Attributing uncertainties in elevation assessments for data-sparse coastal lowlands using global elevation models: A globally applicable approach showcasing the Vietnamese Mekong Delta

Attributing uncertainties in elevation assessments for data-sparse coastal lowlands using global elevation models: A globally applicable approach showcasing the Vietnamese Mekong Delta | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Attributing uncertainties in elevation assessments for data-sparse coastal lowlands using global elevation models: A globally applicable approach showcasing the Vietnamese Mekong Delta Katharina Seeger, Philip S.J. Minderhoud This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7706762/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 04 Feb, 2026 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract River deltas and coastal plains are at risk of sea-level rise and other coastal hazards, often exacerbated by land subsidence. The relative elevation of a coastal lowland to local sea level is a crucial determinant for its overall exposure, making it key input for coastal hazard assessments. However, locally-sourced, high-accuracy elevation data, such as LiDAR, is not available for many data-sparse coastal lowlands worldwide, leaving global digital elevation models as only source of information. While these provide an adequate spatial (i.e. horizontal) resolution for regional, delta-wide coastal assessments, their vertical errors in the range of several metres impede investigations of (relative) sea-level rise impact where changes occur on millimetre- to centimetre-scale. Assessing the quality of available elevation datasets is required to identify the best performing model(s) to use for generating reliable coastal impact and exposure assessments. While data-intrinsic inaccuracy has been extensively addressed both in dataset documentation and literature, the relevance and proper vertical datum conversion from global geoid and ellipsoid to local sea level is often still omitted in many applied studies from coastal research. Similarly, the impact of the actuality of elevation data (i.e. time since data acquisition) on assessments in coastal lowlands is so far understudied although elevation models may become quickly outdated, especially where coastal lowlands are facing high rates of elevation change resulting from the interplay of vertical land motion, (vertical) sediment accretion and sea-level change. Particularly for flat, low-lying subsiding coastal landscapes like the Mekong Delta, being in parts only a few decimetres elevated above sea level and experiencing land subsidence of up to several centimetres per year, the reliability of elevation data and adequate representation of elevation relative to local sea level as well as the consideration of factors impacting elevation over time is of utmost importance. We present a globally applicable approach to quantify and attribute uncertainties in elevation assessment for data-sparse coastal lowlands using global elevation models to sources such as inaccuracy, vertical datum offset and actuality. We showcase this approach by revisiting land elevation in the Vietnamese Mekong Delta (i) by vertically referencing 11 commonly used global elevation models and an updated local elevation model to a common actual, local sea-level datum, and (ii) by conducting a thorough assessment of elevation model performance that not only allows for the quantification of errors and elevation assessment uncertainties but also their attribution to data-intrinsic inaccuracy, vertical datum offset and, tentatively, non-linear impact of elevation change due to vertical land motion (e.g. extraction-induced land subsidence) and sea-level change affecting the actuality of the elevation model. Our approach not only allows to improve the understanding of coastal elevation to further improve relative sea-level rise and flood impact assessments and to substantiate projections of future elevation in the Mekong Delta, but in its design, applying solely open data and commonly used GIS software, facilitates similar assessments of elevation model performance and elevation assessment uncertainties in other (data-sparse) coastal regions in the world. Earth and environmental sciences/Climate sciences Earth and environmental sciences/Environmental sciences Earth and environmental sciences/Natural hazards Digital elevation model (DEM) land elevation change land subsidence relative sea-level rise sea level vertical datum Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Coastal lowlands face an increasing risk of sea-level rise (SLR) as global, climate-induced SLR is often accelerated by coastal subsidence 1 . Especially densely populated river deltas are prone to suffer from magnitudes of land subsidence that outpace dimensions of absolute sea-level change and together with sediment starvation due to sediment trapping in upstream dams culminate in elevation loss (e.g., refs. 1 – 6 ). Consequently, these lowlands are also increasingly exposed to other coastal hazards such as storm surge flooding (e.g., ref. 7 ). The elevation of coastal lowlands above sea level is a critical factor in safeguarding these globally important landscapes from temporary and permanent inundation, salinity intrusion and other cascading effects on the environment and society, affecting for example socio-economic productivity (see also ref. 8 ). Land elevation data, mostly in form of digital elevation models (DEMs), are necessary for any quantitative coastal hazard impact assessment or projection, and the reliability of these assessments is strongly determined by the quality of the underlying elevation data. Therefore, thorough DEM accuracy assessments are needed to ensure the reliability of flood risk and SLR impact evaluations or at least to quantify ranges of uncertainty, both in terms of area, population and assets at risk (e.g., refs. 9 – 12 ). Information on land elevation is obtained either by direct measurements through topographical levelling surveys and Global Navigation Satellite System (GNSS) measurements, or by exploiting remote sensing data from aircrafts or satellites (optical, radar, laser altimetry) to generate DEMs of the Earth’s surface. DEMs are subdivided into models representing a landscape’s surface (so-called digital surface models (DSMs)), i.e. including vegetation and building heights, and digital terrain models (DTMs) that represent elevation of the bare earth (e.g., refs. 13 , 14 ). While the DEM type depends on the acquisition techniques and processing approaches of a given dataset, it is important to select the correct DEM type fitting the purpose of application 13 . Local ground measurements of land elevation data provide elevation information of high vertical accuracy, but suffer from coarse spatial resolutions and time-consuming data acquisition. Through remote sensing techniques such as aerial photogrammetry, and more recently, airborne Light Detection and Ranging (LiDAR), the quality of DEMs has been improved over the years to reach horizontal resolutions and vertical accuracies on centimetre- to decimetre-scale (e.g., refs. 15 – 17 ). While such high-accuracy, locally-source data is publicly available for several coastal regions, for example in the United States, Australia, New Zealand and Europe, it is unfortunately not available for major parts of the Earth’s coast, amongst other the vast, densely populated coastal lowlands of Asia and Africa as well as many Small Island Developing States. Here, global, freely available, satellite-based DEMs are commonly used (e.g., as highlighted by refs. 18,19 ), however often studies do not take limitations of the applicability of these DEMs properly into consideration. Although global DEMs often provide an adequate spatial (i.e. horizontal) resolution (i.e. in the range of ~ 10 to 90 m), sufficient for setting up regional, delta-wide flood models or estimating coastal population and land-use asset exposure, their vertical errors in the range of several metres impede investigations of (relative) SLR impact where changes occur on millimetre- to centimetre-scale. Inaccuracies related to the acquisition of elevation data or DEM processing itself (e.g. sensing or interpolation artefacts) have been addressed by post-processing first-order DEMs such as SRTM 20 , ASTER 21 , 22 , AW3D (ALOS) 23 and TanDEM-X 24 , through applying void-filling and smoothing (e.g. ACE2 25–27 , Copernicus DEM 28 ), filtering elevation data with spatial vegetation, building and/or applying population data in random forest algorithms or neural networks (MERITDEM 29 , FABDEM 30 , Coastal DEM 31 , 32 ) or employing recently-available satellite LiDAR measurements (ICESat-2) (GLL-DTM 33 , 34 ; DeltaDTM 35 . These recent DEMs constitute the newest generation with improved vertical accuracy, specifically targeting applications in coastal lowland contexts, with the FathomDEM 36 , created using advanced postprocessing of the Copernicus DEM, as the youngest sibling. The majority of those DEMs is provided with vertical reference to ellipsoid (e.g. WGS84) or a global geoid (e.g. EGM96, EGM2008), the latter often considered to reflect sea level, thus providing elevation information at global scale with respect to sea level. However, the accuracy of these global geoids to represent the actual local sea-level height varies a lot and is dependent on the amount and accuracy of input data used when generated. Offsets with true local sea-level height in several regions worldwide can be more than 1 metre, a bias which is very often not corrected for in impact assessments using global DEMs. Consequently, in these data-sparse regions, local sea level may differ up to more than a metre from the global geoid (e.g., refs. 11,37–39 ), thereby introducing systematic errors into SLR impact assessments conducted based on global elevation data whose vertical reference was not properly corrected from geoid/ellipsoid to sea level. Finally, the actuality of elevation data, i.e. referring back to the time of data acquisition, impacts the quality of coastal impact assessments. Especially in rapidly subsiding coastal lowlands such as river deltas, DEMs may become quickly outdated, as these landscapes can experience several centimetres to even decimetres subsidence per year 6 , 40 – 42 . In such areas, using for example the SRTM DEM, originally acquired in February 2000, or any of its post-processed versions in a heavily subsiding environment results in substantial misjudgements of actual elevation, and this limitation is hardly considered in the majority of coastal impact assessments based on global DEMs. While several studies have addressed DEM accuracy by comparing different DEMs and investigate their performance in terms of fluvial flood inundation (e.g., refs. 43–47 ), only a few focused on the impact of DEM selection on (relative) SLR impact 11 , 12 , 34 , 38 , 48 . So far, only Minderhoud et al. 38 tentatively attributed the errors associated with a DEM in the Mekong Delta to DEM accuracy and the lack of datum conversion to local sea level. However, due to information paucity on local vertical datums, this was done through a basic transformation, i.e. by lowering the global MERITDEM to equalise the mean elevation of a local DEM (TopoDEM) with sea level datum alignment, rather than systematically converting the vertical reference system from the EGM96 geoid to local sea level as indicated by tide gauge data (i.e. Hon Dau tide gauge). Although tide gauges provide highly local information on sea level, their usage is only recommended if they provide a sufficiently long, continuous and up-to-date monitoring period for small study sites or regions without any significant sea-level variations along the coast 49 . Also the absence of any documentation about the position of a tide gauge with respect to a certain geoid/ellipsoid may hamper the proper alignment of a geoid- or ellipsoid-referenced DEM to local sea level. In their DEM accuracy and (relative) SLR impact assessment for the Ayeyarwady Delta (Myanmar), Seeger et al. 11 employed an open-data vertical datum conversion approach that – in the absence of any suitable tide gauge information – allows for the proper conversion of global DEMs to actual, continuous local mean sea level (MSL) along the Myanmar coast by applying latest, freely available satellite-altimetry-derived mean dynamic topography (MDT). This workflow also includes the beforehand required correction of geoid offset between the different vertical reference systems used by elevation and sea-level datasets and thus improves previous datum conversion workflows that lack this relevant processing step (e.g., refs. 33,34 ). In this study, we investigate land elevation datasets and their performance in data-sparse coastal lowlands, showing a globally applicable approach to quantify and attribute uncertainties in global DEMs to sources such as inaccuracy, vertical datum offset and actuality, which matters in terms of relative SLR impact. We highlight the applicability of this approach by revisiting land elevation in the Vietnamese Mekong Delta where Minderhoud et al. 38 uncovered the delta’s true elevation to be on average only ~ 0.8 m above local sea level whereas previous assessments using global DEMs (e.g. SRTM and MERITDEM) assumed the average elevation of the delta to be considerably higher (i.e. 2.6 m and 3.3 m above sea level, respectively). The previous overestimations stemmed from a combined effect of vertical datum offset, DEM data inaccuracy, and DEM actuality, however the individual contribution of each uncertainty was not quantified (Minderhoud et al., 2019), which we will advance upon in this study. We updated the local Topo DEM of Minderhoud et al. 50 (hereafter referred to as Topo DEM v1), including Ho Chi Minh City and neighbouring provinces, to actual, continuous local MSL by applying an updated vertical datum conversion from Seeger et al. 11 (hereafter referred to as TopoDEM_v2; Fig. 1 ). Consequently, we extend the DEM accuracy assessment to evaluate the performance of in total 11 high-resolution, global DEMs, thereby ensuring to include latest available coastal DEMs such as DeltaDTM while also reassessing those DEMs that Minderhoud et al. 38 investigated in their study (i.e. SRTM and MERITDEM). Furthermore, we investigate the potential impacts of absent and incomplete, incorrect datum conversion, e.g. by including the GLL-DTM v2 referenced to (outdated) MDT as provided by Vernimmen and Hooijer 34 , using the approach of this study. We also consider different versions of recent DEMs and integrate both DeltaDTM v1 and v1.1 as we assume the inclusion of elevations up to 30 m in v1.1 to result in (slight) differences in DEM performance in the flat, low-lying Mekong Delta (see also Pronk et al. 35 ). To enable 1:1 comparison to findings from Minderhoud et al. 38 , we focus on results obtained for the Vietnamese Mekong Delta in the main text while providing also the statistics for the extended area of interest in the supplementary material. Based on this thorough assessment, integrating the DEMs in their original vertical reference and converted to the same vertical datum, our study not only allows to extend and detail previous studies on the Mekong Delta’s elevation but also to attribute and quantify the sources causing uncertainties in the global DEMs to adequately represent the delta’s local elevation. Therewith, we showcase the considerations to be made when handling elevation models in data-sparse local to regional contexts and derive the best compromise of high accuracy and resolution data recommended for further studies on the Mekong Delta such as sea-level rise impact assessments and flood modelling research. Results and interpretation Converting vertical datum to actual continuous local sea level The reliability of elevation data in terms of accuracy (i.e. the absence of uncertainties arising from data acquisition and DEM interpolation) and adequate representation with respect to actual continuous local sea level (i.e. the appropriateness of the vertical reference system to equal actual true sea level experienced along a given coastline) is of utmost importance for flat, low-lying coastal landscapes like the Mekong Delta. Being in parts only a few decimetres elevated above sea level makes elevation-dependent assessments highly sensitive to any uncertainties in the elevation data itself and in the estimation of coastal sea level. To assess the uncertainties related to vertical reference frames and local sea level for the different assessments and DEMs in the Mekong Delta, we focussed on the most common vertical datums used for global DEMs (i.e. EGM96 and EGM2008), which constitute also the original vertical reference of the DEMs used in previous Mekong Delta assessments. We quantified the differences between EGM96, EGM2008 and local MSL, respectively, and determined a mean geoid offset of 1.26 m (median: 1.29 m) for EGM96 and 1.17 m (median: 1.17 m) for EGM2008. Similarly, the offset range is lower for EGM2008 and constitutes only 0.84 m to 1.46 m, with the EGM2008 showing less variability (σ = 0.11 m) while it ranges from 0.77 m to 1.65 m for EGM96 and showing larger deviation of 0.24 m (Fig. 2 ; Table 1 ). This better performance of EGM2008 is probably related to the increased amount of gravitational field data serving as an input and allowing for an improved performance geoid interpolation in the larger data-sparse region. Table 1 Offsets between commonly used geoid models as well as an example of incomplete datum conversion and local mean sea level as indicated by mean dynamic topography of Jousset et al. 51 in the Vietnamese Mekong Delta. The residual error arising from incomplete datum conversion was calculated for the example of GLL-DTM v2 34 . Vertical datum N Mean offset (m) Median offset (m) MAE (m) RMSE (m) Min. offset (m) Max. offset (m) σ (m) EGM96 151369 1.26 1.29 1.26 1.07 0.77 1.65 0.24 EGM2008 151369 1.17 1.17 1.17 1.01 0.84 1.46 0.11 Example of incomplete datum conversion 137689 0.14 0.17 0.25 0.26 -0.39 0.54 0.25 We quantify to what extent the vertical datum offset impacts the reliability of an individual global DEM in the Mekong Delta and compare the respective DEMs both in their original reference and transposed to actual continuous local MSL with TopoDEM_v2 as well as its local point elevations. We observe that the respective global DEM errors reduce after converting them to actual continuous local MSL (Table 2 ). Determining the difference between DEM errors before and after datum conversion allows to quantify the impact the vertical datum issue has on the individual DEM reliability. Tendentially, the smaller the overall DEM errors, the larger the impact of lacking vertical datum conversion is. In a spatially resolved comparison with TopoDEM_v2, the discrepancies introduced range from 11.6% to 147.4%. Thereby, percentages of more than 100.0% are obtained in case the uncertainty of the transposed DEM is smaller than the vertical datum offset of the respective original vertical reference system used (Table 1 ) and the difference in DEM error before and after datum conversion exceeds the uncertainty of the transposed DEM (Table 2 ), specifically for the versions of DeltaDTM (i.e. 101.7% for v1 and 147.4% for v1.1). Similarly, the impact of vertical datum conversion is also high for other DEMs such as CoastalDEM v2.1 (46.3%), FABDEM (48.6%) and MERITDEM (59.7%), thereby indirectly reflecting the effectiveness of accuracy improvement through post-processing which leads to a relative dominance of the vertical datum issue in the DEM-related uncertainty. Similar patterns, however with slightly different percentages, are documented for the comparison with local point elevations, enabling pointwise DEM uncertainty attribution to vertical datum offset over the entire point elevation range as well as by focusing on particularly low-lying elevations (i.e. ≤10 m above MSL). Besides the absence of vertical datum conversion from a global geoid or ellipsoid model to actual continuous local sea level, another error in handling coastal elevation and sea-level is partial or incomplete, and therefore incorrect, datum conversion. From the global DEMs used in this study, this error was only encountered with the GLL-DTM for which geoid offset correction between the elevation and sea-level data used was not conducted during the processing of the original GLL-DTM 33 , 34 , thereby resulting in artefacts impacting the alignment and consequently applicability of this DEM. Comparing the original GLL-DTM v2 34 with the GLL-DTM v2 properly converted to actual continuous local MSL reveals only minor average offsets at large regional to global scale, as smaller-scale offsets in both directions average each other out. However, at local to smaller-regional scale the variability is large and the impact of the incorrect conversion becomes much more relevant (this study). In the Mekong Delta, the incorrect vertical datum conversion affecting the GLL-DTM v2 resulted in offsets of ca. 0.14 m (mean) and 0.18 m (median), with a standard deviation of 0.25 m and overall ranging from − 0.39 m to 0.54 m (Fig. 2 C). RMSE of GLL-DTM v2 improves by ca. 0.12 m (33.3%) (0.08 m (17.6%) in comparison with local spot heights) if datum conversion is conducted completely and correctly. Consequently, errors made in vertical datum conversion account for 33.3% (17.6% in comparison with local spot height) of the DEM’s uncertainty (Table 2 ). With a higher spatial resolution of GLL-DTM, however, we would expect even much greater discrepancies and effects on the quality of the DEM attributable to improper handling of the elevation and sea-level data. Performance of digital elevation models in the Vietnamese Mekong Delta Revisiting local elevation data in the Vietnamese Mekong Delta We updated the existing Topo DEM v1 50 by referencing the data to actual, continuous local MSL (TopoDEM_v2) which enables for an up-to-date assessment of land elevation in the Vietnamese Mekong Delta. Based on visual impression, the overall reflection of land surface height changes only slightly (Figs. 1 and 3 ). The frequency of lower elevations and thus the spatial extent of lowly elevated areas increased. This becomes particularly evident in the Ca Mau peninsula, reflecting more pronounced sea-level variations in this part of the sea (especially in Rach Gia Bay) compared to Hon Dau in northern Vietnam result in higher MSL/lower elevation above MSL. While in comparison the maximum and minimum elevations increased and decreased for TopoDEM_v2, the average elevation of the Mekong Delta is updated to 0.77 m (mean) and 0.72 m (median) compared to 0.80 m (mean) and 0.74 m (median) as previously indicated by Topo DEM v1, resulting in an average height residual of 4–5 cm (Fig. 4 ). The fact that this amount equals annual sinking rates of land subsidence hotspots in the Mekong Delta 40 , 41 highlights the enormous sensitivity of the flat, low-lying and sinking landscape to any error and uncertainty in the elevation data, even if high-accuracy local elevation data is used. Consequently, if properly conducted, converting the vertical datum from MSL indicated by a single tide gauge (and established several years ago) to actual, continuous sea-surface height along the deltaic coast makes the characterisation of local elevation with respect to sea level more precise as potential sea-level variations along the ca. 2000 km long Vietnamese coastline can be accounted for. In the Mekong Delta, this is a crucial step to further narrow down existing uncertainties in the assessment of relative SLR. Which one is the best? – On the local validation of global satellite-based digital elevation models Disentangling DEM uncertainty into DEM-specific accuracy and vertical referencing to actual (continuous) local sea level is crucial to understand the suitability and therefore reliability and applicability of a DEM in a given coastal study area for any coastal hazard or impact assessment. With all elevation data (both DEMs and local point elevations) in the same vertical reference frame, i.e. MSL according to MDT 51 , the impact of respective vertical datum offset on the DEMs’ quality assessment is excluded. Hence, we can characterise and compare their performance and accuracy against the background of their generation (i.e. including data acquisition, DEM interpolation and further processing). Visual inspection of the DEMs available for the Mekong Delta reveals the huge differences in their capability to correctly reflect the delta’s flat, low-lying terrain (Fig. 3 ). Rather, artefacts like stripes from sensing are dominating, especially in SRTM, ASTGTM v003 and AW3D30, and are also preserved in post-processed DEMs such as ACE2, MERITDEM and CoastalDEM v2.1, reflecting that the application of smoothing filters and correction algorithms did not perform well in this coastal lowland (Fig. 3 ). Consequently, the elevation frequency distributions of those DEMs show an almost bimodal shape and are characterised by one mode in the range of 1 m to 2 m (i.e. reflecting the delta’s average elevation) and a second, minor one at the lower end of the frequency distribution with most values in classes − 1 m to -2 m and <-2 m (i.e. including erroneous values resulting from the DEM’s artefacts) (Fig. 3 ). Stripes related to the SRTM DEM are largely hidden in MERITDEM and likely do not become visible in its elevation frequency distribution as related values are in the same range as the indicated delta elevation. While average elevations of SRTM and MERITDEM prior to vertical datum conversion are corresponding with values determined by Minderhoud et al. (2019a) (only for SRTM, mean delta elevation is ~ 0.3 m lower, likely resulting from slightly different pre-processing performance), they are 0.45 m and 1.06 m lower after transposing them to MSL, respectively (Fig. 3 ; Table 2 ). ASTGTM v003 varies in that from SRTM-based DEMs and AW3D30 as it indicates the highest elevations throughout the entire array of datasets. In contrast, visual comparison of TanDEM-X 90m and its post-processed versions such as Copernicus DEM and FABDEM reveal a similar representation of the delta terrain, which is also in line with the local DEM, however, indicating higher mean elevations. Developed for improving global DEM performance in coastal lowlands, the global coastal DEMs of CoastalDEM v2.1, GLL-DTM v2, and DeltaDTM v1 and v1.1 show an overall better representation of the Mekong Delta’s elevation, both in terms of