Coastal hardening and what it means for the world’s sandy beaches

Domain: earth_science
Published: 2024-12-05
Editorial outcome: minor_revision

Abstract
Sea level rise is expected to cause chronic shoreline retreat, affecting over 10% of the world’s population in low-elevation coastal zones (LECZ). Sandy beaches supported by man-made structures are more vulnerable to substantial beach area loss due to restricted landward migration, affecting ecosystem services. However, the global extent of ‘hardened’ sandy coastlines at risk of ‘severe beach loss’ has not been comprehensively quantified. Here, using products and geospatial datasets derived from satellite imagery, we find that, 33% of the world’s sandy coastline is currently hardened. Analysis of the results by IPCC AR6 regions show that the most hardened coastal regions are the Bay of Bengal, Western & Central Europe, the Mediterranean, Western North America, and East Asia. Linking coastal hardening information with existing shoreline retreat projections, we find that under SSP5-8.5, up to 26% of the world’s sandy coastline (~52,100 km) is likely to face severe beach loss by the end of the 21st century. Under a moderate emissions scenario (SSP2-4.5), this projection decreases to 21% (~42,080 km), highlighting the potential mitigation gain. The results also show that the vast majority of projected severe sandy beach losses are in high and upper-middle-income countries, with up to 82% of severe losses under SSP5-8.5 and up to 81% under SSP2-4.5 by the end of the 21st century, relative to the total length of sandy coastline, including both hardened and natural coastlines. The IPCC AR6 regions with the highest projected sandy beach losses include Eastern North America, Northern South America, the Mediterranean, the Bay of Bengal, Western Africa, and South-east Asia.

Introduction
Changes in climatic and non-climatic impact drivers are projected to lead to shoreline retreat around the world during the 21st century, in the absence of abundant sand supply to the coast and/or any physical obstruction to shoreline retreat1,2. In a first-pass assessment of global shoreline changes over the 21st century, Vousdoukas et al.3 projects that under a high emission scenario (RCP 8.5), ~50% of the global sandy coastline could face severe erosion (defined therein as a chronic shoreline retreat exceeding 100 m) by the end of the century. Given that ~10% of the global population currently lives in low-elevation coastal areas (i.e., <10m above MSL), a number that is projected to increase by up to 11.6% by 20504, such severe shoreline retreat is likely to lead to substantial socio-economic impacts. In an earlier study, Hinkel et al.5 estimated the economic cost of forced migration due to sea level rise-driven coastal erosion over the 21st century to be around US$ 1 Trillion. However, none of the aforementioned global shoreline change studies account for the currently existing physical obstructions (such as seawalls, harbor structures, highway and railway revetments) to sandy shoreline retreat, let alone the likely increase of such infrastructure in the coming decades.

Given the rapid increase in population migration to coastal zones since the 1950s6, inevitably leading to large-scale coastal developments that need protection against coastal erosion and flooding, a substantial proportion of the world’s sandy coastline is “hardened” by gray infrastructure. Considering such “hardened coasts” in shoreline retreat assessments is important for at least two reasons: (1) where hardened coasts are present and maintained, shoreline retreat could, at most, occur until the physical obstruction is reached. Projections that assume no physical obstructions to shoreline retreat (as is the case in Hinkel et al.5 and Vousdoukas et al.3), may therefore substantially overpredict the retreat amount at these locations; (2) sandy beaches that have unlimited backshore accommodation space may translate landward as a whole in response to sea level rise7, maintaining their width even as the shoreline retreats. In these latter cases, shoreline retreat need not necessarily result in beach area loss. Whether the projected rate of accelerated SLR beyond mid-century could outpace the rate of landward beach translation is not yet known. However, beaches that are backed by maintained structures could be unable to translate landward, and such beaches may eventually be eroded completely8,9. Indeed, beach loss can already be seen in front of many seawalls around the world. For instance, the entire sandy beach in front of a revetment constructed in Santa Cruz was eroded under wave action10. Along the Atlantic coast’s barrier islands, as well as along the coasts of Oahu, and the states of California and Washington, the process of gradual beach loss in front of the seawall or revetment together with the landward migration of the shoreline has been well documented10–13. Given that sea levels are projected to rise for centuries, even if greenhouse gas emissions are sharply reduced immediately14, sandy beaches fronting hardened coastlines are likely to eventually be lost unless local remedies such as sand nourishments are implemented, or unless there is an ample natural supply of sand to the coast (e.g., via fluvial sediment supply, sand supply from the lower shoreface). To date, studies on coastal hardening have been limited to specific countries or environments. For example, Gittman, et al.15 show that up to 14% of the US coastline (all shoreline types) is hardened. Major coastal cities such as New York, San Diego, Sydney, and Hong Kong currently have 50% or more of their shorelines hardened15–20. A comprehensive global-scale assessment of the number of sandy beaches that are backed by gray infrastructure has hitherto not been undertaken, and consequently, due to the reasons discussed above, the longevity of such beaches remains unknown.

In this work, we aim to answer three specific questions: (1) how much of the global sandy coastline has been artificially hardened? (2) how much of the global sandy coastline is likely to face severe beach loss by the mid- and end-21st century under moderate (SSP2-4.5) and high (SSP5-8.5) emission scenarios? and (3) Which parts of the world are at high risk of facing severe sandy beach loss? In this study, “gray infrastructure” or “hardening” refers to rigid human-made (semi-) impermeable structures that alter the natural landscape, and potentially obstruct shoreline retreat and landward translation of sandy beaches. This includes impervious surfaces such as roads, highways, railways, buildings, and other urbanized developments, which even if they are not resilient enough against erosion in their current form, are valuable assets that are likely to be further protected by engineering structures when they are facing imminent damage, thus obstructing shoreline retreat and landward translation of beaches well into the future.

Results
Hardened sandy beaches
Using GISA-10m in conjunction with the infrastructure data from OSM, we quantified the amount of hardened sandy coast length worldwide. Our results show that 33% (based on GISA-10m plus OSM) world’s sandy coastline is currently hardened (see the text box in Fig. 1a), losing the natural adaptability to accommodate future sea level rise, and is therefore more prone to experience beach loss as the shoreline retreats. At the regional scale, Fig. 1a shows the percentage of hardened sandy coastline in each IPCC AR6 region. The top 5 regions for coastal hardening (in descending order of percentage of sandy coastline hardened in the region) are the Bay of Bengal (84%), Western & Central Europe (68%), the Mediterranean (65%), Western North America (61%), and East Asia (50%). The IPCC AR6 regions with the lowest percentages of artificial hardening are Central Australia (5%), North-eastern North America (5%), North-Eastern Africa (5%), North Australia (3%), and Madagascar (3%). Interestingly, of the top 5 hardened regions, only Western & Central Europe has a noteworthy (>5km2/km) LECZ area per kilometer length of coastline, indicating that the mere presence of large LECZ may not be a driver for coastal hardening (Supplementary Table 524,26,27). However, all of the above top 5 hardened regions are affected by medium to high degrees of storminess, based on the integrated storm wave energy and frequency index28. In addition, most of these regions contain high-income and upper-middle-income countries with major seaports where coastal infrastructure investment can be high, necessitating their protection with engineering structures29. In the Bay of Bengal and the Mediterranean, the population in coastal zones (i.e., within 100 km from the coast) is high (more than 50%–75% of the total population)30, likely making coastal protection a management priority.

The global estimated erodible beach width along hardened coasts, representing the distance from the shoreline to the nearest artificial structure, is 93 m [4 m–497 m] (median [5th–95th percentiles]) (See ‘Global’ boxplot in Fig. 1b). When regions are ordered (lowest to highest) based on the median erodible beach width, artificial structures are found closest to the shoreline in the Northern South America region (Fig. 1b), followed by the Caribbean, Southern Australia, Western Africa, and New Zealand—all within a regionally averaged 56 m from the shoreline. The regions with the widest erodible sandy beaches are found in North-Eastern North America, North-Western South America, Central Australia, South Asia, and North-Eastern Africa (lowest to highest).

Severe sandy beach loss projections
We combine our coastal hardening results with Vousdoukas et al.’s3 shoreline retreat projections to estimate the severe sandy beach loss projections by the middle and end of the 21st century under RCP4.5 (comparable to SSP2-4.5) and RCP8.5 (comparable to SSP5-8.5). We considered all shoreline retreats >100 m at hardened sandy locations to be indicative of potential severe beach loss, in line with Vousdoukas et al.’s3 definition of severe erosion (see Methods). This threshold choice is further supported by the current median global erodible hardened sandy beach width of 93 m derived above from GISA-10m plus OSM (See ‘Global’ box plot in Fig. 1b). Here, we also included locations where the shoreline retreat is less than 100 m but greater than or equal to the distance from present-day shoreline to the nearest gray infrastructure, as these indicate near-complete beach loss. We find that under SSP2-4.5, 4%–15% (5th–95th percentile range) of the world’s sandy coastline could face severe beach loss by mid-century, increasing to 9%–21% by the end of the 21st century (see text boxes in Fig. 2). Under SSP5-8.5, severe sandy beach loss is projected to occur at 4%–19% of sandy coasts by mid-21st century, increasing to 9%–26% by the end of the 21st century (note: percentages indicate the proportion of the global sandy coastline that is projected to experience severe beach loss, here defined as hardened locations where projected shoreline retreat is more than 100 m, or hardened locations where projected shoreline retreat is less than 100 m but is equal to or greater than the distance from the present-day shoreline to the nearest artificial structure). The median projections, statistically representing the most likely or expected value, show that 7% of the world’s sandy coastline could face severe beach loss by mid-century under SSP2-4.5, increasing to 8% under SSP5-8.5. By the end of the 21st century, the most likely projections are 14% under SSP2-4.5 and 19% under SSP5-8.5. Of only the hardened sandy coasts, by mid-century, 20% [11%–43%] are projected to face severe beach loss under SSP2-4.5, increasing to 23% [10%–55%] under SSP5-8.5. By the end-century, 42% [24%–63%] and 55% [27%–78%] of the hardened sandy coasts are projected to face severe beach loss under SSP2-4.5 and SSP5-8.5, respectively.

