Coastal flood impacts and lost ecosystem services along Europe’s outermost regions and overseas countries and territories

Domain: earth_science
Published: 2026-01-07
Editorial outcome: major_revision

Abstract
Climate change is expected to result in rising seas, exacerbating coastal floods and erosion. 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 policymakers, 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 3000 km2 is expected to be flooded, one third of which by tidal flooding, while 150 km2 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.

Introduction
Climate change and consequent sea level rise (SLR) are projected to have a substantial impact on coastal communities through erosion1,2 and flooding3–5. Sea levels have been rising at an accelerating pace6, and even under stringent climate mitigation policies, the world is committed to a further increase in mean sea level (MSL) of at least 50cm by the year 21507,8. 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 change9,10. Given the small size of their economies and their geographic distance from continental areas, island states can face a wide range of limitations in terms of risk reduction and climate change adaptation9,11. Such areas often lack the economic resources to implement all the sustainable solutions needed12, while general awareness about the challenges they face is limited due to their remoteness, as well as their small size and population, among others. As a result, despite having contributed the least to global greenhouse gas emissions and pollution, they are the most affected areas by climate change13,14. At the same time, they are among the least studied regions in terms of such challenges worldwide15.

The above apply also to Europe’s 9 outermost regions (ORs) and 13 overseas countries and territories (OCTs), which are scattered across the Atlantic, Caribbean, Indian Ocean, and Pacific (Fig. 1). 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, which 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 (FAs) with exposure and vulnerability data to estimate flood economic impacts and population exposure. Finally, we assess coastal erosion and the consequent ecosystem service losses in monetary terms.

Results
Direct coastal flood impacts
At present, annually 582.29 [370.52−900.15] km2 (values in brackets express the very likely range defined by the 5th–95th percentiles) of land along coastlines of ORs and OCTs is expected to be flooded (Fig. 2c), 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 FAs correspond to 14,366 ± [9611–39,444] people (Fig. 2b and Table 1), around 0.24% ± [0.16%–0.67%] of the OCTs and ORs population, who are annually exposed to coastal floods, with 88% of the 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 and Table 1), of which 69% occurs in ORs. OCTs host several uninhabited or sparsely populated islands (e.g. the French Southern and Atlantic Lands), so FAs do not always translate into population exposure and damage to infrastructure. On the other hand, certain ORs like the Canary Islands, host urban centres in low lying coastal areas, where extreme events can be more impactful economically (Fig. 3a). 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).

By the year 2050 overall EAFA for all regions increases by more than 70% compared to now and will reach 1020.00 ± [737.83–1364.38], 1043.17 ± [763.42–1386.11], 1057.85 ± [778.02–1403.60], and 1085.11 ± [799.04–1440.66] km2, 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. FAs keep growing in time and will reach 1399.75 ± [767.38–2230.23], 1575.64 ± [925.34–2472.98], 1771.39 ± [1087.30–2744.34], and 1949.07 ± [1231.70–3019.80] km2 by 2100 under 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 2.5–3.5 times more than today. By 2150, the size of FAs will amount to 1757.58 ± [767.38–3129.40], 2105.84 ± [1009.34–3658.46], 2527.97 ± [1305.60–4294.59], and 2838.58 ± [1517.26–4886.42] km2, hence ranging between 0.31% and 0.5%, or 5 times higher than today in the worst-case scenario.

As sea levels rise and FAs grow in size, the number of people exposed is projected to increase around 5 times by the year 2050, 10–17 times by 2100 and 15–28 times by 2150 (Fig. 2b). 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 FAs, 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 seven 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. 3d). 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 3.1–5.9 € billion, corresponding to 2.3–4.4% of the OCT/ORs GDP, or a 21–40 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 a 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. 4c).

Presently, the regions with the highest FA 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] km2, respectively (Fig. 3c). At least 60% of the FA 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 km2, respectively and climb to 651.4–964.3, 484.9–916.9, and 305.1–440.3 km2, 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 km2, respectively (Fig. 4c). 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 1737, 60, 1332, 1327 and 2588 people, respectively. At least one third of the people affected are found in the Canary Islands (4797 people), 18% are in Reunion (2588), and the remaining 31% distributed along Guadeloupe (1327), Mayotte (1737) and French Guiana (1332) (Fig. 4b).

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. 4a). 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. 4a).

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, 478–1125; 16,865–30,497; and 1068–1794 people, respectively (Fig. 4b). 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. 4a). 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. 3c), 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 loss of coastal ecosystem services
The current coastal ecosystem services in OCT/ORs are estimated at around 147.9 € billion annually, half of which comes from New Caledonia and 25% from French Guiana and French Polynesia (Fig. 3e). More than 90% of the ecosystem services are from tropical tree cover. 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 ± [38.77–1053.49], 615.64 ± [164.19–1250.29], 748.72 ± [268.68–1434.20], and 867.60 ± [366.63–1626.72] € 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 ORs (64.90%–67.58%, depending on the scenario), followed by the OCTs (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. 3f), 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. 4d). 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. 3f). 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.

