Coastal regions globally face significant risks from rising sea levels, exacerbated by factors such as land subsidence. Accurately assessing this risk requires precise land elevation data. However, until recently, such data were limited, leading to substantial uncertainty in projecting the extent of land and populations vulnerable to inundation. This paper addresses this critical gap by employing a novel global elevation model derived from satellite LiDAR data – the Global LiDAR Lowland DTM (GLL_DTM_v1). The study aims to provide a more accurate assessment of global coastal vulnerability to sea-level rise, with a specific focus on identifying regions at disproportionately high risk. The importance of this research lies in its potential to inform effective adaptation strategies and policy decisions for coastal communities worldwide, especially those in the tropics, which are projected to experience the most significant impacts.

Existing Global Digital Elevation Models (GDEMs), such as SRTM, MERIT, and CoastalDEM, have been used in previous studies to assess coastal flood risk. However, these models have limitations in accuracy, particularly in coastal lowlands, leading to underestimations of vulnerable areas. Studies have highlighted the discrepancies between GDEMs and the actual coastal topography, emphasizing the need for improved elevation data. The inconsistencies in these models have resulted in differing projections of vulnerable land areas and populations, creating uncertainty in risk assessments. For example, studies on the Ganges-Brahmaputra-Meghna delta illustrate the high flood risk in this region and the limitations of existing GDEMs in capturing the full extent of the problem. Furthermore, previous analyses often overlooked the extensive low-lying areas in regions like Indonesia, underestimating their vulnerability. The lack of accurate and comprehensive elevation data has hindered effective adaptation planning and policy development, particularly in tropical regions.

This study utilizes the GLL_DTM_v1, a global digital elevation model (DEM) derived from ICESat-2 satellite LiDAR data collected between October 14, 2018, and May 13, 2020. The GLL_DTM_v1 offers significantly improved accuracy compared to previous GDEMs, particularly in coastal lowlands. The analysis covers the entire GLL_DTM_v1 extent (88°N-88°S) to determine global areas and population numbers. Tropical regions (23.5°N-23.5°S) are separately analyzed. Coastal lowlands at highest flood risk are defined as areas below 2 meters above mean sea level (MSL), following Syvitski et al. (2009). Population data for 2000 and 2020 are obtained from the UN's Gridded Population of the World database. Future projections consider a 1-meter relative sea-level rise (RSLR) by 2100, combining projected absolute SLR and land surface subsidence (LSS). This value represents a middle ground derived from IPCC RCP2.6/RCP8.5 projections for SLR and reported LSS rates. A deterministic method, commonly used in flood risk assessments, is employed along with a modified deterministic method to account for vertical uncertainty in the GLL_DTM_v1. Uncertainty is assessed by calculating areas and populations at both 68% and 95% confidence levels using RMSE values from the elevation model. Comparisons with other GDEMs (SRTM9013, MERIT14, CoastalDEM15, and TanDEM-X23) are conducted to evaluate the accuracy and differences in findings.

The key findings of this study reveal a significantly higher level of coastal vulnerability to sea-level rise than previously understood. The GLL_DTM_v1 data show that 649,000 km² of land, or 62% of the global land area less than 2 m above mean sea level, lies within tropical regions. This represents a substantial increase compared to estimations using previous GDEMs. Indonesia is identified as having the largest extent of land below 2 m +MSL globally at 118,200 km², significantly more than estimates from SRTM. The study projects a significant increase in the global population residing on land below 2 m +MSL, from 267 million in 2020 to at least 410 million by 2100 under a conservative 1-meter RSLR scenario. A disproportionate share of this increase (72%) would be concentrated in the tropics, with a significant portion (59%) in tropical Asia. The analysis highlights specific regions like the Ganges-Brahmaputra-Meghna delta and other major tropical deltas (e.g., Mekong, Chao Phraya, Pearl River, Amazon, and Niger), where millions of people are already at high risk of frequent flooding and face even greater risks from future sea-level rise. Comparisons with other GDEMs demonstrate the significant improvements in accuracy offered by the GLL_DTM_v1, particularly in low-lying coastal areas. Figure 3 illustrates the differences in area estimates obtained from various GDEMs, highlighting the substantial discrepancies in the assessment of vulnerable areas.

These findings underscore the significant and disproportionate vulnerability of tropical regions, particularly in Asia, to sea-level rise. The substantially larger extent of low-lying land areas revealed by the GLL_DTM_v1 data necessitates a reassessment of coastal flood risk globally. The projected population increase in vulnerable areas emphasizes the urgent need for effective adaptation measures. The results challenge the existing understanding of coastal vulnerability and highlight the limitations of previously used GDEMs. The high accuracy of the GLL_DTM_v1 offers a more robust basis for spatial planning and policy development, providing crucial information for decision-making related to coastal resilience. The study's findings have significant implications for disaster risk reduction, infrastructure planning, and the development of effective adaptation strategies in the face of rising sea levels.

This study, using the first globally consistent high-resolution LiDAR-based elevation model, reveals that tropical regions face a significantly higher risk from sea-level rise than previously estimated. The considerably larger areas of land below 2 m above mean sea level, coupled with substantial population growth projections, necessitate urgent implementation of adaptation strategies. Future research should focus on refining the GLL_DTM_v1 resolution and accuracy, incorporating further data and analyzing the combined effect of sea-level rise and other factors, such as storm surges and riverine flooding, to improve coastal risk assessments and inform more effective adaptation plans.

While the GLL_DTM_v1 represents a significant advancement in global elevation data, some limitations exist. The analysis uses a conservative estimate of RSLR, and future studies could incorporate more refined projections incorporating different emission scenarios. The study assumes a stable population distribution; future work might include scenarios reflecting potential population migrations or changes in population density within vulnerable regions. Additionally, the study focuses on elevation, and other factors, such as infrastructure, social and economic vulnerability, need to be incorporated for a complete assessment of coastal risk.