Geographically, most sandy beaches under threat are located in Eastern North America, Northern South America, Mediterranean, Bay of Bengal, Western Africa, and South-East Asia regions (Fig. 2). In Eastern North America, under SSP2-4.5, 29% [19%–32%] of the regional sandy coastline is projected to suffer severe beach loss by mid-century, this percentage remaining unchanged till the end-century (Supplementary Table 1). Under SSP5-8.5, the mid-century projection for this region increases to 30% [17%–33%], while the end-century projection increases to 31% [26%–33%]. In Northern South America, 21% [11%–28%] of the sandy coastline is projected to experience severe beach loss under SSP2-4.5 by mid-century, and 22% [16%–28%] by end-century. Under SSP5-8.5, the severe beach loss percentage is projected to increase to 22% [10%–30%] by mid-century, and to 24% [17%–29%] by end-century. In Mediterranean, 20% [8%–38%] of the sandy coastline is projected to face severe beach loss under SSP2-4.5 by mid-century, and 37% [24%–48%] by end-century. Under SSP5-8.5, these projections increase to 24% [8%–44%] for mid-century, and to 44% [26%–54%] for the end of the century. In the Bay-of-Bengal, under SSP2-4.5, 13% [9%–23%] of the sandy coastline is projected to suffer severe beach loss by mid-century, and 34% [20%–58%] by end-century. Under SSP5-8.5, the mid-century projection for this region increases to 15% [10%–41%] by mid-century, while the end-century projection increases to 49% [21%–74%]. (The regional distribution of severe beach loss as % of only the hardened sandy coast line is given in Supplementary Table 2).

Fig. 2 indicates that transitioning from high to moderate emissions could yield a more substantial impact at the end of the century compared to mid-century. Although there is no discernible difference in the sandy coastline length at risk resulting from the shift from SSP5-8.5 to SSP2-4.5 by mid-century, this mitigation strategy exhibits potential reductions of up to 5% representing ~10,020 km of sandy coast by the end-century. Similar observations hold at the regional level, where the benefits are more pronounced in the long term than in the near future, with projections indicating a maximum reduction of ~5% in the sandy coastline length that could experience severe beach loss from the SSP5-8.5 to SSP2-4.5 transition (Supplementary Table 1). Bay of Bengal, Western & Central Europe, Western North America, Eastern Australia, South-Eastern South America, and North-Eastern South America are the regions projected to benefit the most from mitigation. Fig. 2 also shows that, in general, projected sandy beach losses are highest in regions with a high level of coastline hardening. However, it is important to note that this does not imply that coastline hardening is the sole direct cause of beach loss, as our definition of severe beach loss is strongly influenced by both shoreline retreat projections and the available erodible beach width.

Socio-economic dimensions of the findings
We further included country income in our analysis to investigate the socio-economic dimensions of our findings on coastal hardening and severe beach loss. By examining these aspects through the lens of country income status, we can determine whether extensive infrastructure investments in high-income countries inadvertently result in maladaptation. Considering the World Bank’s classification of income groups by 2022 gross national income (GNI) per capita—low income (≤$1135), lower-middle-income ($1136 to $4465), upper-middle-income ($4466 to $13,845), and high income (≥$13,846)29—, we find that 56% of the global hardened sandy coasts are found in high-income countries, 26% in upper-middle-income countries, 15% in lower-middle-income countries, and only 3% in low-income countries (Fig. 3a). By mid-century, the majority of severe beach loss is projected to occur in high-income and upper-middle-income countries under both emission scenarios considered (81% [76%–83%]) (Fig. 3b and Supplementary Table 3). There were no major changes to these mid-century projections by the end of the century.

The largest share of hardened coastlines and sandy beaches facing severe beach loss in high-income countries primarily reflects the fact that higher-income countries are more likely to have the resources to invest in and maintain coastal hardening structures, which, as our results show, may lead to unintended consequences and higher beach losses in future. It should be noted that the correlation between income groups and severe beach loss is a finding from our analysis and is not related to how shoreline retreat was computed by Vousdoukas et al.3, as their analysis does not consider shoreline hardening. It is important to acknowledge the potential underestimation in the percentage of hardened sandy beaches and, consequently, in the percentage of sandy beaches facing severe loss in low and lower-middle-income countries due to the higher possibility of unmapped areas in community-based OSM data. This limitation should be borne in mind when interpreting Fig. 3.

Considering the current projections of higher population and urbanization in coastal zones over the 21st century31,32, the development of critical infrastructure or coastal protection measures is expected to increase proportionally. This underscores the potential for even more sandy beach losses than our current estimate, which does not account for any future changes in coastal hardening.

Methods
Data
SDS. We used an updated version of Luijendijk et al.’s33 Satellite-Derived Shorelines (SDS) dataset to locate sandy beach locations. In this dataset, sandy beaches are detected based on supervised pixel classification of Sentinel-2 imagery at 500 m spaced transects orthogonal to the global shoreline from the OSM dataset of 2016. In the preprocessing step, we removed contentious data points (i.e., points that the original SDS dataset categorizes as ‘sandy’ when they are not sandy and vice versa) from the original SDS dataset based on cross-checks with Google Earth satellite imagery. Whatever non-rocky cliffs (e.g., sandstone) may have remained in Luijendijk et al.’s33 SDS classifications would also have been included in our analysis. Most of these removed data points were located on the Canadian east and west coasts, the south coast of Chile, the coastline of Guyana, small island nations in the South Pacific, and the Indian Ocean (including the Solomon Islands, Vanuatu, and New Caledonia), and the Lesser Antilles. Overall, this resulted in 25% fewer locations compared to the original SDS dataset. The final sandy location dataset contains 1,053,613 data points.

GISA. We used three versions of the GISA dataset: (1) Version 1.0 at 30m resolution (GISA-30m-V1)21; (2) Version 2.0 at 30m resolution (GISA-30m-V2)23; (3) Version 3.0 at 10m resolution (GISA-10m)22. We emphasize here the results based on GISA-10m since it is reported to have an accuracy >86%. GISA-10m is derived from over 2.7 million Sentinel optical and radar images. It was generated using 58 million training samples extracted through temporal, spatial, spectral, and geometric rules on various sources of geospatial datasets. In addition to its higher resolution, it has the added benefit of detecting transportation networks resulting from the integration of OSM data in training samples for classification. Therefore, GISA-10m is particularly well suited for our study as it captures coastal structures and revetments along highways and railway that could impede shoreline retreat and beach translation. We used GISA-30m-V1 and GISA-30m-V2 only to test the sensitivity of our results to different spatial resolutions and image classification techniques. The details of GISA-30m-V1 and GISA-30m-V2, along with associated results, are discussed in the “Sensitivity of severe sandy beach loss percentage on GISA versions and spatial resolutions” section of the Supplementary Information.

OSM. We used the community-built global geospatial OSM24 in conjunction with the GISA data to rectify any imagery misclassifications. Although GISA-10m has integrated information from OSM into its training samples, it still has lower accuracy in rural and arid areas. Through manual inspection, we identified a few misclassifications of sandy beaches as urbanized pixels. Therefore, to more accurately define hardened sandy beaches, we incorporated infrastructure data from OSM to establish the ‘hardened’ classification. Specifically, we utilized OSM keys ‘building’, ‘land use’, ‘highway’, and ‘railway’ (Supplementary Table 4). OSM attributes that did not align with a gray infrastructure definition were filtered out manually.

Global shoreline retreat projections. In this study, we used Vousdoukas et al.’s3 projections of chronic shoreline retreat due to the combined effects of Relative SLR (R) and ambient changes (AC) at mid- and end-21st century under RCP4.5 and 8.5 scenarios. Several previous studies have suggested such addition of the RSLR-driven and ambient contributions to net shoreline change to estimate the total future shoreline change34,35. Vousdoukas et al.3 dataset provide projections of shoreline retreat for sandy locations globally at 500 m alongshore resolution. R is estimated using an improved version of the Bruun Rule36 with a correction for its known overestimation compared to projections from physics-based models. The ambient changes, which are added to the RSLR-driven shoreline retreat, essentially represent the manifestation of all other background processes, such as fluvial sediment supply, headland bypassing, lower shoreface transport, etc., on shoreline position change. AC was obtained from historical shoreline change rates (1984-2016) reported by Luijendijk, et al.29 and Mentaschi et al.37, also at 500 m alongshore spacing. It should be noted that these projections do not account for physical obstructions to shoreline retreat (e.g., seawalls, revetments), an aspect explicitly addressed in our study (please see “Hardened sandy beaches” in Methods).

Computational framework
Our analysis covers 1,053,613 locations spaced at 500 m along the ice-free global coastline. First, we verified whether the coast location of interest is sandy using the updated SDS data. If confirmed as sandy, we then examined the presence (or not) of an artificial structure within the corresponding backshore search area, leveraging information from both GISA and OSM. If an artificial structure is identified, the sandy location is classified as hardened, and shoreline retreat projections are integrated into the analysis, followed by a further assessment of the retreat amount. In cases where the shoreline retreat amount is considered to be severe (i.e., 100 m or more, following the same definition used by Vousdoukas et al.3 or when it is less than 100 m, but equals or exceeds the distance from the present-day shoreline to the nearest infrastructure), we flag the location as a sandy beach susceptible to severe future beach loss. The overall workflow is illustrated in Fig. 4. More details are given in the following sections.

Hardened sandy beaches
First, 1 km long shore-normal transects were defined at intervals of 500 m along a global coastline obtained from OSM as shown in Supplementary Fig. 2. On each sandy shore-normal transect, a 90° search quadrant with a 1 km radius was defined, starting at the shoreline (as identified on OSM) (See Supplementary Fig. 2). To define whether a sandy transect is hardened or not, we implemented a logical “AND” condition. This condition requires the presence of both a GISA impervious pixel and an OSM feature within the search sector. Only when this condition is met, we flag the transect as ‘hardened’. These transects are subsequently utilized for global and regional-level quantifications of the percentage of sandy coast that is hardened and may face severe beach loss, based on the presence of GISA and OSM structures within the defined search sector. The distance to the nearest gray infrastructure is computed as an average of the distances to GISA impervious pixel and the OSM feature from the shoreline. As there are no global datasets of sandy beach width available at present, we applied a uniform maximum search distance of 1 km landward from the shoreline to search for any gray infrastructure, and if found, we defined the sandy point as hardened. We performed a sensitivity analysis on the number of hardened structures encountered using different landward search distances. This was done by creating sets of transects with lengths of 250 m, 500 m, and 750 m, generating search sectors with the corresponding radius, and repeating the subsequent steps of searching for gray infrastructure and defining hardened points. The results of our sensitivity analysis indicate that, on a global scale, the increase in the total global hardened coastline percentage is 6% in increasing the search distance from 250 m to 500 m, 4% from 500 m to 750 m, and 3% from 750 m to 1 km (Supplementary Fig. 3). At a regional level too, convergence is reached between 750 m and 1 km search distances, with difference of 0%–5% in computed regional percentage of hardened coastline (For smaller search distances, the difference per 250 m increase in search radius can be up to 12%) (Supplementary Fig. 3).

Severe sandy beach loss projections
At hardened sandy locations, we used the 500 m alongshore resolution shoreline retreat projections from Vousdoukas et al.’s3 to identify locations that may face severe beach loss. A sandy and hardened location is classified as a severe beach loss location if the projected retreat value is (a) >100 m (i.e., following the definition of severe shoreline retreat adopted by Vousdoukas et al.’s3), or (b) <100 m, but equal or greater than the distance from the present-day shoreline to the nearest artificial hardening structure.