approaching average elevation and elevation frequency distribution as well as in lower error statistics (Fig. 3 ; Table 2 ). All of these coastal DEMs have been trained on or processed with high-accuracy ICESat-2 satellite LiDAR. However, CoastalDEM v2.1 still suffers from underlying SRTM artefacts (which are transferred via NASADEM that was used as source DEM for generating CoastalDEM v2.1) although it reflects the delta’s average elevation well. Consequently, indicated elevations are characterised by a left-skewed frequency distribution and comparably high standard deviation which is twice as high as for other coastal DEMs (Table 2 ). Both GLL-DTM v2 and DeltaDTM yield a more accurate representation of the delta’s terrain. Especially GLL-DTM v2 (converted to MSL by this study) and DeltaDTM v1 reveal the best results, with mean errors less than 0.10 m and RMSE of 0.35 m and 0.49 m, respectively (Table 2 ). Table 2 Performance of local and global DEMs in the Vietnamese Mekong Delta validated by the local TopoDEM_v2 and DEM-specific vertical datum offsets quantified as differences in RMSE and discrepancies from RMSE of DEMs referenced to mean dynamic topography (MDT). The statistics were extracted from DEMs masked for water bodies and outcrops and resampled to 500 m × 500 m spatial resolution. N – number of grid cells in the study area, with no-data values excluded for each DEM, respectively; Mean DEM – mean DEM elevation in the study area; Median DEM – median DEM elevation in the study area; Min. DEM – minimum DEM elevation in the study area; Max. DEM – maximum DEM elevation in the study area; σ DEM – standard deviation of DEM elevation in the study area; HR – height residual; MAE – mean absolute error; RMSE – root mean square error. DEM N Mean DEM (m) Median DEM (m) Min. DEM (m) Max. DEM (m) σ DEM (m) Max. negative HR (m) Max. positive HR (m) Mean error (m) MAE (m) Median error (m) RMSE (m) Difference in RMSE (m) Discrepancy (%) TopoDEM_v2 152028 0.77 0.72 -0.37 8.97 0.57 Topo DEM v1 147582 0.80 0.74 -0.52 6.98 0.54 -7.03 1.62 0.04 0.06 0.05 0.10 SRTM MDT 144113 1.04 0.91 -6.89 143.06 2.56 -10.12 142.12 0.29 1.87 0.17 2.55 0.45 17.6 SRTM EGM96 145672 2.27 2.00 -7.00 145.00 2.63 -9.56 144.06 1.52 2.26 1.40 3.00 ACE2 MDT 133927 1.04 0.93 -6.99 140.23 2.32 -8.90 139.29 0.29 1.65 0.17 2.26 0.50 22.0 ACE2 EGM96 134211 2.30 2.20 -7.00 141.70 2.38 -10.02 140.76 1.55 2.09 1.55 2.76 MERITDEM MDT 146263 2.04 1.96 -6.65 139.47 1.33 -7.89 138.53 1.29 1.40 1.28 1.80 1.06 58.7 MERITDEM EGM96 146269 3.29 3.26 -6.46 140.94 1.40 -7.44 140.00 2.54 2.57 2.56 2.86 ASTGTM v003 MDT 145981 8.55 7.44 -1.43 152.33 4.99 -3.88 151.39 7.80 7.80 6.68 9.30 1.08 11.6 ASTGTM v003 EGM96 145981 9.80 9.00 0.00 154.00 5.00 -2.60 153.06 9.05 9.05 7.98 10.38 AW3D30 MDT 109349 1.22 1.00 -6.96 111.90 2.46 -8.79 111.36 0.46 1.72 0.29 2.46 0.54 22.1 AW3D30 EGM96 110184 2.46 2.00 -7.00 113.00 2.54 -9.64 112.47 1.70 2.22 1.52 3.00 TanDEM-X MDT 138263 1.77 1.24 -6.81 163.51 1.85 -7.81 162.57 1.05 1.15 0.48 2.05 0.83 40.6 TanDEM-X EGM96 138313 3.02 2.52 -6.51 164.98 1.86 -7.66 164.04 2.29 2.30 1.75 2.88 Copernicus DEM MDT 114885 1.76 1.09 -5.35 138.51 2.03 -6.40 137.57 0.96 1.18 0.24 2.19 0.71 32.4 Copernicus DEM EGM2008 114887 2.94 2.29 -4.27 139.93 2.02 -5.23 138.99 2.14 2.15 1.42 2.90 FABDEM MDT 108892 1.31 1.01 -4.09 139.50 1.41 -5.00 138.55 0.50 0.66 0.21 1.43 0.69 48.6 FABDEM EGM2008 113820 2.48 2.21 -2.55 144.54 1.38 -3.50 143.60 1.68 1.69 1.40 2.12 CoastalDEM v2.1 MDT 145917 0.75 0.77 -5.50 159.20 1.26 -6.80 158.26 -0.00 0.77 0.03 1.20 0.56 46.3 CoastalDEM v2.1 EGM96 146009 2.01 2.01 -4.05 159.96 1.32 -5.35 159.02 1.26 1.38 1.32 1.76 GLL-DTM v2 MDT (this study) 137689 0.80 0.76 -1.48 8.09 0.51 -5.41 6.86 0.08 0.26 0.08 0.35 0.12 33.3 GLL-DTM v2 MDT (original) 138749 0.94 0.89 -1.67 8.15 0.64 -5.37 6.93 0.22 0.37 0.26 0.47 DeltaDTM v1 MDT 107553 0.74 0.67 -3.21 7.87 0.63 -5.13 6.56 -0.07 0.35 -0.07 0.49 0.72 147.4 DeltaDTM v1 EGM2008 114856 1.91 1.83 -1.88 9.56 0.63 -3.98 7.98 1.11 1.13 1.11 1.21 DeltaDTM v1.1 MDT 114585 0.61 0.53 -6.08 28.70 0.69 -7.05 27.64 -0.20 0.33 -0.19 0.55 0.56 101.7 DeltaDTM v1.1 EGM2008 114587 1.78 1.69 -4.88 30.00 0.70 -5.84 29.06 0.98 1.00 0.98 1.11 By subtracting TopoDEM_v2 from each of the other DEMs referenced to MSL, we quantify all differences between local and global elevation datasets. This not only substantiates the inappropriateness of SRTM, ACE2 and AW3D30 to be applied in the Mekong Delta as inaccuracies are in the range of several metres and are particularly evident from − 2.5 m to -1 m and from 1 m to 2.5 m (Fig. 4 ). However, given their bimodal character, they do not show up in any average height residual statistics but only standard deviations and RMSE in the range of ≥ 2 m (Fig. 4 ; Table 2 ). Aside from ASTGTM v003, whose elevation is in many parts of the Mekong Delta more than 2.5 m higher than indicated by TopoDEM_v2 (with inaccuracies of more than 10 m particularly in the southwestern Ca Mau peninsula), also MERITDEM represents the Mekong Delta on average 1.29 m higher (Fig. 4 ; Table 2 ). This is surprising as MERITDEM was created based on SRTM and involved a correction for vegetation heights 29 . Thus, we would have expected similar or lower offsets to TopoDEM_v2. However, it rather seems that vegetation was not effectively eliminated while artefact correction and smoothing was applied. Also the TanDEM-X based elevation models reveal higher elevations than TopoDEM_v2. Seeing the sequence of DEM generations from TanDEM-X 90 m to Copernicus DEM to FABDEM, involving corrections such as void filling and vegetation removals, their comparison with local elevation data reveals their improvement in accuracy, particularly in terms of lowered mean errors and average height residuals and especially following vegetation correction in FABDEM (Fig. 4 ; Table 2 ). While difference mapping for GLL-DTM v2 (Fig. 4 k and l) resolves how inadequate consideration of geoid offset in vertical datum conversion impacted also coastal inland elevation, DeltaDTM as the most recent coastal DEM, generated by integrating ICESat-2 satellite LiDAR into Copernicus DEM, reveals the overall best performance, benefiting from some of the lowest inaccuracies, height residuals and standard deviations while yielding high resolution of 30 m × 30 m. To further investigate the performance of DeltaDTM, and see whether the inclusion of elevations up to 30 m in v1.1 has an impact of its performance in the low-lying Mekong Delta, we included both available v1 and v1.1. We find that although both versions perform very similar, they show slight differences as DeltaDTM v1 outperforms v1.1 in overall lower height residuals and slightly higher accuracy (RMSE = 0.49 m (v1), RMSE = 0.55 m (v1.1). However, since DeltaDTM v1.1 included higher elevations, we would expect its height residuals to be similar to or higher than for v1, i.e. indicating elevations also similar to or higher than TopoDEM_v2. Instead, DeltaDTM v1.1 is on average 0.20 m lower than TopoDEM v2 and 0.13 m lower than DeltaDTM v1which are considerably larger differences than the minor, global differences in the range of ~ 2 cm with the global-scale validation comparison of Pronk et al. 52 . Discussion, conclusions and outlook The previous elevation assessment of the Vietnamese Mekong Delta by Minderhoud et al. 38 set a benchmark by unravelling the delta’s elevation relative to local sea level and its exposure to relative SLR, which had been grossly underestimated in previous SLR impact assessments based on global DEMs, as they overestimated the delta’s elevation to sea level by several metres. In addition, this work sensitised the coastal research community for proper converting the vertical datum to local sea level by conducting a tentative vertical datum conversion for one of the two assessed global DEMs. In the recent past years there have been considerable advancements in scientific research on data-sparse coastal lowlands for regions in the world where high-quality data (e.g. LiDAR) is limited or unavailable. The recent advancements range from the publication of newly processed DEMs specifically targeting global coastal lowlands 32 – 35 , raising awareness on considerations to be made about elevation assessment uncertainty due to DEM inaccuracy and vertical datum offsets (e.g., refs. 53,54 ) and introducing approaches and showcases how to handle elevation data properly in data-sparse coastal lowlands 9 , 11 , 55 . Building on these new DEMs and insights we revisited the land elevation in the Vietnamese Mekong Delta, which provides an excellent test-case being a lowly-elevated coastal lowland with local validation data (i.e. Topo DEM) available, thereby updating and extending the previous elevation assessment of the delta 38 . We took the majority of currently available global DEMs, including the older, most commonly used DEMs and the latest generation of DEMs specifically designed to target coastal lowlands. We compared them against a vertically high-resolution DEM from local origin (i.e. TopoDEM_v2), with all DEMs properly referenced to a common vertical datum, in our case to actual local continuous MSL (as indicated by MDT 51 ). In addition, we also compared all DEMs while omitting the necessary vertical datum conversion (i.e. DEMs are kept in their original reference system, which is mostly a global geoid). This is to mimic and evaluate the impacts of the common, erroneous practice present in the majority of global coastal impact assessments (highlighted in Minderhoud et al. 38 ). This approach not only allowed us to characterise the presently best performing DEMs but also to attribute and quantify the sources causing uncertainties in a global DEM to adequately represent the delta’s local elevation (Fig. 5 ). Unravelling and quantifying the specific contributions of these different factors to uncertainties of elevation assessments using global DEMs, has, to our knowledge, not been performed before. We deem such an assessment crucial, not only because it reveals which DEMs perform best for a certain area but also as it pinpoints errors in DEM processing steps (e.g. omitting vertical datum conversion) and indicates how much DEM performance (and its applicability) can be improved if certain processing steps are prioritised and properly applied. For example, the RMSE of ACE2 and MERITDEM are very similar if used in the EGM96 geoid as they are provided 27 , 29 . However, our assessment highlights that vertical datum conversion to MSL in the Mekong Delta is much more effective to improve the results when using the MERITDEM, while for ACE2 still 80% of the documented uncertainties is controlled by inaccuracy in the DEM generation process (i.e. related to acquisition and DEM interpolation), meaning that even after proper referencing to sea level, land elevation will be substantially overestimated by several metres (Fig. 5 ; Table 2 ). For global DEMs that included accuracy improvements like vegetation removal or integration of terrain data in their processing (e.g., FABDEM, CoastalDEM v2.1, DeltaDTM v1 and v1.1), vertical datum offset and consequently the need for conversion mount up to 30% to 60% uncertainty, reflecting it – at least for DeltaDTM – as an either equal or the most critical factor to consider in order to improve DEM performance. As this attribution highly depends on the magnitude of datum offset to local sea level, which varies around the world, as well as local spatial patterns and landscape features impacting the accuracy of DEM processing and post-processing, this attribution does not allow to be simply transferred to other areas and requires a site-specific evaluation following our presented approach. Land elevation is not static but changing over time and the vast majority of coastal lowlands such as river deltas and coastal plains are prone to land subsidence, which may reach several centimetres to locally even decimetres per year (e.g., refs. 6,56–58 ), together with global SLR resulting in elevated relative SLR 4 . Consequently, the capability of a DEM to adequately represent coastal-deltaic land elevation will change significantly if over years, accumulated subsidence together with SLR exceeds the DEM-inherent uncertainty. Therefore, the actuality of the elevation data used is as crucial as the actuality of sea-level information in order to guarantee up-to-date assessments of coastal hazards and impacts. However, this factor is often overlooked as still a huge number of those assessments applies elevation data more than ten to even 25 years old (e.g., as highlighted in Hawker et al. 45 ). The fact that some of the more recently published DEMs constitute post-processed elevation data such as SRTM, which was acquired in 2000, may cause confusion as users may consider the post-processed DEM to represent more recently acquired elevation data while these DEMs still rely on original, earlier acquired elevation data, leaving the potential impacts of post-acquisition elevation dynamics unaddressed (Fig. 6 ). We demonstrate the potential impact of outdated elevation on DEM performance using the Mekong Delta and updated the DEMs referenced to continuous local MSL as indicated by MDT (i.e. as of 2007) to MSL 2025. We include both land subsidence and SLR, by using simulated, non-linear extraction-induced land subsidence 60 , 61 (B1 scenario of Minderhoud et al. 61 ) and annual rates of local, delta-average SLR estimated from PSMSL data of Vung Tau tide gauge 62 and IPCC AR6 total rates of sea-level change 63 – 65 (Supplementary Table 6). For SRTM and its follow-up versions, consideration of extraction-induced land subsidence and absolute SLR since 2000 until MSL of ~ 2007 already contributes 3–4% to overall elevation assessment uncertainty, while relative SLR until 2025 increases elevation assessment uncertainty relatively by 9–16% (Fig. 5 ). The impact is less for most recent, TanDEM-X based DEMs (3–6%) until its dimension approaches that of vertical datum offset and accuracy as GLL-DTM v2 shows a smaller vertical datum offset than DEMs originally referenced to a global geoid and reveals a comparably high accuracy (Fig. 5 ; Table 2 ). Only for ASTGTM v003 which suffers from the largest inaccuracies, consideration of land elevation and sea-level change does not result in remarkable performance improvement. We deem our integration of vertical land motion and sea-level change for the Mekong delta only as a first, preliminary attempt of considering and estimating the effect of elevation dynamics on coastal land elevation datasets. Though we consider the non-linear spatio-temporal behaviour of land subsidence in the Mekong Delta, this assessment only accounts for moderate rates of simulated subsidence induced by groundwater extraction 60 , 61 but does not include contributions from other origins such as natural compaction 66 , 67 , other land-use induced shallow processes 58 , or urban differential compaction 68 . The assessment also does not consider the potential contribution of sediment aggradation to elevation gain, whose potential, however, has been shown to be only minor with respect to the current conditions in the delta 69 . Still, the assessment provides a first demonstration of the entire picture of uncertainties related to elevation data in coastal lowland contexts and how these uncertainties impact the individual DEMs relatively. To further substantiate these quantifications requires to address the limitations mentioned above and consider process- and data-driven spatial variability 40 , 58 , 61 (Fig. 4 ). Observational studies reveal that contemporary, total subsidence rates in the Mekong Delta are in places considerably larger than only previously simulated extraction-induced subsidence and delta-wide averages, up to 5–6 cm/yr 6,40,41,56 . Therefore, we expect relative SLR to have a much higher relative impact on elevation assessment uncertainty, especially in case outdated (global) DEMs are used. While some studies handle elevation in the low-lying Mekong Delta more carefully, for example by using the local Topo DEM of Minderhoud et al. 38 , more recent global DEMs, or addressing vertical datum conversion tentatively (e.g., refs. 70–73 ), a considerable number of studies still apply outdated and/or inaccurate DEMs that suffer from artefacts (e.g., refs. 74–79 ), a phenomenon we also observed for coastal hazard and impact assessments in other regions of the world. With our revisit of land elevation in the Mekong Delta we provide an exemplar showcase of a globally applicable approach to attribute uncertainties in elevation assessment for data-sparse coastal lowlands using global elevation models. Our assessment not only details previous studies on the delta’s elevation further, moreover it highlights how to handle coastal elevation and sea-level data properly: (i) by vertically referencing the DEMs to a common actual, local sea-level datum, and (ii) by conducting a thorough assessment of DEM performance that not only allows for the quantification of errors and elevation assessment uncertainties but also their attribution to DEM inaccuracy, vertical datum offset and, tentatively, non-linear impact of elevation change due to vertical land motion (e.g. extraction-induced land subsidence) and sea-level change affecting DEM actuality. This improved understanding of coastal elevation serves as a starting point to further improve impact assessments of flooding and relative SLR (e.g., refs. 38,73 ) and substantiating projections of future elevation in the Mekong Delta 61 . With all quantifications of DEM performance and vertical datum offsets based solely on open data and approaches that can be applied in any GIS environment, our demonstrated approach may set a benchmark to initiate similar assessments of DEM performance in other (data-sparse) coastal regions in the world. As elevation forms the basis for any coastal impact assessment, it needs to be handled carefully and with scrutiny, a practice which is currently often lacking in the coastal research community. Similar to sea level, land elevation is not static but dynamic and this should be considered properly in coastal impact assessment, depends on the respective DEM dataset and vertical reference used, as well as the relative SLR and surface elevation change. As new elevation datasets become available, as do vertical reference systems such as geoid models and sea-level datasets, it should be prioritised to provide end users not only with latest available elevation data but also reference them to latest vertical reference systems or sea level, depending on the intended end use. Providing the coastal research community with regular updates of most recent DEMs, already referenced to an actual sea-level datum, such as latest available MDT, will take complex vertical datum conversion steps away from non-specialist end users and thereby reduce overall uncertainties in coastal elevation and elevation-related SLR and other coastal hazard impact assessment. Methods Updating the vertical datum of local elevation data to continuous local mean sea level TopoDEM_v2 was generated, similar to Topo DEM v1 38 , using elevation data acquired between 2001 to 2003 and referenced to mean sea level (MSL) defined by Hon Dau tide gauge, which is located ca. 1400 km north of the study area 80 . To convert the geodetic heights to MSL as defined by latest available mean dynamic topography (MDT) (MDT HYBRID-CNES-CLS2022; 1993–2021 51 ), they were first corrected for SLR that occurred at Hon Dau since datum establishment which is MSL over 1950 to 2005 80,81 . Consequently, MSL for MDT 51 dates around April 2007 while it is 1992 for Hon Dau datum. Tide gauge information of Hon Dau obtained from the Permanent Sea Level Stations repository indicates 1.8 mm/yr of SLR and that sea level has risen by 0.027 m between 1992 (i.e. year for MSL of Hon Dau datum) and 2007 (i.e. year for MSL of MDT time period) 82 . Subtracting this amount from the elevation points converts them to actual MSL at Hon Dau (Fig. 7). We assume that this determined actual MSL equalises actual MSL as given by MDT 51 . However, in order to convert the local elevation data to continuous MSL along the coast of the Mekong Delta, we use the difference (= 1.173 m) between Hon Dau MSL (measured at the tide gauge) and GOCO06s geoid at the tide gauge location and assume the same difference between geoid (GOCO06s) and local MSL for the Mekong Delta. Geoid height above the WGS84 ellipsoid was obtained from the openly accessible calculation service of the International Centre for Global Earth Models (ICGEM) at a spatial resolution of 0.085 deg 83 . For each geoid, the obtained point data was interpolated into a raster using multiquadric radial basis functions which showed the best interpolation performance. To be comparable to the spatial resolution of the digital elevation models (DEMs) used in this study, the geoid rasters were resampled to 90 m × 90 m and 1000 m × 1000 m resolution using bilinear resampling. Geoid offsets were calculated based on subtraction of the resampled geoid rasters. Given that the quality of satellite altimetry measurements along the coast may be susceptible to land contamination and limitations due to geophysical and environmental corrections 84 – 86 , we do not apply an individual MDT value but apply a radius of 100 km and add the average offset of 1.173 m. Consequently, all elevation points are referenced to the GOCO06s geoid. They were converted to continuous local MSL as represented by MDT HYBRID-CNES-CLS2022 by subtracting MDT 51 and following the updated approach of Seeger et al. 11 . To avoid introducing artefacts due to the differential spatial resolution of MDT and elevation data and to enable datum conversion to MDT not only for the most coastal point elevations but also inland, we resampled the MDT dataset to a resolution of 90 m using bilinear resampling and extrapolated the data up to 500 km inland using Inverse Distance Weighting and applying a smoothing factor of 0.5. The distance threshold was chosen to provide the best compromise in covering vast coastal lowlands (thereby also including the largest delta in the world, i.e. the Ganges-Brahmaputra-Meghna Delta), while also keeping sufficient quality in face of decreasing extrapolation performance with increasing distance. The resampled and extrapolated MDT dataset was subtracted from the pre-processed elevation measurements and therewith concluded the datum conversion process to actual, continuous local MSL along the Mekong Delta coast. TopoDEM_v2 was generated through a two-phased interpolation using Empirical Bayesian Kriging with empirical transformation and exponential modelling within the Geostatistical Wizard in the ArcGIS Pro Analysis 3.1.3 environment. Firstly, the DEM for the delta plain was interpolated after excluding elevations > 10 m to ensure high DEM accuracy for the delta plain and minimise the risk of higher elevations from outcrops impacting DEM interpolation in the flat, low-lying surroundings (similar to Topo DEM v1 38 ). Secondly, the entire area was interpolated with no data exclusion. Subsequently, the extent of the delta plain was clipped from the first interpolation to replace the elevation data in the second interpolation, thereby ensuring the best delta plain representation. TopoDEM_v2 has a spatial resolution of 500 m × 500 m, justified by the data density per km 2 (ref. 87 ) and to facilitate proper DEM comparison for the delta plain, all elevations > 10 m, outcrops and water bodies were masked following the same procedure as described in Topo DEM v1 38 . Assessment of global elevation model performance We revisit land elevation in the Vietnamese Mekong Delta by extending the assessment of DEM performance, integrate a lot more elevation datasets than previous works, updating them to latest available MSL, and widening the spatial coverage to include also surrounding provinces. Global DEMs are often used to overcome the limited availability or absence of local elevation data or its coarse spatial resolution impacting the precision of follow-up applications. However, as the low accuracy of global DEMs is often not or only inadequately addressed, we assessed the quality for the majority of open and freely available DEMs in the Vietnamese Mekong Delta and surrounding provinces, quantifying both respective DEM accuracy and the impact of absent or incomplete and incorrect vertical datum conversion. In total, 11 global DEMs were used for the comparison with Topo DEM v1 and v2, namely SRTM 20 , ACE2 25–27 , MERITDEM 29 , ASTGTM v003 21,22 , AW3D30 23 , TanDEM-X 90m 88 , Copernicus DEM 28 , FABDEM v1.0 30 , CoastalDEM v2.1 31,32 , GLL-DTM v2 34 , DeltaDTM 35 . While both SRTM and MERITDEM have been addressed by studies of Minderhoud et al. 38 , the quantified uncertainties could only be attributed tentatively. More recent DEMs such as FABDEM have been applied in the Mekong Delta (e.g., ref. 73 ), however without assessing their quality. To quantify also the impacts of incomplete vertical datum conversion and difference in elevation threshold applied in DEM interpolation, we integrate two versions of the GLL-DTM v2, the first referenced to MDT (CNES-CLS13 MDT 89 ) as performed by Vernimmen and Hooijer 34 and a second following the approach of this study, as well as both DeltaDTM v1 and v1.1 35,52 . Our quality assessments included both validation with the local TopoDEM_v2 to assess and quantify spatial patterns and differences, as well as spot height comparison with elevation at locations of point measurements that fed the local DEM interpolation. Beforehand, the global DEMs were pre-processed. Single DEM tiles covering the area of interest were mosaicked and projected to UTM 48N based on the WGS84 ellipsoid. Subsequently, the vertical datums of the DEMs (i.e. EGM96 and EGM2008) were converted to MSL as defined by MDT 51 and considering to correct for respective geoid offsets. Although GLL-DTM v2 is already provided with a reference to MSL 34 , we obtained the DEM referenced to EGM96 and could therefore correct for the previously unconsidered geoid offset by applying the same workflow as for the other global DEMs (Fig. 7). All DEMs, including versions with the vertical reference as provided in the respective data repositories as well as with respect to MSL (this study), were masked by applying their respective water body masks. For ACE2, MERITDEM and CoastalDEM v2.1 and FABDEM v1.0, water bodies were excluded based on masks from the underlying source data, which is SRTM and only in case of FABDEM Copernicus. GLL-DTM v2 and DeltaDTM v1 and v1.1 did not require this processing step as the spatial resolution of GLL-DTM v2 is too coarse to adequately resolve the deltaic river and channel network while in DeltaDTM, these values are already eliminated. Largely negative and therefore likely erroneous elevation values were excluded by applying a threshold of <-7 m above the respective vertical datum 11 , 33 , 34 . As for Topo DEM v 1 and v2, we applied the same outcrop mask to the global DEMs. In addition, all global DEMs were resampled to 500 m × 500 m spatial resolution using bilinear resampling and snapping to the TopoDEMs’ extent. Only for GLL-DTM v2 with a resolution of ~ 1000 m × ~1000 m, we resampled the local TopoDEM_v2 using bilinear resampling to match the same spatial resolution as GLL-DTM v2. All these pre-processing steps enable a proper conduction of DEM comparison in terms of difference mapping with the local TopoDEM_v2 and ensure comparability. Only for extracting point elevations from each DEM and quantifying geoid offset per DEM, the respective spatial resolution of the original DEM was used. Quantification of vertical datum offsets To quantify the overall discrepancies between geoid models and actual continuous local sea-level height along the coast for the entire Mekong Delta, we used geoid information for EGM96, EGM2008 and GOCO06s obtained in form of height anomaly to the WGS84 ellipsoid from the ICGEM calculation service 83 . The respective offsets between EGM96 and EGM2008 geoids to GOCO06s were calculated and subsequently subtracted from mean dynamic topography, i.e. MDT – (EGM–GOCO06s). As the processing of the geoid rasters included bilinear resampling to 90 m spatial resolution, all statistics for the respective offsets of EGM96 and EGM2008 to MSL are given at 90 m × 90 m spatial resolution. However, in order to account for an adequate vertical datum conversion of GLL-DTM v2 and compare it to the original GLL-DTM v2 34 , which serves as an example of incomplete vertical datum conversion, we used EGM96 geoid data at 1000 m × 1000 m spatial resolution. Consequently, the resulting statistics are provided at the same resolution. Declarations Data availability The Digital Elevation Models for the Mekong Delta based on global digital elevation models and converted to local mean sea level as indicated by mean dynamic topography (following the approach of this study), are temporarily accessible here for the review process: https://drive.google.com/drive/folders/14Ut5M8KBL_mR69HazYeYl4YedNK7OOTc?usp=sharing [Link to open repository will be provided upon publication]. TopoDEM_v2 is temporarily accessible here for the review process: https://drive.google.com/drive/folders/14Ut5M8KBL_mR69HazYeYl4YedNK7OOTc?usp=sharing [Link to open repository will be provided upon publication]. Research funding Philip S.J. Minderhoud received funding from the Netherlands Science Foundation (NWO) Drowning Deltas project (NWO-Veni-TTW-2022 No. 20231). Author contributions statement K.S. and P.S.J.M. jointly conceptualised this study. K.S. designed the methodology (updating the vertical datum of local elevation data, DEM assessment), performed the analyses, created the figures and wrote the original draft. P.S.J.M. acquired funding and supervised the investigation. Both authors managed project administration, assessed the results and reviewed and edited the manuscript. Competing interests The authors declare that they have no competing interests. Acknowledgement [Reviewers will be acknowledged if they are not anonymous]. Philip S.J. Minderhoud received funding from the Netherlands Science Foundation (NWO) Drowning Deltas project (NWO-Veni-TTW-2022 No. 20231). References Shirzaei, M. et al. Measuring, modelling and projecting coastal land subsidence. Nat. Rev. Earth Environ. 