canarias, Cayenne of French Guiana, Ponta Delgada in Azores, Mamoudzou and Dzaoudzi of Mayotte, Saint-Denis and Saint-Pierre of Reunion, Mata-Utu of Wallis and Saint-Pierre, the capital of Saint Pierre and Miquelon. In all these cities the average elevation doesn’t exceed 10 m and even if large parts will not be directly affected by floods, the disruption of every-day life will be substantial by the inundation of critical assets like ports, roads and energy infrastructure, among others. The resilience of the latter is a critical issue since such assets tend to be in flat, low-lying areas, given the absence of higher altitude plains, especially in volcanic islands. Several OCTs/ORs airports lie below 10 m of elevation and will experience increasing flood risk, even under moderate sea level rise scenarios, e.g. the Dzaoudzi Pamandzi International Airport – Mayotte, Flamingo International Airport – Bonaire, Faa'a International Airport - French Polynesia, Pointe-à-Pitre International Airport – Guadeloupe and Saint-Pierre Airport – Saint Pierre and Miquelon. Airports, together with ports, which are anyway near mean sea level, are the lifelines of islandic communities, either for economic activity by ensuring the access of tourists to the island; or the other way around, by providing to the inhabitant’s access to critical supplies and health services, among others. All the above imply that most of the affected areas cannot be abandoned and will have to be protected, however, acknowledging the challenge is only the first step. By understanding the specific vulnerabilities of each region and working collaboratively towards adaptation strategies, the ORs and OCTs can build resilience against the rising tide. The spectrum of potential actions is broad, from investing in coastal defenses and early warning systems to exploring nature-based solutions (NBS) like land reclamation and mangrove restoration. Detailed methods Coastal flood risk modelling framework We assess impacts from Sea Level Rise (SLR) and episodic flooding along coastlines during the 21st century, considering both permanent inundation from SLR and tides and episodic flooding from coastal extremes. The analysis is based on the modular framework LISCOAST (Large-scale Integrated Sea-level and Coastal Assessment Tool). It combines state-of-the-art large-scale modelling tools and datasets to quantify hazard, exposure and vulnerability in coastal areas and compute consequent risks 1 . We consider the five principal Shared Socio-economic Pathway scenarios that span a range from ambitious mitigation to no emission policies: low-emissions (SSP1-2.6), reaching net-zero emissions after 2050 and achieving the Paris Agreement goal of holding the increase in global temperature to below 2°C compared to pre-industrial levels; ‘moderate emissions’ (SSP2-4.5), implying stable emissions until mid-century, when they start to be reduced without reaching net-zero; high emissions (SSP3-7.0), with emissions rising constantly to almost double from current levels by the end of the century; and a high fossil-fuel development world throughout the 21st century (SSP5-8.5) 14 . For each of these scenarios we generate probabilistic projections of mean and extreme sea levels that give rise to permanent inundation or episodic flooding and combine them with exposure and vulnerability to quantify economic losses. In addition, we produce projections of coastal erosion and we assess how the latter will affect future ecosystem services. More details on the different steps of the analysis are provided below. Present day extreme sea levels Coastal areas are exposed to rising mean sea level (MSL) and episodic high sea levels under extreme atmospheric conditions. Extreme sea levels (ESL) are driven by the combined effect of MSL, tides and water level fluctuations due to waves and storm surges. We derive the contribution of each of these drivers with state-of-the-art modelling tools and datasets and combine them to obtain ESLs every 1 km along the coastline. For the baseline period, spanning from 1980 until 2020, we run a reanalysis of waves and storm surges based on a two-way coupled ocean model using an unstructured grid with a resolution ranging from ~50 km offshore to ~2 km nearshore. The coupled model system includes the Semi-implicit Cross-scale Hydroscience Integrated System Model (SCHISM) 15 , configured in its two-dimensional barotropic mode and the 3rd-generation spectral wave model (WWM-V) 16 . The model accounts for the combined effects of wind, atmospheric pressure gradients, and tides. Bathymetric data are available from the European Marine Observation and Data Network (EMODnet) in angular coordinates at a resolution of 1/8 arc-minute (0.0021° of latitude and longitude; http://www.emodnet.eu/bathymetry) and are interpolated onto the computational grid. We apply the coupled model to produce a reanalysis of waves and storm surges, forced by sea level pressure and wind speed data from ERA5 17 . The reanalysis is carried out without tidal forcing in order to make sure that our hindcast resolves the weather-driven component of ESLs; without the stochastic modulation of tidal variations. Further details about the model setup and the validation can be found in Mentaschi et al. 18 . Since, it is known that non-linear interactions between tides, waves and storm surges can be important in some areas, we apply a correction for the above effects following an approach similar to Arns 19 . To that end, we run a shorter 10-year reanalysis including tidal forces and from the overlapping time series we use copulas to produce a correction function for non-linear tidal effects on the water level anomaly and the significant wave height. In order to improve the accuracy of the reanalysis data we implement some additional steps as detailed below. Using satellite altimetry data we apply a Quantile Mapping Bias Correction on both the water level anomaly and the significant wave height. This is done after compiling all coinciding model and satellite values along 1o x 1o cells. To further improve the cyclone related storm surge estimates which have not been sufficiently resolved by our reanalysis, we did additional simulations of tropical cyclone driven sea level anomalies using the Delft3D-FM model 20 forced by the IBTrACS best-track archive 21 . The reanalysis values are corrected by considering the tropical cyclone runs values when they are higher than those of our ERA5 runs. More information about the approach and data can be found in Vousdoukas et al 8,22 . Spectral wave parameters provide one characteristic estimate for wave height, direction and period from the whole spectrum and therefore lack the detail needed to describe wave processes along complex shorelines. To overcome this shortcoming, we use the spectral peaks from the WWM-V model output and we propagate each peak along a global transect dataset with 1 km alongshore resolution. The transect dataset includes information on the shoreline position, orientation, submerged and subaerial slope, among others. Details of the data and methods used to generate transects are provided in Athanasiou et al. 23 . We benefit from the complete spectral information from the wave model to propagate each peak at each time stamp along its corresponding transect using Snell’s law 24 . We then estimate the wave breaking height combining the peak wave parameters with the submerged profile slope. Subsequently, we obtain the wave run-up height R 2 based on the Stockdon empirical formula 25 , after combining the breaking wave height and period with the subaerial beach profile slope. The above steps result in wave runup height estimates for each spectral peak and we consider the highest value as the characteristic for the specific time stamp. We then combine the wave runup with the storm surge to obtain the meteorological tide and apply non-stationary extreme value analysis 26 to the time series to obtain estimates for different return periods. Baseline ESLs are produced by combining the final meteorological tide time series with tidal elevations obtained from the FES2014 model 27 . Following the approach of Vousdoukas et al. 22 , the high tide water level is considered taking into account the range due to the spring-neap tide cycle. Sea level rise projections Relative SLR projections are obtained from latest IPCC AR6 assessment 28 – 30 and incorporate the effects of the various components of future SLR, as simulated by climate models from the Coupled Model Intercomparison Project phase 6 (CMIP6), including steric SLR, dynamic sea-level change, contributions from glaciers and ice-caps, land-water storage and Glacial Isostatic Adjustment, among others. Projections of ESLs up to 2100 All ESL components (RSLR, tide, surges and R 2 ) are expressed as probability density functions (PDFs) that account for the different sources of uncertainty, and they are combined through Monte Carlo simulations in order to generate probabilistic estimates of ESLs for the SSP scenarios in each coastal segment (1 km alongshore resolution). Non-stationary extreme value analysis 26 is then applied to obtain for a range of return periods (i.e. 1, 2, 5, 10, 20, 50, 100, 200, 500, 1000 and 5000 years) PDFs of the corresponding return values of ESL throughout this century. Coastal flooding We use hydraulic 2-D simulations along the entire coastline to estimate inundation extent and depth, following the approach presented by Vousdoukas et al. 31 , running the Lisflood-ACC (LFP) model 32 at 30 m spatial resolution, using the estimated ESLs as forcing and considering hydraulic roughness derived from land-use maps 33 . Up to high-tide water levels (i.e. combination of mean sea level and high-tide) we apply the bathtub approach, and land below this sea water level and the corresponding assets are considered permanently inundated due to the effects of sea level rise. For episodic flooding, Liscflood-ACC is applied for each coastal segment with the model domain extending up to 200 km landwards in order to ensure the inclusion of all potentially hydrologically connected areas that may lie inland and away from the coast. The flood simulations are based on the recently published GLO-30 DEM 34 that is based on Synthetic Aperture Radar measurements and known to reduce vertical bias of SRTM based products. In addition, we apply post-processing using global LIDAR observations to further remove vertical bias, correcting for buildings and vegetation. Exposure and vulnerability The resulting flood inundation maps are combined with exposure and vulnerability information to estimate population exposure and direct flood damages. For today’s population exposure we overlay the present inundation maps with the WorldPop 2020 population dataset (www.worldpop.org), which is an open and high-resolution geospatial dataset of population and demographic dynamics, with a focus on low and middle income countries. The vulnerability to flooding is expressed through depth-damage functions (DDFs) 35 , which define the relation between direct damage and flood inundation depth for different land use classes. Asset values are further scaled according to the GDP per capita available at 5 arc-min resolution 36 in order to account for differences in the spatial distribution