We implemented Monte Carlo simulations to probabilistically determine the percentages of severe beach loss projections at the IPCC AR6 sub-region level. At every sandy, hardened transect in an IPCC AR6 region, 1000 random samples of projected shoreline change were generated based on the probability distribution for that location given in Vousdoukas et al.’s3. These shoreline retreat distributions were then collated and converted to a probability distribution. This distribution represents the percentages of shoreline retreat exceeding 100 m and those that are equal to or greater than the distance from the present-day shoreline to the nearest artificial structure when retreat is <100 m. We then take the median (50th percentile, statistically known as the “expected value”) of this probability distribution in each region as the most likely estimate of the percentage of locations that may experience severe beach loss in that region. In addition, for each IPCC AR6 region, we take the percentage of locations corresponding to the 5th percentile as best-case estimates (i.e., low estimate of percentage of severe beach loss locations); and the percentage of beach loss equal to the 95th percentile as worst-case estimates (i.e., high estimate of percentage of severe beach loss locations).

Limitations
Our global quantification of hardened sandy coasts and projections of sandy beaches that may face severe beach loss have certain unavoidable limitations due to the input data and methodological assumptions, which are discussed below. It is important that these limitations are taken into account when considering our results.

Firstly, our results do not include possible future changes in the exposure data, i.e., future changes in gray coastal infrastructure. We also could not project total beach loss since the actual sandy beach width per transect is unknown at a global scale. We acknowledge the possibility of locations where gray infrastructure might exist landward of the shoreline retreat threshold of 100 m used here. At such locations, although severe beach loss may not occur within the time horizons considered in this study, as there is a committed SLR for centuries to come, severe beach loss may occur at some point beyond our limited time horizon. For that reason, we included such locations in our quantification of severe beach loss projections.

Another limitation arises from the input datasets utilized to identify hardened sandy locations. Although GISA-10m demonstrates robust performance in urban settings, its accuracy is lower in rural and arid areas22. To address this limitation and enhance the precision of identifying hardened objects in coastal areas, we integrated community-contributed OSM data. Despite these efforts, there are still globally unmapped areas, notably prevalent in low and lower-middle-income countries. This incompleteness may impact our assessment of hardened coastlines and, consequently, our projections for sandy beach loss, potentially underestimating the extent of hardened sandy coastlines within these under-mapped regions.

Similarly, the stated limitations inherent in the shoreline retreat projections from Vousdoukas et al.’s3 consequently propagate into our assessment of locations that may experience severe sandy beach loss in the future. As mentioned before (see Methods), these retreat projections are the combination of two components: AC (retreat estimated by extrapolating the shoreline dynamics over the past 32 years into the future) and R (retreat due to sea level rise). The fact that AC was derived from dynamics over 32 years cannot fully capture changes caused by rare extreme weather events or substantial human interventions. Moreover, the spatial resolution of the underlying satellite images to compute the AC component, set at 30 m, might be insufficient to detect smaller changes in less energetic areas.