2 (1), 40–58. https://doi.org/10.1038/s43017-020-00115-x (2021). Ericson, J. P., Vörösmarty, C. J., Dingman, S. L., Ward, L. G. & Meybeck, M. Effective sea-level rise and deltas: Causes of change and human dimension implications. Global Planet. Change . 50 (1–2), 63–82. https://doi.org/10.1016/j.gloplacha.2005.07.004 (2006). Syvitski, J. P. M. et al. Sinking deltas due to human activities. Nat. Geosci. 2 , 681–686. https://doi.org/10.1038/ngeo629 (2009). Nicholls, R. J. et al. A global analysis of subsidence, relative sea-level change and coastal flood exposure. Nat. Clim. Change . 11 (4), 338–342. https://doi.org/10.1038/s41558-021-00993-z (2021). Chan, F. K. S. et al. Building resilience in Asian mega-deltas. Nat. Rev. Earth Environ. 5 (7), 522–537. https://doi.org/10.1038/s43017-024-00561-x (2024). Ohenhen, L. et al. The Global Threat of Sinking Deltas. Research Square (Preprint Version 1, 07 April 2025), (2025). https://doi.org/10.21203/rs.3.rs-6223150/v1 Edmonds, D. A., Caldwell, R. L., Brondizio, E. S. & Siani, S. M. O. Coastal flooding will disproportionately impact people on river deltas. Nat. Commun. 11 , 4741. https://doi.org/10.1038/s41467-020-18531-4 (2020). Renaud, F. G. et al. Tipping from the Holocene to the Anthropocene: How threatened are major world deltas? Curr. Opin. Environ. Sustain. 5 (6), 644–654. https://doi.org/10.1016/j.cosust.2013.11.007 (2013). Kulp, S. & Strauss, B. H. Global DEM errors underpredict coastal vulnerability to sea level rise and flooding. Front. Earth Sci. 4 , 36. https://doi.org/10.3389/feart.2016.00036 (2016). Gesch, D. Assessing global elevation models for mapping the low elevation coastal zone. Geomorphometry , (2023). http://dx.doi.org/10.5281/zenodo.8011577 Seeger, K. et al. Nay Win Oo & Brill, D. Assessing land elevation in the Ayeyarwady Delta (Myanmar) and its relevance for studying sea level rise and delta flooding. Hydrol. Earth Syst. Sci. 27 (11), 2257–2281. https://doi.org/10.5194/hess-27-2257-2023 (2023). Hauser, L. et al. A. A scoping study on coastal vulnerability to relative sea-level rise in the Gulf of Guinea. AFD Res. Papers . 283 , 1–42 (2023). https://www.afd.fr/en/ressources/scoping-study-coastal-vulnerability-relative-sealevel-rise-gulf-guinea Wilson, J. P. Digital terrain modeling. Geomorphology 137 (1), 107–121. https://doi.org/10.1016/j.geomorph.2011.03.012 (2012). Guth, P. L. et al. Digital elevation models: Terminology and definitions. Remote Sens. 13 (18), 3581. https://doi.org/10.3390/rs13183581 (2021). Hodgson, M. E. & Bresnahan, P. Accuracy of airborne lidar-derived elevation. Photogramm Eng. Remote Sens. 70 (3), 331–339. https://doi.org/10.14358/PERS.70.3.331 (2004). Buffington, K. J., Dugger, B. D., Thorne, K. M. & Takekawa, J. Y. Statistical correction of lidar-derived digital elevation models with multispectral airborne imagery in tidal marshes. Remote Sens. Environ. 186 , 616–625. https://doi.org/10.1016/j.rse.2016.09.020 (2016). Ren, H. C. et al. Study on analysis from sources of error for Airborne LIDAR. IOP Conf. Ser. : Earth Environ. Sci. 46 (1), 012030. http://doi.org/10.1088/1755-1315/46/1/012030 (2016). Almar, R. et al. Coastal zone changes in West Africa: Challenges and opportunities for satellite earth observations. Surv. Geophys. 44 (1), 249–275. https://doi.org/10.1007/s10712-022-09721-4 (2023). Becker, M. et al. Coastal flooding in Asian Megadeltas: Recent advances, persistent challenges, and call for actions admidst local and global changes. Rev. Geophys. 62(4), e (2024). RG00846 https://doi.org/10.1029/2024RG000846 (2024). Farr, T. G. et al. The Shuttle Radar Topography Mission. Rev. Geophys. 45(2), RG (2004). https://doi.org/10.1029/2005RG000183 (2007). Abrams, M., Bailey, B., Tsu, H. & Hato, M. The ASTER Global DEM. Photogramm Eng. Remote Sens. 76 (4), 344–348 (2010). Tachikawa, T. et al. ASTER Global Elevation Model Version 2 – Summary of Validation Results. National Aeronautics and Space Administration (NASA) Land Processes Distributed Active Archive Center, and Joint Japan-US ASTER Science Team, 26 pp., (2011). https://lpdaac.usgs.gov/documents/220/Summary_GDEM2_validation_report_final.pdf Tadono, T. et al. Generation of the 30 m-mesh global digital surface model by ALOS Prism. Int. Arch. Photogramm Remote Sens. Spat. Inf. Sci. XLI-B4 , 157–162. https://doi.org/10.5194/isprs-archives-XLI-B4-157-2016 (2016). Wessel, B. TanDEM-X Ground Segment – DEM Products Specification Document. EOC, DLR, Oberpfaffenhofen, Germany, Public Document TD-GS-PS-0021, Issue 3.2, (2018). https://tandemx-science.dlr.de/ Smith, R. G. & Berry, P. A. M. ACE2: Global Digital Elevation Model User Guide. 22 pp. (2009). Berry, P. A. M., Smith, R. G. & Benveniste, J. ACE2: The New Global Digital Elevation Model. In: Gravity, Geoid and Earth Observation, International Association of Geodesy Symposia 135, edited by Mertiklas, S.P., Springer, Berlin, Heidelberg, Germany, 231–237, (2010). https://doi.org/10.1007/978-3-642-10634-7_30 Berry, P. A. M., Smith, R., Benveniste, J., Altimeter Corrected & Elevations Version 2 (ACE2) [dataset] (Palisades, 2019). https://doi.org/10.7927/H40G3H78 NASA Socioeconomic Data and Applications Center (SEDAC). Airbus Defence and Space: Copernicus Digital Elevation Model Product Handbook Version 3.0. Airbus, 38 pp. (2020). Yamazaki, D. et al. D. A high-accuracy map of global terrain elevations. Geophys. Res. Lett. 44 (11), 5844–5853. https://doi.org/10.1002/2017GL072874 (2017). Hawker, L. et al. A 30 m global map of elevation with forests and buildings removed. Environ. Res. Lett. 17 (2), 024016. https://doi.org/10.1088/1748-9326/ac4d4f (2022). Kulp, S. A. & Strauss, B. H. CoastalDEM: A global coastal digital elevation model improved from SRTM using a neural network. Remote Sens. Environ. 206 (1), 231–239. https://doi.org/10.1016/j.rse.2017.12.026 (2018). Kulp, S. A. & Strauss, B. H. CoastalDEM v2.1: A high-accuracy and high-resolution global coastal elevation model trained on ICESat-2 satellite lidar. Climate Central Scientific Report, 17 pp., (2021). https://assets.ctfassets.net/cxgxgstp8r5d/3f1LzJSnp7ZjFD4loDYnrA/71eaba2b8f8d642dd9a7e6581dce0c66/CoastalDEM_2.1_Scientific_Report_.pdf Vernimmen, R., Hooijer, A. & Pronk, M. New ICESat-2 Satellite LiDAR Data Allow First Global Lowland DTM Suitable for Accurate Coastal Flood Risk Assessment. Remote Sens. 12 (17), 2827. https://doi.org/10.3390/rs12172827 (2020). Vernimmen, R. & Hooijer, A. New LiDAR-based elevation model shows greatest increase in global coastal exposure to flooding to be caused by early‐stage sea‐level rise. Earth's Future . 11 (1). https://doi.org/10.1029/2022EF002880 (2023). e2022EF002880, DOI. Pronk, M. et al. DeltaDTM: A global coastal digital terrain model. Sci. Data . 11 (1), 273. https://doi.org/10.1038/s41597-024-03091-9 (2024a). Uhe, P. et al. FathomDEM: an improved global terrain map using a hybrid vision transformer model. Environ. Res. Lett. 20 (3), 034002. https://doi.org/10.1088/1748-9326/ada972 (2025). Üstün, A., Abbak, R. A. & Zeray Östürk, E. Height biases of SRTM DEM related to EGM96: from a global perspective to regional practice. Surv. Rev. 50 , 26–35. https://doi.org/10.1080/00396265.2016.1218159 (2016). Minderhoud, P. S. J., Coumou, L., Erkens, G., Middelkoop, H. & Stouthamer, E. Mekong delta much lower than previously assumed in sea-level rise impact assessments. Nat. Commun. 10 , 3847. https://doi.org/10.1038/s41467-019-11602-1 (2019a). Fusami, A. A., Edan, J. D. & Takana, A. Local orthometric height based on a combination of GPS-derived ellipsoidal height and geoid model: A review paper. J. Geodetic Sci. 13 (1), 20220158. https://doi.org/10.1515/jogs-2022-0158 (2023). Minderhoud, P. S. J., Hlavacova, I., Kolomaznik, J. & Neussner, O. Towards unraveling total subsidence of a mega-delta–the potential of new PS InSAR data for the Mekong delta. Proc. Int. Assoc. Hydrol. Sci. 382, 327–332, (2020a). https://doi.org/10.5194/piahs-382-327-2020 Dörr, N., Schenk, A. & Hinz, S. Land subsidence in the Mekong delta derived from advanced persistent scatterer interferometry with an infrastructural reference network. IEEE J. Sel. Top. Appl. Earth Obs Remote Sens. 17 , 12077–12091 (2024). https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10578013 Copernicus Emergency Management Service Risk & Recovery Mapping Non-standardised products (FLEX). Lot 1 Final Report. EMSN-091: Assessing changes in subsidence rates in the low Pampanga river basin and Manila area, Philippines. (2021). https://mapping.emergency.copernicus.eu/activations/EMSN091/ Schumann, G. et al. Comparison of remotely sensed water stages from LiDAR, topographic contours and SRTM. ISPRS J. Photogramm Remote Sens. 63 (3), 283–296. https://doi.org/10.1016/j.isprsjprs.2007.09.004 (2008). Cook, A. & Merwade, V. Effect of topographic data, geometric configuration and modeling approach on flood inundation mapping. J. Hydrol. 377 (1–2), 131–142. https://doi.org/10.1016/j.jhydrol.2009.08.015 (2009). Hawker, L. et al. Implications of simulating global digital elevation models for flood inundation studies. Water Resour. Res. 54 (10), 7910–7928. https://doi.org/10.1029/2018WR023279 (2018a). Azizian, A. & Brocca, L. Determining the best remotely sensed DEM for flood inundation mapping in data sparse regions. Int. J. Remote Sens. 41 (5), 1884–1906. https://doi.org/10.1080/01431161.2019.1677968 (2020). Xu, K. et al. The importance of digital elevation model selection in flood simulation and a proposed method to reduce DEM errors: a case study in Shanghai. Int. J. Disaster Risk Sci. 12 , 890–902. https://doi.org/10.1007/s13753-021-00377-z (2021). Almar, R. et al. A global analysis of extreme coastal water levels with implications for potential coastal overtopping. Nat. Commun. 12 (1), 3775. https://doi.org/10.1038/s41467-021-24008-9 (2021). Wöppelmann, G. & Pirazzoli, P. Tide gauges (ML Schwartz (ed.), Encyclopedia of Coastal Sciences, 2005). Minderhoud, P. S. J., Coumou, L., Erkens, G., Middelkoop, H. & Stouthamer, E. Digital elevation model of the Vietnamese Mekong delta based on elevation points from a national topographical map [dataset]. PANGAEA (2019b). https://doi.org/10.1594/PANGAEA.902136 Jousset, S. et al. New Global Mean Dynamic Topography CNES-CLS-22 Combining Drifters, Hydrological Profiles and High Frequency Radar Data. ESS Open Archive , (2023). https://doi.org/10.22541/essoar.170158328.85804859/v2 Pronk, M. DeltaDTM v1.1: A global coastal digital terrain model (Version 4) [dataset]. 4TU.ResearchData (2024b). https://doi.org/10.4121/21997565.V4 Gesch, D. B. Best practices for elevation-based assessments of sea-level rise and coastal flooding exposure. Front. Earth Sci. 6 , 230. https://doi.org/10.3389/feart.2018.00230 (2018). Seeger, K., Minderhoud, P. S. J., Hauser, L. T., Teatini, P. & Woillez, M. N. How to assess coastal flood risk in data-sparse coastal lowlands? Accurate information on land elevation is key. Question Dev. 71 , 1–4 (2024). https://www.afd.fr/en/ressources/how-assess-coastal-flood-risk-data-sparse-coastal-lowlands-accurate-information-land-elevation-key Avornyo, S. Y. et al. The contribution of coastal land subsidence to potential sea-level rise impact in data-sparse settings: The case of Ghana’s Volta delta. Quat Sci. Adv. 14 , 100175. https://doi.org/10.1016/j.qsa.2024.100175 (2024). Erban, L. E., Gorelick, S. M. & Zebker, H. A. Groundwater extraction, land subsidence, and sea-level rise in the Mekong Delta, Vietnam. Environ. Res. Lett. 9 (8), 084010. https://doi.org/10.1088/1748-9326/9/8/084010 (2014). Herrera-García, G. et al. Mapping the global threat of land subsidence. Science 371 (6524), 34–36. https://doi.org/10.1126/science.abb8549 (2021). Minderhoud, P. S. J. et al. The relation between land use and subsidence in the Vietnamese Mekong delta. Sci. Total Environ. 634 , 715–726. https://doi.org/10.1016/j.scitotenv.2018.03.372 (2018). Hawker, L., Bates, P., Neal, J. & Rougier, J. Perspectives on digital elevation model (DEM) simulation for flood modeling in the absence of a high-accuracy open access global DEM. Front. Earth Sci. 6 , 233. https://doi.org/10.3389/feart.2018.00233 (2018b). Minderhoud, P. S. J. et al. Impacts of 25 years of groundwater extraction on subsidence in the Mekong delta, Vietnam. Environ. Res. Lett. 12 (6), 064006. https://doi.org/10.1088/1748-9326/aa7146 (2017). Minderhoud, P. S. J., Middelkoop, H., Erkens, G. & Stouthamer, E. Groundwater extraction may drown mega-delta: projections of extraction-induced subsidence and elevation of the Mekong delta for the 21st century. Environ. Res. Commun. 2 (1), 011005. https://doi.org/10.1088/2515-7620/ab5e21 (2020b). Permanent Service for Mean Sea Level (PSMSL). Permanent Service for Mean Sea Level – Tide Gauge Data: Vung Tau, (2025a). https://psmsl.org/data/obtaining/stations/1495.php Fox-Kemper, B. et al. Cryosphere and Sea Level Change. In: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, edited by Masson-Delmotte, V., Zhai, P., Pirani, A., Connors, S. L., Péan, C., Berger, S., Caud, N., Chen, Y., Goldfarb, L., Gomis, M. I., Huang, M., Leitzell, K., Lonnoy, E., Matthews, J. B. R.,, and Cambridge University Press, Cambridge, 9-1-9-257 (2021). Garner, G. G. et al. IPCC AR6 Sea-Level Rise Projections, Version 20210809, PO.DAAC, CA, USA, Dataset, (2021). https://podaac.jpl.nasa.gov/announcements/2021-08-09-Sea-level-projections-from-the-IPCC-6th-Assessment-Report Garner, G. G. et al. Framework for Assessing Changes To Sea-level (FACTS), Geoscientific Model Development, Zenodo https://zenodo.org/record/6419954 (2022). Zoccarato, C., Minderhoud, P. S. J. & Teatini, P. The role of sedimentation and natural compaction in a prograding delta: insights from the mega Mekong delta, Vietnam. Sci. Rep. 8 (1), 11437. https://doi.org/10.1038/s41598-018-29734-7 (2018). Baldan, S., Minderhoud, P. S. J., Xotta, R., Zoccarato, C. & Teatini, P. Data-driven 3D modelling of long‐term Holocene delta evolution and sediment compaction: The Mekong Delta. Earth Surf. Processes Land. 50 (1), e6046. https://doi.org/10.1002/esp.6046 (2025). de Wit, K. et al. Identifying Causes of Urban Differential Subsidence in the Vietnamese Mekong Delta by Combining InSAR and Field Observations. Remote Sens. 13 (2), 189. https://doi.org/10.3390/rs13020189 (2021). Dunn, F. E. & Minderhoud, P. S. J. Sedimentation strategies provide effective but limited mitigation of relative sea-level rise in the Mekong delta. Commun. Earth Environ. 3(1), 2, (2022). https://doi.org/10.1038/s43247-021-00331-3 Eslami, S. et al. Dynamics of salt intrusion in the Mekong Delta: results of field observations and integrated coastal–inland modelling. Earth Surf. Dyn. 9 (4), 953–976. https://doi.org/10.5194/esurf-9-953-2021 (2021). Dang, A. T., Reid, M. & Kumar, L. Assessing potential impacts of sea level rise on mangrove ecosystems in the Mekong Delta, Vietnam. Reg. Environ. Change . 22 (2), 70. https://doi.org/10.1007/s10113-022-01925-z (2022). Le, H. A., Nguyen, T., Gratiot, N., Deleersnijder, E. & Soares-Frazão, S. The multi-channel system of the Vietnamese Mekong Delta: impacts on the flow dynamics under relative sea-level rise scenarios. Water 15 (20), 3597. https://doi.org/10.3390/w15203597 (2023). Wood, M. et al. J. M. Risk of compound flooding substantially increases in the future Mekong River delta. Nat. Hazards Earth Syst. Sci. 24 (10), 3627–3649. https://doi.org/10.5194/nhess-24-3627-2024 (2024). Dang, T. D., Cochrane, T. A., Arias, M. E. & Tri, V. P. D. Future hydrological alterations in the Mekong Delta under the impact of water resources development, land subsidence and sea level rise. J. Hydrol. : Reg. Stud. 15 , 119–133. https://doi.org/10.1016/j.ejrh.2017.12.002 (2018). Tanaka, K., Fujihara, Y., Hoshikawa, K. & Fujii, H. Development of a flood water level estimation method using satellite images and a digital elevation model for the Mekong floodplain. Hydrol. Sci. J. 64 (2), 241–253. https://doi.org/10.1080/02626667.2019.1578463 (2019). Thanh, V. Q. et al. T. P. Flooding in the Mekong Delta: the impact of dyke systems on downstream hydrodynamics. Hydrol. Earth Syst. Sci. 24 (1), 189–212. https://doi.org/10.5194/hess-24-189-2020 (2020). Wu, S. & Lei, Y. Multiscale Flood Disaster Risk Assessment in the Lancang-Mekong River Basin: A Focus on Watershed and Community Levels. Atmosphere 14 (4), 657. https://doi.org/10.3390/atmos14040657 (2023). Chen, A. et al. Impact of tropical cyclone precipitation on fluvial discharge in the Lancang–Mekong River Basin. Geophys. Res. Lett. 52(8), e (2024). GL113199 https://doi.org/10.1029/2024GL113199 (2024). Yun, X., Song, J., Wang, J. & Bao, H. Modelling to assess the suitability of hydrological-hydrodynamic model under the hydropower development impact in the Lancang-Mekong River basin. J. Hydrol. 637 , 131393. https://doi.org/10.1029/2024GL113199 (2024). Vu, D. T., Bruinsma, S., Bonvalot, S., Remy, D. & Vergos, G. S. A. Quasigeoid-derived transformation model accounting for land subsidence in the Mekong delta towards height system unification in Vietnam. Remote Sens. 12 (5), 817. https://doi.org/10.3390/rs12050817 (2020). Vu, D. T., Bruinsma, S., Bonvalot, S., Bui, L. K. & Balmino, G. Determination of the geopotential value on the permanent GNSS stations in Vietnam based on the Geodetic Boundary Value Problem approach. Geophys. J. Int. 226 (2), 1206–1219. https://doi.org/10.1093/gji/ggab166 (2021). Permanent Service for Mean Sea Level (PSMSL). Permanent Service for Mean Sea Level – Tide Gauge Data: Hon Dau, (2025b). https://psmsl.org/data/obtaining/stations/841.php Ince, E. S. et al. ICGEM – 15 years of successful collection and distribution of global gravitational models, associated services and future plans. Earth Syst. Sci. Data . 11 , 647–674. https://doi.org/10.5194/essd-11-647-2019 (2019). Vignudelli, S., Kostianoy, A. G., Cipollini, P. & Benveniste, J. Coastal altimetry (Springer, 2011). https://doi.org/10.1007/978-3-642-12796-0 Vignudelli, S. et al. Satellite altimetry measurements of sea level in the coastal zone. Surv. Geophys. 40 , 1319–1349. https://doi.org/10.1007/s10712-019-09569-1 (2019). Birol, F. et al. Understanding uncertainties in the satellite altimeter measurement of coastal sea level: insights from a round-robin analysis. Ocean. Sci. 21 (1), 133–150. https://doi.org/10.5194/os-21-133-2025 (2025). Aguilar, F. J., Aguilar, M. A., Agüera, F. & Sánchez, J. The accuracy of grid digital elevation models linearly constructed from scattered sample data. Int. J. Geogr. Inf. Sci. 20 (2), 169–192. https://doi.org/10.1080/13658810500399670 (2006). Wessel, B. TanDEM-X ground segment–DEM products specification document. (2013). Rio, M. H., Mulet, S. & Picot, N. Beyond GOCE for the ocean circulation estimate: Synergetic use of altimetry, gravimetry, and in situ data provides new insight into geostrophic and Ekman currents. Geophys. Res. Lett. 41 (24), 8918–8925. https://doi.org/10.1002/2014GL061773 (2014). Additional Declarations No competing interests reported. Supplementary Files SciRepManuscriptSupplementaryInformation.pdf Cite Share Download PDF Status: Published Journal Publication published 04 Feb, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 19 Nov, 2025 Reviews received at journal 18 Nov, 2025 Reviews received at journal 07 Nov, 2025 Reviewers agreed at journal 22 Oct, 2025 Reviewers agreed at journal 17 Oct, 2025 Reviewers invited by journal 17 Oct, 2025 Editor assigned by journal 17 Oct, 2025 Editor invited by journal 17 Oct, 2025 Submission checks completed at journal 08 Oct, 2025 First submitted to journal 08 Oct, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7706762","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":535711177,"identity":"3908ffd6-176a-4c89-a3fb-1395b686bdc2","order_by":0,"name":"Katharina Seeger","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+0lEQVRIiWNgGAWjYBACCQYGNggFA/wQygKPFmY0LZINMLPwa0ECBgcIaJFsP3/swc89Fnn8DewPH3z4ZZNvfCPH7APjDtxapHmS2Q17nkkUSxzgMTac2Zdmue1GjvEMxjO4tcgxJLNJ8ByQSGw4wMMmzdtz2MDsdo4xA2MbHi38j9kk/wC1zD/A/vz3357/BsazCWiRlkhmkwbZsuEAgxkzw48DBgbSBLRIznhsJi0D1LLxMI+xZG9DsoHE/WfFDIl4tEicT3wm+eZAXeK84+0PP/z4Y2fA33N4M8PHNhucWhCAGYgZ26CcBCI0QMEf4pWOglEwCkbByAEA00hOFAGXa7MAAAAASUVORK5CYII=","orcid":"","institution":"Wageningen University \u0026 Research","correspondingAuthor":true,"prefix":"","firstName":"Katharina","middleName":"","lastName":"Seeger","suffix":""},{"id":535711179,"identity":"3c9201ac-f05a-4e78-bab0-0e1b85cfb689","order_by":1,"name":"Philip S.J. Minderhoud","email":"","orcid":"","institution":"Wageningen University \u0026 Research","correspondingAuthor":false,"prefix":"","firstName":"Philip","middleName":"S.J.","lastName":"Minderhoud","suffix":""}],"badges":[],"createdAt":"2025-09-24 20:08:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7706762/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7706762/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-026-38315-y","type":"published","date":"2026-02-04T15:57:22+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":94823907,"identity":"2f869f3d-3b38-47f9-a437-411b735dabda","added_by":"auto","created_at":"2025-10-31 06:48:16","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":6594471,"visible":true,"origin":"","legend":"","description":"","filename":"SciRepManuscriptrevision.docx","url":"https://assets-eu.researchsquare.com/files/rs-7706762/v1/8903b5a77bc0dacf4e6819c0.docx"},{"id":94763345,"identity":"ff147b3c-3ccd-4ca0-8ced-2544a41d7bfa","added_by":"auto","created_at":"2025-10-30 12:20:40","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":7212,"visible":true,"origin":"","legend":"","description":"","filename":"7109644f65494f9b98781155a0bb9a4d.json","url":"https://assets-eu.researchsquare.com/files/rs-7706762/v1/d740b9de3c923c2a7c471077.json"},{"id":94824674,"identity":"3c59ec3c-866d-498d-ade8-18aef3dbb8e8","added_by":"auto","created_at":"2025-10-31 06:49:12","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":4154923,"visible":true,"origin":"","legend":"","description":"","filename":"SciRepManuscriptSupplementaryInformation.