Sea level much higher than assumed in most coastal hazard assessments

Literature research and evaluation

We conducted a systematic and reproducible literature research and evaluation, adhering to standards of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) process. All details of the literature research, selection and evaluation are provided in Supplementary Tables 1 and 2. We used the Scopus search engine and used a search term for titles, abstracts and keywords that covered keywords from low-lying coastal environments, processes and hazards, as well as elevation information and elevation dynamics, impact and/or hazard assessments, projections or evaluations (Supplementary Table 1). The Scopus search engine was used as it constitutes one of the most transparent search engines. We are aware of the limitation that Scopus does not include the entire available (scientific) literature and therefore might introduce a bias into our investigation. Therefore, we performed a second independent literature evaluation on 96 additional studies (90 after excluding publications by the authors; non-systematically collected through other search engines) (Extended Data Fig. 8) to ensure the results from the search were bias-free. All results presented in the main paper are based on the Scopus-based systematically researched literature only. From the initial 35,001 documents that were identified through Scopus, we included only peer-reviewed scientific articles, reviews including original analyses, or data papers. Moreover, we limited our search to the most recent 15 years (2009–2025) to include only the most recent research from the period during which both global DEMs and global MDT data were available and well-established, thereby ensuring a levelled playing field of global data availability across the globe to evaluate datum conversion and sea-level reference methodologies for assessments. Further filters applied refer to the exclusion of certain unrelated subject areas, keywords and limiting the search to the English language. This refined search term reduced the amount of literature to 7,241 documents that were subsequently screened in two consecutive rounds following established inclusion and exclusion criteria (Supplementary Fig. 1 and Supplementary Table 1). The first screening was conducted based on title, abstract and keywords, and the second screening was conducted based on in-depth analysis of the full article and, when needed, its references and supplementary material. The screening was consistently performed by K.S., whereas a subset of 1,000 articles (first screening) was cross-checked and validated by P.S.J.M., hereby not encountering conflicting decisions. Publications that were categorized as ‘yes’ and ‘maybe’ in the first screening were included in the second, full-article screening. The inclusion criteria were focus on coastal land and coastal sea level, focus on coastal hazard and impact assessment, focus on coastal lowlands and the use of satellite-borne elevation information. The main exclusion criteria were the absence of elevation data use, use of airborne lidar without combination with satellite-borne elevation data, methodological and technical articles without application, articles referring to elevation data cited in tertiary source or beyond (only primary or secondary sourced studies included) and focus on secondary hazards (cascading effects). The finally included literature, consisting of 385 publications, underwent an in-depth evaluation of their respective research data and methodology, scrutinizing them, and—where needed—also their secondary references, to assess all relevant details related to study area, distance to shore, study focus and/or flood type, number of DEMs, DEMs used, actuality of DEM, DEM type, DEM coverage, acquisition and technique, performance of DEM accuracy assessment, DEM accuracy, spatial resolution, DEM availability, horizontal datum used, vertical datum documentation, vertical datum conversion, vertical datum type, vertical datum used, implementation of sea-level reference, and—where needed—additional comments (Supplementary Table 2).

The individual papers were accessed by either the Scopus search engine, ISI Web of Science, Science Direct, Google Scholar or ResearchGate. The complete evaluation of the selected 385 publications and the subsequent analysis of the results following the developed evaluation criteria and coding are provided in Supplementary Table 2. Only publications that handle all datasets in a consistent vertical reference frame (that is, performing a correct vertical datum conversion) and refer to the latest available sea-level information at their time (that is, using an up-to-date sea-level reference and correct vertical datum conversion) were considered as proper. As vertical datum conversion always requires additional data next to a DEM-based coastal elevation dataset (for example, vertical datum information, offset between geoid/geoids and tidal level, sea-level height data) as well as a conversion procedure (including a specific GIS or coding environment or conversion service), we expect researchers who perform a datum conversion to document the additional datasets and procedures in their papers and/or supplementary information or supplementary data. In case a paper does not provide any documentation of additional datasets or required conversion steps, we presume that no datum conversion was performed. This assumption was confirmed to be correct for numerous papers for which the absence of datum conversion becomes apparent in the results of the study.