Methods
Coastal flood risk modelling framework
We assess impacts from 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 risks4. 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)23. For each of these scenarios, we generate probabilistic projections of mean and ESLs 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 assess how the latter will affect future ecosystem services.

More details on the different steps of the analysis are provided below and in the flow diagram (Fig. 5).

Present-day ESLs
Coastal areas are exposed to rising MSL and episodic high sea levels under extreme atmospheric conditions. ESLs 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)24, configured in its two-dimensional barotropic mode and the 3rd-generation spectral wave model (WWM-V)25. 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 ERA526. The reanalysis is carried out without tidal forcing in order to make sure that our hindcast isolates the weather-driven component of ESLs from the tidal variations. Further details about the model setup and the validation can be found in Mentaschi et al.27.

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 Arns28. 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 coincident model and satellite values along 10×10 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 model29 forced by the IBTrACS best-track archive30. 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.9,31.

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 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.32. We benefit from the complete spectral information from the wave model to propagate each peak at each timestamp along its corresponding transect using Snell’s law33. We then estimate the wave breaking height by combining the peak wave parameters with the submerged profile slope. Subsequently, we obtain the wave run-up height R based on the Stockdon empirical formula34, after combining the breaking wave height and period with the subaerial beach profile slope. The above steps result in wave run up height estimates for each spectral peak, and we consider the highest value as the characteristic for the specific timestamp. We then combine the wave run up with the storm surge to obtain the meteorological tide and apply non-stationary extreme value analysis35 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 model36. Following the approach of Vousdoukas et al.31, the high tide water level is considered, taking into account the range due to the spring-neap tide cycle.

SLR projections
Relative SLR projections are obtained from the latest IPCC AR6 assessment37–39 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 ) 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 estimates of ESLs for the SSP scenarios in each coastal segment (1 km alongshore resolution). Non-stationary extreme value analysis35 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.40, running the Lisflood-ACC (LFP) model41 at 30 m spatial resolution, using the estimated ESLs as forcing and considering hydraulic roughness derived from land-use maps42. Up to high-tide water levels (i.e. combination of MSL 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 SLR. 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 DEM43, which is based on Synthetic Aperture Radar measurements and known to reduce the 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)44, 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-minute resolution45 in order to account for differences in the spatial distribution of wealth within countries. Baseline global land cover is available from the European Space Agency46 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 Extent47 (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 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 EAFA, EAPE and direct economic Damage (EAD). We present and discuss our results about risk at global, regional, as well as country levels, and we focus on the median, 5th and 95th percentiles (very likely range).

Land cover and ecosystem valuation
We use land cover data from the WorldCover 2020 dataset from the European Space Agency46. The dataset is an algorithmic classification of Sentinel-2 and Sentinel-1 images, identifying 10 land cover classes at 10 m resolution with a 74.4% overall accuracy48. 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.49. 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 2. 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 types located within a 10 km radius from the coastline.