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7706762/v1/f0e1cfff9e1a93dc57de01b0.pdf"},{"id":94824773,"identity":"bdee9191-7174-45fd-88ec-a60d0ca3132b","added_by":"auto","created_at":"2025-10-31 06:49:19","extension":"xml","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":241022,"visible":true,"origin":"","legend":"","description":"","filename":"7109644f65494f9b98781155a0bb9a4d1enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-7706762/v1/4879063bd7acb22446529d09.xml"},{"id":94824925,"identity":"39b6cc40-e5fe-4a79-8443-a700abd9dbd2","added_by":"auto","created_at":"2025-10-31 06:49:34","extension":"png","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1425198,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7706762/v1/a8cf750cde655b72b37d7ac9.png"},{"id":94763354,"identity":"c1da97aa-7fd0-4e3b-93c9-c72a7c9d33ed","added_by":"auto","created_at":"2025-10-30 12:20:40","extension":"png","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":497534,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7706762/v1/77542fcd1faa3f1bb5ae7f45.png"},{"id":94823901,"identity":"5d9386ee-b63f-4d0a-8f85-409f881041a2","added_by":"auto","created_at":"2025-10-31 06:48:16","extension":"png","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":2116345,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7706762/v1/bf0312a0f52d369ac034d2ea.png"},{"id":94763352,"identity":"119e2bf9-e37f-4623-a2a2-7e635325f2b1","added_by":"auto","created_at":"2025-10-30 12:20:40","extension":"png","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1962670,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7706762/v1/bad987e7f7ef67af8340ad29.png"},{"id":94763357,"identity":"87ac6c1c-a27e-4fbd-ba57-dc556bec0822","added_by":"auto","created_at":"2025-10-30 12:20:40","extension":"png","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":160069,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7706762/v1/1cf0c4d282360369c88522be.png"},{"id":94823613,"identity":"da291887-79c5-4410-93b0-9a06d27b9361","added_by":"auto","created_at":"2025-10-31 06:47:39","extension":"png","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":172998,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7706762/v1/05abfe6760951266704ab089.png"},{"id":94763363,"identity":"721b15ad-dabd-476e-874d-44ae8e186d26","added_by":"auto","created_at":"2025-10-30 12:20:41","extension":"png","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":153173,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-7706762/v1/fe7ee898a174905e2b87cf15.png"},{"id":94763362,"identity":"b303d6b9-d0a6-421d-8627-a6d886f6533f","added_by":"auto","created_at":"2025-10-30 12:20:41","extension":"png","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":196084,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7706762/v1/d40a41655e40748c63addac1.png"},{"id":94763368,"identity":"9ae70c01-3b1c-470f-b864-8217b5ebb370","added_by":"auto","created_at":"2025-10-30 12:20:41","extension":"png","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":109450,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7706762/v1/aad996b9c17a5a6b31ce038e.png"},{"id":94763366,"identity":"22b1f6d8-faa2-458c-904c-1e629a86fa33","added_by":"auto","created_at":"2025-10-30 12:20:41","extension":"png","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":512040,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7706762/v1/4fe27774d65792dbe47db27e.png"},{"id":94763371,"identity":"64d4dc3b-d472-47b2-9924-fda56137d4d0","added_by":"auto","created_at":"2025-10-30 12:20:41","extension":"png","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":457638,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7706762/v1/732d8f2542c8aabf3699aca9.png"},{"id":94763360,"identity":"380bd826-1ac9-4749-ac0b-3302db53339c","added_by":"auto","created_at":"2025-10-30 12:20:41","extension":"png","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":61790,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7706762/v1/7eacf5cbe0c865deae289277.png"},{"id":94763359,"identity":"e7251cdd-dc57-4327-914e-748755f60e95","added_by":"auto","created_at":"2025-10-30 12:20:41","extension":"png","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":59165,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7706762/v1/e05404fa8ac382a279a25de2.png"},{"id":94824510,"identity":"c496d224-a9b5-4f2f-96dc-4347323b010b","added_by":"auto","created_at":"2025-10-31 06:49:03","extension":"png","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":57101,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-7706762/v1/44ef52641a755e2037062749.png"},{"id":94824001,"identity":"475b10ee-777c-4626-8800-76fcfe322694","added_by":"auto","created_at":"2025-10-31 06:48:22","extension":"xml","order_by":18,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":235870,"visible":true,"origin":"","legend":"","description":"","filename":"7109644f65494f9b98781155a0bb9a4d1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7706762/v1/62934186ea24f04916c37a00.xml"},{"id":94823894,"identity":"c93c9fe9-452c-4285-aa86-9ff413d5027b","added_by":"auto","created_at":"2025-10-31 06:48:15","extension":"html","order_by":19,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":255195,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7706762/v1/0caf3b755f70a668b7149418.html"},{"id":94824214,"identity":"ef1096ce-c218-4bbe-b99d-01f01914f764","added_by":"auto","created_at":"2025-10-31 06:48:40","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1002786,"visible":true,"origin":"","legend":"\u003cp\u003eLocal elevation model of the Vietnamese Mekong Delta, updated from Minderhoud et al.\u003csup\u003e38\u003c/sup\u003e by referencing to actual local, continuous mean sea level indicated by mean dynamic topography\u003csup\u003e51\u003c/sup\u003e.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-7706762/v1/4334e4566a7820b37b521f19.png"},{"id":94763342,"identity":"999244b1-ce39-41e8-8946-d68d23138ebf","added_by":"auto","created_at":"2025-10-30 12:20:40","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":675049,"visible":true,"origin":"","legend":"\u003cp\u003eOffsets between commonly used geoid models (EGM96 (a); EGM2008 (b)) as well as an example of incomplete datum conversion (c) and local mean sea level as indicated by mean dynamic topography of Jousset et al.\u003csup\u003e51\u003c/sup\u003e in the Vietnamese Mekong Delta. The residual error arising from incomplete datum conversion was calculated for the example of GLL-DTM v2\u003csup\u003e34\u003c/sup\u003e.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-7706762/v1/8fd64c936d65ccd204350fb5.png"},{"id":94763344,"identity":"c4f0b9af-557d-4c30-b5c2-fd7aadc98a12","added_by":"auto","created_at":"2025-10-30 12:20:40","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":594278,"visible":true,"origin":"","legend":"\u003cp\u003eDigital elevation models included in this study, all referenced to actual, local continuous mean sea level as indicated by mean dynamic topography\u003csup\u003e51\u003c/sup\u003e.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-7706762/v1/e2106936e146a2d948fd4d7b.png"},{"id":94763347,"identity":"18ee919f-ddcd-4151-9f1a-b30da86b9aa8","added_by":"auto","created_at":"2025-10-30 12:20:40","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":555744,"visible":true,"origin":"","legend":"\u003cp\u003eHeight residuals of DEMs referenced to mean sea level compared to TopoDEM_v2.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-7706762/v1/06cbb381f8425d89da824790.png"},{"id":94824796,"identity":"79d7168b-c0b5-47f9-9d44-c4bfd0fbf0c1","added_by":"auto","created_at":"2025-10-31 06:49:20","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":519165,"visible":true,"origin":"","legend":"\u003cp\u003eQuantification and attribution of uncertainties associated with global DEMs in their performance to correctly quantify the elevation of the Vietnamese Mekong Delta. Quantifications were conducted relative to root mean square error (RMSE) of DEMs in comparison to TopoDEM_v2 based on their original vertical reference. Sources of elevation assessment uncertainty include vertical datum offset, inaccuracy resulting from DEM acquisition and processing, as well as time since DEM generation over which land subsidence and sea-level change have resulted in elevation change. (a) Relative uncertainties for DEMs referenced to mean sea level as indicated by mean dynamic topography (MDT), which provide the local sea level average over the period 1993–07/2021, corresponding to April 2007. (b) Relative uncertainties for DEMs referenced to mean sea level as indicated by mean dynamic topography (MDT), including land subsidence and sea-level rise since April 2007. For these tentative calculations, simulated, non-linear, extraction-induced land subsidence\u003csup\u003e60,61\u003c/sup\u003e, tide-gauge observations\u003csup\u003e62\u003c/sup\u003e and IPCC-projections of sea-level rise\u003csup\u003e63–65\u003c/sup\u003e were used.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-7706762/v1/6b9bbe3412e0192914785083.png"},{"id":94763349,"identity":"0c89e263-8479-49b7-a217-80dc6b9ef029","added_by":"auto","created_at":"2025-10-30 12:20:40","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":553862,"visible":true,"origin":"","legend":"\u003cp\u003eTimeline for data acquisition and publication of global digital elevation models (DEMs) investigated in this study as well as their original vertical reference systems and updated vertical reference to local mean sea level (MSL) as indicated by mean dynamic topography (MDT). Note that although EGM2008 geoid provides a slightly more recent datum than the average of latest available continuous local sea-level information, which MDT\u003csup\u003e51\u003c/sup\u003e indicates as it dates around April 2007, the geoid does not reflect local sea level well in many data-sparse coastal lowlands.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-7706762/v1/29943bf31f95850c35da883f.png"},{"id":94763351,"identity":"189a4857-a487-4a8a-847a-ac8e5682fd96","added_by":"auto","created_at":"2025-10-30 12:20:40","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":489267,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 6. \u003c/strong\u003eVertical datums of elevation and sea-level datasets used for the example of the Mekong Delta together with steps of datum conversion for local (1–3) and global elevation data (a–b) and sources of uncertainty associated with global elevation data (A–E). To convert local geodetic heights from mean sea level (MSL) of Hon Dau datum, which is MSL over 1950 to 2005\u003csup\u003e80,81\u003c/sup\u003e, to actual, continuous local MSL as defined by latest available mean dynamic topography (MDT) (1993–2021)\u003csup\u003e 51\u003c/sup\u003e, sea-level rise at Hon Dau tide gauge between 1992 (i.e. year for MSL of Hon Dau datum) and 2007 (i.e. year for MSL of MDT time period) had to be considered (step 1)\u003csup\u003e82\u003c/sup\u003e. Subsequent steps of converting the local geodetic heights to MDT included the determination of offset to the global GOCO06s geoid (step 2) and subtraction of MDT data (step 3). To convert global digital elevation models (DEMs) from global geoid models such as EGM96 or EGM2008 to actual, continuous local MSL as defined by MDT, they were first converted to GOCO06s (step a) and then corrected to MDT (step b). Arrows indicate the relations between vertical reference systems that are relevant to convert elevation and sea-level datasets (solid and short-dashed lines). This figure also illustrates the various sources of uncertainty associated with global DEMs in coastal lowlands, which are vertical datum offset (A), inaccuracy (B), and actuality in terms of impacts due to land elevation change (C) and sea-level change (D).\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-7706762/v1/72c51ef22234d34c21b2d677.png"},{"id":102233977,"identity":"a01949a5-3efd-476f-ac68-105a6b811b07","added_by":"auto","created_at":"2026-02-09 16:01:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5682059,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7706762/v1/eafdb18d-477f-4cf7-acd9-261ee032a8bc.pdf"},{"id":94763350,"identity":"9145751d-9a8e-4811-8b9a-e1ebc97b702f","added_by":"auto","created_at":"2025-10-30 12:20:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":4154923,"visible":true,"origin":"","legend":"","description":"","filename":"SciRepManuscriptSupplementaryInformation.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7706762/v1/541fca75a111f0cb24dca5a6.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Attributing uncertainties in elevation assessments for data-sparse coastal lowlands using global elevation models: A globally applicable approach showcasing the Vietnamese Mekong Delta","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCoastal lowlands face an increasing risk of sea-level rise (SLR) as global, climate-induced SLR is often accelerated by coastal subsidence\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Especially densely populated river deltas are prone to suffer from magnitudes of land subsidence that outpace dimensions of absolute sea-level change and together with sediment starvation due to sediment trapping in upstream dams culminate in elevation loss (e.g., refs.\u003csup\u003e\u003cspan additionalcitationids=\"CR2 CR3 CR4 CR5\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e). Consequently, these lowlands are also increasingly exposed to other coastal hazards such as storm surge flooding (e.g., ref.\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e). The elevation of coastal lowlands above sea level is a critical factor in safeguarding these globally important landscapes from temporary and permanent inundation, salinity intrusion and other cascading effects on the environment and society, affecting for example socio-economic productivity (see also ref.\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e). Land elevation data, mostly in form of digital elevation models (DEMs), are necessary for any quantitative coastal hazard impact assessment or projection, and the reliability of these assessments is strongly determined by the quality of the underlying elevation data. Therefore, thorough DEM accuracy assessments are needed to ensure the reliability of flood risk and SLR impact evaluations or at least to quantify ranges of uncertainty, both in terms of area, population and assets at risk (e.g., refs.\u003csup\u003e\u003cspan additionalcitationids=\"CR10 CR11\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e).\u003c/p\u003e\u003cp\u003eInformation on land elevation is obtained either by direct measurements through topographical levelling surveys and Global Navigation Satellite System (GNSS) measurements, or by exploiting remote sensing data from aircrafts or satellites (optical, radar, laser altimetry) to generate DEMs of the Earth\u0026rsquo;s surface. DEMs are subdivided into models representing a landscape\u0026rsquo;s surface (so-called digital surface models (DSMs)), i.e. including vegetation and building heights, and digital terrain models (DTMs) that represent elevation of the bare earth (e.g., refs.\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e). While the DEM type depends on the acquisition techniques and processing approaches of a given dataset, it is important to select the correct DEM type fitting the purpose of application\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eLocal ground measurements of land elevation data provide elevation information of high vertical accuracy, but suffer from coarse spatial resolutions and time-consuming data acquisition. Through remote sensing techniques such as aerial photogrammetry, and more recently, airborne Light Detection and Ranging (LiDAR), the quality of DEMs has been improved over the years to reach horizontal resolutions and vertical accuracies on centimetre- to decimetre-scale (e.g., refs.\u003csup\u003e\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e). While such high-accuracy, locally-source data is publicly available for several coastal regions, for example in the United States, Australia, New Zealand and Europe, it is unfortunately not available for major parts of the Earth\u0026rsquo;s coast, amongst other the vast, densely populated coastal lowlands of Asia and Africa as well as many Small Island Developing States. Here, global, freely available, satellite-based DEMs are commonly used (e.g., as highlighted by refs. \u003csup\u003e18,19\u003c/sup\u003e), however often studies do not take limitations of the applicability of these DEMs properly into consideration. Although global DEMs often provide an adequate spatial (i.e. horizontal) resolution (i.e. in the range of ~\u0026thinsp;10 to 90 m), sufficient for setting up regional, delta-wide flood models or estimating coastal population and land-use asset exposure, their vertical errors in the range of several metres impede investigations of (relative) SLR impact where changes occur on millimetre- to centimetre-scale. Inaccuracies related to the acquisition of elevation data or DEM processing itself (e.g. sensing or interpolation artefacts) have been addressed by post-processing first-order DEMs such as SRTM\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e, ASTER\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e, AW3D (ALOS)\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e and TanDEM-X\u003csup\u003e24\u003c/sup\u003e, through applying void-filling and smoothing (e.g. ACE2\u003csup\u003e25\u0026ndash;27\u003c/sup\u003e, Copernicus DEM\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e), filtering elevation data with spatial vegetation, building and/or applying population data in random forest algorithms or neural networks (MERITDEM\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e, FABDEM\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e, Coastal DEM\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e) or employing recently-available satellite LiDAR measurements (ICESat-2) (GLL-DTM\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e; DeltaDTM\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. These recent DEMs constitute the newest generation with improved vertical accuracy, specifically targeting applications in coastal lowland contexts, with the FathomDEM\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e, created using advanced postprocessing of the Copernicus DEM, as the youngest sibling.\u003c/p\u003e\u003cp\u003eThe majority of those DEMs is provided with vertical reference to ellipsoid (e.g. WGS84) or a global geoid (e.g. EGM96, EGM2008), the latter often considered to reflect sea level, thus providing elevation information at global scale with respect to sea level. However, the accuracy of these global geoids to represent the actual local sea-level height varies a lot and is dependent on the amount and accuracy of input data used when generated. Offsets with true local sea-level height in several regions worldwide can be more than 1 metre, a bias which is very often not corrected for in impact assessments using global DEMs. Consequently, in these data-sparse regions, local sea level may differ up to more than a metre from the global geoid (e.g., refs. \u003csup\u003e11,37\u0026ndash;39\u003c/sup\u003e), thereby introducing systematic errors into SLR impact assessments conducted based on global elevation data whose vertical reference was not properly corrected from geoid/ellipsoid to sea level. Finally, the actuality of elevation data, i.e. referring back to the time of data acquisition, impacts the quality of coastal impact assessments. Especially in rapidly subsiding coastal lowlands such as river deltas, DEMs may become quickly outdated, as these landscapes can experience several centimetres to even decimetres subsidence per year\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan additionalcitationids=\"CR41\" citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. In such areas, using for example the SRTM DEM, originally acquired in February 2000, or any of its post-processed versions in a heavily subsiding environment results in substantial misjudgements of actual elevation, and this limitation is hardly considered in the majority of coastal impact assessments based on global DEMs.\u003c/p\u003e\u003cp\u003eWhile several studies have addressed DEM accuracy by comparing different DEMs and investigate their performance in terms of fluvial flood inundation (e.g., refs. \u003csup\u003e43\u0026ndash;47\u003c/sup\u003e), only a few focused on the impact of DEM selection on (relative) SLR impact\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e,\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. So far, only Minderhoud et al.\u003csup\u003e38\u003c/sup\u003e tentatively attributed the errors associated with a DEM in the Mekong Delta to DEM accuracy and the lack of datum conversion to local sea level. However, due to information paucity on local vertical datums, this was done through a basic transformation, i.e. by lowering the global MERITDEM to equalise the mean elevation of a local DEM (TopoDEM) with sea level datum alignment, rather than systematically converting the vertical reference system from the EGM96 geoid to local sea level as indicated by tide gauge data (i.e. Hon Dau tide gauge). Although tide gauges provide highly local information on sea level, their usage is only recommended if they provide a sufficiently long, continuous and up-to-date monitoring period for small study sites or regions without any significant sea-level variations along the coast\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. Also the absence of any documentation about the position of a tide gauge with respect to a certain geoid/ellipsoid may hamper the proper alignment of a geoid- or ellipsoid-referenced DEM to local sea level. In their DEM accuracy and (relative) SLR impact assessment for the Ayeyarwady Delta (Myanmar), Seeger et al.\u003csup\u003e11\u003c/sup\u003e employed an open-data vertical datum conversion approach that \u0026ndash; in the absence of any suitable tide gauge information \u0026ndash; allows for the proper conversion of global DEMs to actual, continuous local mean sea level (MSL) along the Myanmar coast by applying latest, freely available satellite-altimetry-derived mean dynamic topography (MDT). This workflow also includes the beforehand required correction of geoid offset between the different vertical reference systems used by elevation and sea-level datasets and thus improves previous datum conversion workflows that lack this relevant processing step (e.g., refs. \u003csup\u003e33,34\u003c/sup\u003e).\u003c/p\u003e\u003cp\u003eIn this study, we investigate land elevation datasets and their performance in data-sparse coastal lowlands, showing a globally applicable approach to quantify and attribute uncertainties in global DEMs to sources such as inaccuracy, vertical datum offset and actuality, which matters in terms of relative SLR impact. We highlight the applicability of this approach by revisiting land elevation in the Vietnamese Mekong Delta where Minderhoud et al.\u003csup\u003e38\u003c/sup\u003e uncovered the delta\u0026rsquo;s true elevation to be on average only\u0026thinsp;~\u0026thinsp;0.8 m above local sea level whereas previous assessments using global DEMs (e.g. SRTM and MERITDEM) assumed the average elevation of the delta to be considerably higher (i.e. 2.6 m and 3.3 m above sea level, respectively). The previous overestimations stemmed from a combined effect of vertical datum offset, DEM data inaccuracy, and DEM actuality, however the individual contribution of each uncertainty was not quantified (Minderhoud et al., 2019), which we will advance upon in this study. We updated the local Topo DEM of Minderhoud et al.\u003csup\u003e50\u003c/sup\u003e (hereafter referred to as Topo DEM v1), including Ho Chi Minh City and neighbouring provinces, to actual, continuous local MSL by applying an updated vertical datum conversion from Seeger et al. \u003csup\u003e11\u003c/sup\u003e (hereafter referred to as TopoDEM_v2; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Consequently, we extend the DEM accuracy assessment to evaluate the performance of in total 11 high-resolution, global DEMs, thereby ensuring to include latest available coastal DEMs such as DeltaDTM while also reassessing those DEMs that Minderhoud et al.\u003csup\u003e38\u003c/sup\u003e investigated in their study (i.e. SRTM and MERITDEM). Furthermore, we investigate the potential impacts of absent and incomplete, incorrect datum conversion, e.g. by including the GLL-DTM v2 referenced to (outdated) MDT as provided by Vernimmen and Hooijer\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e, using the approach of this study. We also consider different versions of recent DEMs and integrate both DeltaDTM v1 and v1.1 as we assume the inclusion of elevations up to 30 m in v1.1 to result in (slight) differences in DEM performance in the flat, low-lying Mekong Delta (see also Pronk et al.\u003csup\u003e35\u003c/sup\u003e). To enable 1:1 comparison to findings from Minderhoud et al.\u003csup\u003e38\u003c/sup\u003e, we focus on results obtained for the Vietnamese Mekong Delta in the main text while providing also the statistics for the extended area of interest in the supplementary material. Based on this thorough assessment, integrating the DEMs in their original vertical reference and converted to the same vertical datum, our study not only allows to extend and detail previous studies on the Mekong Delta\u0026rsquo;s elevation but also to attribute and quantify the sources causing uncertainties in the global DEMs to adequately represent the delta\u0026rsquo;s local elevation. Therewith, we showcase the considerations to be made when handling elevation models in data-sparse local to regional contexts and derive the best compromise of high accuracy and resolution data recommended for further studies on the Mekong Delta such as sea-level rise impact assessments and flood modelling research.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Results and interpretation","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eConverting vertical datum to actual continuous local sea level\u003c/h2\u003e\u003cp\u003eThe reliability of elevation data in terms of accuracy (i.e. the absence of uncertainties arising from data acquisition and DEM interpolation) and adequate representation with respect to actual continuous local sea level (i.e. the appropriateness of the vertical reference system to equal actual true sea level experienced along a given coastline) is of utmost importance for flat, low-lying coastal landscapes like the Mekong Delta. Being in parts only a few decimetres elevated above sea level makes elevation-dependent assessments highly sensitive to any uncertainties in the elevation data itself and in the estimation of coastal sea level.