of wealth within countries. Baseline global land cover is available from the European Space Agency 37 at 10 m resolution. Given that more than 95% of the damages relate to built-up areas, the land use is corrected to take into account 30 m resolution gridded information on Global Human Built-up And Settlement Extent 38 (reference year 2010). Risk assessment For each coastal segment, the area flooded, number of people affected and direct flood losses are calculated at ~100 m resolution by combining flood inundation estimates with population and land use maps and the vulnerability functions. For areas that are inundated on a regular basis (which could happen in the future with SLR), defined as lying below the present high tide water level, assets are considered as fully damaged and the maximum loss according to the DDFs is applied. For areas inundated only during extreme events, the damage is estimated by applying the DDFs combined with the simulated inundation depth and land use information. MSLs and ESLs, and the corresponding flood depths, are available as PDFs for different return periods (RP = 1, 2, 5, 10, 20, 50, 100, 200, 500, 1000 and 5000 years). Consequently, for each coastal segment we obtain probabilistic estimates of flooded area (FA), population exposed (PE) and impact (D) from 1981 up to 2100. Integrating FA, PE and D over the return periods allows obtaining the Expected Annual Flooded area (EAFA), Population Exposed (EAPE) and direct economic Damage (EAD). We present and discuss our results about risk at global, regional, as well as country level, and we focus on the median, 5 th and 95 th percentiles (very likely range). Land cover and ecosystem valuation We use land cover data from WorldCover 2020 dataset from the European Space Agency 37 . The dataset is an algorithmic classification of Sentinel-2 and Sentinel-1 images, identifying 10 land cover classes at 10 meters resolution with an 74.4% overall accuracy 39 . We calculate the value of each land cover class using mean ecosystem value per year per hectare from the Ecosystem Services Valuation Database (ESVD), as presented by de Groot et al. 40 . The original values are in 2020 US dollars, which was first updated to 2022 dollars using the GDP deflator for the United States, and then converted to Euro at average price level of the European Union (27 countries) using the purchasing power parity conversion rates for 2022 (Eurostat, https://ec.europa.eu/eurostat). ESVD uses a different land cover classification, therefore it is assigned to the ESA dataset as shown in Table 1. ESVD differentiates between tropical and temperate forests, which is not explicitly shown in WorldCover 2020, hence we assume that all forests are tropical except those located in regions with a temperate climate, i.e. the Azores, Madeira, the Canary Islands, Saint Pierre and Miquelon, and Kerguelen Islands (which are the only part of the French Southern and Antarctic Lands analysed here). The total value of ecosystem services is the value of all inland land cover type located within the 10 km radius from the coastline. Table 1. Ecosystem service values (in 2022 euros at EU27 price level per hectare per year) according to ESA WorldCover class. ESA WorldCover class ESVD land cover type Value/year/ha Tree cover Tropical forests 82,638 Tree cover Temperate forests 3736 Shrubland Woodland and shrubland 534 Grassland Grassland 1108 Cropland Cultivated areas 5570 Built-up - 0 Bare / sparse vegetation Inland Un- or Sparsely Vegetated 234 Snow and ice Inland Un- or Sparsely Vegetated 234 Permanent water bodies Rivers and lakes 75,202 Herbaceous wetland Inland wetlands 33,761 Mangroves Mangroves 54,167 Moss and lichen Tundra 568 Erosion extent Projections of shoreline change are produced according to the methodology of Vousdoukas et al. 2 , using the latest IPCC SLR projections, as in the flood risk analysis. The methodology combines estimates of ambient change and SLR retreat in a probabilistic framework, with the former assessed through a probabilistic extrapolation of historical behavior detected from satellites and the latter by using a modified version of the Bruun Rule. Detailed description of the approach and data used can be found in Vousdoukas et al. 2 . Erosion magnitude at different locations is connected with the base coastline derived from ESA World Cover. The coastline is smoothed and split into detailed segments with an average length of 30 meters. Each segment of the coastline is assigned a sector of the inland land cover based on nearest-neighbor analysis. However, as unlimited backshore space for shoreline retreat is often not a realistic assumption due to topography or human coastline protection activities, we constrain the erosion extent by introducing two types of barriers into the analysis in a similar manner to Paprotny et al. 41 . Firstly, artificial barriers representing various anthropogenic structures are extracted from the GHS built-up surface (R2023) dataset for reference year 2018 at 10-meter resolution 42 , which is part of the Global Human Settlement Layer database. Any occurrence of built-up surfaces in a grid cell is considered a barrier for erosion. Second, topographic barriers representing high ground that is unlikely to be easily eroded are derived from DeltaDTM 34 . Each coastal sector corresponding to a 30-meter (on average) section of the coastline is intersected with the barriers, erasing areas that are invulnerable to erosion. Parts of the sector that are no longer connected directly to the coastline were removed. The resulting layer of areas where erosion is possible is used to clip the buffer around each coastline segment generated according to the erosion extent per scenario in a given location. Finally, the constrained erosion layer is intersected with the land cover dataset and the value of ecosystem services is calculated according to the eroded area per land cover class. Avoiding overlaps between flooding and erosion Given that the current computational and modelling capabilities do not allow carrying out the erosion and flooding assessments together, one of the inevitable limitations of this study is that we do not directly resolve the interactions between the two hazards. Coastal erosion is projected to be mainly driven by SLR, in the contrary to floods for which meteorological and astronomical tides are the main drivers. More than 95% of the coastal flood losses come from artificial areas, which also act as the landward limit of coastal erosion. This means that in case of shoreline retreat in front of urbanized areas, the double counting of the economic damages from floods and lost ecosystem services is very small, since the second number is much higher than the first (retreat takes place only in non artificial areas where flood losses are very small). On the other hand, in natural coastlines shoreline retreat will continue unimpeded resulting in even higher losses from ecosystem services. But also in this case coastal flood losses are less significant due to the lack of built areas. One unresolved issue remains the fact that in the case of shoreline accretion in front of artificial coastlines, the additional land will provide natural protection which will reduce the amount of flooded areas backshore. But such cases of shoreline accretion in front of urbanization are rarely natural but are result of human intervention which imply also shoreward expansion of the built area. Given all the above, and considering the scope of the work, we consider that the nonlinear interactions are insignificant compared to the other sources of uncertainty. References Vousdoukas, M. I. et al. Climatic and socioeconomic controls of future coastal flood risk in Europe. Nat. Clim. Change (2018) doi:10.1038/s41558-018-0260-4. Vousdoukas, M. I. et al. Sandy coastlines under threat of erosion. Nat. Clim. Change 10 , 260–263 (2020). Schinko, T. et al. Economy-wide effects of coastal flooding due to sea level rise: a multi-model simultaneous treatment of mitigation, adaptation, and residual impacts. Environ. Res. Commun. 2 , 015002 (2020). Frederikse, T. et al. The causes of sea-level rise since 1900. Nature 584 , 393–397 (2020). DeConto, R. M. et al. The Paris Climate Agreement and future sea-level rise from Antarctica. Nature 593 , 83–89 (2021). Kopp, R. E. et al. Communicating future sea-level rise uncertainty and ambiguity to assessment users. Nat. Clim. Change in press , (2023). Martyr-Koller, R., Thomas, A., Schleussner, C.-F., Nauels, A. & Lissner, T. Loss and damage implications of sea-level rise on Small Island Developing States. Curr. Opin. Environ. Sustain. 50 , 245–259 (2021). Vousdoukas, M. I. et al. Small Island Developing States under threat by rising seas even in a 1.5 °C warming world. Nat. Sustain. (2023) doi:10.1038/s41893-023-01230-5. Tebaldi, C. et al. Climate model projections from the Scenario Model Intercomparison Project (ScenarioMIP) of CMIP6. Earth Syst Dynam 12 , 253–293 (2021). Koks, E. E. et al. The macroeconomic impacts of future river flooding in Europe. Environ. Res. Lett. 14 , 084042 (2019). Seddon, N. et al. Global recognition of the importance of nature-based solutions to the impacts of climate change. Glob. Sustain. 3 , e15 (2020). Pappenberger, F. et al. The monetary benefit of early flood warnings in Europe. Environ. Sci. Policy 51 , 278–291 (2015). Ciavola, P., Ferreira, O., Haerens, P., Van Koningsveld, M. & Armaroli, C. Storm impacts along European coastlines. Part 2: lessons learned from the MICORE project. Environ. Sci. Policy 14 , 924–933 (2011). Meinshausen, M. et al. The shared socio-economic pathway (SSP) greenhouse gas concentrations and their extensions to 2500. Geosci Model Dev 13 , 3571–3605 (2020). Zhang, Y. J., Ye, F., Stanev, E. V. & Grashorn, S. Seamless cross-scale modeling with SCHISM. Ocean Model. 102 , 64–81 (6AD). Roland, A. et al. A fully coupled 3D wave-current interaction model on unstructured grids. J. Geophys. Res. Oceans 117 , 2156–2202 (2012). Hersbach, H. et al. The ERA5 global reanalysis. Q. J. R. Meteorol. Soc. 146 , 1999–2049 (2020). Mentaschi, L. et al. A global unstructured, coupled, high-resolution hindcast of waves and storm surge. Front. Mar. Sci. 10 , (2023). Arns, A. et al. Non-linear interaction modulates global extreme sea levels, coastal flood exposure, and impacts. Nat. Commun. 11 , 1918 (2020). Muis, S., Verlaan, M., Winsemius, H. C., Aerts, J. C. J. H. & Ward, P. J. A global reanalysis of storm surges and extreme sea levels. Nat Commun 7 , (2016). Knapp, K. R., Kruk, M. C., Levinson, D. H., Diamond, H. J. & Neumann, C. J. The International Best Track Archive for Climate Stewardship (IBTrACS). Bull. Am. Meteorol. Soc. 91 , 363–376 (2010). Vousdoukas, M. I. et al. Global probabilistic projections of extreme sea levels show intensification of coastal flood hazard. Nat. Commun. 9 , 2360 (2018). Athanasiou, P. et al. Global Coastal Characteristics (GCC): A global dataset of geophysical, hydrodynamic, and socioeconomic coastal indicators. Earth Syst. Sci. Data Discuss. 