Sandy Beach locations are here taken from Luijendijk et al.’s33 SDS database. Over time, some discrepancies have been noted in this SDS dataset where non-sandy locations had been identified as sandy locations. While, in our study, we removed such known contentious locations from the analysis, there may still be others that we had not removed. It must be recognized that when performing very large-scale assessments such as that done here, which are essential for macroscale decision-making, some local scale imperfections necessarily need to be accepted.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Nature Communications Review: Coastal hardening and what it means for the world’s sandy beaches\nIntroduction\nR1.1 This study provides a much needed global analysis on the extent and potential impacts of shoreline hardening on sandy beach ecosystems. The authors have done a significant amount of novel and valuable geospatial analysis and are presenting significant and valuable results that can be used by coastal managers and policymakers.\n\nR1.2 Given the scale of the study and the short format of articles in Nature Communications, it was likely challenging for the authors to pick which results are the most important to display, and how to display them in a way that will have reach to the widest audience.\n\nR1.3 The comments here are mainly concerned with clarifying the link between research questions #1 (how much of the global sandy coastline has been artificially hardened?) and #2 (how much of the global sandy coastline is likely to face severe beach loss by the mid and-end-21st century under moderate and high emissions scenarios?) in the presentation and discussion of results. Discussion of results presented needs to be expanded to convey the significance of these findings.\n\nAbstract\nR 1.4 Line 12-14: Please clarify if projected sandy shoreline retreat is only for hardened sandy shoreline, which the first part of this sentence implies: “Connecting the coastal hardening information with existing projections of sandy shoreline retreat, we find that under SSP5-8.5, up to 24% (representing ̴ 48,095 13 km) of the world’s (hardened?) sandy coastline is likely to face severe beach loss by the end of the 21st century.”\nR 1.4.1 Same for line 15, 17, and 19\n\nR 1.5 Previous projections like Hinkel et al. 2013 and Vousdoukas et al. 2020 may over-predict retreat because physical obstructions are not accounted for. Can you compare your predictions to Vousdoukas et al. 2020 to see how accounting for obstructions changes shoreline retreat projections? Did Vousdoukas et al. 2020 actually over-predict?\n\nR 1.6 Line 41: You discuss landward translation here but there is no mention of landward translation later when results are discussed. Did the Vousdoukas et al. 2020 shoreline projection model project landward translation in any areas that you identified as hardened? It would be extremely valuable to see if any of the highly hardened areas you identified would otherwise be predicted to translate landward, because it would suggest that certain regions have shoreline hardening that is working “against” beach management goals.\n\nR 1.7 Line 63: If your research question #2 is for global sandy shorelines, and not global hardened sandy shorelines, the link between research question #1 and #2 is not clear.\n\nR 1.8 Line 86-88: This is a very important and novel finding! But the claim that this 33% of the world’s global shoreline is “more prone to experience beach loss as the shoreline retreats” needs more justification.\n• Is this just based on evidence that shore protection structures like seawalls and revetments block landward translation (As you discussed in Lines 41-54)? There is no mention that some of these vertical structures worsen scouring and erosion due to wave reflection, although this seems implied in lines 86-88.\n\nR 1.8.1 This 33% includes other kinds of gray infrastructure and hardening, like roads, housing, other kinds of development which may not be a direct block to landward translation depending on how dense the infrastructure is. It would be helpful to clearly state how you are defining gray infrastructure.\n\nR 1.9 Lines 89-93, and Line 101 (Figure 1): Is there a reason why these are the top 5 regions that have the most shoreline hardening? This is worth making a comment, to convey the significance of your results. Is it because they have coastal dependent economies (shipping or fisheries)? Is it due to these regions having lots of low-lying areas that need infrastructure to prevent flooding? Is it because they are in storm-prone areas (infrastructure is mainly seawalls)?\n\nR 1.10 Line 101 (Figure 1b): Maybe order the regions by lowest-highest distance from shoreline so figure is easier to read, or possibly a map figure with different colored color bar from Min-Max erodible beach width in front of nearest artificial structure\n\nR 1.11 Lines 106-121: It seems like a greater percentage of hardened sandy coasts are predicted to experience severe (>100m) beach loss under both SSP scenarios and by both mid-and-end century, when compared to “all” or “global” beaches. Why do you not highlight this finding? Is it because the uncertainty range around the percentages makes the difference between “hardened” and “global” beaches not significant? I would also suggest making a comparison between “hardened” and not hardened beaches. This is the kind of analysis that is missing that would connect your first and second research questions.\n\nR 1.12 Lines 122-138 (including Figure 2): Again, these are novel valuable findings, and it is great to see them grouped by IPCC AR6 regions and continent in the figure. What is missing is the link to shoreline hardening. The regions that have high % of (“all”) sandy coast projected to face severe beach loss seem to also be regions that have high % of hardening, yet this link is not made clear. It would be good to use Figure 2 to make this link. Could you plot the percentages from Supplementary Table 2 over (or next to) the bars in Figure 2 in a different color?\n\nR 1.12.1 Also worth making a comment about why these regions are ones that have the greatest % of beaches under threat.\n\nR 1.13 Lines 139-149 (including Figure 3): Why is country income (Gross National Income from the World Bank) chosen as the demographic lens through which you view your % sandy shoreline hardening and % sandy beach facing severe erosion?\n\nR 1.13.1 What is the significance of high-income countries having greater percentages of both hardened beaches and beaches facing severe erosion? Discussion of these results needs a comment on this.\n\nR 1.13.2 I think you need to make it clear that the correlation between the two is not related to how erosion was predicted/calculated, since the methods of Vousdoukas (2020) do not take into account shoreline hardening.\n\nR 1.13.3 You mention in the limitations section that less developed countries are more likely to have unmapped regions, which would make both your %hardened and % beach facing erosion estimations potentially underestimated. I think you should mention that when you show the graph\n\nR 1.13.4 Could you look at another demographic, that would have a less strong relationship than GNI to unmapped-ness, like population density?\n\nMethods\nR 1.14 Lines 174-192: Some of this text on GISA datasets can be cut down and moved to Supplemental, to make room for more detail on “Global shoreline retreat projections” and detail on your workflow.\n\nR 1.15 Lines 200-212: Slightly more detail here would be helpful, how was Bruun rule modified?\n\nR 1.16 Line 212: After “an aspect explicitly addressed in our study”, it would be helpful to add a reference to where in the Methods section this is addressed in your workflow.\n\nR 1.17 Lines 229-230 (Figure 4): Figure 4 can be moved to supplemental section. A flow chart figure illustrating your workflow steps would be more helpful here.\n\nR 1.18 Lines 243-258: It is unclear in your description of the workflow how hardening is accounted for.\nExample #1: if your 1000km transect detects infrastructure at 50m, yet your shoreline retreat projection predicts 100m of erosion, does your analysis cut off erosion at 50m for that location data point? If erosion is cutoff at 50m, then this location does not make the cut for “severe” beach erosion, yet it should count as “severe” because all beach is lost.\nExample #2: if your 1000km transect detects infrastructure at 50m, and shoreline retreat projection predicts 50m of erosion, this also does not make the “severe” beach erosion cutoff, yet it should count as “severe” because all beach is lost.\n\nSupplemental\nR 1.19 Mistake: “more” should be “less” according to Figure S5.", "reviewer_2": "Summary\nThis paper investigates global sandy shorelines and quantifies the proportion of these shorelines that are hardened (backed by infrastructure/buildings) within a 1km search grid. It quantifies the proportion of all sandy beaches and hardened sandy beaches that are at risk of severe beach loss (erosion of >100 m) by mid and end of the century according to data from Vousdoukas et al. 2020. Lastly, the study categorizes these values based on IPCC AR6 reference regions and the income groupings from the World Bank\nIt is overall a large undertaking of data curation and analysis at a global scale and generally well documented. However, some further clarification on a few areas could improve the manuscript.\nMy primary concern is the close resemblance of this paper to the study published by Lansu et al. in Nature Communications in January 2024. While I realize this publication may have been just before the submission of this paper, I am not sure that I see how this current paper contributes differently than what has already been published. From my understanding, I’ve added a very brief comparison on the methodology below for reference:\nLansu et al. 2024 uses the same dataset from Luijendijk et al. 2018 to identify locations of sandy beaches, then uses the same dataset on shoreline change projections by Vousdoukas et al. 2020 (but at 1000m increments instead of 500m used here), and lastly uses the same OSM dataset as used here to determine coastal hardening. Although here, the Global impervious surface area (GISA) raster dataset is primarily used with OSM as a complement to these data, providing an alternative way to determine coastal hardening. Analysis by Lansu et al. is categorized by countries, whereas this is categorized by IPCC AR6 reference regions. The overall conclusions are similar - both studies find 33% global sandy beaches to be hardened. Lansu et al. finds 23% (30%) of beaches to be lost by the end of the 21st century under RCP4.5 (RPC 8.5). The current study considers ‘severe’ beach loss and finds 18% (24%) of beaches to likely face severe beach loss by the end of the 21st century under SSP2-4.5 (SSP5-8.5). Interestingly, Lansu et al. 2024, with their method of coastal hardening, find a median distance of infrastructure free space to be 392 meters, while this papers find it to be 93 meters.\nBelow are comments on each section\nThis paper provides a nice robust statistical approach to determine the probabilities (and most likely scenario) of % of beaches facing severe erosion.\nA few points I believe could benefit some slight clarification:\n- Use of OSM and GISA datasets: The methods section should clearly explain how the OpenStreetMap (OSM) and Global Impervious Surface Area (GISA) datasets are used together. Providing details on how they complement each other and resolve discrepancies would add clarity. Specifically, there were instances where it’s stated that GISA-10 pixels inaccurately indicated hardening on sandy pixels, and OSM data was used to improve accuracy. I think clarity is needed on 1) how it was determined whether a ‘hardened’ pixel was sandy or not at a global scale? And 2) How exactly the GISA and OSM datasets were combined? For instance, were GISA pixels disregarded in favor of OSM data when they were found on sandy beaches, or were the GISA pixels modified to exclude false indicators of hardening?\n- If possible, include the number of transects analyzed in each IPCC reference region in one of the tables or figures. This would help contextualize the findings.\nClarification of percentages: In the main text, briefly explain what the percentages represent when they are first introduced (line 113). While the methods section may provide a full explanation, a brief summary in the main text would help guide readers through the presented data.\n- Address/clarify the discrepancy between Vousdoukas et al.'s finding that nearly 50% of the global sandy coastline could face severe erosion, and the results here that indicate 3-14% of beaches globally are at risk with the same dataset.\n- Consider examining how many beaches would be completely lost due to erosion, taking into account the proximity of coastal hardening. 100 m of erosion means vastly different things for different coastal environments. Analyzing the complete loss of these ecosystems could provide a more comprehensive understanding. But I also understand the complexities with a large-scale analysis like this.\nBelow are in line comments\nLine 9: Replace \"using satellite imagery\" with \"products and geospatial datasets derived from satellite imagery\" to clarify that the study does not use raw satellite imagery directly.\nLine 37: Broadly define grey infrastructure. Does it encompass all man-made structures?\nLine 49: This sentence reads to me like it is all barrier islands, perhaps reword.\nLine 59-61: see the comments from earlier regarding the comparison with Lansu et al.'s study.\nLine 69: Instead of \"state-of-the-art image classification model,\" provide more specific details on the segmentation or classification methods/models used in the study if you include it in the methodology.\nLine 136: Reword the figure description for clarity. Specify whether the plot is for hardened sandy beaches only or for all sandy beaches. If it's for hardened sandy beaches, clearly state that. If it's for all sandy beaches, remove the reference to GISA.\nLine 150: I don't know that figure 3 supports the statement in line 150-152 - that transitioning from high to moderate emissions will yield more impact at the end of the century. From my understanding Figure 3 demonstrates that the majority of hardened beaches occur in the high income areas. It then shows that a higher percentage of beaches in high and upper-middle income areas are facing severe erosion, which further increases under higher emission scenarios.