\u003c/p\u003e\u003cp\u003eTo assess the uncertainties related to vertical reference frames and local sea level for the different assessments and DEMs in the Mekong Delta, we focussed on the most common vertical datums used for global DEMs (i.e. EGM96 and EGM2008), which constitute also the original vertical reference of the DEMs used in previous Mekong Delta assessments. We quantified the differences between EGM96, EGM2008 and local MSL, respectively, and determined a mean geoid offset of 1.26 m (median: 1.29 m) for EGM96 and 1.17 m (median: 1.17 m) for EGM2008. Similarly, the offset range is lower for EGM2008 and constitutes only 0.84 m to 1.46 m, with the EGM2008 showing less variability (σ\u0026thinsp;=\u0026thinsp;0.11 m) while it ranges from 0.77 m to 1.65 m for EGM96 and showing larger deviation of 0.24 m (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This better performance of EGM2008 is probably related to the increased amount of gravitational field data serving as an input and allowing for an improved performance geoid interpolation in the larger data-sparse region.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eOffsets between commonly used geoid models as well as an example of incomplete datum conversion and local mean sea level as indicated by mean dynamic topography of Jousset et al.\u003csup\u003e51\u003c/sup\u003e in the Vietnamese Mekong Delta. The residual error arising from incomplete datum conversion was calculated for the example of GLL-DTM v2\u003csup\u003e34\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVertical datum\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMean offset\u003c/p\u003e\u003cp\u003e(m)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMedian offset\u003c/p\u003e\u003cp\u003e(m)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMAE\u003c/p\u003e\u003cp\u003e(m)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRMSE\u003c/p\u003e\u003cp\u003e(m)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eMin. offset\u003c/p\u003e\u003cp\u003e(m)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eMax. offset\u003c/p\u003e\u003cp\u003e(m)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eσ\u003c/p\u003e\u003cp\u003e(m)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEGM96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e151369\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.24\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEGM2008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e151369\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExample of incomplete datum conversion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e137689\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWe quantify to what extent the vertical datum offset impacts the reliability of an individual global DEM in the Mekong Delta and compare the respective DEMs both in their original reference and transposed to actual continuous local MSL with TopoDEM_v2 as well as its local point elevations. We observe that the respective global DEM errors reduce after converting them to actual continuous local MSL (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Determining the difference between DEM errors before and after datum conversion allows to quantify the impact the vertical datum issue has on the individual DEM reliability. Tendentially, the smaller the overall DEM errors, the larger the impact of lacking vertical datum conversion is. In a spatially resolved comparison with TopoDEM_v2, the discrepancies introduced range from 11.6% to 147.4%. Thereby, percentages of more than 100.0% are obtained in case the uncertainty of the transposed DEM is smaller than the vertical datum offset of the respective original vertical reference system used (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) and the difference in DEM error before and after datum conversion exceeds the uncertainty of the transposed DEM (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), specifically for the versions of DeltaDTM (i.e. 101.7% for v1 and 147.4% for v1.1). Similarly, the impact of vertical datum conversion is also high for other DEMs such as CoastalDEM v2.1 (46.3%), FABDEM (48.6%) and MERITDEM (59.7%), thereby indirectly reflecting the effectiveness of accuracy improvement through post-processing which leads to a relative dominance of the vertical datum issue in the DEM-related uncertainty. Similar patterns, however with slightly different percentages, are documented for the comparison with local point elevations, enabling pointwise DEM uncertainty attribution to vertical datum offset over the entire point elevation range as well as by focusing on particularly low-lying elevations (i.e. \u0026le;10 m above MSL).\u003c/p\u003e\u003cp\u003eBesides the absence of vertical datum conversion from a global geoid or ellipsoid model to actual continuous local sea level, another error in handling coastal elevation and sea-level is partial or incomplete, and therefore incorrect, datum conversion. From the global DEMs used in this study, this error was only encountered with the GLL-DTM for which geoid offset correction between the elevation and sea-level data used was not conducted during the processing of the original GLL-DTM\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e, thereby resulting in artefacts impacting the alignment and consequently applicability of this DEM. Comparing the original GLL-DTM v2\u003csup\u003e34\u003c/sup\u003e with the GLL-DTM v2 properly converted to actual continuous local MSL reveals only minor average offsets at large regional to global scale, as smaller-scale offsets in both directions average each other out. However, at local to smaller-regional scale the variability is large and the impact of the incorrect conversion becomes much more relevant (this study). In the Mekong Delta, the incorrect vertical datum conversion affecting the GLL-DTM v2 resulted in offsets of ca. 0.14 m (mean) and 0.18 m (median), with a standard deviation of 0.25 m and overall ranging from \u0026minus;\u0026thinsp;0.39 m to 0.54 m (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). RMSE of GLL-DTM v2 improves by ca. 0.12 m (33.3%) (0.08 m (17.6%) in comparison with local spot heights) if datum conversion is conducted completely and correctly. Consequently, errors made in vertical datum conversion account for 33.3% (17.6% in comparison with local spot height) of the DEM\u0026rsquo;s uncertainty (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). With a higher spatial resolution of GLL-DTM, however, we would expect even much greater discrepancies and effects on the quality of the DEM attributable to improper handling of the elevation and sea-level data.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003ePerformance of digital elevation models in the Vietnamese Mekong Delta\u003c/h3\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003eRevisiting local elevation data in the Vietnamese Mekong Delta\u003c/h2\u003e\u003cp\u003eWe updated the existing Topo DEM v1\u003csup\u003e50\u003c/sup\u003e by referencing the data to actual, continuous local MSL (TopoDEM_v2) which enables for an up-to-date assessment of land elevation in the Vietnamese Mekong Delta. Based on visual impression, the overall reflection of land surface height changes only slightly (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The frequency of lower elevations and thus the spatial extent of lowly elevated areas increased. This becomes particularly evident in the Ca Mau peninsula, reflecting more pronounced sea-level variations in this part of the sea (especially in Rach Gia Bay) compared to Hon Dau in northern Vietnam result in higher MSL/lower elevation above MSL. While in comparison the maximum and minimum elevations increased and decreased for TopoDEM_v2, the average elevation of the Mekong Delta is updated to 0.77 m (mean) and 0.72 m (median) compared to 0.80 m (mean) and 0.74 m (median) as previously indicated by Topo DEM v1, resulting in an average height residual of 4\u0026ndash;5 cm (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The fact that this amount equals annual sinking rates of land subsidence hotspots in the Mekong Delta\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e,\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e highlights the enormous sensitivity of the flat, low-lying and sinking landscape to any error and uncertainty in the elevation data, even if high-accuracy local elevation data is used. Consequently, if properly conducted, converting the vertical datum from MSL indicated by a single tide gauge (and established several years ago) to actual, continuous sea-surface height along the deltaic coast makes the characterisation of local elevation with respect to sea level more precise as potential sea-level variations along the ca. 2000 km long Vietnamese coastline can be accounted for. In the Mekong Delta, this is a crucial step to further narrow down existing uncertainties in the assessment of relative SLR.\u003c/p\u003e\u003cp\u003e\u003cb\u003eWhich one is the best? \u0026ndash; On the local validation of global satellite-based digital elevation models\u003c/b\u003e\u003c/p\u003e\u003cp\u003eDisentangling DEM uncertainty into DEM-specific accuracy and vertical referencing to actual (continuous) local sea level is crucial to understand the suitability and therefore reliability and applicability of a DEM in a given coastal study area for any coastal hazard or impact assessment. With all elevation data (both DEMs and local point elevations) in the same vertical reference frame, i.e. MSL according to MDT\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e, the impact of respective vertical datum offset on the DEMs\u0026rsquo; quality assessment is excluded. Hence, we can characterise and compare their performance and accuracy against the background of their generation (i.e. including data acquisition, DEM interpolation and further processing).\u003c/p\u003e\u003cp\u003eVisual inspection of the DEMs available for the Mekong Delta reveals the huge differences in their capability to correctly reflect the delta\u0026rsquo;s flat, low-lying terrain (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Rather, artefacts like stripes from sensing are dominating, especially in SRTM, ASTGTM v003 and AW3D30, and are also preserved in post-processed DEMs such as ACE2, MERITDEM and CoastalDEM v2.1, reflecting that the application of smoothing filters and correction algorithms did not perform well in this coastal lowland (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Consequently, the elevation frequency distributions of those DEMs show an almost bimodal shape and are characterised by one mode in the range of 1 m to 2 m (i.e. reflecting the delta\u0026rsquo;s average elevation) and a second, minor one at the lower end of the frequency distribution with most values in classes \u0026minus;\u0026thinsp;1 m to -2 m and \u0026lt;-2 m (i.e. including erroneous values resulting from the DEM\u0026rsquo;s artefacts) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Stripes related to the SRTM DEM are largely hidden in MERITDEM and likely do not become visible in its elevation frequency distribution as related values are in the same range as the indicated delta elevation. While average elevations of SRTM and MERITDEM prior to vertical datum conversion are corresponding with values determined by Minderhoud et al. (2019a) (only for SRTM, mean delta elevation is ~\u0026thinsp;0.3 m lower, likely resulting from slightly different pre-processing performance), they are 0.45 m and 1.06 m lower after transposing them to MSL, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). ASTGTM v003 varies in that from SRTM-based DEMs and AW3D30 as it indicates the highest elevations throughout the entire array of datasets. In contrast, visual comparison of TanDEM-X 90m and its post-processed versions such as Copernicus DEM and FABDEM reveal a similar representation of the delta terrain, which is also in line with the local DEM, however, indicating higher mean elevations. Developed for improving global DEM performance in coastal lowlands, the global coastal DEMs of CoastalDEM v2.1, GLL-DTM v2, and DeltaDTM v1 and v1.1 show an overall better representation of the Mekong Delta\u0026rsquo;s elevation, both in terms of approaching average elevation and elevation frequency distribution as well as in lower error statistics (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). All of these coastal DEMs have been trained on or processed with high-accuracy ICESat-2 satellite LiDAR. However, CoastalDEM v2.1 still suffers from underlying SRTM artefacts (which are transferred via NASADEM that was used as source DEM for generating CoastalDEM v2.1) although it reflects the delta\u0026rsquo;s average elevation well. Consequently, indicated elevations are characterised by a left-skewed frequency distribution and comparably high standard deviation which is twice as high as for other coastal DEMs (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Both GLL-DTM v2 and DeltaDTM yield a more accurate representation of the delta\u0026rsquo;s terrain. Especially GLL-DTM v2 (converted to MSL by this study) and DeltaDTM v1 reveal the best results, with mean errors less than 0.10 m and RMSE of 0.35 m and 0.49 m, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePerformance of local and global DEMs in the Vietnamese Mekong Delta validated by the local TopoDEM_v2 and DEM-specific vertical datum offsets quantified as differences in RMSE and discrepancies from RMSE of DEMs referenced to mean dynamic topography (MDT). The statistics were extracted from DEMs masked for water bodies and outcrops and resampled to 500 m \u0026times; 500 m spatial resolution. N \u0026ndash; number of grid cells in the study area, with no-data values excluded for each DEM, respectively; Mean DEM \u0026ndash; mean DEM elevation in the study area; Median DEM \u0026ndash; median DEM elevation in the study area; Min. DEM \u0026ndash; minimum DEM elevation in the study area; Max. DEM \u0026ndash; maximum DEM elevation in the study area; σ DEM \u0026ndash; standard deviation of DEM elevation in the study area; HR \u0026ndash; height residual; MAE \u0026ndash; mean absolute error; RMSE \u0026ndash; root mean square error.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"15\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDEM\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003cp\u003eDEM\u003c/p\u003e\u003cp\u003e(m)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMedian\u003c/p\u003e\u003cp\u003eDEM\u003c/p\u003e\u003cp\u003e(m)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMin.\u003c/p\u003e\u003cp\u003eDEM\u003c/p\u003e\u003cp\u003e(m)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMax.\u003c/p\u003e\u003cp\u003eDEM\u003c/p\u003e\u003cp\u003e(m)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eσ\u003c/p\u003e\u003cp\u003eDEM\u003c/p\u003e\u003cp\u003e(m)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eMax.\u003c/p\u003e\u003cp\u003enegative HR\u003c/p\u003e\u003cp\u003e(m)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eMax.\u003c/p\u003e\u003cp\u003epositive HR\u003c/p\u003e\u003cp\u003e(m)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003cp\u003eerror\u003c/p\u003e\u003cp\u003e(m)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003eMAE\u003c/p\u003e\u003cp\u003e(m)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c12\"\u003e\u003cp\u003eMedian error\u003c/p\u003e\u003cp\u003e(m)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c13\"\u003e\u003cp\u003eRMSE\u003c/p\u003e\u003cp\u003e(m)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c14\"\u003e\u003cp\u003eDifference\u003c/p\u003e\u003cp\u003ein RMSE\u003c/p\u003e\u003cp\u003e(m)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c15\"\u003e\u003cp\u003eDiscrepancy\u003c/p\u003e\u003cp\u003e(%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTopoDEM_v2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e152028\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e8.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTopo DEM v1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e147582\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e6.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-7.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e1.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSRTM\u003csub\u003eMDT\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e144113\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-6.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e143.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-10.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e142.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e1.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e2.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c15\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e17.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSRTM\u003csub\u003eEGM96\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e145672\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-7.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e145.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-9.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e144.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e1.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e2.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e1.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e3.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eACE2\u003csub\u003eMDT\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e133927\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-6.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e140.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-8.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e139.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e1.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e2.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c15\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e22.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eACE2\u003csub\u003eEGM96\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e134211\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-7.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e141.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-10.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e140.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e1.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e2.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e1.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e2.76\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMERITDEM\u003csub\u003eMDT\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e146263\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-6.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e139.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-7.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e138.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e1.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e1.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e1.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e1.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e1.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c15\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e58.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMERITDEM\u003csub\u003eEGM96\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e146269\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-6.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e140.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-7.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e140.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e2.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e2.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e2.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e2.86\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eASTGTM v003\u003csub\u003eMDT\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e145981\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-1.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e152.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e4.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-3.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e151.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e7.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e7.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e6.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e9.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e1.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c15\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e11.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eASTGTM v003\u003csub\u003eEGM96\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e145981\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e154.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e5.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-2.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e153.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e9.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e9.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e7.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e10.38\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAW3D30\u003csub\u003eMDT\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e109349\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-6.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e111.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-8.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e111.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e1.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e0.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e2.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c15\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e22.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAW3D30\u003csub\u003eEGM96\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e110184\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-7.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e113.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-9.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e112.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e1.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e2.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e1.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e3.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTanDEM-X\u003csub\u003eMDT\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e138263\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-6.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e163.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-7.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e162.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e1.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e1.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e0.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e2.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c15\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e40.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTanDEM-X\u003csub\u003eEGM96\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e138313\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-6.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e164.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-7.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e164.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e2.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e2.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e1.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e2.88\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCopernicus DEM\u003csub\u003eMDT\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e114885\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-5.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e138.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-6.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e137.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e1.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e0.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e2.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c15\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e32.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCopernicus DEM\u003csub\u003eEGM2008\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e114887\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-4.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e139.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-5.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e138.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e2.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e2.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e1.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e2.90\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFABDEM\u003csub\u003eMDT\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e108892\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-4.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e139.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-5.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e138.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e1.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c15\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e48.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFABDEM\u003csub\u003eEGM2008\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e113820\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-2.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e144.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-3.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e143.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e1.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e1.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e1.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e2.12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCoastalDEM v2.1\u003csub\u003eMDT\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e145917\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-5.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e159.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-6.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e158.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e-0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e1.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c15\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e46.