1–32 (2023) doi:10.5194/essd-2023-313. US Army Corps of Engineers. Coastal Engineering Manual . (U.S. Army Corps of Engineers, Washington, DC, 2002). Stockdon, H. F., Holman, R. A., Howd, P. A. & Sallenger, J. A. H. Empirical parameterization of setup, swash, and runup. Coast. Eng. 53 , 573–588 (2006). Mentaschi, L. et al. The transformed-stationary approach: a generic and simplified methodology for non-stationary extreme value analysis. Hydrol. Earth Syst. Sci. 20 , 3527–3547 (2016). Carrere, L., Lyard, F., Cancet, M., Guillot, A. & Picot, N. FES 2014, a new tidal model – Validation results and perspectives for improvements. in (Prague, 2016). Fox-Kemper, B. et al. Ocean, Cryosphere and Sea Level Change. in Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (eds. Masson-Delmotte, V. et al.) 1211–1362 (Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 2021). doi:10.1017/9781009157896.01. Garner, G. G. et al. IPCC AR6 Sea-Level Rise Projections. (2021). Kopp, R. E. et al. The Framework for Assessing Changes To Sea-level (FACTS) v1.0-rc: A platform for characterizing parametric and structural uncertainty in future global, relative, and extreme sea-level change. EGUsphere 2023 , 1–34 (2023). Vousdoukas, M. I. et al. Developments in large-scale coastal flood hazard mapping. Nat. Hazards Earth Syst. Sci. 16 , 1841–1853 (2016). Bates, P. D., Horritt, M. S. & Fewtrell, T. J. A simple inertial formulation of the shallow water equations for efficient two-dimensional flood inundation modelling. J. Hydrol. 387 , 33–45 (6AD). European Space Agency. Land Cover CCI Product User Guide Version 2. Tech. Rep. (2017). Pronk, M. et al. DeltaDTM: A global coastal digital terrain model. Sci. Data 11 , 273 (2024). Huizinga, H. J. Flood Damage Functions for EU Member States . 67 (2007). Kummu, M., Taka, M. & Guillaume, J. H. A. Gridded global datasets for Gross Domestic Product and Human Development Index over 1990–2015. Sci. Data 5 , 180004 (2018). European Space Agency. ESA WorldCover 2020. (2020). Wang, P., Huang, C., Brown de Colstoun, E. C., Tilton, J. C. & Tan, B. Global Human Built-up And Settlement Extent (HBASE) Dataset From Landsat. https://doi.org/10.7927/H4DN434S (2017). European Space Agency. Product Validation Report D12-PVR . https://worldcover2020.esa.int/data/docs/WorldCover_PVR_V1.1.pdf (2021). R. de Groot, L. Brander, & S. Solomonides. Update of Global Ecosystem Service Valuation Database (ESVD). FSD Report No 2020-06 . https://www.esvd.info/_files/ugd/53b4f9_c7518bbf3f884375828524c50773cff0.pdf (2020). Paprotny, D., Terefenko, P., Giza, A., Czapliński, P. & Vousdoukas, M. I. Future losses of ecosystem services due to coastal erosion in Europe. Sci. Total Environ. 760 , 144310 (2021). Pesaresi, M. & Politis, P. GHS-BUILT-S R2023A - GHS built-up surface grid, derived from Sentinel2 composite and Landsat, multitemporal (1975-2030). (2023) doi:10.2905/9F06F36F-4B11-47EC-ABB0-4F8B7B1D72EA. Additional Declarations There is NO Competing Interest. Cite Share Download PDF Status: Published Journal Publication published 07 Jan, 2026 Read the published version in Nature Communications → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5158614","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":381125769,"identity":"9f50023a-e7b9-4272-8a82-53e90fc5e04b","order_by":0,"name":"Michalis I. Vousdoukas","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2ElEQVRIiWNgGAWjYLACHgYGAwYG5gNApoQMKVrYEkBaeEjRwmMAZRMABsd7jD+8qThszN9/5vOrGzUWPAzsh49uwKvlzBkzyTlnDptJ3MjdZp1zDOgwnrS0G/i0SM7IMWPmbTtsw3CDd5txDhtQiwSPGSEtxp9BWuTPn3lmnPOPCC38EjkG0kAtZgYHcpgf57YRo4XnWBnQL+nGhjfSzJhz+yR42Aj5hY29eTMwxKwN550//Phzzrc6OX72w8fwakHRLgEmiVUOAswfSFE9CkbBKBgFIwcAAGdkRMdAFKCcAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0003-2655-6181","institution":"University of the Aegean","correspondingAuthor":true,"prefix":"","firstName":"Michalis","middleName":"I.","lastName":"Vousdoukas","suffix":""},{"id":381125770,"identity":"1df24c25-3884-4b03-a581-af7c55b6ce0f","order_by":1,"name":"Dominik Patrotny","email":"","orcid":"","institution":"Potsdam Institute for Climate Impact Research (PIK)","correspondingAuthor":false,"prefix":"","firstName":"Dominik","middleName":"","lastName":"Patrotny","suffix":""},{"id":381125771,"identity":"923f734f-c68b-4e2b-a712-77be0c4dbd7f","order_by":2,"name":"Lorenzo Mentaschi","email":"","orcid":"","institution":"University of Bologna","correspondingAuthor":false,"prefix":"","firstName":"Lorenzo","middleName":"","lastName":"Mentaschi","suffix":""},{"id":381125772,"identity":"a7c1148f-7d2f-45c5-ac73-9d3b456e42ac","order_by":3,"name":"Isavela N Monioudi","email":"","orcid":"","institution":"University of the Aegean, Mytilene, Greece","correspondingAuthor":false,"prefix":"","firstName":"Isavela","middleName":"N","lastName":"Monioudi","suffix":""},{"id":381125773,"identity":"24f49398-6442-4316-9c6a-8794886357d0","order_by":4,"name":"Luc Feyen","email":"","orcid":"https://orcid.org/0000-0003-4225-2962","institution":"Joint Research Centre","correspondingAuthor":false,"prefix":"","firstName":"Luc","middleName":"","lastName":"Feyen","suffix":""}],"badges":[],"createdAt":"2024-09-26 12:10:05","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5158614/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5158614/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41467-025-66391-7","type":"published","date":"2026-01-07T05:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":69820329,"identity":"b4b126d8-a9e2-4642-8def-839288274df2","added_by":"auto","created_at":"2024-11-25 14:09:49","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":149980,"visible":true,"origin":"","legend":"\u003cp\u003eEurope’s Outermost Regions and Overseas Countries and Territories will be increasingly exposed to rising seas until 2100: (a) Pie plot indicating the countries with the highest share of the total present-day Expected Annual Damage (EAD) - colors in a-d are unique for each country and only the countries with the 10 highest values are shown; (b) present-day EAD as percentage of the GDP (median values in bars with black whiskers indicating the 5\u003csup\u003eth\u003c/sup\u003e–95\u003csup\u003eth\u003c/sup\u003e confidence interval); (c) Pie plot indicating the highest country contributions to the total 2100 EAD under SSP1-2.6; (d) Ten countries with the highest 2100 EAD as percentage of the GDP (median values in circles with black whiskers indicating the 5\u003csup\u003eth\u003c/sup\u003e-95\u003csup\u003eth\u003c/sup\u003e quantile range; bars are grouped in stacks of 5 with one each for the 4 scenarios studied: ‘low-emissions’ (SSP1-2.6; blue), ‘moderate-emissions’ (SSP2-4.5; purple), ‘high-emissions’ (SSP3-7.0; orange) and ‘very-high-emissions’ (SSP5-8.5; red); (e) Pie plot indicating the highest country contributions to the total present-day Coastal Ecosystem Services; (d) Ten countries with the highest 2100 lost ecosystem services under SSP1-2.6 (median values in circles with black whiskers indicating the 5\u003csup\u003eth\u003c/sup\u003e-95\u003csup\u003eth\u003c/sup\u003e quantile range).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5158614/v1/4d8abcd335566a81248c66eb.png"},{"id":69820374,"identity":"caf8799f-039f-4959-a2b4-76fb90996723","added_by":"auto","created_at":"2024-11-25 14:09:55","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":236047,"visible":true,"origin":"","legend":"\u003cp\u003eEvolution of impacts from coastal floods along Europe’s OCTs and ORs until the year 2150 under all four scenarios (‘low-emissions’ (SSP1-2.6), ‘moderate-emissions’ (SSP2-4.5), ‘high-emissions’ (SSP3-7.0) and ‘very-high-emissions (SSP5-8.5)). Expected Annual Damage (a), Expected Annual Population Exposed (b), Expected Annual Flooded Area (c) and Expected Annual Value of Lost Ecosystem Services. The lines express the ensemble median projections and the coloured areas the 5\u003csup\u003eth\u003c/sup\u003e-95\u003csup\u003eth\u003c/sup\u003e confidence interval.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5158614/v1/cdce8449f2b55c5a04286944.png"},{"id":69820899,"identity":"947236d8-a492-40a0-856d-de700990c839","added_by":"auto","created_at":"2024-11-25 14:17:49","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":195873,"visible":true,"origin":"","legend":"\u003cp\u003eCountry level expected annual alues of coastal impacts along Europe’s OCTs and ORs by the year 2100 under all four scenarios (‘low-emissions’ (SSP1-2.6), ‘moderate-emissions’ (SSP2-4.5), ‘high-emissions’ (SSP3-7.0) and ‘very-high-emissions’ (SSP5-8.5)). Expected annual damage (a), Expected Annual Number of People Exposed (b), Expected Annual Number of Flooded Area (c) and Expected Annual Value of Lost Ecosystem Services (d). The bars for each region are grouped at four stacks corresponding to median values for the present, 2050, 2100 and 2150 and the colors express the four scenarios.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5158614/v1/aef7ab8fa2d0b1c38ac51bb4.png"},{"id":99766596,"identity":"9582692d-2917-4d4a-8f32-0064eb2d6688","added_by":"auto","created_at":"2026-01-08 08:13:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1213030,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5158614/v1/e3e85ea7-6cb9-4161-a3fa-d901ba21d42b.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Coastal flood impacts and lost ecosystem services along Europe’s Outermost Regions and Overseas Countries and Territories","fulltext":[{"header":"Introduction","content":"\u003cp\u003eClimate change and consequent sea level rise (SLR) are projected to have a substantial impact on coastal communities through erosion\u003csup\u003e2\u003c/sup\u003e and flooding\u003csup\u003e3\u003c/sup\u003e. Sea levels have been rising with an accelerating pace\u003csup\u003e4\u003c/sup\u003e and even under stringent climate mitigation policies the world is committed to a further increase in Mean Sea Level (MSL) of at least 50 cm by the year 2150\u003csup\u003e5,6\u003c/sup\u003e. While damages from coastal flooding are projected to rise sharply worldwide if current coastal protection is not improved, island states are projected to bear the highest burden of climate change\u003csup\u003e7\u003c/sup\u003e\u003csup\u003e8\u003c/sup\u003e. At the same time, some of these remote areas are among the less studied regions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis is the case for Europe\u0026rsquo;s 9 Outermost Regions (ORs) and 13 Overseas Countries and Territories (OCTs), which are scattered across the Atlantic, Caribbean, Indian Ocean, and Pacific. ORs are an integral part of the EU while OCTs are part of the territory of an EU member state (Denmark, France and the Netherlands), but not of the EU, although they are associated with it. ORs and OCTs are all islands or archipelagos, except for French Guiana that is located on the north Atlantic coast of South America. They are diverse in geography and geology, ranging from volcanic landscapes to coral reefs, and rich in biodiversity and natural resources. Several ORs and OCTs are low-lying atolls e.g. in French Polynesia and the Dutch OCTs (e.g. Aruba and Bonaire) and hence are inherently susceptible to coastal erosion and inundation. Economically, they vary from tourism-driven economies in the Canary Islands and French Polynesia to more traditional fishing and agriculture in others (e.g. Saint Pierre and Miquelon and French Guiana). Geographically, they are dispersed, creating challenges and opportunities in connectivity, climate adaptation, and sustainable development.