\nLine 158: I think this sentence is missing a ‘to’ (“projected TO benefit”)\nLine 166: Reword the sentence to clarify that the dataset method detects sandy beaches. You could use the phrase \"Here, sandy beaches are detected based on the SDS dataset method...\" or another similar phrasing.\nLine 167: How were contentious data points determined? And are cliffy areas also removed?\nLine 194: perhaps remove the reference to remote sensing and simply state \"any misclassifications.\"\nLine 286: If there are citations for discrepancies in the sandy shoreline dataset published, include them as references.\nSUPPLEMENTARY Table S1, Line 1: Add \"percentage of ALL sandy beaches\" for clarity. Remove the final sentence about hardened shoreline locations as it is not directly relevant to the table.\nPapers referenced\nLansu, Eva M., Valérie C. Reijers, Solveig Höfer, Arjen Luijendijk, Max Rietkerk, Martin J. Wassen, Evert Jan Lammerts, and Tjisse Van Der Heide. “A Global Analysis of How Human Infrastructure Squeezes Sandy Coasts.” Nature Communications 15, no. 1 (January 10, 2024): 432. https://doi.org/10.1038/s41467-023-44659-0."}, "author_response": "Responses to Reviewers' comments\nWe thank the Reviewers very much for their positive and highly constructive reviews of the\nmanuscript which have helped us to improve it greatly. Below we provide our responses to all\nreviewer comments. Original reviewer comments are shown in black bold font. Our responses and\nmodifications affected in response to Reviewer comments are shown in blue font here and highlighted\nin the revised manuscript. Reviewer comments are numbered for convenience.\nReviewer #1(Remarks to the Author):\nNature Communications Review: Coastal hardening and what it means for the world’s sandy\nbeaches\nIntroduction\nR1.1 This study provides a much needed global analysis on the extent and potential impacts of\nshoreline hardening on sandy beach ecosystems. The authors have done a significant amount of\nnovel and valuable geospatial analysis and are presenting significant and valuable results that\ncan be used by coastal managers and policymakers.\nWe thank the Reviewer for this very positive assessment.\nR1.2 Given the scale of the study and the short format of articles in Nature Communications, it\nwas likely challenging for the authors to pick which results are the most important to display,\nand how to display them in a way that will have reach to the widest audience.\nWe thank the Reviewer for this understanding statement.\nR1.3 The comments here are mainly concerned with clarifying the link between research\nquestions #1 (how much of the global sandy coastline has been artificially hardened?) and #2\n(how much of the global sandy coastline is likely to face severe beach loss by the mid and-end-\n21st century under moderate and high emissions scenarios?) in the presentation and discussion\nof results. Discussion of results presented needs to be expanded to convey the significance of\nthese findings.\nWe thank the Reviewer for these encouraging comments. We have addressed all these points in our\nresponses to line-by-line comments below.\nAbstract\nR 1.4 Line 12-14: Please clarify if projected sandy shoreline retreat is only for hardened sandy\nshoreline, which the first part of this sentence implies: “Connecting the coastal hardening\ninformation with existing projections of sandy shoreline retreat, we find that under SSP5-8.5, up\nto 24% (representing ̴ 48,095 13 km) of the world’s (hardened?) sandy coastline is likely to face\nsevere beach loss by the end of the 21st century.”\nR 1.4.1 Same for line 15, 17, and 19\nWe thank the Reviewer for this suggestion. We presented the severe beach loss percentages (both\nregional and global) based on the total length of the sandy coastline. This approach was chosen to\nmaintain alignment with the title: 'Coastal Hardening and What it Means for the World’s Sandy\nBeaches' and the focus of our paper.\nIn response to this comment, we have now revised the sentence in lines 7-9 of the revised manuscript\nas “The global extent of sandy coasts that has been “hardened” and can face “severe beach loss” has\nnot been comprehensively quantified to date.” And also included a further clarifying statement in lines\n18-19 as “(Percentages are relative to the total sandy coast length, which include both hardened and\nnatural coasts.)”.\nR 1.5 Previous projections like Hinkel et al. 2013 and Vousdoukas et al. 2020 may over-predict\nretreat because physical obstructions are not accounted for. Can you compare your predictions\nto Vousdoukas et al. 2020 to see how accounting for obstructions changes shoreline retreat\nprojections? Did Vousdoukas et al. 2020 actually over-predict?\nIn our opinion, the two diagnostics used in the two studies cannot be directly compared. Vousdoukas\net al. (2020) projected shoreline retreat at all sandy beaches and quantified severe 'erosion' at locations\nwhere the retreat exceeded 100 m. In contrast, we combined the shoreline retreat projections from\nVousdoukas et al. (2020) specifically at sandy beaches with existing infrastructure to quantify severe\nbeach \"loss.\"\nFor example, almost no sandy beaches in Northern Australia have man-made infrastructures, with only\n3% being hardened (please see Figure 1(a)). Vousdoukas et al. (2020) projected a median shoreline\nretreat of over 200 meters for this region under RCP 8.5 by 2100. Thus, sandy beaches along the\nNorthern Australia shoreline were included as areas of severe \"erosion\" in Vousdoukas et al. (2020)\n(please refer to Figures 1 and 3 of their study). In contrast, our analysis does not consider the retreat\namount at non-hardened beaches, so Northern Australia does not show a high percentage of sandy\nbeaches facing severe beach \"loss\" in our results (please refer to Figure 2, and supplementary Table 1).\nThis is because the entire beach can migrate landward when there are no physical obstructions like\nstructures and therefore, such locations do not qualify as areas of potential severe beach loss, despite\nthe high projected shoreline retreat. Therefore, the shoreline retreat projections of Vousdoukas et al.\n(2020) should not be directly compared with our projections of sandy beaches that could face severe\nloss.\nTo enhance clarity, we have now better explained how we define “severe beach loss” in lines 301-305\nas “A sandy and hardened location is classified as a severe beach loss location if the projected retreat\nvalue is (a) greater than 100 m (i.e. following the definition of severe shoreline retreat adopted by\nVousdoukas et al.’s (2020) 3), or (b) less than 100 m, but equal or greater than the distance from the\npresent-day shoreline to the nearest artificial hardening structure.”\nR 1.6 Line 41: You discuss landward translation here but there is no mention of landward\ntranslation later when results are discussed. Did the Vousdoukas et al. 2020 shoreline projection\nmodel project landward translation in any areas that you identified as hardened? It would be\nextremely valuable to see if any of the highly hardened areas you identified would otherwise be\npredicted to translate landward, because it would suggest that certain regions have shoreline\nhardening that is working “against” beach management goals.\nThere appears to be a slight misunderstanding here. Although we have mentioned landward translation\nof whole beach systems to provide context in our study, we do not present any information on\nlandward translation. This is because currently there are no global-scale assessments of beach\ntranslation. Vousdoukas et al. (2020)’s shoreline projection model also does not provide any\ninformation on landward translation, only shoreline retreat relative to the present-day shoreline.\nWe fully agree with the Reviewer’s insight that it would be extremely valuable to incorporate natural\nlandward translation rates at now hardened sandy locations along with the shoreline change\nprojections. This would help identify regions where human-made structures will in the long term\nworsen the situation. Unfortunately, the lack of data on landward beach translation rates prevents us\nfrom performing such an analysis at this time.\nR 1.7 Line 63: If your research question #2 is for global sandy shorelines, and not global\nhardened sandy shorelines, the link between research question #1 and #2 is not clear.\nThe reason why we frame our research questions in terms of total sandy beaches (including both\nhardened and natural coastlines) rather than specifically focusing on 'hardened sandy beaches' is to\nemphasize our analysis of worldwide sandy beaches, aligning with the title of our paper, \"Coastal\nHardening and What it Means for the World’s Sandy Beaches.\" Additionally, framing our questions in\nthis manner helps readers to easily contextualize the results in terms of the worldwide sandy coastline\nlength. Please also see our responses to R1.4 and R1.4.1.\nResearch question #2, which focuses on quantifying the extent of the global sandy coastline that is\nunder threat of severe beach loss, can only be addressed after deriving the coastal hardening\ninformation from research question #1, as the presence of hardening is a defining characteristic of the\ncoastlines we classify as being under threat of severe beach loss. Therefore, the link is more evident in\nhow we answered research question #2.\nR 1.8 Line 86-88: This is a very important and novel finding! But the claim that this 33% of the\nworld’s global shoreline is “more prone to experience beach loss as the shoreline retreats” needs\nmore justification.\n• Is this just based on evidence that shore protection structures like seawalls and\nrevetments block landward translation (As you discussed in Lines 41-54)? There is no\nmention that some of these vertical structures worsen scouring and erosion due to wave\nreflection, although this seems implied in lines 86-88.\nWe thank the Reviewer for this very positive comment on our findings. Yes, it is indeed based on the\nevidence mentioned in lines 41-54 (Please refer to lines 41-56 of the revised manuscript). We did not\ninclude the phenomena of wave reflection and scouring from wave-structure interaction because, while\nthese can definitely worsen erosion, they occur only when waves reach and interact with the structure.\nThis interaction typically happens in case of extremely narrow or absence of supratidal beach in front\nof the structure. We acknowledge that in some cases, structures are located right next to the shoreline,\nand wave-structure interaction could still be relevant. However, such locations are included in our\nquantification of 'hardened' and 'severe beach loss' percentages following our methodology. We did\nnot include a detailed discussion on this aspect in the manuscript because the treatment of such\ndetailed and localised process is well outside the scope of our global-scale analysis.\nR 1.8.1 This 33% includes other kinds of gray infrastructure and hardening, like roads, housing,\nother kinds of development which may not be a direct block to landward translation depending\non how dense the infrastructure is. It would be helpful to clearly state how you are defining gray\ninfrastructure.\nWe thank the Reviewer for this suggestion. With \"grey infrastructure\" or \"hardening,\" we refer to any\nrigid and impermeable human-made structures that can alter the natural landscape and obstruct\nshoreline retreat and landward translation of sandy beaches.\nWe chose the GISA datasets to define hardening because, where the impervious surface area is defined\nas \"artificial structures with impermeable characteristics, mainly including roofs, paved surfaces,\nroads, and hardened grounds\" (Huang et al., 2019). Following this definition, these artificial structures\nare here assumed to be sufficiently rigid and impermeable to block or alter sediment transport and the\nlandward translation of beaches under sea level rise. Also, even if some of these structures may not be\nresilient enough against erosion in their current form, it is likely that such valuable assets be further\nprotected by engineering structures when they are facing imminent damage. Therefore, we used this\nimpervious surface definition from the GISA dataset together with OSM to carefully select OSM\nfeatures that align with this definition, as described in the OSM feature descriptions. We also detailed\nwhich keys and corresponding attributes are included in the OSM dataset in the methods and\nSupplementary Table 4.\nTo improve clarity, we have now included our definition of hardening or grey infrastructure in the\nIntroduction section of the revised manuscript as follows.\nLine 65-71: In this study, \"grey infrastructure\" or \"hardening\" refers to rigid human-made (semi-\n)impermeable structures that alter the natural landscape, and potentially obstruct shoreline retreat and\nlandward translation of sandy beaches. This includes impervious surfaces such as roads, highways,\nrailways, buildings, and other urbanized developments, which even if they are not resilient enough\nagainst erosion in their current form, are valuable assets that are likely to be further protected by\nengineering structures when they are facing imminent damage, thus obstructing shoreline retreat and\nlandward translation of beaches well into the future.\nR 1.9 Lines 89-93, and Line 101 (Figure 1): Is there a reason why these are the top 5 regions that\nhave the most shoreline hardening? This is worth making a comment, to convey the significance\nof your results. Is it because they have coastal-dependent economies (shipping or fisheries)? Is it\ndue to these regions having lots of low-lying areas that need infrastructure to prevent flooding?\nIs it because they are in storm-prone areas (infrastructure is mainly seawalls)?\nWe thank the Reviewer for this insightful comment. Regarding the Reviewer's question about coastal-\ndependent economies (e.g., shipping or fisheries), determining the specific purpose of each hardened\nlocation is infeasible at global scale, because the information is not available from the GISA datasets,\nwhich are binary in nature (pixel is impervious or not), and the attributes for each OSM feature,\nalthough detailed for a small minority of countries, are mostly too incomplete to make a thorough\nanalysis at the global scale.