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 based on extrapolation of historical behaviour 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 the ESA WorldCover. The coastline is smoothed and split into detailed segments with an average length of 30 m. Each segment of the coastline is assigned a sector of the inland land cover based on nearest-neighbour 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.50. Firstly, artificial barriers representing various anthropogenic structures are extracted from the GHS built-up surface (R2023) dataset for reference year 2018 at 10-m resolution51, which is part of the Global Human Settlement Layer database. Any occurrence of built-up surfaces in a grid cell is considered a barrier to erosion. Second, topographic barriers representing high ground that is unlikely to be easily eroded are derived from Delta DTM43. Each coastal sector corresponding to a 30-m (on average) section of the coastline is intersected with the barriers, erasing areas that are vulnerable 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.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The manuscript addresses coastal flood impacts and the loss of ecosystem services in Europe’s Outermost Regions and Overseas Countries and Territories.\nIt investigates areas that are poorly studied in terms of flooding, submergence of flat regions, and coastal erosion along coastlines.\nThe manuscript is rich in useful information, presents a very detailed methodology, and can serve as a guideline for conducting similar analyses in comparable contexts.\nFor these reasons, I recommend its publication in Nature Communications after minor revisions.\nMinor comments:\nIntroduction: I suggest including a figure that shows the Outermost Regions and Overseas Countries and Territories analyzed in the article.\nDetailed methods: A flow diagram illustrating the applied methodology could help readers reproduce the analysis.\nFigure 1: Please review the caption for Figure 1.\nDiscussion: Could the results be grouped by location? This grouping might help identify geographical areas most affected by coastal risks or verify if there are regions with common characteristics. In this regard, I encourage a more detailed discussion on this aspect.", "reviewer_2": "Key Results\nThe manuscript provides a detailed, quantitative analysis of the socio-economic and ecological impacts of sea-level rise and coastal flooding on Europe’s Outermost Regions (ORs) and Overseas Countries and Territories (OCTs). Using a state-of-the-art data-modeling framework, it highlights the significant vulnerabilities of these regions, projecting future damages under multiple emissions scenarios and underscoring the critical importance of mitigation and adaptation strategies.\nMajor Comments\n1. Significance and Context\nStrengths:\nThe manuscript addresses an underrepresented research gap, offering a novel focus on ORs and OCTs. It employs cutting-edge methodologies to quantify impacts in these regions, contributing valuable insights into their vulnerabilities.\n Suggestions:\na) Expand the discussion to connect the findings with other vulnerable regions, such as Small Island Developing States (SIDS). For example, compare projected GDP losses, population displacement, or ecosystem service impacts between ORs/OCTs and SIDS.\nb) Cite recent studies that emphasize island-specific climate change vulnerabilities to situate the work within a broader research context.\n2. Methodological Soundness\nStrengths:\nThe manuscript utilizes robust models (e.g., LISCOAST) and credible data sources (e.g., IPCC AR6, ESA WorldCover). Limitations, such as decoupling of erosion and flooding processes, are acknowledged.\nSuggestions:\na) Explicitly discuss the potential biases introduced by treating erosion and flooding separately. For example, would integrating these processes amplify or mitigate the projected impacts? A brief qualitative analysis in the discussion would strengthen the methodology’s transparency.\nb) Consider providing a supplementary appendix with simplified flowcharts or diagrams of the methodological framework to help readers navigate complex processes like hydrodynamic modeling and vulnerability assessments.\n3. Policy Relevance and Adaptation Strategies\nStrengths:\nThe manuscript mentions nature-based solutions (NBS) as a critical tool for adaptation. \nSuggestions:\na) Include concrete examples of NBS tailored to the studied regions. For example, mangrove restoration in tropical areas, coral reef rehabilitation, or managed retreat strategies for low-lying atolls.\nb) Discuss potential barriers to implementing these solutions in ORs and OCTs (e.g., financial, governance, or technical challenges) and suggest strategies to overcome them.\nc) Relate adaptation strategies to policy frameworks in the EU and other governing bodies of OCTs/ORs to make the findings actionable for stakeholders.\n4. Data Transparency and Reproducibility\nStrengths: The manuscript uses extensive datasets and robust modeling frameworks.\nSuggestions:\na) Clearly state where datasets and model codes are available for public access. If sharing full data is infeasible, consider releasing derived datasets or visualizations of model outputs to enhance transparency.\nb) Suggest repositories such as Zenodo or Figshare for hosting key datasets and codes, aligning with open science practices.\n5. Data Presentation and Figures\nStrengths: Figures are informative and well-designed for specialists.\nSuggestions:\na) Simplify complex figures like Figures 1 and 2 by focusing on representative scenarios (e.g., SSP1-2.6 and SSP5-8.5) or regions. Figure 1 and 2 are very hard to digest at the moment.\nb) Provide more intuitive legends and labels. For example, use consistent color coding across scenarios and add arrows or annotations to highlight key trends.