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDespite these differences, their economies rely, to varying degrees, on tourism, agriculture, and fisheries, all sectors profoundly impacted by rising sea levels. Meanwhile, even seemingly elevated regions like the Canaries are not immune, with coastal infrastructure, settlements, beaches, and agricultural land all under increasing pressure. In the present work, we quantify the socio-economic impacts of rising seas for Europe\u0026apos;s OCTs and ORs. To that end, we use state-of-the-art estimates of Extreme Sea Levels (ESLs), produced by a coupled wave-ocean circulation model, and the latest SLR projections from IPCC AR6. We apply a hydrodynamic model to simulate coastal flooding considering the latest available information on the islands\u0026rsquo; topography and then combine simulated flooded areas with exposure and vulnerability data to estimate flood economic impacts and population exposure. Finally, we asses coastal erosion and the consequent ecosystem service losses in monetary terms.\u003c/p\u003e"},{"header":"Direct coastal flood impacts","content":"\u003cp\u003eAt present, annually 582.29 [370.52 - 900.15] km\u003csup\u003e2\u003c/sup\u003e (values in brackets express the very likely range defined by the 5\u003csup\u003eth\u003c/sup\u003e-95\u003csup\u003eth\u003c/sup\u003e percentiles) of land along coastlines of ORs and OCTs is expected to be flooded, corresponding to 0.10 ±[0.07 - 0.16%] % of the region's total area. Almost 84% of this total Expected Annual Flooded Area (EAFA) is located in OCTs and 16% in ORs. These flooded areas correspond to 14,366 ±[9,611 - 39,444] people, around 0.24% ±[0.16% -0.67%] ofthe OCTs and ORs population, who are annually exposed to coastal floods, with 88% of Expected Annual Population Exposed (EAPE) living in ORs. The resulting Expected Annual Damage (EAD) of flooding for the OCTs and ORs is 141.9 ±[98.6 - 215.0]\u0026nbsp;€ million (Fig. 2a), of which 69% occurs in ORs.\u0026nbsp;OCTs host several uninhabited or sparsely populated islands (e.g. the French Southern and Atlantic Lands), so flooded areas do not alwaystranslate into population exposure and economic impact. On the other hand, certain ORs like the Canary Islands, host urban centers in low lying coastal areas, where extreme events can be more impactful economically. However, when expressed relative to the size of the economy, impacts in the OCTs (EAD of 0.15% ±[0.06% - 0.30%] of GDP) are larger compared to that in the ORs (0.09% ±[0.08% - 0.12%] of GDP) (Fig. 1b).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBy the year 2050 overall EAFA for all regions increases by more than 70% compared to now and will reach 1,020.00 ±[737.83 - 1,364.38], 1,043.17 ±[763.42 - 1,386.11], 1,057.85 ±[778.02-1,403.60], and 1,085.11 ±[799.04 - 1,440.66] km\u003csup\u003e2\u003c/sup\u003e, under SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5, respectively (Fig. 2c). These values correspond to around 0.18% of the total area. Flooded areas keep growing in time and will reach 1,399.75 ±[767.38 - 2,230.23], 1,575.64 ±[925.34 - 2,472.98], 1,771.39 ±[1,087.30 - 2,744.34], and 1,949.07 ±[1,231.70 - 3,019.80] km\u003csup\u003e2\u003c/sup\u003e by 2100under SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5, respectively. With 0.25%-0.35% of the total land area expected to be flooded, this is 2.5 to 3.5 times more than today. By 2150 the size of flooded areas will amount to 1,757.58 ±[767.38 - 3,129.40], 2,105.84 ±[1009.34 - 3,658.46], 2,527.97 ±[1,305.60 - 4,294.59], and 2,838.58 ±[1,517.26 - 4,886.42] km\u003csup\u003e2\u003c/sup\u003e, hence ranging between 0.31% and 0.5%, or 5 times higher than today in the worst-case scenario.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAs sea levels rise and flooded areas grow in size, the number of people exposed is projected to increase around 5 times by the year 2050, 10 to 17 times by 2100 and 15 to 28 times by 2150. The resulting 2050 EAPE values are between 0.09 and 0.10 million people, depending on the scenario, and by 2150 they are projected to climb to 0.22 ±[0.04 - 0.47], 0.29 ±[0.09 - 0.58], 0.37 ±[0.14 - 0.69], and 0.42 ±[0.18 - 0.80] million people, under SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5, respectively (Fig. 2b). These values correspond to around 3.77% ±[0.69% - 8.03%], 4.92% ±[1.51% - 9.77%], 6.23% ±[2.42% - 11.74%], and 7.13% ±[3.03% - 13.54%] of the total population by 2150, under SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5, respectively.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDirect economic damages increase not only with growing flooded areas, but also with higher inundation depths, so the relative rise in projected EAD is even more pronounced compared to EAFE and EAPE. By 2050, EAD increases at least 7 times compared to present and will reach 1.18 ±[0.54 - 2.09], 1.24 ±[0.58 - 2.15], 1.28 ±[0.61 - 2.19], and 1.35 ±[0.64 - 2.28] € billion under SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5, respectively (Fig. 2a). These impacts remain below 1% of GDP, but by the end of the century they will rise 14 - 24 times compared to present to reach 1.60 ±[0.42% - 3.23%], 1.92% ±[0.65% - 3.66%], 2.31% ±[0.98% - 4.20%], and 2.66% ±[1.26% - 4.74%] of the GDP under SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5, respectively (Fig. 1d). The corresponding EAD values are 2.15 ±[0.56 - 4.33], 2.58 ±[0.87 - 4.92], 3.10 ±[1.31 - 5.64], and 3.58 ±[1.70 - 6.36] € billion, respectively. By 2150, projected EAD reaches 3.1 to 5.9 € billion, corresponding to 2.3-4.4% of the OCT/ORs GDP, or a 21 to 40 times increase compared to the present-day levels (Fig. 2a).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOverall, OCTs contribute at least 73% of the EAFA by 2050, a number which is reduced to 60.75% - 66.10% by the end of the century and to 56.52% - 62.12% by 2150. As several of the OCTs are very sparsely populated, ORs contribute to at least 88% of the total EAPE, a number which rises to 93% by the year 2150. The same year, the corresponding median EAPE values are 208,198 – 395,892, 14,524 – 25,396 people, for ORs and OCTs, respectively, with range expressing the scenario uncertainty. Similarly, ORs contribute to most of the 2150 EAD which is around 2.43 - 4.77 € billion, compared to 0.64 - 1.12 € billion for the OCTs, accounting for 2.32% - 4.55% and 2.16% - 3.79%, of the GDP respectively (Fig. 3c).\u003c/p\u003e\n\u003cp\u003ePresently, the regions with the highest flooded area as percentage of the total are Aruba (1.09% ±[0.01% - 2.54%]), followed by Bonaire, Sint Eustatius and Saba (1.04% ±[0.53% - 5.88%]), French Polynesia (0.94% ±[0.47% - 1.39%]), Guadeloupe (0.54% ±[0.35% - 1.90%]) and New Caledonia (0.52% ±[0.16% - 1.03%]). These values correspond to area equal to 1.97 ±[0.02 - 4.62], 3.31 ±[1.70 - 18.76], 33.50 ±[16.89 - 49.53], 9.04 ±[5.76 - 31.55] and 96.16 ±[29.47 - 190.48] km\u003csup\u003e2\u003c/sup\u003e, respectively (Fig. 3c). At least 60% of the flooded area is found in the French Southern and Antarctic Lands and another 30% is distributed along New Caledonia, French Guiana and French Polynesia.\u003c/p\u003e\n\u003cp\u003eAs for the present-day, also by the end of the century, most of the EAFA is from the French Southern and Antarctic Lands (36.18% - 38.87%, depending on the scenario), followed by the French Guiana (24.1% - 28.7%) and New Caledonia (17.0% - 18.9%). The corresponding median EAFA values are 544.1 - 705.1, 336.8 - 559.1 and 265.1 - 331.2 km\u003csup\u003e2\u003c/sup\u003e, respectively and climb to 651.4 - 964.3, 484.9 - 916.9 and 305.1 - 440.3 km\u003csup\u003e2\u003c/sup\u003e, respectively, by 2150. By the same year, all regions are projected to experience an increase in EAFA between 1 and 40 times compared to the present, depending on the area and scenario. Some smaller islands are projected to have at least 4% of their area annually flooded; e.g. Bonaire, Sint Eustatius and Saba (11.0% - 16.6%), Aruba (8.8% - 15.5%), Guadeloupe (5.0% - 8.6%) and Saint-Martin (4.5% - 6.7%). The corresponding median EAFA values are 35.1 - 53.1, 15.9 - 28.3, 82.5 - 143.6 and 2.3 - 3.4 km\u003csup\u003e2\u003c/sup\u003e, respectively (Fig. 3c). For the above regions the projected increase in EAFA exceeds 7 and can reach up to 15 times compared to the present. But the highest relative increase in EAFA is projected in Reunion (19.3 - 40.0 times compared to the present), Bonaire, Sint Eustatius and Saba (9.6 - 15.0), Martinique (9.4 - 16.7), Saint-Martin (9.3 - 14.4) and Guadeloupe (8.1 - 14.9).\u003c/p\u003e\n\u003cp\u003eIn terms of the present-day EAPE, the region with the highest EAPE as percentage of the total population is Mayotte (0.52% ±[0.38% - 0.67%]), followed by Wallis and Futuna (0.52% ±[0.38% - 0.65%]), French Guiana (0.45% ±[0.33% - 1.10%]), Guadeloupe (0.34% ±[0.22% - 1.41%]) and Reunion (0.30% ±[0.23% - 0.52%]). Such values correspond to EAPE equal to 1,737, 60, 1,332, 1,327 and 2,588 people, respectively. At least one third of the people affected are found in the Canary Islands (4797 people), 18% is in Reunion (2588) and the remaining 31% in distributed along Guadeloupe (1327), Mayotte (1737) and French Guiana (1332) (Fig. 3b).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn terms of EAD, about half of the total present-day damages are estimated in New Caledonia and the Canary Islands (24 ±[7.5 - 50.9] and 40 ±[34.4 - 46.5] € million, respectively) and one third in French Guiana, Reunion, and French Polynesia (20 ±[16.9 - 22.9], 17 ±[14.4 - 19.4], and 15 ±[7.7 - 23.3] € million, respectively) (Fig. 3a). Some of the above countries feature also among the ones with the highest EAD as percentage of the GDP: French Guiana (0.50% ±[0.43% - 0.58%]), followed by New Caledonia (0.24% ±[ 0.07% - 0.50%]), Mayotte (0.23% ±[0.20% - 0.27%]), Saint Pierre and Miquelon (0.22% ±[0.19% - 0.25%), and French Polynesia (0.20% ±[0.10% - 0.30%]) (Fig. 3a).\u003c/p\u003e\n\u003cp\u003eBy the end of the century, most of the EAPE comes from the Canary Islands (34.53% - 37.09% of the total, depending on the scenario), followed by the Reunion (28.81% - 32.01%). The corresponding median EAPE values are 57,857 – 88,908 and 44,946 – 82,405 people, respectively. The same values increase further by 2150 to reach 78,537 – 138,868 and 69,894 – 142,677 people, respectively; representing a 15 - 28 and 26 - 54 times increase from the present. While the above values express the highest absolute EAPE, the highest EAPE as percentage of the population in 2150 are estimated for Reunion (8% - 17%), French Guiana (7% - 15%), St Barthelemy (5% - 12%), Guadeloupe (4% - 8%) and Bonaire, Sint Eustatius and Saba (4% - 7%), with corresponding median EAPE values being 69,894 – 142,677, 21,130 – 43,167, 478 – 1,125, 16,865 – 30,497 and 1,068 – 1,794 people, respectively (Fig. 3b). The highest relative increase in EAPE comes from St Barthelemy (42 – 100 times from the present), Reunion (26 - 54), Saint Pierre and Miquelon (25 - 49), and Canary Islands (15 - 28).