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCoastalDEM v2.1\u003csub\u003eEGM96\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e146009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-4.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e159.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-5.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e159.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e1.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e1.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e1.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e1.76\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGLL-DTM v2\u003csub\u003eMDT (this study)\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e137689\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-1.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e8.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-5.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e6.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e0.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c15\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e33.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGLL-DTM v2\u003csub\u003eMDT (original)\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e138749\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-1.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e8.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-5.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e6.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e0.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e0.47\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDeltaDTM v1\u003csub\u003eMDT\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e107553\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-3.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e7.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-5.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e6.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e-0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e-0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e0.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c15\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e147.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDeltaDTM v1\u003csub\u003eEGM2008\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e114856\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-1.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e9.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-3.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e7.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e1.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e1.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e1.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e1.21\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDeltaDTM v1.1\u003csub\u003eMDT\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e114585\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-6.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e28.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-7.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e27.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e-0.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e-0.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e0.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c15\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e101.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDeltaDTM v1.1\u003csub\u003eEGM2008\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e114587\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-4.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e30.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-5.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e29.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e0.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e1.11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eBy subtracting TopoDEM_v2 from each of the other DEMs referenced to MSL, we quantify all differences between local and global elevation datasets. This not only substantiates the inappropriateness of SRTM, ACE2 and AW3D30 to be applied in the Mekong Delta as inaccuracies are in the range of several metres and are particularly evident from \u0026minus;\u0026thinsp;2.5 m to -1 m and from 1 m to 2.5 m (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). However, given their bimodal character, they do not show up in any average height residual statistics but only standard deviations and RMSE in the range of \u0026ge;\u0026thinsp;2 m (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Aside from ASTGTM v003, whose elevation is in many parts of the Mekong Delta more than 2.5 m higher than indicated by TopoDEM_v2 (with inaccuracies of more than 10 m particularly in the southwestern Ca Mau peninsula), also MERITDEM represents the Mekong Delta on average 1.29 m higher (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This is surprising as MERITDEM was created based on SRTM and involved a correction for vegetation heights\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Thus, we would have expected similar or lower offsets to TopoDEM_v2. However, it rather seems that vegetation was not effectively eliminated while artefact correction and smoothing was applied. Also the TanDEM-X based elevation models reveal higher elevations than TopoDEM_v2. Seeing the sequence of DEM generations from TanDEM-X 90 m to Copernicus DEM to FABDEM, involving corrections such as void filling and vegetation removals, their comparison with local elevation data reveals their improvement in accuracy, particularly in terms of lowered mean errors and average height residuals and especially following vegetation correction in FABDEM (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). While difference mapping for GLL-DTM v2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ek and l) resolves how inadequate consideration of geoid offset in vertical datum conversion impacted also coastal inland elevation, DeltaDTM as the most recent coastal DEM, generated by integrating ICESat-2 satellite LiDAR into Copernicus DEM, reveals the overall best performance, benefiting from some of the lowest inaccuracies, height residuals and standard deviations while yielding high resolution of 30 m \u0026times; 30 m. To further investigate the performance of DeltaDTM, and see whether the inclusion of elevations up to 30 m in v1.1 has an impact of its performance in the low-lying Mekong Delta, we included both available v1 and v1.1. We find that although both versions perform very similar, they show slight differences as DeltaDTM v1 outperforms v1.1 in overall lower height residuals and slightly higher accuracy (RMSE\u0026thinsp;=\u0026thinsp;0.49 m (v1), RMSE\u0026thinsp;=\u0026thinsp;0.55 m (v1.1). However, since DeltaDTM v1.1 included higher elevations, we would expect its height residuals to be similar to or higher than for v1, i.e. indicating elevations also similar to or higher than TopoDEM_v2. Instead, DeltaDTM v1.1 is on average 0.20 m lower than TopoDEM v2 and 0.13 m lower than DeltaDTM v1which are considerably larger differences than the minor, global differences in the range of ~\u0026thinsp;2 cm with the global-scale validation comparison of Pronk et al.\u003csup\u003e52\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion, conclusions and outlook","content":"\u003cp\u003eThe previous elevation assessment of the Vietnamese Mekong Delta by Minderhoud et al.\u003csup\u003e38\u003c/sup\u003e set a benchmark by unravelling the delta\u0026rsquo;s elevation relative to local sea level and its exposure to relative SLR, which had been grossly underestimated in previous SLR impact assessments based on global DEMs, as they overestimated the delta\u0026rsquo;s elevation to sea level by several metres. In addition, this work sensitised the coastal research community for proper converting the vertical datum to local sea level by conducting a tentative vertical datum conversion for one of the two assessed global DEMs. In the recent past years there have been considerable advancements in scientific research on data-sparse coastal lowlands for regions in the world where high-quality data (e.g. LiDAR) is limited or unavailable. The recent advancements range from the publication of newly processed DEMs specifically targeting global coastal lowlands\u003csup\u003e\u003cspan additionalcitationids=\"CR33 CR34\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e, raising awareness on considerations to be made about elevation assessment uncertainty due to DEM inaccuracy and vertical datum offsets (e.g., refs. \u003csup\u003e53,54\u003c/sup\u003e) and introducing approaches and showcases how to handle elevation data properly in data-sparse coastal lowlands\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e. Building on these new DEMs and insights we revisited the land elevation in the Vietnamese Mekong Delta, which provides an excellent test-case being a lowly-elevated coastal lowland with local validation data (i.e. Topo DEM) available, thereby updating and extending the previous elevation assessment of the delta\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eWe took the majority of currently available global DEMs, including the older, most commonly used DEMs and the latest generation of DEMs specifically designed to target coastal lowlands. We compared them against a vertically high-resolution DEM from local origin (i.e. TopoDEM_v2), with all DEMs properly referenced to a common vertical datum, in our case to actual local continuous MSL (as indicated by MDT\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e). In addition, we also compared all DEMs while omitting the necessary vertical datum conversion (i.e. DEMs are kept in their original reference system, which is mostly a global geoid). This is to mimic and evaluate the impacts of the common, erroneous practice present in the majority of global coastal impact assessments (highlighted in Minderhoud et al.\u003csup\u003e38\u003c/sup\u003e). This approach not only allowed us to characterise the presently best performing DEMs but also to attribute and quantify the sources causing uncertainties in a global DEM to adequately represent the delta\u0026rsquo;s local elevation (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Unravelling and quantifying the specific contributions of these different factors to uncertainties of elevation assessments using global DEMs, has, to our knowledge, not been performed before. We deem such an assessment crucial, not only because it reveals which DEMs perform best for a certain area but also as it pinpoints errors in DEM processing steps (e.g. omitting vertical datum conversion) and indicates how much DEM performance (and its applicability) can be improved if certain processing steps are prioritised and properly applied. For example, the RMSE of ACE2 and MERITDEM are very similar if used in the EGM96 geoid as they are provided\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. However, our assessment highlights that vertical datum conversion to MSL in the Mekong Delta is much more effective to improve the results when using the MERITDEM, while for ACE2 still 80% of the documented uncertainties is controlled by inaccuracy in the DEM generation process (i.e. related to acquisition and DEM interpolation), meaning that even after proper referencing to sea level, land elevation will be substantially overestimated by several metres (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). For global DEMs that included accuracy improvements like vegetation removal or integration of terrain data in their processing (e.g., FABDEM, CoastalDEM v2.1, DeltaDTM v1 and v1.1), vertical datum offset and consequently the need for conversion mount up to 30% to 60% uncertainty, reflecting it \u0026ndash; at least for DeltaDTM \u0026ndash; as an either equal or the most critical factor to consider in order to improve DEM performance. As this attribution highly depends on the magnitude of datum offset to local sea level, which varies around the world, as well as local spatial patterns and landscape features impacting the accuracy of DEM processing and post-processing, this attribution does not allow to be simply transferred to other areas and requires a site-specific evaluation following our presented approach.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eLand elevation is not static but changing over time and the vast majority of coastal lowlands such as river deltas and coastal plains are prone to land subsidence, which may reach several centimetres to locally even decimetres per year (e.g., refs. \u003csup\u003e6,56\u0026ndash;58\u003c/sup\u003e), together with global SLR resulting in elevated relative SLR\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Consequently, the capability of a DEM to adequately represent coastal-deltaic land elevation will change significantly if over years, accumulated subsidence together with SLR exceeds the DEM-inherent uncertainty. Therefore, the actuality of the elevation data used is as crucial as the actuality of sea-level information in order to guarantee up-to-date assessments of coastal hazards and impacts. However, this factor is often overlooked as still a huge number of those assessments applies elevation data more than ten to even 25 years old (e.g., as highlighted in Hawker et al.\u003csup\u003e45\u003c/sup\u003e). The fact that some of the more recently published DEMs constitute post-processed elevation data such as SRTM, which was acquired in 2000, may cause confusion as users may consider the post-processed DEM to represent more recently acquired elevation data while these DEMs still rely on original, earlier acquired elevation data, leaving the potential impacts of post-acquisition elevation dynamics unaddressed (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003e). We demonstrate the potential impact of outdated elevation on DEM performance using the Mekong Delta and updated the DEMs referenced to continuous local MSL as indicated by MDT (i.e. as of 2007) to MSL 2025. We include both land subsidence and SLR, by using simulated, non-linear extraction-induced land subsidence\u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e,\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e (B1 scenario of Minderhoud et al.\u003csup\u003e61\u003c/sup\u003e) and annual rates of local, delta-average SLR estimated from PSMSL data of Vung Tau tide gauge\u003csup\u003e\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e and IPCC AR6 total rates of sea-level change\u003csup\u003e\u003cspan additionalcitationids=\"CR64\" citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e (Supplementary Table\u0026nbsp;6). For SRTM and its follow-up versions, consideration of extraction-induced land subsidence and absolute SLR since 2000 until MSL of ~\u0026thinsp;2007 already contributes 3\u0026ndash;4% to overall elevation assessment uncertainty, while relative SLR until 2025 increases elevation assessment uncertainty relatively by 9\u0026ndash;16% (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The impact is less for most recent, TanDEM-X based DEMs (3\u0026ndash;6%) until its dimension approaches that of vertical datum offset and accuracy as GLL-DTM v2 shows a smaller vertical datum offset than DEMs originally referenced to a global geoid and reveals a comparably high accuracy (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Only for ASTGTM v003 which suffers from the largest inaccuracies, consideration of land elevation and sea-level change does not result in remarkable performance improvement.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eWe deem our integration of vertical land motion and sea-level change for the Mekong delta only as a first, preliminary attempt of considering and estimating the effect of elevation dynamics on coastal land elevation datasets. Though we consider the non-linear spatio-temporal behaviour of land subsidence in the Mekong Delta, this assessment only accounts for moderate rates of simulated subsidence induced by groundwater extraction\u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e,\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e but does not include contributions from other origins such as natural compaction\u003csup\u003e\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e,\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e, other land-use induced shallow processes\u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e, or urban differential compaction\u003csup\u003e\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u003c/sup\u003e. The assessment also does not consider the potential contribution of sediment aggradation to elevation gain, whose potential, however, has been shown to be only minor with respect to the current conditions in the delta\u003csup\u003e\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u003c/sup\u003e. Still, the assessment provides a first demonstration of the entire picture of uncertainties related to elevation data in coastal lowland contexts and how these uncertainties impact the individual DEMs relatively. To further substantiate these quantifications requires to address the limitations mentioned above and consider process- and data-driven spatial variability\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e,\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e,\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Observational studies reveal that contemporary, total subsidence rates in the Mekong Delta are in places considerably larger than only previously simulated extraction-induced subsidence and delta-wide averages, up to 5\u0026ndash;6 cm/yr\u003csup\u003e6,40,41,56\u003c/sup\u003e. Therefore, we expect relative SLR to have a much higher relative impact on elevation assessment uncertainty, especially in case outdated (global) DEMs are used. While some studies handle elevation in the low-lying Mekong Delta more carefully, for example by using the local Topo DEM of Minderhoud et al.\u003csup\u003e38\u003c/sup\u003e, more recent global DEMs, or addressing vertical datum conversion tentatively (e.g., refs. \u003csup\u003e70\u0026ndash;73\u003c/sup\u003e), a considerable number of studies still apply outdated and/or inaccurate DEMs that suffer from artefacts (e.g., refs. \u003csup\u003e74\u0026ndash;79\u003c/sup\u003e), a phenomenon we also observed for coastal hazard and impact assessments in other regions of the world.\u003c/p\u003e\u003cp\u003eWith our revisit of land elevation in the Mekong Delta we provide an exemplar showcase of a globally applicable approach to attribute uncertainties in elevation assessment for data-sparse coastal lowlands using global elevation models. Our assessment not only details previous studies on the delta\u0026rsquo;s elevation further, moreover it highlights how to handle coastal elevation and sea-level data properly: (i) by vertically referencing the DEMs to a common actual, local sea-level datum, and (ii) by conducting a thorough assessment of DEM performance that not only allows for the quantification of errors and elevation assessment uncertainties but also their attribution to DEM inaccuracy, vertical datum offset and, tentatively, non-linear impact of elevation change due to vertical land motion (e.g. extraction-induced land subsidence) and sea-level change affecting DEM actuality. This improved understanding of coastal elevation serves as a starting point to further improve impact assessments of flooding and relative SLR (e.g., refs. \u003csup\u003e38,73\u003c/sup\u003e) and substantiating projections of future elevation in the Mekong Delta\u003csup\u003e\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e. With all quantifications of DEM performance and vertical datum offsets based solely on open data and approaches that can be applied in any GIS environment, our demonstrated approach may set a benchmark to initiate similar assessments of DEM performance in other (data-sparse) coastal regions in the world. As elevation forms the basis for any coastal impact assessment, it needs to be handled carefully and with scrutiny, a practice which is currently often lacking in the coastal research community. Similar to sea level, land elevation is not static but dynamic and this should be considered properly in coastal impact assessment, depends on the respective DEM dataset and vertical reference used, as well as the relative SLR and surface elevation change. As new elevation datasets become available, as do vertical reference systems such as geoid models and sea-level datasets, it should be prioritised to provide end users not only with latest available elevation data but also reference them to latest vertical reference systems or sea level, depending on the intended end use. Providing the coastal research community with regular updates of most recent DEMs, already referenced to an actual sea-level datum, such as latest available MDT, will take complex vertical datum conversion steps away from non-specialist end users and thereby reduce overall uncertainties in coastal elevation and elevation-related SLR and other coastal hazard impact assessment.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eUpdating the vertical datum of local elevation data to continuous local mean sea level\u003c/h2\u003e\u003cp\u003eTopoDEM_v2 was generated, similar to Topo DEM v1\u003csup\u003e38\u003c/sup\u003e, using elevation data acquired between 2001 to 2003 and referenced to mean sea level (MSL) defined by Hon Dau tide gauge, which is located ca. 1400 km north of the study area\u003csup\u003e\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e\u003c/sup\u003e. To convert the geodetic heights to MSL as defined by latest available mean dynamic topography (MDT) (MDT HYBRID-CNES-CLS2022; 1993\u0026ndash;2021\u003csup\u003e51\u003c/sup\u003e), they were first corrected for SLR that occurred at Hon Dau since datum establishment which is MSL over 1950 to 2005\u003csup\u003e80,81\u003c/sup\u003e. Consequently, MSL for MDT\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e dates around April 2007 while it is 1992 for Hon Dau datum. Tide gauge information of Hon Dau obtained from the Permanent Sea Level Stations repository indicates 1.8 mm/yr of SLR and that sea level has risen by 0.027 m between 1992 (i.e. year for MSL of Hon Dau datum) and 2007 (i.e. year for MSL of MDT time period)\u003csup\u003e\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e\u003c/sup\u003e. Subtracting this amount from the elevation points converts them to actual MSL at Hon Dau (Fig.\u0026nbsp;7). We assume that this determined actual MSL equalises actual MSL as given by MDT\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. However, in order to convert the local elevation data to continuous MSL along the coast of the Mekong Delta, we use the difference (=\u0026thinsp;1.173 m) between Hon Dau MSL (measured at the tide gauge) and GOCO06s geoid at the tide gauge location and assume the same difference between geoid (GOCO06s) and local MSL for the Mekong Delta. Geoid height above the WGS84 ellipsoid was obtained from the openly accessible calculation service of the International Centre for Global Earth Models (ICGEM) at a spatial resolution of 0.085 deg\u003csup\u003e\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e\u003c/sup\u003e. For each geoid, the obtained point data was interpolated into a raster using multiquadric radial basis functions which showed the best interpolation performance. To be comparable to the spatial resolution of the digital elevation models (DEMs) used in this study, the geoid rasters were resampled to 90 m \u0026times; 90 m and 1000 m \u0026times; 1000 m resolution using bilinear resampling. Geoid offsets were calculated based on subtraction of the resampled geoid rasters.\u003c/p\u003e\u003cp\u003eGiven that the quality of satellite altimetry measurements along the coast may be susceptible to land contamination and limitations due to geophysical and environmental corrections\u003csup\u003e\u003cspan additionalcitationids=\"CR85\" citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e\u003c/sup\u003e, we do not apply an individual MDT value but apply a radius of 100 km and add the average offset of 1.173 m. Consequently, all elevation points are referenced to the GOCO06s geoid. They were converted to continuous local MSL as represented by MDT HYBRID-CNES-CLS2022 by subtracting MDT\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e and following the updated approach of Seeger et al.\u003csup\u003e11\u003c/sup\u003e. To avoid introducing artefacts due to the differential spatial resolution of MDT and elevation data and to enable datum conversion to MDT not only for the most coastal point elevations but also inland, we resampled the MDT dataset to a resolution of 90 m using bilinear resampling and extrapolated the data up to 500 km inland using Inverse Distance Weighting and applying a smoothing factor of 0.5. The distance threshold was chosen to provide the best compromise in covering vast coastal lowlands (thereby also including the largest delta in the world, i.e. the Ganges-Brahmaputra-Meghna Delta), while also keeping sufficient quality in face of decreasing extrapolation performance with increasing distance. The resampled and extrapolated MDT dataset was subtracted from the pre-processed elevation measurements and therewith concluded the datum conversion process to actual, continuous local MSL along the Mekong Delta coast.