\nc) Add a supplementary table summarizing key numerical findings (e.g., GDP losses, population exposure, ecosystem service values) for each region. This will make the data more accessible for policymakers and non-specialist audiences.\nMinor Comments\n1. Abstract: Include a specific statement on the mitigation benefits, such as: \"Under SSP1-2.6, economic damages could be reduced by X%, highlighting the importance of stringent emissions reductions.\"\n2. Introduction: Add a brief paragraph contrasting ORs and OCTs with continental regions in terms of vulnerabilities to sea-level rise and flooding.\n3. Language and Jargon: While the manuscript is generally well-written, some sections (e.g., \"Detailed Methods\") are dense with technical jargon. Simplify descriptions where possible, such as rephrasing “probabilistic extrapolation of historical behavior detected from satellites” to “using historical satellite data to estimate future shoreline changes.” Give the manuscript a little rest and then with fresh eyes, try to simplify the use of language as much as possible.\n4. References: The reference list is comprehensive, but it could include more studies on successful adaptation strategies in island contexts.\n5. Adaptation Strategies: Add a dedicated subsection or paragraph on adaptation strategies with specific examples and their feasibility for ORs and OCTs.\n6. Uncertainty and Scope: Expand the discussion on uncertainty. For example, discuss how socio-economic variability (e.g., migration patterns, GDP growth) might influence the projections. You might want to address limitations of the decoupled flooding and erosion analysis by outlining potential pathways for integrating these processes in future studies.\n7. Accessibility: You might wish to include a \"Key Findings\" section summarizing the main results in plain language for policymakers and stakeholders where you best see fit.\n8. Figures and Tables: Redesign Figure 2 to highlight mitigation benefits across scenarios more clearly. For example, use bar charts to show absolute differences in projected damages under SSP1-2.6 versus SSP5-8.5.\nOverall Recommendation\nThe manuscript is a significant contribution to climate change research and provides essential insights into the vulnerabilities of ORs and OCTs. While the study is methodologically sound and highly relevant, revisions are needed to enhance clarity, accessibility, and policy relevance. These improvements will ensure the manuscript meets the high standards of Nature Communications and achieves maximum impact in the scientific and policy communities."}, "author_response": "Response to the Reviewers’ comments\nAuthors: We would like to thank the reviewers and the editor for the\npositive comments. We have revised the manuscript according to the\nreviews and we discuss each point in the sections to follow. We would\nlike to thank you again for the constructive effort and we are at your\nconvenience for any additional changes needed.\nReviewer #1\nThe manuscript addresses coastal flood impacts and the loss of ecosystem\nservices in Europe’s Outermost Regions and Overseas Countries and\nTerritories.\nIt investigates areas that are poorly studied in terms of flooding, submergence\nof flat regions, and coastal erosion along coastlines.\nThe manuscript is rich in useful information, presents a very detailed\nmethodology, and can serve as a guideline for conducting similar analyses in\ncomparable contexts.\nFor these reasons, I recommend its publication in Nature Communications\nafter minor revisions.\nAuthors: We would like to thank the reviewer for the positive comments\nMinor comments:\nIntroduction: I suggest including a figure that shows the Outermost Regions\nand Overseas Countries and Territories analyzed in the article.\nAuthors: We have added the map as suggested by the reviewer and it is\nnow Figure 1.\nDetailed methods: A flow diagram illustrating the applied methodology could\nhelp readers reproduce the analysis.\nAuthors: We have added such a flow diagram in Figure 5.\nFigure 1: Please review the caption for Figure 1.\nAuthors: We have reviewed the caption and made several changes. We\nhope it now reads better.\nDiscussion: Could the results be grouped by location? This grouping might\nhelp identify geographical areas most affected by coastal risks or verify if\nthere are regions with common characteristics. In this regard, I encourage a\nmore detailed discussion on this aspect.\nAuthors: As shown in the map the reviewer suggested (Figure 1) the\nislands are quite scattered to be grouped geographically, while in\naddition they are characterized by distinct characteristics. This is a\ndifference with SIDS which are more in number and form clear\ngeographical groups. Still, we have expanded the discussion to several\ndimensions, as also suggested by both reviewers.\nReviewer #2 (Remarks to the Author):\nKey Results\nThe manuscript provides a detailed, quantitative analysis of the socio-\neconomic and ecological impacts of sea-level rise and coastal flooding on\nEurope’s Outermost Regions (ORs) and Overseas Countries and Territories\n(OCTs). Using a state-of-the-art data-modeling framework, it highlights the\nsignificant vulnerabilities of these regions, projecting future damages under\nmultiple emissions scenarios and underscoring the critical importance of\nmitigation and adaptation strategies.\nMajor Comments\n1. Significance and Context\nStrengths:\nThe manuscript addresses an underrepresented research gap, offering a\nnovel focus on ORs and OCTs. It employs cutting-edge methodologies to\nquantify impacts in these regions, contributing valuable insights into their\nvulnerabilities.\nAuthors: We would like to thank the reviewer for the positive comments\nabout our work. We have tried to improve our manuscript as much as\npossible following the suggestions.