\u003c/p\u003e\n\u003cp\u003eGuadeloupe accounts for most of the total OCT/ORs EAD by the year 2050 (i.e. around 19%), but after that year it is replaced by Reunion, i.e. 20.00% - 23.69% and 23.07% - 26.36%, by 2100 and 2150, respectively (range expresses the different emission scenarios). Other countries with high contributions to the total EAD\u0026nbsp;(by 2100 and 2150)\u0026nbsp;are Guadeloupe (14.9% - 16.5%), Canary Islands (14.4% - 15.8%), French Guiana (12.2% - 13.3%), and New Caledonia (8.6% - 9.8%). The corresponding 2150 median EAD values are 0.7 - 1.5, 0.5 - 1.0, 0.5 - 0.9, 0.4 - 0.8 and 0.3 - 0.5 € billion, respectively (Fig. 3a). These values correspond to a 41 - 89, 139 - 263, 11 - 20, 18 – 37 and 11 - 21 times increase from the present, respectively.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBy the end of the century, the highest EAD as percentage is projected in French Guiana (6.0% - 11.1%), Guadeloupe (4.5% - 7.1%), Bonaire, Sint Eustatius and Saba (4.4% - 6.4%), Saint Pierre and Miquelon (2.9% - 4.3%), and Saint-Martin (2.5% - 3.5%) (e.g. Fig. 1c), corresponding to a 11.1 - 21.2, 111.3 – 176.3, 47.6 – 69.9, 12.1 - 18.5, and 83.7 – 116.7 times increase from the present, respectively. Guadeloupe and Saint-Martin are also the countries where the highest increase is projected, followed by Martinique (85.0 – 133.5), Aruba (71.0 – 109.7), and Curacao (49 – 70.5). Overall, the above rankings extend also to 2150.\u003c/p\u003e"},{"header":"Anticipated lost coastal ecosystem services","content":"\u003cp\u003eThe current coastal ecosystem services in OCT/ORs are estimated around 147.9 € billion annually, half of which come from New Caledonia and 25% from French Guiana and French Polynesia. More than 90% of the ecosystem services are from tropical tree cover (Fig. 1e). Rising seas are projected to drive shoreline retreat, which will deplete a part of such ecosystem services. An estimated 198.35 ±[20.24 - 435.04], 214.49 ±[33.92 - 451.33], 222.41 ±[37.21- 461.86], and 245.24 ±[47.57 - 492.98] € million of such ecosystem services are projected to be lost by the year 2050, under SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5, respectively (Fig. 2d). The same estimates climb to 474.80 ±[38.77 - 1,053.49], 615.64 ±[164.19 - 1,250.29], 748.72 ±[268.68 - 1,434.20], and 867.60 ±[366.63 - 1,626.72] € million, respectively, by the end of the century. The above estimates imply emission mitigation benefits of at least 38.18% for the entire most likely range of the projections. By the year 2150, ecosystem services of 736.41 ±[20.58 - 1702.10], 1020.10 ±[243.47 - 2131.25], 1314.44 ±[445.63 - 2574.21], and 1499.61 ±[571.23 - 2970.29] € million are projected to be lost, under SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5, respectively (Fig. 2d).\u003c/p\u003e\n\u003cp\u003eBy the years 2100 and 2150, the highest contributions come from the Outermost Regions (64.90% - 67.58%, depending on the scenario), followed by the Overseas countries and territories (32.42% - 35.10%). By the end of the century the corresponding median values are 308.15 – 578.47, and 166.65 – 289.13 € million, respectively (Fig. 1f) and by the year 2150 these values climb to 488.20 – 1013.47, and 248.21 – 486.14 € million, respectively. The country with the most losses in coastal ecosystem services by the end of the century is projected to be French Guiana (45.85% - 47.82% of the total, range expresses SSP uncertainty), characterized by a long, low-lying coastline (Fig. 3d). It is followed by the New Caledonia (17.1% - 18.4%), Martinique (8.1% - 8.2%), French Polynesia (7.8% - 7.9%), and French Southern and Antarctic Lands (4.2% - 4.5%), respectively. The corresponding median lost ecosystem services values are 217.70 – 414.87 € million, 87.17 – 148.72 € million, 38.47 – 70.98 € million, 37.33 – 67.37 € million, and 21.35 – 36.83 € million, respectively (Fig. 1f). The corresponding 2150 median values are 349.02 – 732.13 € million, 128.16 – 247.75 € million, 60.13 – 123.29 € million, 57.34 – 115.71 € million, and 31.66 – 61.74 € million, respectively.\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur results show a strong increase in impacts from rising seas for Europe\u0026rsquo;s OCTs and ORs. In the short term, the effect of climate mitigation is limited and impacts of high emissions in 2050 are marginally worse compared to implementing stringent climate mitigation policies. The benefits of mitigation become more substantial in time. By 2100, our findings indicate an additional 139 km\u003csup\u003e2\u003c/sup\u003e of land lost to erosion, 102,774 more people exposed to floods and 1,542 \u0026euro; billion of additional direct flood losses for the highest emissions scenario compared to the lowest emissions scenario. Such, deviations are projected to keep increasing beyond the present century as sea levels will keep rising long after emission\u0026rsquo;s have been reduced. Therefore, even under strong mitigation pathways such as SSP1-1.9, for which the maximum global average temperature is expected to occur before 2100\u003csup\u003e9\u003c/sup\u003e, coastal impacts of SLR will continue to grow in time (at least until 2150 according to our analysis). For the lowest emission scenario (SSP1-1.9), and assuming present socioeconomic exposure in our analysis, impacts by 2150 will be 40% larger compared to impacts in 2100.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eImpacts of rising sea levels will vary strongly among OCTs and ORs. Even for the lowest emissions scenario and as early as 2050, several OCTs and ORs are projected to experience expected annual direct economic impacts that exceed 1% of their GDP (e.g. French Guiana, Saint Pierre and Miquelon, Aruba, Guadeloupe, Martinique and New Caledonia). As time proceeds and under higher emissions scenarios, direct economic impacts could rapidly grow to several percentage points (more than 10% in the extreme case of French Guiana). These values reflect longer term average impacts, while actual extreme flood events and their impacts can constitute substantial shocks for any economy, without even considering indirect impacts, like business interruption and other spill-over effects\u003csup\u003e10\u003c/sup\u003e. The most affected areas in OCTs and ORs are flatter low-lying coastal parts characterized by high population density. This includes, for example, the cities of Las Palmas de Gran Canarias, Cayenne of French Guiana, Ponta Delgada in Azores, Mamoudzou and Dzaoudzi of Mayotte, Saint-Denis and Saint-Pierre of Reunion, Mata-Utu of Wallis and Saint-Pierre, the capital of Saint Pierre and Miquelon.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn all these cities the average elevation doesn\u0026rsquo;t exceed 10 m and even if large parts will not be directly affected by floods, the disruption of every-day life will be substantial by the inundation of critical assets like ports, roads and energy infrastructure, among others. The resilience of the latter is a critical issue since such assets tend to be in flat, low-lying areas, given the absence of higher altitude plains, especially in volcanic islands. Several OCTs/ORs airports lie below 10 m of elevation and will experience increasing flood risk, even under moderate sea level rise scenarios, e.g. the Dzaoudzi Pamandzi International Airport \u0026ndash; Mayotte, Flamingo International Airport \u0026ndash; Bonaire, Faa\u0026apos;a International Airport - French Polynesia, Pointe-\u0026agrave;-Pitre International Airport \u0026ndash; Guadeloupe and Saint-Pierre Airport \u0026ndash; Saint Pierre and Miquelon. Airports, together with ports, which are anyway near mean sea level, are the lifelines of islandic communities, either for economic activity by ensuring the access of tourists to the island; or the other way around, by providing to the inhabitant\u0026rsquo;s access to critical supplies and health services, among others.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll the above imply that most of the affected areas cannot be abandoned and will have to be protected, however, acknowledging the challenge is only the first step. By understanding the specific vulnerabilities of each region and working collaboratively towards adaptation strategies, the ORs and OCTs can build resilience against the rising tide. The spectrum of potential actions is broad, from investing in coastal defenses and early warning systems to exploring nature-based solutions (NBS) like land reclamation and mangrove restoration.\u0026nbsp;\u003c/p\u003e"},{"header":"Detailed methods","content":"\u003ch3\u003eCoastal flood risk modelling framework\u003c/h3\u003e\n\u003cp\u003eWe assess impacts from Sea Level Rise (SLR) and episodic flooding along coastlines during the 21st century, considering both permanent inundation from SLR and tides and episodic flooding from coastal extremes. The analysis is based on the modular framework LISCOAST (Large-scale Integrated Sea-level and Coastal Assessment Tool). It combines state-of-the-art large-scale modelling tools and datasets to quantify hazard, exposure and vulnerability\u0026nbsp;in coastal areas and compute consequent risks\u003csup\u003e1\u003c/sup\u003e. We consider the five principal Shared Socio-economic Pathway scenarios that span a range from ambitious mitigation to no emission policies: low-emissions (SSP1-2.6), reaching net-zero emissions after 2050 and achieving the Paris Agreement goal of holding the increase in global temperature to below 2\u0026deg;C compared to pre-industrial levels; \u0026lsquo;moderate emissions\u0026rsquo; (SSP2-4.5), implying stable emissions until mid-century, when they start to be reduced without reaching net-zero; high emissions (SSP3-7.0), with emissions rising constantly to almost double from current levels by the end of the century; and a high fossil-fuel development world throughout the 21st century (SSP5-8.5)\u003csup\u003e14\u003c/sup\u003e. For each of these scenarios we generate probabilistic projections of mean and extreme sea levels that give rise to permanent inundation or episodic flooding and combine them with exposure and vulnerability to quantify economic losses. In addition, we produce projections of coastal erosion and we assess how the latter will affect future ecosystem services.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMore details on the different steps of the analysis are provided below.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003ePresent day extreme sea levels\u003c/h3\u003e\n\u003cp\u003eCoastal areas are exposed to rising mean sea level (MSL) and episodic high sea levels under extreme atmospheric conditions. Extreme sea levels (ESL) are driven by the combined effect of MSL, tides and water level fluctuations due to waves and storm surges. We derive the contribution of each of these drivers with state-of-the-art modelling tools and datasets and combine them to obtain ESLs every 1 km along the coastline.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor the baseline period, spanning from 1980 until 2020, we run a reanalysis of waves and storm surges based on a two-way coupled ocean model using an unstructured grid with a resolution ranging from ~50 km offshore to ~2 km nearshore. The coupled model system includes the Semi-implicit Cross-scale Hydroscience Integrated System Model (SCHISM)\u003csup\u003e15\u003c/sup\u003e, configured in its two-dimensional barotropic mode and the 3rd-generation spectral wave model (WWM-V)\u003csup\u003e16\u003c/sup\u003e. The model accounts for the combined effects of wind, atmospheric pressure gradients, and tides. Bathymetric data are available from the European Marine Observation and Data Network (EMODnet) in angular coordinates at a resolution of 1/8 arc-minute (0.0021\u0026deg; of latitude and longitude; http://www.emodnet.eu/bathymetry) and are interpolated onto the computational grid.