\u003c/p\u003e\u003cp\u003eTopoDEM_v2 was generated through a two-phased interpolation using Empirical Bayesian Kriging with empirical transformation and exponential modelling within the Geostatistical Wizard in the ArcGIS Pro Analysis 3.1.3 environment. Firstly, the DEM for the delta plain was interpolated after excluding elevations\u0026thinsp;\u0026gt;\u0026thinsp;10 m to ensure high DEM accuracy for the delta plain and minimise the risk of higher elevations from outcrops impacting DEM interpolation in the flat, low-lying surroundings (similar to Topo DEM v1\u003csup\u003e38\u003c/sup\u003e). Secondly, the entire area was interpolated with no data exclusion. Subsequently, the extent of the delta plain was clipped from the first interpolation to replace the elevation data in the second interpolation, thereby ensuring the best delta plain representation. TopoDEM_v2 has a spatial resolution of 500 m \u0026times; 500 m, justified by the data density per km\u003csup\u003e2\u003c/sup\u003e (ref. \u003csup\u003e87\u003c/sup\u003e) and to facilitate proper DEM comparison for the delta plain, all elevations\u0026thinsp;\u0026gt;\u0026thinsp;10 m, outcrops and water bodies were masked following the same procedure as described in Topo DEM v1\u003csup\u003e38\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eAssessment of global elevation model performance\u003c/h3\u003e\n\u003cp\u003eWe revisit land elevation in the Vietnamese Mekong Delta by extending the assessment of DEM performance, integrate a lot more elevation datasets than previous works, updating them to latest available MSL, and widening the spatial coverage to include also surrounding provinces. Global DEMs are often used to overcome the limited availability or absence of local elevation data or its coarse spatial resolution impacting the precision of follow-up applications. However, as the low accuracy of global DEMs is often not or only inadequately addressed, we assessed the quality for the majority of open and freely available DEMs in the Vietnamese Mekong Delta and surrounding provinces, quantifying both respective DEM accuracy and the impact of absent or incomplete and incorrect vertical datum conversion.\u003c/p\u003e\u003cp\u003eIn total, 11 global DEMs were used for the comparison with Topo DEM v1 and v2, namely SRTM\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e, ACE2\u003csup\u003e25\u0026ndash;27\u003c/sup\u003e, MERITDEM\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e, ASTGTM v003\u003csup\u003e21,22\u003c/sup\u003e, AW3D30\u003csup\u003e23\u003c/sup\u003e, TanDEM-X 90m\u003csup\u003e\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e\u003c/sup\u003e, Copernicus DEM\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e, FABDEM v1.0\u003csup\u003e30\u003c/sup\u003e, CoastalDEM v2.1\u003csup\u003e31,32\u003c/sup\u003e, GLL-DTM v2\u003csup\u003e34\u003c/sup\u003e, DeltaDTM\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. While both SRTM and MERITDEM have been addressed by studies of Minderhoud et al.\u003csup\u003e38\u003c/sup\u003e, the quantified uncertainties could only be attributed tentatively. More recent DEMs such as FABDEM have been applied in the Mekong Delta (e.g., ref. \u003csup\u003e73\u003c/sup\u003e), however without assessing their quality. To quantify also the impacts of incomplete vertical datum conversion and difference in elevation threshold applied in DEM interpolation, we integrate two versions of the GLL-DTM v2, the first referenced to MDT (CNES-CLS13 MDT\u003csup\u003e\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e\u003c/sup\u003e) as performed by Vernimmen and Hooijer\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e and a second following the approach of this study, as well as both DeltaDTM v1 and v1.1\u003csup\u003e35,52\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eOur quality assessments included both validation with the local TopoDEM_v2 to assess and quantify spatial patterns and differences, as well as spot height comparison with elevation at locations of point measurements that fed the local DEM interpolation. Beforehand, the global DEMs were pre-processed. Single DEM tiles covering the area of interest were mosaicked and projected to UTM 48N based on the WGS84 ellipsoid. Subsequently, the vertical datums of the DEMs (i.e. EGM96 and EGM2008) were converted to MSL as defined by MDT\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e and considering to correct for respective geoid offsets. Although GLL-DTM v2 is already provided with a reference to MSL\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e, we obtained the DEM referenced to EGM96 and could therefore correct for the previously unconsidered geoid offset by applying the same workflow as for the other global DEMs (Fig.\u0026nbsp;7). All DEMs, including versions with the vertical reference as provided in the respective data repositories as well as with respect to MSL (this study), were masked by applying their respective water body masks. For ACE2, MERITDEM and CoastalDEM v2.1 and FABDEM v1.0, water bodies were excluded based on masks from the underlying source data, which is SRTM and only in case of FABDEM Copernicus. GLL-DTM v2 and DeltaDTM v1 and v1.1 did not require this processing step as the spatial resolution of GLL-DTM v2 is too coarse to adequately resolve the deltaic river and channel network while in DeltaDTM, these values are already eliminated. Largely negative and therefore likely erroneous elevation values were excluded by applying a threshold of \u0026lt;-7 m above the respective vertical datum\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. As for Topo DEM v 1 and v2, we applied the same outcrop mask to the global DEMs. In addition, all global DEMs were resampled to 500 m \u0026times; 500 m spatial resolution using bilinear resampling and snapping to the TopoDEMs\u0026rsquo; extent. Only for GLL-DTM v2 with a resolution of ~\u0026thinsp;1000 m \u0026times; ~1000 m, we resampled the local TopoDEM_v2 using bilinear resampling to match the same spatial resolution as GLL-DTM v2. All these pre-processing steps enable a proper conduction of DEM comparison in terms of difference mapping with the local TopoDEM_v2 and ensure comparability. Only for extracting point elevations from each DEM and quantifying geoid offset per DEM, the respective spatial resolution of the original DEM was used.\u003c/p\u003e\n\u003ch3\u003eQuantification of vertical datum offsets\u003c/h3\u003e\n\u003cp\u003eTo quantify the overall discrepancies between geoid models and actual continuous local sea-level height along the coast for the entire Mekong Delta, we used geoid information for EGM96, EGM2008 and GOCO06s obtained in form of height anomaly to the WGS84 ellipsoid from the ICGEM calculation service\u003csup\u003e\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e\u003c/sup\u003e. The respective offsets between EGM96 and EGM2008 geoids to GOCO06s were calculated and subsequently subtracted from mean dynamic topography, i.e. MDT \u0026ndash; (EGM\u0026ndash;GOCO06s). As the processing of the geoid rasters included bilinear resampling to 90 m spatial resolution, all statistics for the respective offsets of EGM96 and EGM2008 to MSL are given at 90 m \u0026times; 90 m spatial resolution. However, in order to account for an adequate vertical datum conversion of GLL-DTM v2 and compare it to the original GLL-DTM v2\u003csup\u003e34\u003c/sup\u003e, which serves as an example of incomplete vertical datum conversion, we used EGM96 geoid data at 1000 m \u0026times; 1000 m spatial resolution. Consequently, the resulting statistics are provided at the same resolution.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eData availability\u003c/h2\u003e\u003cp\u003eThe Digital Elevation Models for the Mekong Delta based on global digital elevation models and converted to local mean sea level as indicated by mean dynamic topography (following the approach of this study), are temporarily accessible here for the review process: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://drive.google.com/drive/folders/14Ut5M8KBL_mR69HazYeYl4YedNK7OOTc?usp=sharing\u003c/span\u003e\u003cspan address=\"https://drive.google.com/drive/folders/14Ut5M8KBL_mR69HazYeYl4YedNK7OOTc?usp=sharing\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e [Link to open repository will be provided upon publication]. TopoDEM_v2 is temporarily accessible here for the review process: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://drive.google.com/drive/folders/14Ut5M8KBL_mR69HazYeYl4YedNK7OOTc?usp=sharing\u003c/span\u003e\u003cspan address=\"https://drive.google.com/drive/folders/14Ut5M8KBL_mR69HazYeYl4YedNK7OOTc?usp=sharing\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e [Link to open repository will be provided upon publication].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eResearch funding\u003c/h2\u003e\u003cp\u003ePhilip S.J. Minderhoud received funding from the Netherlands Science Foundation (NWO) Drowning Deltas project (NWO-Veni-TTW-2022 No. 20231).\u003c/p\u003e\u003c/div\u003e\n\u003ch2\u003eAuthor contributions statement\u003c/h2\u003e\u003cp\u003eK.S. and P.S.J.M. jointly conceptualised this study. K.S. designed the methodology (updating the vertical datum of local elevation data, DEM assessment), performed the analyses, created the figures and wrote the original draft. P.S.J.M. acquired funding and supervised the investigation. Both authors managed project administration, assessed the results and reviewed and edited the manuscript.\u003c/p\u003e\u003ch2\u003eCompeting interests\u003c/h2\u003e\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003e[Reviewers will be acknowledged if they are not anonymous]. Philip S.J. Minderhoud received funding from the Netherlands Science Foundation (NWO) Drowning Deltas project (NWO-Veni-TTW-2022 No. 20231).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eShirzaei, M. et al. Measuring, modelling and projecting coastal land subsidence. \u003cem\u003eNat. Rev. Earth Environ.\u003c/em\u003e \u003cb\u003e2\u003c/b\u003e (1), 40\u0026ndash;58. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s43017-020-00115-x\u003c/span\u003e\u003cspan address=\"10.1038/s43017-020-00115-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEricson, J. P., V\u0026ouml;r\u0026ouml;smarty, C. J., Dingman, S. L., Ward, L. G. \u0026amp; Meybeck, M. Effective sea-level rise and deltas: Causes of change and human dimension implications. \u003cem\u003eGlobal Planet. Change\u003c/em\u003e. \u003cb\u003e50\u003c/b\u003e (1\u0026ndash;2), 63\u0026ndash;82. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.gloplacha.2005.07.004\u003c/span\u003e\u003cspan address=\"10.1016/j.gloplacha.2005.07.004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2006).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSyvitski, J. P. M. et al. Sinking deltas due to human activities. \u003cem\u003eNat. Geosci.\u003c/em\u003e \u003cb\u003e2\u003c/b\u003e, 681\u0026ndash;686. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/ngeo629\u003c/span\u003e\u003cspan address=\"10.1038/ngeo629\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2009).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNicholls, R. J. et al. A global analysis of subsidence, relative sea-level change and coastal flood exposure. \u003cem\u003eNat. Clim. Change\u003c/em\u003e. \u003cb\u003e11\u003c/b\u003e (4), 338\u0026ndash;342. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41558-021-00993-z\u003c/span\u003e\u003cspan address=\"10.1038/s41558-021-00993-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChan, F. K. S. et al. Building resilience in Asian mega-deltas. \u003cem\u003eNat. Rev. Earth Environ.\u003c/em\u003e \u003cb\u003e5\u003c/b\u003e (7), 522\u0026ndash;537. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s43017-024-00561-x\u003c/span\u003e\u003cspan address=\"10.1038/s43017-024-00561-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOhenhen, L. et al. The Global Threat of Sinking Deltas. Research Square (Preprint Version 1, 07 April 2025), (2025). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.21203/rs.3.rs-6223150/v1\u003c/span\u003e\u003cspan address=\"10.21203/rs.3.rs-6223150/v1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEdmonds, D. A., Caldwell, R. L., Brondizio, E. S. \u0026amp; Siani, S. M. O. Coastal flooding will disproportionately impact people on river deltas. \u003cem\u003eNat. Commun.\u003c/em\u003e \u003cb\u003e11\u003c/b\u003e, 4741. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41467-020-18531-4\u003c/span\u003e\u003cspan address=\"10.1038/s41467-020-18531-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRenaud, F. G. et al. Tipping from the Holocene to the Anthropocene: How threatened are major world deltas? \u003cem\u003eCurr. Opin. Environ. Sustain.\u003c/em\u003e \u003cb\u003e5\u003c/b\u003e (6), 644\u0026ndash;654. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.cosust.2013.11.007\u003c/span\u003e\u003cspan address=\"10.1016/j.cosust.2013.11.007\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2013).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKulp, S. \u0026amp; Strauss, B. H. Global DEM errors underpredict coastal vulnerability to sea level rise and flooding. \u003cem\u003eFront. Earth Sci.\u003c/em\u003e \u003cb\u003e4\u003c/b\u003e, 36. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/feart.2016.00036\u003c/span\u003e\u003cspan address=\"10.3389/feart.2016.00036\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2016).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGesch, D. Assessing global elevation models for mapping the low elevation coastal zone. \u003cem\u003eGeomorphometry\u003c/em\u003e, (2023). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.5281/zenodo.8011577\u003c/span\u003e\u003cspan address=\"10.5281/zenodo.8011577\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSeeger, K. et al. Nay Win Oo \u0026amp; Brill, D. Assessing land elevation in the Ayeyarwady Delta (Myanmar) and its relevance for studying sea level rise and delta flooding. \u003cem\u003eHydrol. Earth Syst. Sci.\u003c/em\u003e \u003cb\u003e27\u003c/b\u003e (11), 2257\u0026ndash;2281. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5194/hess-27-2257-2023\u003c/span\u003e\u003cspan address=\"10.5194/hess-27-2257-2023\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHauser, L. et al. A. A scoping study on coastal vulnerability to relative sea-level rise in the Gulf of Guinea. \u003cem\u003eAFD Res. Papers\u003c/em\u003e. \u003cb\u003e283\u003c/b\u003e, 1\u0026ndash;42 (2023). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.afd.fr/en/ressources/scoping-study-coastal-vulnerability-relative-sealevel-rise-gulf-guinea\u003c/span\u003e\u003cspan address=\"https://www.afd.fr/en/ressources/scoping-study-coastal-vulnerability-relative-sealevel-rise-gulf-guinea\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWilson, J. P. Digital terrain modeling. \u003cem\u003eGeomorphology\u003c/em\u003e \u003cb\u003e137\u003c/b\u003e (1), 107\u0026ndash;121. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.geomorph.2011.03.012\u003c/span\u003e\u003cspan address=\"10.1016/j.geomorph.2011.03.012\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2012).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGuth, P. L. et al. Digital elevation models: Terminology and definitions. \u003cem\u003eRemote Sens.\u003c/em\u003e \u003cb\u003e13\u003c/b\u003e (18), 3581. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/rs13183581\u003c/span\u003e\u003cspan address=\"10.3390/rs13183581\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHodgson, M. E. \u0026amp; Bresnahan, P. Accuracy of airborne lidar-derived elevation. \u003cem\u003ePhotogramm Eng. Remote Sens.\u003c/em\u003e \u003cb\u003e70\u003c/b\u003e (3), 331\u0026ndash;339. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.14358/PERS.70.3.331\u003c/span\u003e\u003cspan address=\"10.14358/PERS.70.3.331\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2004).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBuffington, K. J., Dugger, B. D., Thorne, K. M. \u0026amp; Takekawa, J. Y. Statistical correction of lidar-derived digital elevation models with multispectral airborne imagery in tidal marshes. \u003cem\u003eRemote Sens. Environ.\u003c/em\u003e \u003cb\u003e186\u003c/b\u003e, 616\u0026ndash;625. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.rse.2016.09.020\u003c/span\u003e\u003cspan address=\"10.1016/j.rse.2016.09.020\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2016).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRen, H. C. et al. Study on analysis from sources of error for Airborne LIDAR. \u003cem\u003eIOP Conf. Ser. : Earth Environ. Sci.\u003c/em\u003e \u003cb\u003e46\u003c/b\u003e (1), 012030. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://doi.org/10.1088/1755-1315/46/1/012030\u003c/span\u003e\u003cspan address=\"10.1088/1755-1315/46/1/012030\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2016).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAlmar, R. et al. Coastal zone changes in West Africa: Challenges and opportunities for satellite earth observations. \u003cem\u003eSurv. Geophys.\u003c/em\u003e \u003cb\u003e44\u003c/b\u003e (1), 249\u0026ndash;275. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10712-022-09721-4\u003c/span\u003e\u003cspan address=\"10.1007/s10712-022-09721-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBecker, M. et al. Coastal flooding in Asian Megadeltas: Recent advances, persistent challenges, and call for actions admidst local and global changes. \u003cem\u003eRev. Geophys.\u003c/em\u003e 62(4), e (2024). RG00846 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1029/2024RG000846\u003c/span\u003e\u003cspan address=\"10.1029/2024RG000846\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFarr, T. G. et al. The Shuttle Radar Topography Mission. \u003cem\u003eRev. Geophys.\u003c/em\u003e 45(2), RG (2004). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1029/2005RG000183\u003c/span\u003e\u003cspan address=\"10.1029/2005RG000183\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2007).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAbrams, M., Bailey, B., Tsu, H. \u0026amp; Hato, M. The ASTER Global DEM. \u003cem\u003ePhotogramm Eng. Remote Sens.\u003c/em\u003e \u003cb\u003e76\u003c/b\u003e (4), 344\u0026ndash;348 (2010).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTachikawa, T. et al. ASTER Global Elevation Model Version 2 \u0026ndash; Summary of Validation Results. National Aeronautics and Space Administration (NASA) Land Processes Distributed Active Archive Center, and Joint Japan-US ASTER Science Team, 26 pp., (2011). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://lpdaac.usgs.gov/documents/220/Summary_GDEM2_validation_report_final.pdf\u003c/span\u003e\u003cspan address=\"https://lpdaac.usgs.gov/documents/220/Summary_GDEM2_validation_report_final.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTadono, T. et al. Generation of the 30 m-mesh global digital surface model by ALOS Prism. \u003cem\u003eInt. Arch. Photogramm Remote Sens. Spat. Inf. Sci.\u003c/em\u003e \u003cb\u003eXLI-B4\u003c/b\u003e, 157\u0026ndash;162. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5194/isprs-archives-XLI-B4-157-2016\u003c/span\u003e\u003cspan address=\"10.5194/isprs-archives-XLI-B4-157-2016\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2016).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWessel, B. TanDEM-X Ground Segment \u0026ndash; DEM Products Specification Document. EOC, DLR, Oberpfaffenhofen, Germany, Public Document TD-GS-PS-0021, Issue 3.2, (2018). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://tandemx-science.dlr.de/\u003c/span\u003e\u003cspan address=\"https://tandemx-science.dlr.de/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSmith, R. G. \u0026amp; Berry, P. A. M. ACE2: Global Digital Elevation Model User Guide. 22 pp. (2009).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBerry, P. A. M., Smith, R. G. \u0026amp; Benveniste, J. ACE2: The New Global Digital Elevation Model. In: Gravity, Geoid and Earth Observation, International Association of Geodesy Symposia 135, edited by Mertiklas, S.P., Springer, Berlin, Heidelberg, Germany, 231\u0026ndash;237, (2010). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/978-3-642-10634-7_30\u003c/span\u003e\u003cspan address=\"10.1007/978-3-642-10634-7_30\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBerry, P. A. M., Smith, R., Benveniste, J., Altimeter Corrected \u0026amp; Elevations \u003cem\u003eVersion 2 (ACE2) [dataset]\u003c/em\u003e (Palisades, 2019). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.7927/H40G3H78\u003c/span\u003e\u003cspan address=\"10.7927/H40G3H78\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003eNASA Socioeconomic Data and Applications Center (SEDAC).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAirbus Defence and Space: Copernicus Digital Elevation Model Product Handbook Version 3.0. Airbus, 38 pp. (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYamazaki, D. et al. D. A high-accuracy map of global terrain elevations. \u003cem\u003eGeophys. Res. Lett.\u003c/em\u003e \u003cb\u003e44\u003c/b\u003e (11), 5844\u0026ndash;5853. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/2017GL072874\u003c/span\u003e\u003cspan address=\"10.1002/2017GL072874\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2017).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHawker, L. et al. A 30 m global map of elevation with forests and buildings removed. \u003cem\u003eEnviron. Res. Lett.\u003c/em\u003e \u003cb\u003e17\u003c/b\u003e (2), 024016. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1088/1748-9326/ac4d4f\u003c/span\u003e\u003cspan address=\"10.1088/1748-9326/ac4d4f\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKulp, S. A. \u0026amp; Strauss, B. H. CoastalDEM: A global coastal digital elevation model improved from SRTM using a neural network. \u003cem\u003eRemote Sens. Environ.\u003c/em\u003e \u003cb\u003e206\u003c/b\u003e (1), 231\u0026ndash;239. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.rse.2017.12.026\u003c/span\u003e\u003cspan address=\"10.1016/j.rse.2017.12.026\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2018).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKulp, S. A. \u0026amp; Strauss, B. H. CoastalDEM v2.1: A high-accuracy and high-resolution global coastal elevation model trained on ICESat-2 satellite lidar. Climate Central Scientific Report, 17 pp., (2021). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://assets.ctfassets.net/cxgxgstp8r5d/3f1LzJSnp7ZjFD4loDYnrA/71eaba2b8f8d642dd9a7e6581dce0c66/CoastalDEM_2.1_Scientific_Report_.pdf\u003c/span\u003e\u003cspan address=\"https://assets.ctfassets.net/cxgxgstp8r5d/3f1LzJSnp7ZjFD4loDYnrA/71eaba2b8f8d642dd9a7e6581dce0c66/CoastalDEM_2.1_Scientific_Report_.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVernimmen, R., Hooijer, A. \u0026amp; Pronk, M. New ICESat-2 Satellite LiDAR Data Allow First Global Lowland DTM Suitable for Accurate Coastal Flood Risk Assessment. \u003cem\u003eRemote Sens.\u003c/em\u003e \u003cb\u003e12\u003c/b\u003e (17), 2827. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/rs12172827\u003c/span\u003e\u003cspan address=\"10.3390/rs12172827\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVernimmen, R. \u0026amp; Hooijer, A. New LiDAR-based elevation model shows greatest increase in global coastal exposure to flooding to be caused by early‐stage sea‐level rise. \u003cem\u003eEarth's Future\u003c/em\u003e. \u003cb\u003e11\u003c/b\u003e (1). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1029/2022EF002880\u003c/span\u003e\u003cspan address=\"10.1029/2022EF002880\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023). e2022EF002880, DOI.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePronk, M. et al. DeltaDTM: A global coastal digital terrain model. \u003cem\u003eSci. Data\u003c/em\u003e. \u003cb\u003e11\u003c/b\u003e (1), 273. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41597-024-03091-9\u003c/span\u003e\u003cspan address=\"10.1038/s41597-024-03091-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024a).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eUhe, P. et al. FathomDEM: an improved global terrain map using a hybrid vision transformer model. \u003cem\u003eEnviron. Res. Lett.\u003c/em\u003e \u003cb\u003e20\u003c/b\u003e (3), 034002. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1088/1748-9326/ada972\u003c/span\u003e\u003cspan address=\"10.1088/1748-9326/ada972\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2025).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e\u0026Uuml;st\u0026uuml;n, A., Abbak, R. A. \u0026amp; Zeray \u0026Ouml;st\u0026uuml;rk, E. Height biases of SRTM DEM related to EGM96: from a global perspective to regional practice. \u003cem\u003eSurv. Rev.\u003c/em\u003e \u003cb\u003e50\u003c/b\u003e, 26\u0026ndash;35. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/00396265.2016.1218159\u003c/span\u003e\u003cspan address=\"10.1080/00396265.2016.1218159\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2016).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMinderhoud, P. S. J., Coumou, L., Erkens, G., Middelkoop, H. \u0026amp; Stouthamer, E. Mekong delta much lower than previously assumed in sea-level rise impact assessments. \u003cem\u003eNat. Commun.\u003c/em\u003e \u003cb\u003e10\u003c/b\u003e, 3847. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41467-019-11602-1\u003c/span\u003e\u003cspan address=\"10.1038/s41467-019-11602-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2019a).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFusami, A. A., Edan, J. D. \u0026amp; Takana, A. Local orthometric height based on a combination of GPS-derived ellipsoidal height and geoid model: A review paper. \u003cem\u003eJ. Geodetic Sci.\u003c/em\u003e \u003cb\u003e13\u003c/b\u003e (1), 20220158. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1515/jogs-2022-0158\u003c/span\u003e\u003cspan address=\"10.1515/jogs-2022-0158\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMinderhoud, P. S. J., Hlavacova, I., Kolomaznik, J. \u0026amp; Neussner, O. Towards unraveling total subsidence of a mega-delta\u0026ndash;the potential of new PS InSAR data for the Mekong delta. \u003cem\u003eProc. Int. Assoc. Hydrol. Sci.\u003c/em\u003e 382, 327\u0026ndash;332, (2020a). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5194/piahs-382-327-2020\u003c/span\u003e\u003cspan address=\"10.5194/piahs-382-327-2020\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eD\u0026ouml;rr, N., Schenk, A. \u0026amp; Hinz, S. Land subsidence in the Mekong delta derived from advanced persistent scatterer interferometry with an infrastructural reference network. \u003cem\u003eIEEE J. Sel. Top. Appl. Earth Obs Remote Sens.\u003c/em\u003e \u003cb\u003e17\u003c/b\u003e, 12077\u0026ndash;12091 (2024). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10578013\u003c/span\u003e\u003cspan address=\"https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10578013\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCopernicus Emergency Management Service Risk \u0026amp; Recovery Mapping Non-standardised products (FLEX). Lot 1 Final Report. EMSN-091: Assessing changes in subsidence rates in the low Pampanga river basin and Manila area, Philippines. (2021). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://mapping.emergency.copernicus.eu/activations/EMSN091/\u003c/span\u003e\u003cspan address=\"https://mapping.emergency.copernicus.eu/activations/EMSN091/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSchumann, G. et al. Comparison of remotely sensed water stages from LiDAR, topographic contours and SRTM. \u003cem\u003eISPRS J. Photogramm Remote Sens.\u003c/em\u003e \u003cb\u003e63\u003c/b\u003e (3), 283\u0026ndash;296. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.isprsjprs.2007.09.004\u003c/span\u003e\u003cspan address=\"10.1016/j.isprsjprs.2007.09.004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2008).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCook, A. \u0026amp; Merwade, V. Effect of topographic data, geometric configuration and modeling approach on flood inundation mapping. \u003cem\u003eJ. Hydrol.\u003c/em\u003e \u003cb\u003e377\u003c/b\u003e (1\u0026ndash;2), 131\u0026ndash;142. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jhydrol.2009.08.015\u003c/span\u003e\u003cspan address=\"10.1016/j.jhydrol.2009.08.015\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2009).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHawker, L. et al. Implications of simulating global digital elevation models for flood inundation studies. \u003cem\u003eWater Resour. Res.\u003c/em\u003e \u003cb\u003e54\u003c/b\u003e (10), 7910\u0026ndash;7928. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1029/2018WR023279\u003c/span\u003e\u003cspan address=\"10.1029/2018WR023279\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2018a).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAzizian, A. \u0026amp; Brocca, L. Determining the best remotely sensed DEM for flood inundation mapping in data sparse regions. \u003cem\u003eInt. J. Remote Sens.\u003c/em\u003e \u003cb\u003e41\u003c/b\u003e (5), 1884\u0026ndash;1906. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/01431161.2019.1677968\u003c/span\u003e\u003cspan address=\"10.1080/01431161.2019.1677968\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eXu, K. et al. The importance of digital elevation model selection in flood simulation and a proposed method to reduce DEM errors: a case study in Shanghai. \u003cem\u003eInt. J. Disaster Risk Sci.\u003c/em\u003e \u003cb\u003e12\u003c/b\u003e, 890\u0026ndash;902. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s13753-021-00377-z\u003c/span\u003e\u003cspan address=\"10.1007/s13753-021-00377-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAlmar, R. et al. A global analysis of extreme coastal water levels with implications for potential coastal overtopping. \u003cem\u003eNat. Commun.\u003c/em\u003e \u003cb\u003e12\u003c/b\u003e (1), 3775. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41467-021-24008-9\u003c/span\u003e\u003cspan address=\"10.1038/s41467-021-24008-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eW\u0026ouml;ppelmann, G. \u0026amp; Pirazzoli, P. \u003cem\u003eTide gauges (ML Schwartz\u003c/em\u003e (ed.), Encyclopedia of Coastal Sciences, 2005).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMinderhoud, P. S. J., Coumou, L., Erkens, G., Middelkoop, H. \u0026amp; Stouthamer, E. Digital elevation model of the Vietnamese Mekong delta based on elevation points from a national topographical map [dataset]. \u003cem\u003ePANGAEA\u003c/em\u003e (2019b). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1594/PANGAEA.902136\u003c/span\u003e\u003cspan address=\"10.1594/PANGAEA.902136\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJousset, S. et al. New Global Mean Dynamic Topography CNES-CLS-22 Combining Drifters, Hydrological Profiles and High Frequency Radar Data. \u003cem\u003eESS Open Archive\u003c/em\u003e, (2023). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.22541/essoar.170158328.85804859/v2\u003c/span\u003e\u003cspan address=\"10.22541/essoar.170158328.85804859/v2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePronk, M. DeltaDTM v1.1: A global coastal digital terrain model (Version 4) [dataset]. \u003cem\u003e4TU.ResearchData\u003c/em\u003e (2024b). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4121/21997565.V4\u003c/span\u003e\u003cspan address=\"10.4121/21997565.V4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGesch, D. B. Best practices for elevation-based assessments of sea-level rise and coastal flooding exposure. \u003cem\u003eFront. Earth Sci.\u003c/em\u003e \u003cb\u003e6\u003c/b\u003e, 230. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/feart.2018.00230\u003c/span\u003e\u003cspan address=\"10.3389/feart.2018.00230\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2018).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSeeger, K., Minderhoud, P. S. J., Hauser, L. T., Teatini, P. \u0026amp; Woillez, M. N. How to assess coastal flood risk in data-sparse coastal lowlands? Accurate information on land elevation is key. \u003cem\u003eQuestion Dev.\u003c/em\u003e \u003cb\u003e71\u003c/b\u003e, 1\u0026ndash;4 (2024). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.afd.fr/en/ressources/how-assess-coastal-flood-risk-data-sparse-coastal-lowlands-accurate-information-land-elevation-key\u003c/span\u003e\u003cspan address=\"https://www.afd.fr/en/ressources/how-assess-coastal-flood-risk-data-sparse-coastal-lowlands-accurate-information-land-elevation-key\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAvornyo, S. Y. et al. The contribution of coastal land subsidence to potential sea-level rise impact in data-sparse settings: The case of Ghana\u0026rsquo;s Volta delta. \u003cem\u003eQuat Sci. Adv.\u003c/em\u003e \u003cb\u003e14\u003c/b\u003e, 100175. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.qsa.2024.100175\u003c/span\u003e\u003cspan address=\"10.1016/j.qsa.2024.100175\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eErban, L. E., Gorelick, S. M. \u0026amp; Zebker, H. A. Groundwater extraction, land subsidence, and sea-level rise in the Mekong Delta, Vietnam. \u003cem\u003eEnviron. Res. Lett.\u003c/em\u003e \u003cb\u003e9\u003c/b\u003e (8), 084010. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1088/1748-9326/9/8/084010\u003c/span\u003e\u003cspan address=\"10.1088/1748-9326/9/8/084010\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2014).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHerrera-Garc\u0026iacute;a, G. et al. Mapping the global threat of land subsidence. \u003cem\u003eScience\u003c/em\u003e \u003cb\u003e371\u003c/b\u003e (6524), 34\u0026ndash;36. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1126/science.abb8549\u003c/span\u003e\u003cspan address=\"10.1126/science.abb8549\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMinderhoud, P. S. J. et al. The relation between land use and subsidence in the Vietnamese Mekong delta. \u003cem\u003eSci. Total Environ.\u003c/em\u003e \u003cb\u003e634\u003c/b\u003e, 715\u0026ndash;726. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.scitotenv.2018.03.372\u003c/span\u003e\u003cspan address=\"10.1016/j.scitotenv.2018.03.372\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2018).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHawker, L., Bates, P., Neal, J. \u0026amp; Rougier, J. Perspectives on digital elevation model (DEM) simulation for flood modeling in the absence of a high-accuracy open access global DEM. \u003cem\u003eFront. Earth Sci.\u003c/em\u003e \u003cb\u003e6\u003c/b\u003e, 233. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/feart.2018.00233\u003c/span\u003e\u003cspan address=\"10.3389/feart.2018.00233\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2018b).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMinderhoud, P. S. J. et al. Impacts of 25 years of groundwater extraction on subsidence in the Mekong delta, Vietnam. \u003cem\u003eEnviron. Res. Lett.\u003c/em\u003e \u003cb\u003e12\u003c/b\u003e (6), 064006. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1088/1748-9326/aa7146\u003c/span\u003e\u003cspan address=\"10.1088/1748-9326/aa7146\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2017).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMinderhoud, P. S. J., Middelkoop, H., Erkens, G. \u0026amp; Stouthamer, E. Groundwater extraction may drown mega-delta: projections of extraction-induced subsidence and elevation of the Mekong delta for the 21st century. \u003cem\u003eEnviron. Res. Commun.\u003c/em\u003e \u003cb\u003e2\u003c/b\u003e (1), 011005. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1088/2515-7620/ab5e21\u003c/span\u003e\u003cspan address=\"10.1088/2515-7620/ab5e21\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020b).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePermanent Service for Mean Sea Level (PSMSL). Permanent Service for Mean Sea Level \u0026ndash; Tide Gauge Data: Vung Tau, (2025a). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://psmsl.org/data/obtaining/stations/1495.php\u003c/span\u003e\u003cspan address=\"https://psmsl.org/data/obtaining/stations/1495.php\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFox-Kemper, B. et al. Cryosphere and Sea Level Change. In: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, edited by Masson-Delmotte, V., Zhai, P., Pirani, A., Connors, S. L., P\u0026eacute;an, C., Berger, S., Caud, N., Chen, Y., Goldfarb, L., Gomis, M. I., Huang, M., Leitzell, K., Lonnoy, E., Matthews, J. B. R.,, and Cambridge University Press, Cambridge, 9-1-9-257 (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGarner, G. G. et al. IPCC AR6 Sea-Level Rise Projections, Version 20210809, PO.DAAC, CA, USA, Dataset, (2021). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://podaac.jpl.nasa.gov/announcements/2021-08-09-Sea-level-projections-from-the-IPCC-6th-Assessment-Report\u003c/span\u003e\u003cspan address=\"https://podaac.jpl.nasa.gov/announcements/2021-08-09-Sea-level-projections-from-the-IPCC-6th-Assessment-Report\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGarner, G. G. et al. Framework for Assessing Changes To Sea-level (FACTS), Geoscientific Model Development, \u003cem\u003eZenodo\u003c/em\u003e https://zenodo.org/record/6419954 (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZoccarato, C., Minderhoud, P. S. J. \u0026amp; Teatini, P. The role of sedimentation and natural compaction in a prograding delta: insights from the mega Mekong delta, Vietnam. \u003cem\u003eSci. Rep.\u003c/em\u003e \u003cb\u003e8\u003c/b\u003e (1), 11437. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41598-018-29734-7\u003c/span\u003e\u003cspan address=\"10.1038/s41598-018-29734-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2018).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBaldan, S., Minderhoud, P. S. J., Xotta, R., Zoccarato, C. \u0026amp; Teatini, P. Data-driven 3D modelling of long‐term Holocene delta evolution and sediment compaction: The Mekong Delta. \u003cem\u003eEarth Surf. Processes Land.\u003c/em\u003e \u003cb\u003e50\u003c/b\u003e (1), e6046. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/esp.6046\u003c/span\u003e\u003cspan address=\"10.1002/esp.6046\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2025).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ede Wit, K. et al. Identifying Causes of Urban Differential Subsidence in the Vietnamese Mekong Delta by Combining InSAR and Field Observations. \u003cem\u003eRemote Sens.\u003c/em\u003e \u003cb\u003e13\u003c/b\u003e (2), 189. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/rs13020189\u003c/span\u003e\u003cspan address=\"10.3390/rs13020189\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDunn, F. E. \u0026amp; Minderhoud, P. S. J. Sedimentation strategies provide effective but limited mitigation of relative sea-level rise in the Mekong delta. \u003cem\u003eCommun. Earth Environ.\u003c/em\u003e 3(1), 2, (2022). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s43247-021-00331-3\u003c/span\u003e\u003cspan address=\"10.1038/s43247-021-00331-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEslami, S. et al. Dynamics of salt intrusion in the Mekong Delta: results of field observations and integrated coastal\u0026ndash;inland modelling. \u003cem\u003eEarth Surf. Dyn.\u003c/em\u003e \u003cb\u003e9\u003c/b\u003e (4), 953\u0026ndash;976. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5194/esurf-9-953-2021\u003c/span\u003e\u003cspan address=\"10.5194/esurf-9-953-2021\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDang, A. T., Reid, M. \u0026amp; Kumar, L. Assessing potential impacts of sea level rise on mangrove ecosystems in the Mekong Delta, Vietnam. \u003cem\u003eReg. Environ. Change\u003c/em\u003e. \u003cb\u003e22\u003c/b\u003e (2), 70. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10113-022-01925-z\u003c/span\u003e\u003cspan address=\"10.1007/s10113-022-01925-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLe, H. A., Nguyen, T., Gratiot, N., Deleersnijder, E. \u0026amp; Soares-Fraz\u0026atilde;o, S. The multi-channel system of the Vietnamese Mekong Delta: impacts on the flow dynamics under relative sea-level rise scenarios. \u003cem\u003eWater\u003c/em\u003e \u003cb\u003e15\u003c/b\u003e (20), 3597. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/w15203597\u003c/span\u003e\u003cspan address=\"10.3390/w15203597\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWood, M. et al. J. M. Risk of compound flooding substantially increases in the future Mekong River delta. \u003cem\u003eNat. Hazards Earth Syst. Sci.\u003c/em\u003e \u003cb\u003e24\u003c/b\u003e (10), 3627\u0026ndash;3649. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5194/nhess-24-3627-2024\u003c/span\u003e\u003cspan address=\"10.5194/nhess-24-3627-2024\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDang, T. D., Cochrane, T. A., Arias, M. E. \u0026amp; Tri, V. P. D. Future hydrological alterations in the Mekong Delta under the impact of water resources development, land subsidence and sea level rise. \u003cem\u003eJ. Hydrol. : Reg. Stud.\u003c/em\u003e \u003cb\u003e15\u003c/b\u003e, 119\u0026ndash;133. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ejrh.2017.12.002\u003c/span\u003e\u003cspan address=\"10.1016/j.ejrh.2017.12.002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2018).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTanaka, K., Fujihara, Y., Hoshikawa, K. \u0026amp; Fujii, H. Development of a flood water level estimation method using satellite images and a digital elevation model for the Mekong floodplain. \u003cem\u003eHydrol. Sci. J.\u003c/em\u003e \u003cb\u003e64\u003c/b\u003e (2), 241\u0026ndash;253. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/02626667.2019.1578463\u003c/span\u003e\u003cspan address=\"10.1080/02626667.2019.1578463\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2019).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eThanh, V. Q. et al. T. P. Flooding in the Mekong Delta: the impact of dyke systems on downstream hydrodynamics. \u003cem\u003eHydrol. Earth Syst. Sci.\u003c/em\u003e \u003cb\u003e24\u003c/b\u003e (1), 189\u0026ndash;212. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5194/hess-24-189-2020\u003c/span\u003e\u003cspan address=\"10.5194/hess-24-189-2020\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWu, S. \u0026amp; Lei, Y. Multiscale Flood Disaster Risk Assessment in the Lancang-Mekong River Basin: A Focus on Watershed and Community Levels. \u003cem\u003eAtmosphere\u003c/em\u003e \u003cb\u003e14\u003c/b\u003e (4), 657. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/atmos14040657\u003c/span\u003e\u003cspan address=\"10.3390/atmos14040657\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChen, A. et al. Impact of tropical cyclone precipitation on fluvial discharge in the Lancang\u0026ndash;Mekong River Basin. \u003cem\u003eGeophys. Res. Lett.\u003c/em\u003e 52(8), e (2024). GL113199 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1029/2024GL113199\u003c/span\u003e\u003cspan address=\"10.1029/2024GL113199\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYun, X., Song, J., Wang, J. \u0026amp; Bao, H. Modelling to assess the suitability of hydrological-hydrodynamic model under the hydropower development impact in the Lancang-Mekong River basin. \u003cem\u003eJ. Hydrol.\u003c/em\u003e \u003cb\u003e637\u003c/b\u003e, 131393. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1029/2024GL113199\u003c/span\u003e\u003cspan address=\"10.1029/2024GL113199\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVu, D. T., Bruinsma, S., Bonvalot, S., Remy, D. \u0026amp; Vergos, G. S. A. Quasigeoid-derived transformation model accounting for land subsidence in the Mekong delta towards height system unification in Vietnam. \u003cem\u003eRemote Sens.\u003c/em\u003e \u003cb\u003e12\u003c/b\u003e (5), 817. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/rs12050817\u003c/span\u003e\u003cspan address=\"10.3390/rs12050817\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVu, D. T., Bruinsma, S., Bonvalot, S., Bui, L. K. \u0026amp; Balmino, G. Determination of the geopotential value on the permanent GNSS stations in Vietnam based on the Geodetic Boundary Value Problem approach. \u003cem\u003eGeophys. J. Int.\u003c/em\u003e \u003cb\u003e226\u003c/b\u003e (2), 1206\u0026ndash;1219. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/gji/ggab166\u003c/span\u003e\u003cspan address=\"10.1093/gji/ggab166\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePermanent Service for Mean Sea Level (PSMSL). Permanent Service for Mean Sea Level \u0026ndash; Tide Gauge Data: Hon Dau, (2025b). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://psmsl.org/data/obtaining/stations/841.php\u003c/span\u003e\u003cspan address=\"https://psmsl.org/data/obtaining/stations/841.php\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eInce, E. S. et al. ICGEM \u0026ndash; 15 years of successful collection and distribution of global gravitational models, associated services and future plans. \u003cem\u003eEarth Syst. Sci. Data\u003c/em\u003e. \u003cb\u003e11\u003c/b\u003e, 647\u0026ndash;674. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5194/essd-11-647-2019\u003c/span\u003e\u003cspan address=\"10.5194/essd-11-647-2019\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2019).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVignudelli, S., Kostianoy, A. G., Cipollini, P. \u0026amp; Benveniste, J. \u003cem\u003eCoastal altimetry\u003c/em\u003e (Springer, 2011). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/978-3-642-12796-0\u003c/span\u003e\u003cspan address=\"10.1007/978-3-642-12796-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVignudelli, S. et al. Satellite altimetry measurements of sea level in the coastal zone. \u003cem\u003eSurv. Geophys.\u003c/em\u003e \u003cb\u003e40\u003c/b\u003e, 1319\u0026ndash;1349. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10712-019-09569-1\u003c/span\u003e\u003cspan address=\"10.1007/s10712-019-09569-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2019).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBirol, F. et al. Understanding uncertainties in the satellite altimeter measurement of coastal sea level: insights from a round-robin analysis. \u003cem\u003eOcean. Sci.\u003c/em\u003e \u003cb\u003e21\u003c/b\u003e (1), 133\u0026ndash;150. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5194/os-21-133-2025\u003c/span\u003e\u003cspan address=\"10.5194/os-21-133-2025\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2025).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAguilar, F. J., Aguilar, M. A., Ag\u0026uuml;era, F. \u0026amp; S\u0026aacute;nchez, J. The accuracy of grid digital elevation models linearly constructed from scattered sample data. \u003cem\u003eInt. J. Geogr. Inf. Sci.\u003c/em\u003e \u003cb\u003e20\u003c/b\u003e (2), 169\u0026ndash;192. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/13658810500399670\u003c/span\u003e\u003cspan address=\"10.1080/13658810500399670\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2006).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWessel, B. TanDEM-X ground segment\u0026ndash;DEM products specification document. (2013).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRio, M. H., Mulet, S. \u0026amp; Picot, N. Beyond GOCE for the ocean circulation estimate: Synergetic use of altimetry, gravimetry, and in situ data provides new insight into geostrophic and Ekman currents. \u003cem\u003eGeophys. Res. Lett.\u003c/em\u003e \u003cb\u003e41\u003c/b\u003e (24), 8918\u0026ndash;8925. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/2014GL061773\u003c/span\u003e\u003cspan address=\"10.1002/2014GL061773\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2014).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Digital elevation model (DEM), land elevation change, land subsidence, relative sea-level rise, sea level, vertical datum","lastPublishedDoi":"10.21203/rs.3.rs-7706762/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7706762/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eRiver deltas and coastal plains are at risk of sea-level rise and other coastal hazards, often exacerbated by land subsidence. The relative elevation of a coastal lowland to local sea level is a crucial determinant for its overall exposure, making it key input for coastal hazard assessments. However, locally-sourced, high-accuracy elevation data, such as LiDAR, is not available for many data-sparse coastal lowlands worldwide, leaving global digital elevation models as only source of information. While these provide an adequate spatial (i.e. horizontal) resolution for regional, delta-wide coastal assessments, their vertical errors in the range of several metres impede investigations of (relative) sea-level rise impact where changes occur on millimetre- to centimetre-scale. Assessing the quality of available elevation datasets is required to identify the best performing model(s) to use for generating reliable coastal impact and exposure assessments. While data-intrinsic inaccuracy has been extensively addressed both in dataset documentation and literature, the relevance and proper vertical datum conversion from global geoid and ellipsoid to local sea level is often still omitted in many applied studies from coastal research. Similarly, the impact of the actuality of elevation data (i.e. time since data acquisition) on assessments in coastal lowlands is so far understudied although elevation models may become quickly outdated, especially where coastal lowlands are facing high rates of elevation change resulting from the interplay of vertical land motion, (vertical) sediment accretion and sea-level change. Particularly for flat, low-lying subsiding coastal landscapes like the Mekong Delta, being in parts only a few decimetres elevated above sea level and experiencing land subsidence of up to several centimetres per year, the reliability of elevation data and adequate representation of elevation relative to local sea level as well as the consideration of factors impacting elevation over time is of utmost importance. We present a globally applicable approach to quantify and attribute uncertainties in elevation assessment for data-sparse coastal lowlands using global elevation models to sources such as inaccuracy, vertical datum offset and actuality. We showcase this approach by revisiting land elevation in the Vietnamese Mekong Delta (i) by vertically referencing 11 commonly used global elevation models and an updated local elevation model to a common actual, local sea-level datum, and (ii) by conducting a thorough assessment of elevation model performance that not only allows for the quantification of errors and elevation assessment uncertainties but also their attribution to data-intrinsic inaccuracy, vertical datum offset and, tentatively, non-linear impact of elevation change due to vertical land motion (e.g. extraction-induced land subsidence) and sea-level change affecting the actuality of the elevation model. Our approach not only allows to improve the understanding of coastal elevation to further improve relative sea-level rise and flood impact assessments and to substantiate projections of future elevation in the Mekong Delta, but in its design, applying solely open data and commonly used GIS software, facilitates similar assessments of elevation model performance and elevation assessment uncertainties in other (data-sparse) coastal regions in the world.\u003c/p\u003e","manuscriptTitle":"Attributing uncertainties in elevation assessments for data-sparse coastal lowlands using global elevation models: A globally applicable approach showcasing the Vietnamese Mekong Delta","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-30 12:20:35","doi":"10.21203/rs.3.rs-7706762/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-19T09:51:51+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-18T09:18:04+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-07T14:51:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"161440131510986252474327276436586260892","date":"2025-10-22T14:03:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"74160770996873276345266175071812856012","date":"2025-10-17T14:33:06+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-17T13:42:52+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-17T13:25:39+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-10-17T12:50:52+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-08T12:52:22+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-10-08T12:45:38+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4f96e285-2f50-464e-a319-a9ec889f1ea5","owner":[],"postedDate":"October 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":56957366,"name":"Earth and environmental sciences/Climate sciences"},{"id":56957367,"name":"Earth and environmental sciences/Environmental sciences"},{"id":56957368,"name":"Earth and environmental sciences/Natural hazards"}],"tags":[],"updatedAt":"2026-02-09T16:00:31+00:00","versionOfRecord":{"articleIdentity":"rs-7706762","link":"https://doi.org/10.1038/s41598-026-38315-y","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2026-02-04 15:57:22","publishedOnDateReadable":"February 4th, 2026"},"versionCreatedAt":"2025-10-30 12:20:35","video":"","vorDoi":"10.1038/s41598-026-38315-y","vorDoiUrl":"https://doi.org/10.1038/s41598-026-38315-y","workflowStages":[]},"version":"v1","identity":"rs-7706762","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7706762","identity":"rs-7706762","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}