\nHowever, we further explored whether these regions have extensive low-lying areas requiring\ninfrastructure to prevent flooding or are storm-prone areas, using additional spatial information such as\nthe extent of low-elevation coastal zones (LECZ), storminess, and population proximity to the coast.\nWe also included this discussion based on our findings in lines 101-109 of the revised manuscript and\nSupplementary Table 5 as follows.\nLine 101-109: “Interestingly, of the top 5 hardened regions, only Western & Central Europe has a\nnoteworthy (> 5 km2/km) LECZ area per kilometre length of coastline, indicating that the mere\npresence of large LECZ may not be a driver for coastal hardening (Supplementary Table 5 24,26,27).\nHowever, all of the above top 5 hardened regions are affected by medium to high degrees of\nstorminess, based on the integrated storm wave energy and frequency index 28. In addition, most of\nthese regions contain high-income and upper-middle-income countries with major seaports where\ncoastal infrastructure investment can be high, necessitating their protection with engineering structures\n29. In Bay of Bengal and the Mediterranean, the population in coastal zones (i.e., within 100 km from\nthe coast) is high (more than 50% - 75% of the total population) 30, likely making coastal protection a\nmanagement priority.”\nTable 5 Area of Low-Elevation Coastal Zone (LECZ) per kilometer of coastline for each IPCC region (sorted in descending\norder). LECZ area is computed with MacManus et al. (2021)26,27 and total coastline within each region is calculated based on\nOpen Street Map coastline24.\nNo. IPCC region LECZ area total coastline LECZ area per\n(km2) (km) km coastline\nThe relevant Jupyter Notebook “Table_S5.ipynb” to reproduce the above table is also added to the\ncode repository.\nR 1.10 Line 101 (Figure 1b): Maybe order the regions by lowest-highest distance from shoreline\nso figure is easier to read, or possibly a map figure with different colored color bar from Min-\nMax erodible beach width in front of nearest artificial structure\nIn Figure 1b, the regions are ordered by the median erodible distance from the shoreline (red vertical\nline), from lowest to highest.\nR 1.11 Lines 106-121: It seems like a greater percentage of hardened sandy coasts are predicted\nto experience severe (>100m) beach loss under both SSP scenarios and by both mid-and-end\ncentury, when compared to “all” or “global” beaches. Why do you not highlight this finding? Is\nit because the uncertainty range around the percentages makes the difference between\n“hardened” and “global” beaches not significant? I would also suggest making a comparison\nbetween “hardened” and not hardened beaches. This is the kind of analysis that is missing that\nwould connect your first and second research questions.\nYes, the percentage of severe beach loss is higher when considering only the hardened sandy coastline\ncompared to the total length of all sandy coastline (which includes both hardened and natural\ncoastlines). This difference primarily arises from changes in the denominator value (i.e., total sandy\ncoastline versus only hardened sandy coastline). While the observed differences in percentages are\nnoteworthy, they do not, by themselves, establish a causal link between hardening and severe beach\nloss. (Please also refer to the response to the following comment R1.12). This is because severe beach\nloss also depends a lot on projected shoreline retreat rates which varies substantially within and among\nregions. And so, concluding that a greater percentage of hardened sandy coastline is projected to face\nsevere beach loss might send out a not entirely correct message, we feel (because it depends on the\nshoreline retreat change as well, and that depends on many other things).\nR 1.12 Lines 122-138 (including Figure 2): Again, these are novel valuable findings, and it is\ngreat to see them grouped by IPCC AR6 regions and continent in the figure. What is missing is\nthe link to shoreline hardening. The regions that have high % of (“all”) sandy coast projected to\nface severe beach loss seem to also be regions that have high % of hardening, yet this link is not\nmade clear. It would be good to use Figure 2 to make this link. Could you plot the percentages\nfrom Supplementary Table 2 over (or next to) the bars in Figure 2 in a different color?\nWe thank the Reviewer for this sharp observation. In response to this suggestion, we have integrated\nthe percentages from Supplementary Table 2 adjacent to the bars in Figure 2, using a different color as\nrecommended. (Please note that the values in Supplementary Table 2 and Figure 2 are now the results\nfrom the refined method in response to comment R 1.18.)\nAs shown in the above figure, the bars representing severe beach loss percentages based on hardened\nsandy coastline are consistently higher than those based on all sandy coasts. As mentioned in response\nto R1.11, this is mainly due to changes in the denominator value (i.e., total sandy coastline versus only\nhardened sandy coastline). The percentage of severe beach loss of all sandy coastline is lower than that\nof only hardened sandy coastline in regions with high projected severe beach losses as well as in\nregions with low projected severe beach losses.\nTo address the Reviewer’s point, we have added a subplot to Figure 2 (bottom subplot) showing the\npercentage of hardened sandy coastlines in the revised manuscript. The figure now clearly indicates\nthat many regions with the highest projections of severe beach loss (as a percentage of total sandy\ncoastline) also have a higher percentage of hardened sandy coastlines.\nHowever, as also mentioned in our response to R1.11 above, it is important to note that while regions\nwith a high percentage of hardened sandy beaches appear to be those that are also projected to\nexperience a high percentage of severe sandy beach loss, this does not imply a direct link due to strong\ndependence between severe beach loss and shoreline retreat projections.\nWe have now revised the Figure 2 as follows (lines 162-169 of the revised manuscript).\nFigure 2 Percentages of the sandy coast in each IPCC AR6 region that is projected to face severe beach losses (i.e. either\nhardened and shoreline retreat > 100 m or hardened and up to or more than the distance from the present-day shoreline to\nthe nearest grey infrastructure when shoreline retreat < 100 m) under SSP2-4.5 and SSP5-8.5 by mid- and end-century. The\nbars represent median estimates, indicative of a 'most likely' scenario using GISA-10m plus OSM, along with the error bars\nshowing the 5th – 95th percentile ranges. Globally averaged values, together with the associated 5th – 95th percentile ranges,\nare shown in the text box next to each subplot. (The bottom subplot shows percentages of sandy beaches that are hardened in\neach IPCC AR6 region (grey bars) to illustrate the relationship between hardening and severe beach loss projections.)\nAdditionally, we have included the following text in the revised manuscript in line 178-181 “Figure 2\nalso shows that, in general, projected sandy beach losses are highest in regions with a high level of\ncoastline hardening. However, it is important to note that this does not imply a direct causal link, due\nto the strong dependence between shoreline retreat projections and severe beach loss.”\nR 1.12.1 Also worth making a comment about why these regions are ones that have the greatest\n% of beaches under threat.\nWe thank the reviewer for this insightful comment. We believe that the reason why regions such as\nEastern North America, Northern South America, Mediterranean, Bay of Bengal, Western Africa, and\nSouth East Asia show a higher percentage of beaches under threat is likely because these regions have\nhigh population density, extensive coastal developments, and a lot of human interventions, all of\nwhich may not only enhance ambient coastal erosion but also promote coastal hardening. However,\nour current large-scale assessment does not delve into the specific causes for each region. It is\nimpractical to go into such an analysis in this already heavy analysis. The observed trends highlight\nthe importance of further regional studies to understand the underlying factors that may be\ncontributing to severe beach loss.\nR 1.13 Lines 139-149 (including Figure 3): Why is country income (Gross National Income from\nthe World Bank) chosen as the demographic lens through which you view your % sandy\nshoreline hardening and % sandy beach facing severe erosion?\nWe thank the Reviewer for this important question. We have now included an explanation statement in\nthe revised manuscript as follows.\nLines 183-186: “We further included country income in our analysis to investigate the socio-economic\ndimensions of our findings on coastal hardening and severe beach loss. By examining these aspects\nthrough the lens of country income status, we can determine whether extensive infrastructure\ninvestments in high-income countries inadvertently result in maladaptation.”\nR 1.13.1 What is the significance of high-income countries having greater percentages of both\nhardened beaches and beaches facing severe erosion? Discussion of these results needs a\ncomment on this.\nWe thank the Reviewer for this suggestion. We have now discussed this significance in the revised\nmanuscript as follows.\nLine 192-195: “The largest share of hardened coastlines and sandy beaches facing severe beach loss\nbeing in high-income countries primarily reflects the fact that higher-income countries are more likely\nto have the resources to invest in and maintain coastal hardening structures, which as our results show\nmay lead to unintended consequences and higher beach losses in future.”\nR 1.13.2 I think you need to make it clear that the correlation between the two is not related to\nhow erosion was predicted/calculated, since the methods of Vousdoukas (2020) do not take into\naccount shoreline hardening.\nWe have now revised the manuscript accordingly in lines 195-197 of the revised manuscript as\nfollows.\nLine 195-197: “It should be noted that the correlation between income groups and severe beach loss is\na finding from our analysis and is not related to how shoreline retreat was computed by Vousdoukas et\nal. (2020), as their analysis does not consider shoreline hardening.”\nR 1.13.3 You mention in the limitations section that less developed countries are more likely to\nhave unmapped regions, which would make both your %hardened and % beach facing erosion\nestimations potentially underestimated. I think you should mention that when you show the\ngraph\nWe thank the Reviewer for this suggestion. We have now mentioned that in lines 197-201 of the\nrevised manuscript as follows.\nLine 197-201: “It is important to acknowledge the potential underestimation in the percentage of\nhardened sandy beaches, and consequently in the percentage of sandy beaches facing severe loss in\nless developed countries due to the higher possibility of unmapped areas in community-based OSM\ndata. This limitation should be borne in mind when interpreting Figure 3.”\nR 1.13.4 Could you look at another demographic, that would have a less strong relationship than\nGNI to unmapped-ness, like population density?\nYes, we could explore another demographic factor like population density for this analysis. However,\nwe think the effectiveness of hardening measures depends significantly on the level of infrastructure\ninvestment in coastal areas, which is more influenced by a country's economic capacity than its\npopulation (for e.g. India, Bangladesh). This is unfortunately evident from the higher casualties in\ndensely populated but poorer countries during coastal flooding events. Therefore, we would prefer to\nuse GNI here to reflect the relationship between socio-economics and coastal hardening.\nMethods\nR 1.14 Lines 174-192: Some of this text on GISA datasets can be cut down and moved to\nSupplemental, to make room for more detail on “Global shoreline retreat projections” and\ndetail on your workflow.\nWe thank the Reviewer for this suggestion. We have revised the manuscript accordingly in line 222-\n229 of the revised manuscript and in the “Sensitivity of severe sandy beach loss percentage on GISA\nversions and spatial resolutions” section of the Supplementary Information.\nLine 227-234: “We emphasize here the results based on GISA-10m since it is reported to have an\naccuracy greater than 86%. In addition to its higher resolution, it has the added benefit of detecting\ntransportation networks resulting from the integration of OSM data in training samples for\nclassification. Therefore, GISA-10m is particularly well suited for our study as it captures coastal\nstructures and revetments along highways and railways that could impede shoreline retreat and beach\ntranslation. We used GISA-30m-V1 and GISA-30m-V2 only to test the sensitivity of our results to\ndifferent spatial resolutions and image classification techniques. The details of GISA-30m-V1 and\nGISA-30m-V2, along with associated results, are discussed in the “Sensitivity of severe sandy beach\nloss percentage on GISA versions and spatial resolutions” section of the Supplementary Information.”\nR 1.15 Lines 200-212: Slightly more detail here would be helpful, how was Bruun rule modified?\nVousdoukas et al (2020) used a stochastic correction factor to account for known overestimations of\nthe Bruun Ruled compared to projections from physics-based models. This is stated in lines 253-256\nof the revised manuscript as “Vousdoukas’ et al (2020) dataset provides projections of shoreline\nretreat for sandy locations globally at 500 m alongshore resolution. Long-term shoreline position\nchanges due to RSLR (R) are estimated using an improved version of the Bruun Rule 36 with a\ncorrection for its known overestimation compared to projections from physics-based models.” As this\nrelates to a previously published study we do not feel comfortable delving into the details of that study\nhere. The interested reader could always refer to the original paper if they want more details.\nR 1.16 Line 212: After “an aspect explicitly addressed in our study”, it would be helpful to add a\nreference to where in the Methods section this is addressed in your workflow.