\u003c/p\u003e\n\u003cp\u003eWe apply the coupled model to produce a reanalysis of waves and storm surges, forced by sea level pressure and wind speed data from ERA5\u003csup\u003e17\u003c/sup\u003e. The reanalysis is carried out without tidal forcing in order to make sure that our hindcast resolves the weather-driven component of ESLs; without the stochastic modulation of tidal variations. Further details about the model setup and the validation can be found in Mentaschi et al.\u003csup\u003e18\u003c/sup\u003e. Since, it is known that non-linear interactions between tides, waves and storm surges can be important in some areas, we apply a correction for the above effects following an approach similar to Arns\u003csup\u003e19\u003c/sup\u003e. To that end, we run a shorter 10-year reanalysis including tidal forces and from the overlapping time series we use copulas to produce a correction function for non-linear tidal effects on the water level anomaly and the significant wave height.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn order to improve the accuracy of the reanalysis data we implement some additional steps as detailed below. Using satellite altimetry data we apply a Quantile Mapping Bias Correction on both the water level anomaly and the significant wave height. This is done after compiling all coinciding model and satellite values along 1o x 1o cells. To further improve the cyclone related storm surge estimates which have not been sufficiently resolved by our reanalysis, we did additional simulations of tropical cyclone driven sea level anomalies using the Delft3D-FM model\u003csup\u003e20\u003c/sup\u003e forced by the IBTrACS best-track archive\u003csup\u003e21\u003c/sup\u003e. The reanalysis values are corrected by considering the tropical cyclone runs values when they are higher than those of our ERA5 runs. More information about the approach and data can be found in Vousdoukas et al\u003csup\u003e8,22\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eSpectral wave parameters provide one characteristic estimate for wave height, direction and period from the whole spectrum and therefore lack the detail needed to describe wave processes along complex shorelines. To overcome this shortcoming, we use the spectral peaks from the WWM-V model output and we propagate each peak along a global transect dataset with 1 km alongshore resolution. The transect dataset includes information on the shoreline position, orientation, submerged and subaerial slope, among others. Details of the data and methods used to generate transects are provided in Athanasiou et al.\u003csup\u003e23\u003c/sup\u003e. We benefit from the complete spectral information from the wave model to propagate each peak at each time stamp along its corresponding transect using Snell\u0026rsquo;s law\u003csup\u003e24\u003c/sup\u003e. We then estimate the wave breaking height combining the peak wave parameters with the submerged profile slope. Subsequently, we obtain the wave run-up height R\u003csub\u003e2\u003c/sub\u003e based on the Stockdon empirical formula\u003csup\u003e25\u003c/sup\u003e, after combining the breaking wave height and period with the subaerial beach profile slope. The above steps result in wave runup height estimates for each spectral peak and we consider the highest value as the characteristic for the specific time stamp. We then combine the wave runup with the storm surge to obtain the meteorological tide and apply non-stationary extreme value analysis\u003csup\u003e26\u003c/sup\u003e to the time series to obtain estimates for different return periods.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBaseline ESLs are produced by combining the final meteorological tide time series with tidal elevations obtained from the FES2014 model\u003csup\u003e27\u003c/sup\u003e. Following the approach of Vousdoukas et al.\u003csup\u003e22\u003c/sup\u003e, the high tide water level is considered taking into account the range due to the spring-neap tide cycle.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eSea level rise projections\u003c/h3\u003e\n\u003cp\u003eRelative SLR projections are obtained from latest IPCC AR6 assessment\u003csup\u003e28\u003c/sup\u003e\u003csup\u003e\u0026ndash;\u003c/sup\u003e\u003csup\u003e30\u003c/sup\u003e and incorporate the effects of the various components of future SLR, as simulated by climate models from the Coupled Model Intercomparison Project phase 6 (CMIP6), including steric SLR, dynamic sea-level change, contributions from glaciers and ice-caps, land-water storage and Glacial Isostatic Adjustment, among others.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eProjections of ESLs up to 2100\u0026nbsp;\u003c/h3\u003e\n\u003cp\u003eAll ESL components (RSLR, tide, surges and R\u003csub\u003e2\u003c/sub\u003e) are expressed as probability density functions (PDFs) that account for the different sources of uncertainty, and they are combined through Monte Carlo simulations in order to generate probabilistic estimates of ESLs for the SSP scenarios in each coastal segment (1 km alongshore resolution). Non-stationary extreme value analysis\u003csup\u003e26\u003c/sup\u003e is then applied to obtain for a range of return periods (i.e. 1, 2, 5, 10, 20, 50, 100, 200, 500, 1000 and 5000 years) PDFs of the corresponding return values of ESL throughout this century.\u003c/p\u003e\n\u003ch3\u003eCoastal flooding\u003c/h3\u003e\n\u003cp\u003eWe use hydraulic 2-D simulations along the entire coastline to estimate inundation extent and depth, following the approach presented by Vousdoukas et al.\u003csup\u003e31\u003c/sup\u003e, running the Lisflood-ACC (LFP) model\u003csup\u003e32\u003c/sup\u003e at 30 m spatial resolution, using the estimated ESLs as forcing and considering hydraulic roughness derived from land-use maps\u003csup\u003e33\u003c/sup\u003e. Up to high-tide water levels (i.e. combination of mean sea level and high-tide) we apply the bathtub approach, and land below this sea water level and the corresponding assets are considered permanently inundated due to the effects of sea level rise. For episodic flooding, Liscflood-ACC is applied for each coastal segment with the model domain extending up to 200 km landwards in order to ensure the inclusion of all potentially hydrologically connected areas that may lie inland and away from the coast. The flood simulations are based on the recently published GLO-30 DEM\u003csup\u003e34\u003c/sup\u003e that is based on Synthetic Aperture Radar measurements and known to reduce vertical bias of SRTM based products. In addition, we apply post-processing using global LIDAR observations to further remove vertical bias, correcting for buildings and vegetation.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eExposure and vulnerability\u003c/h3\u003e\n\u003cp\u003eThe resulting flood inundation maps are combined with exposure and vulnerability information to estimate population exposure and direct flood damages. For today\u0026rsquo;s population exposure we overlay the present inundation maps with the WorldPop 2020 population dataset (www.worldpop.org), which is an open and high-resolution geospatial dataset of population and demographic dynamics, with a focus on low and middle income countries. The vulnerability to flooding is expressed through depth-damage functions (DDFs)\u003csup\u003e35\u003c/sup\u003e, which define the relation between direct damage and flood inundation depth for different land use classes. Asset values are further scaled according to the GDP per capita available at 5 arc-min resolution\u003csup\u003e36\u003c/sup\u003e in order to account for differences in the spatial distribution of wealth within countries. Baseline global land cover is available from the European Space Agency\u003csup\u003e37\u003c/sup\u003e at 10 m resolution. Given that more than 95% of the damages relate to built-up areas, the land use is corrected to take into account 30 m resolution gridded information on Global Human Built-up And Settlement Extent\u003csup\u003e38\u003c/sup\u003e (reference year 2010).\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eRisk assessment\u003c/h3\u003e\n\u003cp\u003eFor each coastal segment, the area flooded, number of people affected and direct flood losses are calculated at ~100 m resolution by combining flood inundation estimates with population and land use maps and the vulnerability functions. For areas that are inundated on a regular basis (which could happen in the future with SLR), defined as lying below the present high tide water level, assets are considered as fully damaged and the maximum loss according to the DDFs is applied. For areas inundated only during extreme events, the damage is estimated by applying the DDFs combined with the simulated inundation depth and land use information.\u003c/p\u003e\n\u003cp\u003eMSLs and ESLs, and the corresponding flood depths, are available as PDFs for different return periods (RP = 1, 2, 5, 10, 20, 50, 100, 200, 500, 1000 and 5000 years). Consequently, for each coastal segment we obtain probabilistic estimates of flooded area (FA), population exposed (PE) and impact (D) from 1981 up to 2100. Integrating FA, PE and D over the return periods allows obtaining the Expected Annual Flooded area (EAFA), Population Exposed (EAPE) and direct economic Damage (EAD). We present and discuss our results about risk at global, regional, as well as country level, and we focus on the median, 5\u003csup\u003eth\u003c/sup\u003e and 95\u003csup\u003eth\u003c/sup\u003e percentiles (very likely range).\u003c/p\u003e\n\u003ch3\u003eLand cover and ecosystem valuation\u003c/h3\u003e\n\u003cp\u003eWe use land cover data from WorldCover 2020 dataset from the European Space Agency\u003csup\u003e37\u003c/sup\u003e. The dataset is an algorithmic classification of Sentinel-2 and Sentinel-1 images, identifying 10 land cover classes at 10 meters resolution with an 74.4% overall accuracy\u003csup\u003e39\u003c/sup\u003e. We calculate the value of each land cover class using mean ecosystem value per year per hectare from the Ecosystem Services Valuation Database (ESVD), as presented by de Groot et al.\u003csup\u003e40\u003c/sup\u003e. The original values are in 2020 US dollars, which was first updated to 2022 dollars using the GDP deflator for the United States, and then converted to Euro at average price level of the European Union (27 countries) using the purchasing power parity conversion rates for 2022 (Eurostat, https://ec.europa.eu/eurostat). ESVD uses a different land cover classification, therefore it is assigned to the ESA dataset as shown in Table 1. ESVD differentiates between tropical and temperate forests, which is not explicitly shown in WorldCover 2020, hence we assume that all forests are tropical except those located in regions with a temperate climate, i.e. the Azores, Madeira, the Canary Islands, Saint Pierre and Miquelon, and Kerguelen Islands (which are the only part of the French Southern and Antarctic Lands analysed here). The total value of ecosystem services is the value of all inland land cover type located within the 10 km radius from the coastline.