\nSuggestions:\na) Expand the discussion to connect the findings with other vulnerable\nregions, such as Small Island Developing States (SIDS). For example,\ncompare projected GDP losses, population displacement, or ecosystem\nservice impacts between ORs/OCTs and SIDS.\nAuthors: We have expanded the discussion including comparisons with\nresults for SIDS and thanks to the reviewer’s comment we are now\nconfident that the discussion is more lively and informative.\nb) Cite recent studies that emphasize island-specific climate change\nvulnerabilities to situate the work within a broader research context.\nAuthors: Since we have expanded both the introduction and discussion,\nthe manuscript cites several recent articles that touch SIDS, adaptation\nand other relevant issues.\n2. Methodological Soundness\nStrengths:\nThe manuscript utilizes robust models (e.g., LISCOAST) and credible data\nsources (e.g., IPCC AR6, ESA WorldCover). Limitations, such as decoupling\nof erosion and flooding processes, are acknowledged.\nSuggestions:\na) Explicitly discuss the potential biases introduced by treating erosion and\nflooding separately. For example, would integrating these processes amplify\nor mitigate the projected impacts? A brief qualitative analysis in the discussion\nwould strengthen the methodology’s transparency.\nAuthors: We have a whole paragraph in the methods dedicated on that\nissue. There we discuss the necessity for that assumption and explain\nwhy we believe it doesn’t affect the results to a significant level. We\nhave expanded that section and we hope now it is clearer for the reader.\nb) Consider providing a supplementary appendix with simplified flowcharts or\ndiagrams of the methodological framework to help readers navigate complex\nprocesses like hydrodynamic modeling and vulnerability assessments.\nAuthors: We have added a flow diagram of the methodology as this was\nalso a suggestion by Reviewer 1 (now figure 5).\n3. Policy Relevance and Adaptation Strategies\nStrengths:\nThe manuscript mentions nature-based solutions (NBS) as a critical tool for\nadaptation.\nSuggestions:\na) Include concrete examples of NBS tailored to the studied regions. For\nexample, mangrove restoration in tropical areas, coral reef rehabilitation, or\nmanaged retreat strategies for low-lying atolls.\nb) Discuss potential barriers to implementing these solutions in ORs and\nOCTs (e.g., financial, governance, or technical challenges) and suggest\nstrategies to overcome them.\nc) Relate adaptation strategies to policy frameworks in the EU and other\ngoverning bodies of OCTs/ORs to make the findings actionable for\nstakeholders.\nAuthors: We agree with the reviewer that discussing further NBS\nstrengthens the paper and we elaborate on this in the last paragraphs.\nWe do so, at the same time avoiding proposing solutions and interfering\nwith the political decision making process of adaptation, especially\nbecause some of the authors are part of the EC and have restrictions\nrelative to that.\n4. Data Transparency and Reproducibility\nStrengths: The manuscript uses extensive datasets and robust modeling\nframeworks.\nSuggestions:\na) Clearly state where datasets and model codes are available for public\naccess. If sharing full data is infeasible, consider releasing derived datasets or\nvisualizations of model outputs to enhance transparency.\nb) Suggest repositories such as Zenodo or Figshare for hosting key datasets\nand codes, aligning with open science practices.\nAuthors: Nature journals have a strict policy in open access data and\nopen source tools which we fully comply with. We have received several\nforms to fill on these topics and the published manuscript will satisfy all\ndata transparency and reproducibility criteria.\n5. Data Presentation and Figures\nStrengths: Figures are informative and well-designed for specialists.\nSuggestions:\na) Simplify complex figures like Figures 1 and 2 by focusing on representative\nscenarios (e.g., SSP1-2.6 and SSP5-8.5) or regions. Figure 1 and 2 are very\nhard to digest at the moment.\nAuthors: We understand that especially figure 1 contains a lot of\ninformation, but looking at other published articles in NCOMMS it is not\nan extreme case. After all, most authors are obliged to put a lot of\ninformation in the limited number of figures allowed. Still, if the reviewer\n(or the editor) insists we are available to simplify figure 1 and move\nparts to the SI. From our side we believe that as the figure is cited\nseveral times in the manuscript, it would be more cumbersome for the\nreader to have to address to the SI, rather than having only one more\ncomplex figure in the main manuscript.\nWe don’t think figure 2 is complicated and breaking it up in 4 figures\nwould result in too many figures that would not be justifiable. In any\ncase we have worked and improved the captions.\nb) Provide more intuitive legends and labels. For example, use consistent\ncolor coding across scenarios and add arrows or annotations to highlight key\ntrends.\nAuthors: The color coding of the scenarios is consistent among all\nfigures. We provide eps versions of the figures so any discrepancies\ndue to technical issues (like renderers) canbe resolved by the journals\nediting team.\nc) Add a supplementary table summarizing key numerical findings (e.g., GDP\nlosses, population exposure, ecosystem service values) for each region. This\nwill make the data more accessible for policymakers and non-specialist\naudiences.\nAuthors: Tables of all variables are provided in the public dataset. We\nhave added a simplified table in the manuscript according to the\nreviewer’s comment (now Table 2).\nMinor Comments\n1. Abstract: Include a specific statement on the mitigation benefits, such as:\n\"Under SSP1-2.6, economic damages could be reduced by X%, highlighting\nthe importance of stringent emissions reductions.\"\nAuthors: Such statement is present in the abstract: ‘Our study shows\nthe increasing benefits in time of stringent climate mitigation, which\ncould nearly halve these impacts in the long run’.