\nWe thank the Reviewer for this suggestion. We have now added the required reference in lines 261-\n262 of the revised manuscript. The sentence now reads: “It should be noted that these projections do\nnot account for physical obstructions to shoreline retreat (e.g. seawalls, revetments), an aspect\nexplicitly addressed in our study ( see “Hardened sandy beaches” in Methods).”\nR 1.17 Lines 229-230 (Figure 4): Figure 4 can be moved to supplemental section. A flow chart\nfigure illustrating your workflow steps would be more helpful here.\nWe thank the Reviewer for this suggestion. We have moved the previous Figure 4 to the\nSupplementary Information as Supplementary Figure 2 and added the overall workflow as Figure 4 in\nlines 272-275 of the revised manuscript. (Please note that the overall workflow is now slightly\nmodified in response to comment R 1.18.)\nFigure 4 Overall workflow of global severe sandy beach loss projections\nSupplementary Figure 2 Shore normal transects and search quadrants used in the analysis\nR 1.18 Lines 243-258: It is unclear in your description of the workflow how hardening is\naccounted for.\nExample #1: if your 1000km transect detects infrastructure at 50m, yet your shoreline retreat\nprojection predicts 100m of erosion, does your analysis cut off erosion at 50m for that location\ndata point? If erosion is cutoff at 50m, then this location does not make the cut for “severe”\nbeach erosion, yet it should count as “severe” because all beach is lost.\nExample #2: if your 1000km transect detects infrastructure at 50m, and shoreline retreat\nprojection predicts 50m of erosion, this also does not make the “severe” beach erosion cutoff,\nyet it should count as “severe” because all beach is lost.\nWe thank the Reviewer for pointing this out. Regarding the workflow, under the conditions described\nin the Reviewer’s Example #1, we would indeed categorize our 1 km transect as experiencing ‘severe’\nbeach loss. Specifically, we did not truncate our erosion at 50 m. When the projected shoreline retreat\nis > 100 m, a transect that has hardening < 100 m from the shoreline is taken to face severe beach loss\nin our workflow.\nHowever, we recognize that we did not classify transects that fall into the situation in the Reviewer’s\nExample #2 as ‘severe’ beach loss locations, even though they should be. We greatly appreciate the\nReviewer for bringing this to our attention. We have now added, the locations where the shoreline\nretreat is not greater than 100 m but equals or exceeds the distance from the present-day shoreline to\nthe nearest infrastructure, to the ‘severe’ beach loss counts, as these indicate near complete loss.\nWe have updated the description of our method accordingly in the revised manuscript. The updated\nsections in response to this specific method refinement is highlighted in blue throughout the revised\nmanuscript. The relevant Python Scripts and Jupyter Notebooks to reproduce the results and figures\nwith the refined method (both in the main manuscript and the supplementary information) are also\nupdated in the code repository.\nSupplemental\nR 1.19 Mistake: “more” should be “less” according to Figure S5.\nWe thank the Reviewer for pointing out this error. We have updated the text accordingly in the revised\nSupplementary Information.\nReviewer #1 (Remarks on code availability):\nR 1.20 I do not have the expertise to provide a thorough review of this code.\nWe appreciate the Reviewer for helping us improve the manuscript with very constructive comments.\nReviewer #2 (Remarks to the Author):\nSummary\nR2.1 This paper investigates global sandy shorelines and quantifies the proportion of these\nshorelines that are hardened (backed by infrastructure/buildings) within a 1km search grid. It\nquantifies the proportion of all sandy beaches and hardened sandy beaches that are at risk of\nsevere beach loss (erosion of >100 m) by mid and end of the century according to data from\nVousdoukas et al. 2020. Lastly, the study categorizes these values based on IPCC AR6 reference\nregions and the income groupings from the World Bank.\nIt is overall a large undertaking of data curation and analysis at a global scale and generally well\ndocumented. However, some further clarification on a few areas could improve the manuscript.\nWe thank the reviewer for this very positive assessment.\nR2.2 My primary concern is the close resemblance of this paper to the study published by Lansu\net al. in Nature Communications in January 2024. While I realize this publication may have\nbeen just before the submission of this paper, I am not sure that I see how this current paper\ncontributes differently than what has already been published. From my understanding, I’ve\nadded a very brief comparison on the methodology below for reference:\nLansu et al. 2024 uses the same dataset from Luijendijk et al. 2018 to identify locations of sandy\nbeaches, then uses the same dataset on shoreline change projections by Vousdoukas et al. 2020\n(but at 1000m increments instead of 500m used here), and lastly uses the same OSM dataset as\nused here to determine coastal hardening. Although here, the Global impervious surface area\n(GISA) raster dataset is primarily used with OSM as a complement to these data, providing an\nalternative way to determine coastal hardening. Analysis by Lansu et al. is categorized by\ncountries, whereas this is categorized by IPCC AR6 reference regions. The overall conclusions\nare similar - both studies find 33% global sandy beaches to be hardened. Lansu et al. finds 23%\n(30%) of beaches to be lost by the end of the 21st century under RCP4.5 (RPC 8.5). The current\nstudy considers ‘severe’ beach loss and finds 18% (24%) of beaches to likely face severe beach\nloss by the end of the 21st century under SSP2-4.5 (SSP5-8.5). Interestingly, Lansu et al. 2024,\nwith their method of coastal hardening, find a median distance of infrastructure free space to be\n392 meters, while this papers find it to be 93 meters.\nWe thank the Reviewer for the well-researched and detailed comparison with the study by Lansu et al.\n(2024). As the Reviewer has guessed, both studies were conducted independently and simultaneously,\nwithout knowledge of each other. In fact, we became aware of Lansu et al's work almost at the\ncompletion of our work (which spanned 18 months from beginning to end). This was very unfortunate\ntiming. However, as we had seen other very similar papers being published a few months apart in\nNature Portfolio journals, without mentioning each other (e.g. Luijendijk et al., 2018; Mentaschi et al,\n2018 - https://www.nature.com/articles/s41598-018-24630-6, https://www.nature.com/articles/s41598-\n018-30904-w), we decided to go ahead and submit our paper without comparing and contrasting with\nLansu et al (2024).\nAs the Reviewer has noted, there are some similarities between Lansu et al. (2024) and our study;\nhowever, there are also significant differences in aims, methodological details, and results. Below we\noutline the main differences, highlighting how both studies stand on their own merits:\n• While Lansu et al. (2024) focus on investigating coastal squeeze, our study’s final goal is to\nprovide robust and fully probabilistic projections of sandy beaches that can face 'severe beach\nloss'. Lansu et al. (2024) discussed the median value of the projected sandy beach loss using\nonly the 50th percentile from Vousdoukas et al. (2020) shoreline projections, and only briefly\ndiscussed their results in the Supplementary Information. Our projections are fully\nprobabilistic, utilizing the complete distribution of shoreline retreat projections from\nVousdoukas et al. (2020).\n• Lansu et al. (2024) utilized 1 km spacing along the global coastline for their analysis. In\ncontrast, we employed 500 m spacing. While more computationally intensive and time\nconsuming, this finer resolution allowed us to achieve more definitive results. As noted by the\nReviewer, we found the global median distance from the present-day shoreline to the nearest\ngrey infrastructure to be 93 meters, compared to Lansu et al.'s 392 meters; a large difference.\n• Lansu et al. (2024) focused only on paved streets from OSM and buildings from the Global\nUrban Footprint (12 m resolution), excluding unpaved seawalls and dikes. In our study, we\nincluded any object defined as a grey structure in the OSM data (including buildings,\ntransport, and land use) that can obstruct shoreline retreat and the landward translation of\nsandy beaches. Additionally, we utilized three versions of the Global Impervious Surface Area\n(GISA) dataset with different resolutions (10 m and 30 m). This more detailed analysis\nallowed us to better detect any grey structure that would obstruct shoreline retreat and beach\ntranslation.\n• As the Reviewer has noted, Lansu et al. (2024) presented their results by countries or\ncontinents, whereas we present our results in terms of IPCC AR6 reference regions.\nBased on these facts, we believe that while both studies contribute valuable insights into the topic, the\ndifferences in methodology, focus, and resolution underscore the unique contributions of each study.\nWe respect the efforts of Lansu et al. (2024) and believe that presenting our work independently\nallows both studies to stand on their own merits and provide complementary perspectives on this\nimportant issue of the future of global sandy beaches. We, however, feel a bit uncomfortable including\nthe above points in a revised manuscript, because such a methodological comparison will most likely\nbe construed by the authors of Lansu et al. (2024) as a harsh criticism of their work, which is not how\nwe like to do science. Also, please note that both teams are based in the Netherlands, and we would\nnot like to antagonize a fellow Dutch team, if we can help it. Therefore, we hope that the above\nclarifications help the Reviewer to appreciate the differences between the two seemingly similar\nstudies, and that it would not be necessary to include such a comparison in our revised manuscript. As\nwe are facing this issue now, we were very pleased to come across a 2020 editorial in NCOMMs that\nspecifically addressed similar dilemmas (https://www.nature.com/articles/s41467-020-17817-x).\nBelow are comments on each section\nThis paper provides a nice robust statistical approach to determine the probabilities (and most\nlikely scenario) of % of beaches facing severe erosion.\nWe thank the reviewer for this very positive comment.\nA few points I believe could benefit some slight clarification:\nR 2.3 Use of OSM and GISA datasets: The methods section should clearly explain how the\nOpenStreetMap (OSM) and Global Impervious Surface Area (GISA) datasets are used together.\nProviding details on how they complement each other and resolve discrepancies would add\nclarity. Specifically, there were instances where it’s stated that GISA-10 pixels inaccurately\nindicated hardening on sandy pixels, and OSM data was used to improve accuracy. I think\nclarity is needed on 1) how it was determined whether a ‘hardened’ pixel was sandy or not at a\nglobal scale? And 2) How exactly the GISA and OSM datasets were combined? For instance,\nwere GISA pixels disregarded in favor of OSM data when they were found on sandy beaches, or\nwere the GISA pixels modified to exclude false indicators of hardening?\nWe thank the Reviewer for this suggestion to enhance clarity.\n1) Sandy locations were identified using an updated version of Luijendijk et al.’s (2018) Satellite-\nDerived Shoreline (SDS) dataset (Lines 212-213 of the revised manuscript). Additionally, to\nprovide further clarity, we have included a workflow diagram as Figure 4 in line 272-275 of the\nrevised manuscript. As outlined in Line 212-213 and depicted in Figure 4 of the revised\nmanuscript, we first determine whether the transect is sandy or not, and only proceed with\ndetermining 'hardened' status at the sandy locations.\n2) We have now included a clarification on how the GISA and OSM datasets were combined in\nthe revised manuscript as follows. Lines 280-282: “To define whether a sandy transect is\nhardened or not, we implemented a logical \"AND\" condition. This condition requires the\npresence of both a GISA impervious pixel and an OSM feature within the search sector. Only\nwhen this condition is met, we flag the transect as 'hardened'.”\nR 2.4 If possible, include the number of transects analyzed in each IPCC reference region in one\nof the tables or figures. This would help contextualize the findings.\nWe thank the Reviewer for this suggestion. The number of transects in each IPCC reference region is\nnow added to Figure 1 in the Supplementary Information. The relevant Jupyter Notebook to reproduce\nthe figure “Fig_S1.ipynb” is also updated in the code repository.\nR 2.5 Clarification of percentages: In the main text, briefly explain what the percentages\nrepresent when they are first introduced (line 113). While the methods section may provide a full\nexplanation, a brief summary in the main text would help guide readers through the presented\ndata.\nWe thank the Reviewer for this suggestion. We have now added a brief explanation of the percentages\nin Lines 135-139 of the revised manuscript as follows. “(note: percentages indicate the proportion of\nthe global sandy coastline that is projected to experience severe beach loss, here defined as hardened\nlocations where projected shoreline retreat is more than 100 m, or hardened locations where projected\nshoreline retreat is less than 100 m but is equal to or greater than the distance from the present-day\nshoreline to the nearest artificial structure).”\nR 2.6 Address/clarify the discrepancy between Vousdoukas et al.'s finding that nearly 50%\nof the global sandy coastline could face severe erosion, and the results here that indicate 3-14%\nof beaches globally are at risk with the same dataset.\nWe thank the Reviewer for this suggestion. However, we don’t think the two diagnostics used in the\ntwo studies can be directly compared. Vousdoukas et al. (2020) projected shoreline retreat at all sandy\nbeaches and quantified severe 'erosion' at locations where the retreat exceeded 100 m. In contrast, we\ncombined Vousdoukas et al. (2020) shoreline retreat projections only at the sandy coast that has been\n‘hardened’ to quantify sandy beaches that can potentially face severe 'loss', where our metric is a\nprojected shoreline retreat being more than 100 m or being equal or exceeding the distance from the\npresent-day shoreline to the nearest structure. Therefore, Vousdoukas et al. (2020) shoreline retreat\nprojections should not be compared with our projections of sandy beaches that could face severe\nloss.