Finally, we evaluated more than 90 additional publications that fitted the selection criteria, but were not included in the Scopus-searched literature. This subset was separately collected by the authors through additional, non-systematic literature searches, for example, to include relevant IPCC-referenced assessments, and served as an independent dataset to evaluate potential bias present in our systematic Scopus-searched literature dataset (Supplementary Data 1). The additional literature dataset underwent the same scrutinizing evaluation (Supplementary Table 1). We found nearly equivalent findings on the occurrence and percentage distribution for the various vertical datum and sea-level reference issues (Extended Data Fig. 8) as for the systematic review (Fig. 1), which suggests that our Scopus-based systematic review is unbiased and provides representative results for the existing body of literature.

Vertical datum conversion and processing of DEMs

To obtain land elevation above continuous local sea level globally and quantify the discrepancies to land elevation with respect to global geoids or datum conversion errors, we processed four of the most recent DEMs at a global scale by converting them from their original vertical reference system to MSL as indicated by the MDT product. The DEMs included were CoastalDEM v.2.1 (original vertical reference system: EGM96, spatial resolution: 90 m × 90 m)⁴⁰, FABDEM v.1.0 (original vertical reference system: EGM2008, spatial resolution: 30 m × 30 m)⁴¹, GLL-DTM v.2 (original vertical reference system: MDT⁵³, spatial resolution: 1° × 1°, that is, about 1,000 m × 1,000 m)⁴² and DeltaDTM v.1 (original vertical reference system: EGM2008, spatial resolution: 30 m × 30 m)⁴³. The publicly available GLL-DTM v.2 (referenced to CNES-CLS13 MDT⁵³) contains a geoid conversion error (that is, the EGM-DIR R4 geoid was assumed to equal the EGM96) and does not apply the latest available MDT dataset, at the time. Therefore, we obtained the pre-converted GLL-DTM v.2 (referenced to EGM96) from the authors and conducted the vertical datum conversion to MSL⁴⁴.

We used the latest available global MDT HYBRID-CNES-CLS2022 dataset⁴⁴, which provides sea surface height above geoid (GOCO06s) across the globe, measured by satellite altimetry and combined with gravitational field information, oceanographic data from drifting buoys, high-frequency radar velocities and hydrological profiles. The MDT dataset⁴⁴ provides spatially continuous information of sea surface height above the GOCO06s geoid at a resolution of 0.125° averaged over a period from 1993 to 2021. Therefore, it provides the latest available information on global MSL and an accurate substitute for local tide gauge information in those regions. Several studies confirm the accuracy of MDT data in the range of cm (see, for example, refs. ^54,55), although vertical accuracy decreases up to about 4 cm within 10 km of the coast⁵⁶. It serves as an open-accessible product that—if properly aligned with elevation information—can be used to adjust elevation with respect to local sea level continuously along the coastlines of the world.

The following section documents a proper, consistent and reproducible vertical datum conversion of the four DEMs, providing all required datasets, datum information, processing steps and used software environments and may serve as an example for future studies or existing studies aiming to re-evaluate previous assessments. For all our computations, we used the ArcGIS Pro environment. To reference coastal elevation of the DEMs to MSL as given by MDT, the offsets of the underlying respective vertical reference systems (that is, EGM96, EGM2008 and GOCO06s) were determined (Extended Data Fig. 2).