\nWe feel that the way it is written is more appropriate to the style of the\njournal (‘stringent climate mitigation’), than referring to actual SSP\nscenarios, but if the reviewer or the editor insists we can modify the\nsentence.\n2. Introduction: Add a brief paragraph contrasting ORs and OCTs with\ncontinental regions in terms of vulnerabilities to sea-level rise and flooding.\nAuthors: We have expanded the first paragraph of the introduction\naccording to the reviewer’s comment. We now believe that such\nchallenges are better presented not only in this, but also other parts of\nthe introduction.\n3. Language and Jargon: While the manuscript is generally well-written, some\nsections (e.g., \"Detailed Methods\") are dense with technical jargon. Simplify\ndescriptions where possible, such as rephrasing “probabilistic extrapolation of\nhistorical behavior detected from satellites” to “using historical satellite data to\nestimate future shoreline changes.” Give the manuscript a little rest and then\nwith fresh eyes, try to simplify the use of language as much as possible.\nAuthors: We tried to do modifications to the proposed direction without\nhaving important technical details being lost. After all it is the Methods\nwhere experts may look for specific information, while we are confident\nthat the main manuscript is written in a simpler, more pleasant style.\n4. References: The reference list is comprehensive, but it could include more\nstudies on successful adaptation strategies in island contexts.\nAuthors: We have added several references since we have expanded the\nintroduction and discussion, according to the reviewers’ comments.\n5. Adaptation Strategies: Add a dedicated subsection or paragraph on\nadaptation strategies with specific examples and their feasibility for ORs and\nOCTs.\nAuthors: We have expanded the discussion to that direction, also\naccording to the reviewer’s comment 3c. At the same time we try to\navoid to propose solutions and interfere with the political decision\nmaking process of adaptation, especially because some of the authors\nare part of the EC and have restrictions relative to that.\n6. Uncertainty and Scope: Expand the discussion on uncertainty. For\nexample, discuss how socio-economic variability (e.g., migration patterns,\nGDP growth) might influence the projections. You might want to address\nlimitations of the decoupled flooding and erosion analysis by outlining\npotential pathways for integrating these processes in future studies.\nAuthors: We have added a section dedicated to uncertainty in the\nmethods.\n7. Accessibility: You might wish to include a \"Key Findings\" section\nsummarizing the main results in plain language for policymakers and\nstakeholders where you best see fit.\nAuthors: We checked the journal’s guidelines, and such a section is not\nrecommended. We discern from the guidelines that the abstract should\nserve that purpose, but if the editor recommends adding a Key Findings\nsection we will do it.\n8. Figures and Tables: Redesign Figure 2 to highlight mitigation benefits\nacross scenarios more clearly. For example, use bar charts to show absolute\ndifferences in projected damages under SSP1-2.6 versus SSP5-8.5.\nAuthors: We understand the nature of the suggestion, but we think that\nwhile changing the figure would make the ‘benefits of mitigation’\nmessage stronger, it would also come with disadvantages, e.g. not\nshowing information of the temporal evolution of Impacts and\ndifferences among all scenarios, that some readers might be interested\nin. For that reason, we prefer to keep the figure as it is, since anyway\nsuch figures are ambiguous in IPCC reports and most readers are\nalready familiar with them.\nOverall Recommendation\nThe manuscript is a significant contribution to climate change research and\nprovides essential insights into the vulnerabilities of ORs and OCTs. While the\nstudy is methodologically sound and highly relevant, revisions are needed to\nenhance clarity, accessibility, and policy relevance. These improvements will\nensure the manuscript meets the high standards of Nature Communications\nand achieves maximum impact in the scientific and policy communities.\nAuthors: We would like to thank the reviewer for the constructive\ncomments and we are also confident that the manuscript has improved\nsubstantially after implementing them."}, {"round": 2, "reviewer_comments": {"reviewer_3": "Key Results\nThis study investigates coastal flooding impacts and the subsequent change in expected annual damages and ecosystem services in Europe’s Outermost Regions and Oversees Countries and Territories. It investigates areas that are poorly studied but are disproportionately impacted by changing climatic conditions. The paper is dense, providing a very high level of detail. This makes it harder to read but allows the reader to extract more information about changes across very large and spatially diverse areas. Much of the methodology is detailed and the study does an excellent job highlighting the significant vulnerabilities in these regions. It also clearly communicates the importance of adaptation in the areas.\nMajor Comments\nPrevious Efforts to address previous reviewer comments: I think the efforts to address the first round of comments were mostly sufficient. I added additional comments related to estimation and interpretation of economic results.\nSignificance and Context\nThis study represents an important topic in highly vulnerable regions (OOs & OCTs). Many of the methodologies are cutting edge. The economic analyses seems to lag the other analysis, especially as it relates to ecosystem service valuation and the comparison of expected damages to GDP over time.