\n(Please also see our explanation with the Northern Australia example in response to R 1.5.)\nR 2.7 Consider examining how many beaches would be completely lost due to erosion, taking\ninto account the proximity of coastal hardening. 100 m of erosion means vastly different things\nfor different coastal environments. Analyzing the complete loss of these ecosystems could\nprovide a more comprehensive understanding. But I also understand the complexities with a\nlarge-scale analysis like this.\nWe thank the Reviewer for this insightful suggestion. We have now refined our definition of severe\nbeach loss to include hardened locations where the shoreline retreat is less than 100 m but greater than\nor equal to the distance from the present-day shoreline to the nearest hardened structure since these\nindicate near-complete loss (in addition the hardened locations where the projected shoreline retreat is\n> 100 m, as before). This has led to changes in our original results and associated discussion.\nWe have updated the description of our methods accordingly, as well affected consequential changes\nin the revised manuscript and also in the code repository. The updated sections in response to this\nchange are highlighted in blue in the revised manuscript.\nBelow are in line comments\nR 2.8 Line 9: Replace \"using satellite imagery\" with \"products and geospatial datasets derived\nfrom satellite imagery\" to clarify that the study does not use raw satellite imagery directly.\nWe thank the reviewer for this suggestion. We modified the text in line 9-10 of the revised manuscript\naccordingly.\nLine 9-10: “Here, using products and geospatial datasets derived from satellite imagery, we find that,\n33% of the world’s sandy coastline is currently hardened.”\nR 2.9 Line 37: Broadly define grey infrastructure. Does it encompass all man-made structures?\nWith \"grey infrastructure\" or \"hardening,\" we refer to any rigid and impermeable human-made\nstructures that can alter the natural landscape and obstruct shoreline retreat and landward translation of\nsandy beaches.\nWe have now included the definition of hardening or grey infrastructure in the Introduction section of\nthe revised manuscript as “In this study, \"grey infrastructure\" or \"hardening\" refers to rigid human-\nmade (semi-)impermeable structures that alter the natural landscape, and potentially obstruct shoreline\nretreat and landward translation of sandy beaches. This includes impervious surfaces such as roads,\nhighways, railways, buildings, and other urbanized developments, which even if they are not resilient\nenough against erosion in their current form, are valuable assets that are likely to be further protected\nby engineering structures when they are facing imminent damage, thus obstructing shoreline retreat\nand landward translation of beaches well into the future.” in lines 65-71.\nR 2.10 Line 49: This sentence reads to me like it is all barrier islands, perhaps reword.\nWe thank the Reviewer for this suggestion. That sentence has been revised in the manuscript as\nfollows.\nLine 49-52: “Along the Atlantic coast’s barrier islands, as well as along the coasts of Oahu, and the\nstates of California and Washington, the process of gradual beach loss in front of the seawall or\nrevetment together with the landward migration of the shoreline has been well documented.”\nR 2.11 Line 59-61: see the comments from earlier regarding the comparison with Lansu et al.'s\nstudy.\nWe became aware of the mentioned study only at the very completion of our analysis. Furthermore,\nwe recognize that referencing Lansu et al.’s study at this stage would require a comparison and\ncontrast, which would not only devalue the authors' efforts and goals but also divert from our initial\nresearch focus. As detailed in our response to comment R2.2, although the studies appear similar, they\nhave distinct differences in methodological details and aims. Therefore, out of respect for Lansu et al.\n(2024), we prefer to present our work independently, acknowledging that both studies contribute\nvaluable insights in their respective contexts. Please see our more detailed response to this matter\nabove at R 2.2.\nR 2.12 Line 69 Instead of \"state-of-the-art image classification model,\" provide more specific\ndetails on the segmentation or classification methods/models used in the study if you include it in\nthe methodology.\nWe didn’t include a detailed background on the image classification techniques of the GISA dataset in\nour methodology because each version of the dataset is extensively documented in the associated\noriginal publications. Instead, we have included relevant references that guide readers to these datasets\nand their publications, which contain full descriptions of the classification methods. However, indeed\nour choice of words could be improved. Therefore, we have now rephrased this sentence as “The\ndatasets are produced by applying robust image classification techniques on millions of historical\nsatellite images.” in lines 73-74 of the revised manuscript.\nR 2.13 Line 136: Reword the figure description for clarity. Specify whether the plot is for\nhardened sandy beaches only or for all sandy beaches. If it's for hardened sandy beaches, clearly\nstate that. If it's for all sandy beaches, remove the reference to GISA.\nWe thank the Reviewer for this suggestion. The figure caption has been changed to “Figure 2.\nPercentages of the sandy coast in each IPCC AR6 region that is projected to face severe beach losses\n(i.e. either hardened and shoreline retreat > 100 m or hardened and up to or more than the distance\nfrom the present-day shoreline to the nearest grey infrastructure when shoreline retreat < 100 m) under\nSSP2-4.5 and SSP5-8.5 by mid- and end-century.” in line 163-165 of the revised manuscript.\nR 2.14 Line 150: I don't know that figure 3 supports the statement in line 150-152 - that\ntransitioning from high to moderate emissions will yield more impact at the end of the century.\nFrom my understanding Figure 3 demonstrates that the majority of hardened beaches occur in\nthe high income areas. It then shows that a higher percentage of beaches in high and upper-\nmiddle income areas are facing severe erosion, which further increases under higher emission\nscenarios.\nWe thank the Reviewer for pointing this out. This was a mistake on our part, for which we apologise.\nThe text should be referring to the Figure 2 instead of Figure 3. We have now corrected this in line\n170 of the revised manuscript.\nR 2.15 Line 158: I think this sentence is missing a ‘to’ (“projected TO benefit”)\nWe thank the Reviewer for pointing this out. Yes, the correct sentence should be with ‘to’. This has\nnow been corrected in line 178 of the revised manuscript.\nR 2.16 Line 166: Reword the sentence to clarify that the dataset method detects sandy beaches.\nYou could use the phrase \"Here, sandy beaches are detected based on the SDS dataset\nmethod...\" or another similar phrasing.\nWe thank the Reviewer for pointing this out. We have now revised the sentence as “In this data set,\nsandy beaches are detected based on supervised pixel classification of Sentinel-2 imagery at 500 m\nspaced transects orthogonal to the global shoreline from the OSM dataset of 2016.” in line 213-215 of\nthe revised manuscript.\nR 2.17 Line 167: How were contentious data points determined? And are cliffy areas also\nremoved?\nContentious data points refer to instances where the original SDS dataset categorizes points as 'sandy'\nwhen they are not sandy and vice versa. In the manuscript, we explicitly indicate where these points\nwere identified (lines 218-221 of the revised manuscript). We have also added further clarification in\nline 215-217 of the revised manuscript as “In the pre-processing step, we removed contentious data\npoints (i.e. points which the original SDS dataset categorizes as 'sandy' when they are not sandy and\nvice versa) from the original SDS dataset based on cross-checks with Google Earth satellite imagery.”\nCliffed areas remain in this pre-processed SDS dataset. We have now added a clarification statement\nin lines 217-218 of the revised manuscript: “Whatever non-rocky cliffs (e.g. sandstone) that may have\nremained in Luijendijk et al.’s (2018) SDS classifications would also have been included in our\nanalysis.”\nR 2.18 Line 194: perhaps remove the reference to remote sensing and simply state \"any\nmisclassifications.\"\nWe thank the Reviewer for this suggestion. We have made the suggested change as in lines 242-243 of\nthe revised manuscript.\nLines 242-243: “We used the community-built global geospatial OpenStreetMap (OSM) in\nconjunction with the GISA data to rectify any imagery misclassifications.”\nR 2.19 Line 286: If there are citations for discrepancies in the sandy shoreline dataset published,\ninclude them as references.\nUnfortunately, we don’t have citations for this. We are aware of these discrepancies through working\nwith the SDS dataset at various locations around the world in a number of different studies/projects.\nR 2.20 SUPPLEMENTARY Table S1, Line 1: Add \"percentage of ALL sandy beaches\"\nfor clarity. Remove the final sentence about hardened shoreline locations as it is not directly\nrelevant to the table.\nWe thank the Reviewer for this suggestion. We have made the suggested changes in the revised\nSupplementary Information. For consistency, we have also made corresponding amendments to the\ncaptions of Supplementary Tables 2 and 3.\nReviewer #2 (Remarks on code availability):\nR 2.21 The code repo is extensive and fairly well documented. There is a README with\ninstructions for where to download the publicly available datasets (i.e. OSM, GISA raster etc.),\nand how and which scripts to run to replicate it. The data used for each figure is provided in the\ndata folder and there are jupyter notebooks to reproduce those figures and tables.\nWe thank the reviewer for this positive comment."}, {"round": 2, "reviewer_comments": {"reviewer_1": "Thank you for consideration of comments on the manuscript. All comments have been addressed and concerns resolved in the revised manuscript.\n(Remarks on code availability)\nI don't have the expertise to review the code.", "reviewer_2": "I have read the revised manuscript and the responses to the comments. I am satisfied with the responses and changes that were made to the manuscript and now suggest publication of this article.\n--------------\nI have two minor suggestions, but these are not a must\nIn line 17 in the abstract there is a whole sentence in a parentheses. I would maybe consider incorporating into the previous sentence, something like:\n\"Results also show [......] up to 81% under SSP2-4.5 by the end of the 21st century relative to the total sandy coast length)\".\nIn line 181, the last part of the paragraph is unclear, especially in explaining why there isn't a \"direct causal link\" due to the \"strong dependence between shoreline retreat projections and severe beach loss.\". It would be helpful if this sentence could be revised to better clarify how this dependence relates to the match between shoreline hardening and projected beach loss, if that is the intended meaning.\n(Remarks on code availability)", "reviewer_3": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": "Responses to Reviewers' comments\nWe thank the Reviewers very much for their positive reviews of the revised manuscript. Below we\nprovide our final responses to all reviewer comments. Original reviewer comments are shown in black\nbold font. Our responses and modifications affected in response to Reviewer comments are shown in\nblue font here and highlighted in the revised manuscript. Reviewer comments are numbered for\nconvenience.\nReviewer #1(Remarks to the Author):\nR 1.1 Thank you for consideration of comments on the manuscript. All comments have been\naddressed and concerns resolved in the revised manuscript.\nWe thank the Reviewer for this positive assessment.\nReviewer #1 (Remarks on code availability):\nR 1.2 I don't have the expertise to review the code.\nWe appreciate the Reviewer for helping us improve the manuscript with very constructive comments.\nReviewer #2 (Remarks to the Author):\nR 2.1 I have read the revised manuscript and the responses to the comments. I am satisfied with\nthe responses and changes that were made to the manuscript and now suggest publication of this\narticle.\nWe thank the Reviewer for this positive assessment and for helping us improve the manuscript.\nI have two minor suggestions, but these are not a must\nR 2.2 In line 17 in the abstract there is a whole sentence in a parentheses. I would maybe\nconsider incorporating into the previous sentence, something like:\n\"Results also show [......] up to 81% under SSP2-4.5 by the end of the 21st century relative to the\ntotal sandy coast length)\".\nWe thank the Reviewer for this suggestion. We have now revised the sentence as: “The results also\nshow that the vast majority of projected severe sandy beach losses are in high and upper-middle-\nincome countries, with up to 82% of severe losses under SSP5-8.5 and up to 81% under SSP2-4.5 by\nthe end of the 21st century, relative to the total length of sandy coastline, including both hardened and\nnatural coastlines.” in line 16-19 of the revised manuscript.\nR 2.3 In line 181, the last part of the paragraph is unclear, especially in explaining why there\nisn't a \"direct causal link\" due to the \"strong dependence between shoreline retreat projections\nand severe beach loss.\". It would be helpful if this sentence could be revised to better clarify how\nthis dependence relates to the match between shoreline hardening and projected beach loss, if\nthat is the intended meaning.\nWe intended to clarify that although there is an association between coastline hardening and severe\nbeach loss, the relationship is complex and shaped by additional factors influencing shoreline retreat\nprojections beyond hardening alone. Accordingly, we have revised the sentence to read: “However, it\nis important to note that this does not imply that coastline hardening is the sole direct cause of beach\nloss, as our definition of severe beach loss is strongly influenced by both shoreline retreat projections,\nand the available erodible beach width.” in lines 183-184 of the revised manuscript."}]

Decision letter