Geoid information was obtained from the openly accessible calculation service of the International Centre for Global Earth Models from GFZ Helmholtz Centre for Geosciences⁵⁷. Point data on geoid height anomaly to the WGS84 ellipsoid was obtained for the entire globe at a resolution of 0.085° for the EGM96, EGM2008 and GOCO06s geoids, respectively. For each geoid, the height anomaly points were interpolated into a global raster by using multiquadric radial basis functions, which gave the most accurate interpolation results and is also used by gravitational field modelling studies^58,59. Subsequently, the global geoid raster files were resampled to a common spatial resolution of 90 m × 90 m (for CoastalDEM v.2.1, FABDEM v.1.0 and DeltaDTM v.1) and 1,000 m × 1,000 m (for GLL-DTM v2) by using bilinear resampling to be comparable with the spatial resolution of the DEMs throughout the entire datum conversion process. Geoid offsets were determined by subtracting the GOCO06s geoid height anomaly raster from the EGM96 and EGM2008 geoid height anomaly rasters.

Using MSL as indicated by MDT as a vertical datum for land elevation data requires the extrapolation of MDT data over land. We extracted point values from the MDT HYBRID-CNES-CLS2022 raster using bilinear interpolation at point locations and subsequently extrapolated them over land using an inverse distance weighting algorithm and a smooth neighbourhood type with a smoothing factor of 0.5. The resulting raster dataset was resampled to two spatial resolutions of 90 m × 90 m (for CoastalDEM v.2.1, FABDEM v.1.0 and DeltaDTM v.1) and 1,000 m × 1,000 m (for GLL-DTM v.2) by using bilinear resampling to enable comparability with the spatial resolution of the DEMs and to avoid the introduction of potential artefacts stemming from large differences in spatial resolution in the datum conversion process.

The processing of DEMs involved two main processes (Extended Data Fig. 2d). First, the DEMs were converted from the EGM96 and EGM2008 geoid to the GOCO06s geoid by adding the computed respective geoid offsets. Therewith, the vertical references of DEM-derived land elevation and MDT-derived sea surface height are aligned to a common datum (that is, GOCO06s), which is a prerequisite to obtain land elevation above MSL by subtracting the MDT data from the respective DEM (Extended Data Fig. 2d). To automate the processing of the vertical datum conversion of the DEMs, we applied two ArcGIS Pro model workflows to convert DEM tiles from the EGM geoids to GOCO06s and subsequently to MDT, while preserving their original spatial resolution and properties (the Python codes are provided online).

As the performance and the reliability of MDT extrapolation over land decreases with increasing distance from the sea, we applied a distance threshold of 500 km from the coastline (as defined by Open Street Map⁶⁰), for which we consider this approach of DEM vertical datum conversion valid. We converted all land elevation information within the distance threshold to MSL, thereby ensuring adequate extrapolation performance and full coverage of vast low-lying coastal plains and river deltas such as the Ganges–Brahmaputra–Meghna Delta.

Assessment of coastal land elevation and sea-level height, SLR impact and LECZ from global DEMs

We investigated coastal land elevation and sea-level height globally by extracting point elevation data from the original and vertically converted DEMs and their respective differences, at a 90 m interval along the coastline using bilinear interpolation of values at point locations and excluding no data values and water bodies before rasterization. As some of the evaluated DEMs contain large negative, unrealistic elevation values that are likely artefacts from the source DEM acquisition and post-process steps, we excluded these by applying a minimum elevation threshold of 7 m below MSL, which represents some of the lowest elevations in the coastal lowlands worldwide, such as the Netherlands (see, for example, ref. ²¹). The elevation statistics (including minimum, maximum, mean, median and standard deviation) were calculated for global (both including and excluding Antarctica), continental and regional scales (using administrative boundaries provided in ref. ⁵¹). The global statistics provided in the main text and figures exclude the results for Antarctica, as there are no people living there.

To show the impacts of neglected conversion to a sea-level datum, we investigated the impact of 1 m RSLR on area and population when simulated for DEMs with their original vertical reference system and after conversion to MSL, apart from computing area and population at present already below sea level. Similarly, we investigated the discrepancies in area and population within the 10 m LECZ when different DEMs with and without proper vertical datum alignment are used. After no data values and large negative values (that is, ≤7 m below MSL) were excluded and water bodies masked, we reclassified the DEMs applying thresholds of ≤1 m and ≤10 m, respectively. Area in km² was calculated for global, continental and regional scales (Supplementary Fig. 8) using zonal statistics. We limit our assessment to relative elevation only, and do not apply a hydrodynamic inundation (for example, bathtub) approach, nor report on flood or inundation extent or impacts in our results.