\nUncertainty in Economic Results\nThe manuscript seems insufficient in accounting for uncertainty associated with the economic results. My biggest issue is that this study spends a significant amount of effort discussing the economic implications of change within and outside of markets, but it does not adequately communicate how uncertainty manifests itself in the estimates. This could be more explicit in the paper. The literature related to economic uncertainty seems insufficient and largely ignores uncertainty in the non-market context (i.e. ecosystem services). In addition, damages are reported relative to GDP using a baseline that appears to omit location-specific adaptation and to treat GDP similarly across locations applying the different risk profiles but without accounting for heterogeneous economic growth or adaptive capacity.\nAssessment of Uncertainty in Changes in Damage Relative to GDP: The analysis is excellent in accounting for potential uncertainty in the physical hazard chain related to exposure, vulnerability, and damages. The literature cited also shows the importance of adaptation and the study has extensive discussion of adaptation following the results. It seems that the manuscript can expand on the description of the uncertainty in the economic results and what drives that uncertainty. For example, Bachner et al (2022) appears to be relevant to this application. Bachner et al. (2022) seem to describe a no adaptation scenario as an implausible reference point when forecasting damage and GDP changes from sea level rise into the future. It is important to then consider how a no-adaptation dollar estimate can be interpreted since these areas will be forced to adapt out of necessity. This likely has implications for how a reader should interpret the economic results, with damage estimates representing potential upper bounds if realistic adaptation is not accounted for. Further, economic differences across locations are likely to lead to different rates of GDP growth, which could be significant over a long time horizon. That makes comparisons between GDP and expected damages less certain between locations over the long time horizon. Newell et al (2021) also provides relevant background information for explaining potential model uncertainties. Newell investigates many plausible models linking temperature and GDP. While temperature impacts are different than damages from sea level rise and erosion, that work discusses both level effects and growth effects on GDP. Newell results show estimates of GDP changes to be uncertain because growth responses are sensitive to modeling assumptions. Since this paper compares damage estimates to GDP, this seems like a relevant topic, even if it is only referred to in the detailed methodology as a limitation and area of future research. The manuscript already mentions differences in financial capacity across locations in the discussion, so this seems like a natural extension that acknowledges the complexity of economic outcomes when faced with these risks.\nAssessment of Ecosystem Services\nIn the paper, the manuscript first provides an estimate for, and then highlights changes in, aggregate ecosystem service value from changing coastal conditions. Costanza et al. (2014) argue that for raising awareness, Total Aggregate Values applied to regional or global scales are appropriate and low precision is acceptable. I find this a reasonable argument, but the paper should be explicit in communicating this so not to mislead readers or overstate its policy applications beyond highlighting value at risk. I do have some concerns that the manuscript uses global mean values for ecosystem services rather than following some type of transfer values that are adjusted--possibly even values using a more spatially explicit transfer function that accounts for differences in sites and populations. Johnston et al. (2021) provide details into best practices for benefit transfer. The manuscript makes no note of the assumptions necessary for the transfer of mean global values in this context or the implications of using these types of values. The manuscript really glosses over the limitations of those estimates.\nAdditionally, the manuscript does not provide estimates of uncertainty global mean values for ecosystem services. It could still rely on mean values in analysis while also communicating variability using standard errors or confidence intervals.\nFenichel and Abbot (2014) argue that as ecosystem services become scarcer over time, the marginal value of those services can increase non-linearly. Drupp et al. (2024) extend that argument. This highlights the importance of feedbacks between ecosystems and economic behavior within and outside markets. I think it is important for readers to realize that the marginal value of ecosystem services rises with both increased scarcity and increased wealth. You could add these types of references to the detailed methodology to communicate uncertainty over long time periods.\nPolicy Relevance\nThis is a very important topic for these impacted regions. There will need to be significant efforts for both protection and adaptation in the study areas. While I have concerns over how policy makers should interpret the economic estimates, they provide an excellent opportunity to raise awareness. The manuscript can both clearly state the uncertainties and the extensive assets at risk.\nMinor Comments:\n• On line 81-82, you state that these “flooded areas do not always translate into population exposure and economic impacts.” I am assuming that you are referring to market based economic impacts. This is slightly confusing since you also include ecosystem service valuations in the paper and ecosystem service values can manifest on local, regional, or global levels. This is very minor, but slight rewording may be helpful.