\u003c/p\u003e\n\u003cp\u003eTable 1. Ecosystem service values (in\u0026nbsp;2022 euros at EU27 price level per hectare per year) according to ESA\u0026nbsp;WorldCover class.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"601\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38.9351%;\"\u003e\n \u003cp\u003eESA WorldCover class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39.6007%;\"\u003e\n \u003cp\u003eESVD land cover type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4642%;\"\u003e\n \u003cp\u003eValue/year/ha\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38.9351%;\"\u003e\n \u003cp\u003eTree cover\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39.6007%;\"\u003e\n \u003cp\u003eTropical forests\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4642%;\"\u003e\n \u003cp\u003e82,638\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38.9351%;\"\u003e\n \u003cp\u003eTree cover\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39.6007%;\"\u003e\n \u003cp\u003eTemperate forests\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4642%;\"\u003e\n \u003cp\u003e3736\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38.9351%;\"\u003e\n \u003cp\u003eShrubland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39.6007%;\"\u003e\n \u003cp\u003eWoodland and shrubland\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4642%;\"\u003e\n \u003cp\u003e534\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38.9351%;\"\u003e\n \u003cp\u003eGrassland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39.6007%;\"\u003e\n \u003cp\u003eGrassland\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4642%;\"\u003e\n \u003cp\u003e1108\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38.9351%;\"\u003e\n \u003cp\u003eCropland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39.6007%;\"\u003e\n \u003cp\u003eCultivated areas\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4642%;\"\u003e\n \u003cp\u003e5570\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38.9351%;\"\u003e\n \u003cp\u003eBuilt-up\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39.6007%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4642%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38.9351%;\"\u003e\n \u003cp\u003eBare / sparse vegetation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39.6007%;\"\u003e\n \u003cp\u003eInland Un- or Sparsely Vegetated\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4642%;\"\u003e\n \u003cp\u003e234\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38.9351%;\"\u003e\n \u003cp\u003eSnow and ice\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39.6007%;\"\u003e\n \u003cp\u003eInland Un- or Sparsely Vegetated\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4642%;\"\u003e\n \u003cp\u003e234\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38.9351%;\"\u003e\n \u003cp\u003ePermanent water bodies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39.6007%;\"\u003e\n \u003cp\u003eRivers and lakes\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4642%;\"\u003e\n \u003cp\u003e75,202\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38.9351%;\"\u003e\n \u003cp\u003eHerbaceous wetland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39.6007%;\"\u003e\n \u003cp\u003eInland wetlands\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4642%;\"\u003e\n \u003cp\u003e33,761\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38.9351%;\"\u003e\n \u003cp\u003eMangroves\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39.6007%;\"\u003e\n \u003cp\u003eMangroves\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4642%;\"\u003e\n \u003cp\u003e54,167\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38.9351%;\"\u003e\n \u003cp\u003eMoss and lichen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39.6007%;\"\u003e\n \u003cp\u003eTundra\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4642%;\"\u003e\n \u003cp\u003e568\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch3\u003eErosion extent\u003c/h3\u003e\n\u003cp\u003eProjections of shoreline change are produced according to the methodology of Vousdoukas et al.\u003csup\u003e2\u003c/sup\u003e, using the latest IPCC SLR projections, as in the flood risk analysis. The methodology combines estimates of ambient change and SLR retreat in a probabilistic framework, with the former assessed through a probabilistic extrapolation of historical behavior detected from satellites and the latter by using a modified version of the Bruun Rule. Detailed description of the approach and data used can be found in Vousdoukas et al.\u003csup\u003e2\u003c/sup\u003e. Erosion magnitude at different locations is connected with the base coastline derived from ESA World Cover. The coastline is smoothed and split into detailed segments with an average length of 30 meters. Each segment of the coastline is assigned a sector of the inland land cover based on nearest-neighbor analysis. However, as unlimited backshore space for shoreline retreat is often not a realistic assumption due to topography or human coastline protection activities, we constrain the erosion extent by introducing two types of barriers into the analysis in a similar manner to Paprotny et al.\u003csup\u003e41\u003c/sup\u003e. Firstly, artificial barriers representing various anthropogenic structures are extracted from the GHS built-up surface (R2023) dataset for reference year 2018 at 10-meter resolution\u003csup\u003e42\u003c/sup\u003e, which is part of the Global Human Settlement Layer database. Any occurrence of built-up surfaces in a grid cell is considered a barrier for erosion. Second, topographic barriers representing high ground that is unlikely to be easily eroded are derived from DeltaDTM\u003csup\u003e34\u003c/sup\u003e. Each coastal sector corresponding to a 30-meter (on average) section of the coastline is intersected with the barriers, erasing areas that are invulnerable to erosion. Parts of the sector that are no longer connected directly to the coastline were removed. The resulting layer of areas where erosion is possible is used to clip the buffer around each coastline segment generated according to the erosion extent per scenario in a given location. Finally, the constrained erosion layer is intersected with the land cover dataset and the value of ecosystem services is calculated according to the eroded area per land cover class.\u003c/p\u003e\n\u003ch3\u003eAvoiding overlaps between flooding and erosion\u003c/h3\u003e\n\u003cp\u003eGiven that the current computational and modelling capabilities do not allow carrying out the erosion and flooding assessments together, one of the inevitable limitations of this study is that we do not directly resolve the interactions between the two hazards. Coastal erosion is projected to be mainly driven by SLR, in the contrary to floods for which meteorological and astronomical tides are the main drivers. More than 95% of the coastal flood losses come from artificial areas, which also act as the landward limit of coastal erosion. This means that in case of shoreline retreat in front of urbanized areas, the double counting of the economic damages from floods and lost ecosystem services is very small, since the second number is much higher than the first (retreat takes place only in non artificial areas where flood losses are very small). On the other hand, in natural coastlines shoreline retreat will continue unimpeded resulting in even higher losses from ecosystem services. But also in this case coastal flood losses are less significant due to the lack of built areas. One unresolved issue remains the fact that in the case of shoreline accretion in front of artificial coastlines, the additional land will provide natural protection which will reduce the amount of flooded areas backshore. But such cases of shoreline accretion in front of urbanization are rarely natural but are result of human intervention which imply also shoreward expansion of the built area. Given all the above, and considering the scope of the work, we consider that the nonlinear interactions are insignificant compared to the other sources of uncertainty.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eVousdoukas, M. I. \u003cem\u003eet al.\u003c/em\u003e Climatic and socioeconomic controls of future coastal flood risk in Europe. \u003cem\u003eNat. Clim. Change\u003c/em\u003e (2018) doi:10.1038/s41558-018-0260-4.\u003c/li\u003e\n \u003cli\u003eVousdoukas, M. I. \u003cem\u003eet al.\u003c/em\u003e Sandy coastlines under threat of erosion. \u003cem\u003eNat. Clim. Change\u003c/em\u003e\u003cstrong\u003e10\u003c/strong\u003e, 260\u0026ndash;263 (2020).\u003c/li\u003e\n \u003cli\u003eSchinko, T. \u003cem\u003eet al.\u003c/em\u003e Economy-wide effects of coastal flooding due to sea level rise: a multi-model simultaneous treatment of mitigation, adaptation, and residual impacts. \u003cem\u003eEnviron. Res. Commun.\u003c/em\u003e\u003cstrong\u003e2\u003c/strong\u003e, 015002 (2020).\u003c/li\u003e\n \u003cli\u003eFrederikse, T. \u003cem\u003eet al.\u003c/em\u003e The causes of sea-level rise since 1900. \u003cem\u003eNature\u003c/em\u003e\u003cstrong\u003e584\u003c/strong\u003e, 393\u0026ndash;397 (2020).\u003c/li\u003e\n \u003cli\u003eDeConto, R. M. \u003cem\u003eet al.\u003c/em\u003e The Paris Climate Agreement and future sea-level rise from Antarctica. \u003cem\u003eNature\u003c/em\u003e\u003cstrong\u003e593\u003c/strong\u003e, 83\u0026ndash;89 (2021).\u003c/li\u003e\n \u003cli\u003eKopp, R. E. \u003cem\u003eet al.\u003c/em\u003e Communicating future sea-level rise uncertainty and ambiguity to assessment users. \u003cem\u003eNat. Clim. Change\u003c/em\u003e\u003cstrong\u003ein press\u003c/strong\u003e, (2023).\u003c/li\u003e\n \u003cli\u003eMartyr-Koller, R., Thomas, A., Schleussner, C.-F., Nauels, A. \u0026amp; Lissner, T. Loss and damage implications of sea-level rise on Small Island Developing States. \u003cem\u003eCurr. Opin. Environ. Sustain.\u003c/em\u003e\u003cstrong\u003e50\u003c/strong\u003e, 245\u0026ndash;259 (2021).\u003c/li\u003e\n \u003cli\u003eVousdoukas, M. I. \u003cem\u003eet al.\u003c/em\u003e Small Island Developing States under threat by rising seas even in a 1.5\u0026thinsp;\u0026deg;C warming world. \u003cem\u003eNat. Sustain.\u003c/em\u003e (2023) doi:10.1038/s41893-023-01230-5.\u003c/li\u003e\n \u003cli\u003eTebaldi, C. \u003cem\u003eet al.\u003c/em\u003e Climate model projections from the Scenario Model Intercomparison Project (ScenarioMIP) of CMIP6. \u003cem\u003eEarth Syst Dynam\u003c/em\u003e\u003cstrong\u003e12\u003c/strong\u003e, 253\u0026ndash;293 (2021).\u003c/li\u003e\n \u003cli\u003eKoks, E. E. \u003cem\u003eet al.\u003c/em\u003e The macroeconomic impacts of future river flooding in Europe. \u003cem\u003eEnviron. Res. Lett.\u003c/em\u003e\u003cstrong\u003e14\u003c/strong\u003e, 084042 (2019).\u003c/li\u003e\n \u003cli\u003eSeddon, N. \u003cem\u003eet al.\u003c/em\u003e Global recognition of the importance of nature-based solutions to the impacts of climate change. \u003cem\u003eGlob. Sustain.