To estimate population currently below MSL with 1 m RSLR and within the LECZ, we use three global population datasets and thereby avoid potential bias in absolute population counts arising from single datasets^25,61,62. Uncertainties in and between population data stem from resolution (grid cell size) and quality of input data (for example, census data) and ancillary products as well as models to calculate statistics (ref. ⁶³ and references therein), daily population dynamics and inconsistencies in administrative boundary data^61,62. Therefore, we apply a multi-dataset approach and use unconstrained WorldPop data from 2020 at 100 m spatial resolution⁵² as well as the LandScan Global dataset (800 m spatial resolution) for the years 2020 (to be comparable to the WorldPop 2020 data)⁶⁴ and the latest available 2023 data⁶⁵. For the WorldPop dataset, we used data for individual countries as the global dataset provides only aggregated data that would lead to substantial overestimation in population. Population estimates were derived for global, continental and regional scales using zonal statistics. Global population was calculated by computing binary rasters for each DEM (1 if elevation is between 7 m below MSL and ≤0 m, ≤1 m and ≤10 m above MSL, respectively) and aggregating these at the respective (coarser) resolution of the population datasets, using the arithmetic mean. This created the fraction of which each raster cell meets the elevation requirement. Subsequently, the raster was multiplied by the respective population data and spatially summed to estimate population for each extent of interest, implicitly assuming equal distribution of population within a single population data cell. We note that this constitutes an uncertainty factor as people may be distributed disproportionately within a single raster cell, but data resolution restricts further detailing. Another shortcoming is that we do not account for population change in our exposure projections and use static population numbers. Adding a projection of population change in our 1 m RSLR scenarios is not possible, as the projection is spatio-temporally variable. Therefore, the actual number of future exposed population is probably higher, given the predominant projected growth of the human population, particularly in coastal zones of the Global South (see, for instance, refs. ^11,66). All reported coastal elevation values, RSLR impact and LECZ statistics, as well as the respective deviations between estimates for DEMs with and without proper datum conversion, were quantified in absolute value and as discrepancies in percentage (Supplementary Data 2 and 3).

Evaluating the use of investigated studies in IPCC AR6 reports

To investigate links of the evaluated literature to the latest IPCC reporting cycle (AR6), we screened the AR6 working group (WG)I–III and the Special Report on the Ocean and Cryosphere in a Changing Climate (SROCC) reports against our evaluated literature reference list. Through a reproducible, large-language-model-supported, screening protocol (protocol details are available in the Supplementary Information), we used ChatGPT-5 to screen all the chapter-specific literature lists from the AR6 WGI, II, III reports and WGII cross-chapter papers (using .bib files available on https://www.ipcc.ch/report/ar6/wg1/downloads/, https://www.ipcc.ch/report/ar6/wg2/downloads/, https://www.ipcc.ch/report/ar6/wg3/downloads/) and the SROCC report chapters (using PDFs of individual chapters, available on https://www.ipcc.ch/srocc/download/) for using references from our systematic review (385) and additional literature (96). We found that 46 studies in our systematic review and 29 in our additional literature were included as references in the IPCC reporting (Supplementary Table 5, Supplementary Figs. 10 and 11 and Supplementary Data 1 and 4). We then grouped these studies according to their evaluated categories: proper integration of sea-level reference (n = 1 and 2, for systematic review and additional literature, respectively); incorrect integration of sea-level reference (n = 9 and 7, for systematic review and additional literature, respectively); and absence of sea-level reference (n = 36 and 20, for systematic review and additional literature, respectively) (Supplementary Table 5) and performed a quantitative comparison (area and/or population exposure) with several representative and methodologically comparable studies from each category with the results from our meta-analyses, to quantify the magnitude of error in the existing, IPCC-referenced impact assessments as a result of various vertical referencing issues.