\n• Spelling: change asses to assess on line 69\nOverall assessment\nOverall, I found this to be a very strong paper, but it is inadequate in communicating the limitations of the economic results and in providing guidance for how a reader should interpret the economic results. I recommend the authors clarify these areas both in the main portion of the manuscript and in detailed methods. With those changes, this manuscript represents a strong contribution to the climate change literature.\nMy Expertise\nI am an environmental economist.\nCitations\nBachner, G., Lincke, D., & Hinkel, J. (2022). The macroeconomic effects of adapting to high-end sea-level rise via protection and migration. Nature Communications, 13(1), 5705.\nCostanza, R., De Groot, R., Sutton, P., Van der Ploeg, S., Anderson, S. J., Kubiszewski, I., ... & Turner, R. K. (2014). Changes in the global value of ecosystem services. Global environmental change, 26, 152-158.\nDrupp, M. A., Hänsel, M. C., Fenichel, E. P., Freeman, M., Gollier, C., Groom, B., ... & Venmans, F. (2024). Accounting for the increasing benefits from scarce ecosystems. Science, 383(6687), 1062-1064.\nFenichel, E. P., & Abbott, J. K. (2014). Natural capital: from metaphor to measurement. Journal of the Association of Environmental and Resource Economists, 1(1/2), 1-27.\nJohnston, R. J., Boyle, K. J., Loureiro, M. L., Navrud, S., & Rolfe, J. (2021). Guidance to enhance the validity and credibility of environmental benefit transfers. Environmental and Resource Economics, 79(3), 575-624.\nNewell, R. G., Prest, B. C., & Sexton, S. E. (2021). The GDP-temperature relationship: implications for climate change damages. Journal of Environmental Economics and Management, 108, 102445."}, "author_response": "AUTHORS: We would like to thank the reviewer for his positive comments.\nAUTHORS: We would like to thank the reviewer for the positive comments about our work.\nAUTHORS: We agree with the reviewer on this point, and we have improved the\nmanuscript in this aspect. We have added a ‘Limitations’ section in the Methods\nwhere we discuss this and other shortcomings of the paper (some discussed also\nbelow).\nAUTHORS: We would like to thank the reviewer for this constructive comment. We\nhave added a paragraph discussing all the points raised by the reviewer. Here is the\ntext:\n‘While our analysis accounts for uncertainty in the physical hazard chain, it is\nimportant to also consider sources of uncertainty in the economic baseline used for\ncomparison, namely GDP. Regional differences in financial capacity, institutional\nreadiness, and long-term growth trajectories can introduce substantial variability in\nGDP projections, complicating cross-regional comparisons of damages relative to\neconomic output. In addition, macroeconomic impacts of cli-mate change are highly\nsensitive to assumptions about whether climate shocks affect GDP levels or long-\nterm growth rates. Although our results focus on relative damages as a share of GDP,\nreaders should be aware that these estimates remain sensitive to broader economic\nun-certainties not fully captured in the physical impact modeling. Also, our damage\nestimates assume of a no-adaptation scenario and thus they represent a stylized,\nincreasingly implausible reference case intended to isolate potential physical\nimpacts. They are meaningful as they can allow estimating the cost of inaction, but\nshould be interpreted as a potential upper bound of damages57.’\nAUTHORS: We fully agree with the reviewer and we discuss this point in the\n‘Limitations’ section found in the Methods.\n‘In the absence of more detailed information, we assume that the value of ecosystem\ndo not vary spatially. This limitation may be inevitable but is one of the many sources\nof epistemic uncertainty related to quantifying the economic benefits from\necosystems. More spatially explicit valuation methods, such as benefit transfer\nfunctions that adjust for local context, could improve accuracy in future work. Another\nimportant aspect that is omitted here is that the value of ecosystems tends to\nincrease as they become scarcer. Another limitation of our approach is that, given the\nscarcity of such information, we are not considering any uncertainty in the ecosystem\nservice value estimates. Acknowledging all the above, we emphasize that our results\nare intended to signal the potential magnitude of ecosystem service loss under\ncontinued coastal change.’\nAUTHORS: Same the previous comment and is discussed in the ‘Limitations’ section\nfound in the Methods (see previous response for text).\nAUTHORS: Same the previous comment and is discussed in the ‘Limitations’ section\nfound in the Methods (see previous response for text).\nAUTHORS: We would like to thank the reviewer once again for his positive feedback\nand constructive comments. We are confident that the revision is more transparent\nabout uncertainties.\nAUTHORS: We have rephrased the sentence:\n‘so flooded areas do not always translate into population exposure and damage to\ninfrastructure’\nAUTHORS: We have corrected the spelling error."}, {"round": 3, "reviewer_comments": {"reviewer_3": "I have not further comments related to this manuscript. The authors addressed my comments by identifying those issues as limitations in their current analysis and communicate their primary economic results (relative damage as a share of GDP) as upper bounds. Similarly, the authors identify the limitations of ecosystem service valuation under these conditions. Given the primary focus of this paper by identifying the uncertainty in the physical hazard chain, I think it is reasonable to communicate other economic uncertainties as long as it is clear that people in these regions will adapt to changes and that ecosystem service values depend on local context and relative scarcity.\nOverall, I am satisfied with the authors communicating the limitations of their economic analysis. I feel that this is an important topic in an understudied area."}, "author_response": ""}]

Decision letter