\u003c/em\u003e\u003cstrong\u003e3\u003c/strong\u003e, e15 (2020).\u003c/li\u003e\n \u003cli\u003ePappenberger, F. \u003cem\u003eet al.\u003c/em\u003e The monetary benefit of early flood warnings in Europe. \u003cem\u003eEnviron. Sci. Policy\u003c/em\u003e\u003cstrong\u003e51\u003c/strong\u003e, 278\u0026ndash;291 (2015).\u003c/li\u003e\n \u003cli\u003eCiavola, P., Ferreira, O., Haerens, P., Van Koningsveld, M. \u0026amp; Armaroli, C. Storm impacts along European coastlines. Part 2: lessons learned from the MICORE project. \u003cem\u003eEnviron. Sci. Policy\u003c/em\u003e\u003cstrong\u003e14\u003c/strong\u003e, 924\u0026ndash;933 (2011).\u003c/li\u003e\n \u003cli\u003eMeinshausen, M. \u003cem\u003eet al.\u003c/em\u003e The shared socio-economic pathway (SSP) greenhouse gas concentrations and their extensions to 2500. \u003cem\u003eGeosci Model Dev\u003c/em\u003e\u003cstrong\u003e13\u003c/strong\u003e, 3571\u0026ndash;3605 (2020).\u003c/li\u003e\n \u003cli\u003eZhang, Y. J., Ye, F., Stanev, E. V. \u0026amp; Grashorn, S. Seamless cross-scale modeling with SCHISM. \u003cem\u003eOcean Model.\u003c/em\u003e\u003cstrong\u003e102\u003c/strong\u003e, 64\u0026ndash;81 (6AD).\u003c/li\u003e\n \u003cli\u003eRoland, A. \u003cem\u003eet al.\u003c/em\u003e A fully coupled 3D wave-current interaction model on unstructured grids. \u003cem\u003eJ. Geophys. Res. Oceans\u003c/em\u003e\u003cstrong\u003e117\u003c/strong\u003e, 2156\u0026ndash;2202 (2012).\u003c/li\u003e\n \u003cli\u003eHersbach, H. \u003cem\u003eet al.\u003c/em\u003e The ERA5 global reanalysis. \u003cem\u003eQ. J. R. Meteorol. Soc.\u003c/em\u003e\u003cstrong\u003e146\u003c/strong\u003e, 1999\u0026ndash;2049 (2020).\u003c/li\u003e\n \u003cli\u003eMentaschi, L. \u003cem\u003eet al.\u003c/em\u003e A global unstructured, coupled, high-resolution hindcast of waves and storm surge. \u003cem\u003eFront. Mar. Sci.\u003c/em\u003e\u003cstrong\u003e10\u003c/strong\u003e, (2023).\u003c/li\u003e\n \u003cli\u003eArns, A. \u003cem\u003eet al.\u003c/em\u003e Non-linear interaction modulates global extreme sea levels, coastal flood exposure, and impacts. \u003cem\u003eNat. Commun.\u003c/em\u003e\u003cstrong\u003e11\u003c/strong\u003e, 1918 (2020).\u003c/li\u003e\n \u003cli\u003eMuis, S., Verlaan, M., Winsemius, H. C., Aerts, J. C. J. H. \u0026amp; Ward, P. J. A global reanalysis of storm surges and extreme sea levels. \u003cem\u003eNat Commun\u003c/em\u003e\u003cstrong\u003e7\u003c/strong\u003e, (2016).\u003c/li\u003e\n \u003cli\u003eKnapp, K. R., Kruk, M. C., Levinson, D. H., Diamond, H. J. \u0026amp; Neumann, C. J. The International Best Track Archive for Climate Stewardship (IBTrACS). \u003cem\u003eBull. Am. Meteorol. Soc.\u003c/em\u003e\u003cstrong\u003e91\u003c/strong\u003e, 363\u0026ndash;376 (2010).\u003c/li\u003e\n \u003cli\u003eVousdoukas, M. I. \u003cem\u003eet al.\u003c/em\u003e Global probabilistic projections of extreme sea levels show intensification of coastal flood hazard. \u003cem\u003eNat. Commun.\u003c/em\u003e\u003cstrong\u003e9\u003c/strong\u003e, 2360 (2018).\u003c/li\u003e\n \u003cli\u003eAthanasiou, P. \u003cem\u003eet al.\u003c/em\u003e Global Coastal Characteristics (GCC): A global dataset of geophysical, hydrodynamic, and socioeconomic coastal indicators. \u003cem\u003eEarth Syst. Sci. Data Discuss.\u003c/em\u003e 1\u0026ndash;32 (2023) doi:10.5194/essd-2023-313.\u003c/li\u003e\n \u003cli\u003eUS Army Corps of Engineers. \u003cem\u003eCoastal Engineering Manual\u003c/em\u003e. (U.S. Army Corps of Engineers, Washington, DC, 2002).\u003c/li\u003e\n \u003cli\u003eStockdon, H. F., Holman, R. A., Howd, P. A. \u0026amp; Sallenger, J. A. H. Empirical parameterization of setup, swash, and runup. \u003cem\u003eCoast. Eng.\u003c/em\u003e\u003cstrong\u003e53\u003c/strong\u003e, 573\u0026ndash;588 (2006).\u003c/li\u003e\n \u003cli\u003eMentaschi, L. \u003cem\u003eet al.\u003c/em\u003e The transformed-stationary approach: a generic and simplified methodology for non-stationary extreme value analysis. \u003cem\u003eHydrol. Earth Syst. Sci.\u003c/em\u003e\u003cstrong\u003e20\u003c/strong\u003e, 3527\u0026ndash;3547 (2016).\u003c/li\u003e\n \u003cli\u003eCarrere, L., Lyard, F., Cancet, M., Guillot, A. \u0026amp; Picot, N. FES 2014, a new tidal model \u0026ndash; Validation results and perspectives for improvements. in (Prague, 2016).\u003c/li\u003e\n \u003cli\u003eFox-Kemper, B. \u003cem\u003eet al.\u003c/em\u003e Ocean, Cryosphere and Sea Level Change. in \u003cem\u003eClimate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change\u003c/em\u003e (eds. Masson-Delmotte, V. et al.) 1211\u0026ndash;1362 (Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 2021). doi:10.1017/9781009157896.01.\u003c/li\u003e\n \u003cli\u003eGarner, G. G. \u003cem\u003eet al.\u003c/em\u003e IPCC AR6 Sea-Level Rise Projections. (2021).\u003c/li\u003e\n \u003cli\u003eKopp, R. E. \u003cem\u003eet al.\u003c/em\u003e The Framework for Assessing Changes To Sea-level (FACTS) v1.0-rc: A platform for characterizing parametric and structural uncertainty in future global, relative, and extreme sea-level change. \u003cem\u003eEGUsphere\u003c/em\u003e\u003cstrong\u003e2023\u003c/strong\u003e, 1\u0026ndash;34 (2023).\u003c/li\u003e\n \u003cli\u003eVousdoukas, M. I. \u003cem\u003eet al.\u003c/em\u003e Developments in large-scale coastal flood hazard mapping. \u003cem\u003eNat. Hazards Earth Syst. Sci.\u003c/em\u003e\u003cstrong\u003e16\u003c/strong\u003e, 1841\u0026ndash;1853 (2016).\u003c/li\u003e\n \u003cli\u003eBates, P. D., Horritt, M. S. \u0026amp; Fewtrell, T. J. A simple inertial formulation of the shallow water equations for efficient two-dimensional flood inundation modelling. \u003cem\u003eJ. Hydrol.\u003c/em\u003e\u003cstrong\u003e387\u003c/strong\u003e, 33\u0026ndash;45 (6AD).\u003c/li\u003e\n \u003cli\u003eEuropean Space Agency. Land Cover CCI Product User Guide Version 2. Tech. Rep. (2017).\u003c/li\u003e\n \u003cli\u003ePronk, M. \u003cem\u003eet al.\u003c/em\u003e DeltaDTM: A global coastal digital terrain model. \u003cem\u003eSci. Data\u003c/em\u003e\u003cstrong\u003e11\u003c/strong\u003e, 273 (2024).\u003c/li\u003e\n \u003cli\u003eHuizinga, H. J. \u003cem\u003eFlood Damage Functions for EU Member States\u003c/em\u003e. 67 (2007).\u003c/li\u003e\n \u003cli\u003eKummu, M., Taka, M. \u0026amp; Guillaume, J. H. A. Gridded global datasets for Gross Domestic Product and Human Development Index over 1990\u0026ndash;2015. \u003cem\u003eSci. Data\u003c/em\u003e\u003cstrong\u003e5\u003c/strong\u003e, 180004 (2018).\u003c/li\u003e\n \u003cli\u003eEuropean Space Agency. ESA WorldCover 2020. (2020).\u003c/li\u003e\n \u003cli\u003eWang, P., Huang, C., Brown de Colstoun, E. C., Tilton, J. C. \u0026amp; Tan, B. Global Human Built-up And Settlement Extent (HBASE) Dataset From Landsat. https://doi.org/10.7927/H4DN434S (2017).\u003c/li\u003e\n \u003cli\u003eEuropean Space Agency. \u003cem\u003eProduct Validation Report D12-PVR\u003c/em\u003e. https://worldcover2020.esa.int/data/docs/WorldCover_PVR_V1.1.pdf (2021).\u003c/li\u003e\n \u003cli\u003eR. de Groot, L. Brander, \u0026amp; S. Solomonides. \u003cem\u003eUpdate of Global Ecosystem Service Valuation Database (ESVD). FSD Report No 2020-06\u003c/em\u003e. https://www.esvd.info/_files/ugd/53b4f9_c7518bbf3f884375828524c50773cff0.pdf (2020).\u003c/li\u003e\n \u003cli\u003ePaprotny, D., Terefenko, P., Giza, A., Czapliński, P. \u0026amp; Vousdoukas, M. I. Future losses of ecosystem services due to coastal erosion in Europe. \u003cem\u003eSci. Total Environ.\u003c/em\u003e\u003cstrong\u003e760\u003c/strong\u003e, 144310 (2021).\u003c/li\u003e\n \u003cli\u003ePesaresi, M. \u0026amp; Politis, P. GHS-BUILT-S R2023A - GHS built-up surface grid, derived from Sentinel2 composite and Landsat, multitemporal (1975-2030). (2023) doi:10.2905/9F06F36F-4B11-47EC-ABB0-4F8B7B1D72EA.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-5158614/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5158614/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eClimate change is expected to result in rising seas, exacerbating coastal floods\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e and erosion\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Remote islands are projected to be among the most challenged regions, due to their geographic isolation and fragile economies. While, Small Island Developing States have been attracting the attention of scientists and policy makers, Europe\u0026rsquo;s Outermost Regions (ORs) and Overseas Countries and Territories (OCTs) remain poorly studied in terms of their impacts from Sea Level Rise (SLR). Here we carry out a data-modelling framework to comprehensively study risks of flooding, the submergence of flat regions, and coastal erosion along coastlines of ORs and OCTs. Our study shows that under a high emissions scenario by 2150 annually nearly 3,000 km\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e is expected to be flooded, one third of which by tidal flooding, while 150 km\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e of land will be lost by coastal erosion. This translates into an annual exposure to coastal inundation of up to half a million of people and an economic damage of 5.9 \u0026euro; billion per year - a 40-fold increase from today. Our study shows the increasing benefits in time of stringent climate mitigation, which could nearly halve these impacts in the long run. However, sea levels will continue to rise long after net zero carbon is reached, and so will the consequent impacts, highlighting the critical importance of proactive efforts to increase the resilience of these vulnerable regions against rising seas.\u003c/p\u003e","manuscriptTitle":"Coastal flood impacts and lost ecosystem services along Europe’s Outermost Regions and Overseas Countries and Territories","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-25 14:08:56","doi":"10.21203/rs.3.rs-5158614/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-communications","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"NCOMMS","sideBox":"Learn more about [Nature Communications](http://www.nature.com/ncomms/)","snPcode":"","submissionUrl":"https://mts-ncomms.nature.com/","title":"Nature Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Communications","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"861e9824-b877-4f2b-84dc-3ef216438582","owner":[],"postedDate":"November 25th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":40594322,"name":"Earth and environmental sciences/Natural hazards"},{"id":40594323,"name":"Earth and environmental sciences/Climate sciences/Climate change"}],"tags":[],"updatedAt":"2026-01-08T08:13:03+00:00","versionOfRecord":{"articleIdentity":"rs-5158614","link":"https://doi.org/10.1038/s41467-025-66391-7","journal":{"identity":"nature-communications","isVorOnly":false,"title":"Nature Communications"},"publishedOn":"2026-01-07 05:00:00","publishedOnDateReadable":"January 7th, 2026"},"versionCreatedAt":"2024-11-25 14:08:56","video":"","vorDoi":"10.1038/s41467-025-66391-7","vorDoiUrl":"https://doi.org/10.1038/s41467-025-66391-7","workflowStages":[]},"version":"v1","identity":"rs-5158614","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5158614","identity":"rs-5158614","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}