New LiDAR‐Based Elevation Model Shows Greatest Increase in Global Coastal Exposure to Flooding to Be Caused by Early‐Stage Sea‐Level Rise

Vernimmen, Ronald; Hooijer, A.

doi:10.1029/2022ef002880

Cited by 19 publications

(20 citation statements)

References 36 publications

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“…This dataset was obtained by the SRTM [37], a joint survey conducted by NASA and the National Imagery and Mapping Agency with a resolution of 30 m. SRTM reflects the highest elevation of an object on the ground and provides free data for all land areas from 60 GLL_DTM_v2: GLL is a global DEM dataset generated by Vernimmen and Hooijer [3] based on the most accurate new satellite LIDAR ICESat-2 data, with a 0.01 • horizontal resolution (∼1 km near the equator). This DEM data has been shown to have higher vertical accuracy compared to other global DEM [3,34]. The GLL data for this paper are from the open study by Vernimmen and Hooijer [3], downloaded from https://doi.org/10.5281/zenodo.…”

Section: Srtmmentioning

confidence: 99%

“…This DEM data has been shown to have higher vertical accuracy compared to other global DEM [3,34]. The GLL data for this paper are from the open study by Vernimmen and Hooijer [3], downloaded from https://doi.org/10.5281/zenodo. 6534526.…”

Section: Srtmmentioning

confidence: 99%

“…Considering the carbon emission scenarios and the land subsidence caused by drainage and groundwater abstraction, relative sea-level rise (hereafter: SLR) will reach 1 m before 2150 and 4 m in centuries to come [1][2][3] in the most densely populated coastal areas. This immediately implies the increased coastal exposure to flooding and the cost of climate change adaptation measures [4].…”

Section: Introductionmentioning

confidence: 99%

“…The SRTM was the first global DEM but was found to have a global mean error of 1.9 m when using the DEM from NASA's ICESat satellite as ground truth [21]. Despite the improvement in accuracy compared to SRTM, ALOS and the improved SRTM-based NASADEM still lack the necessary precision and minimum uncertainty for SLR assessment [3,22]. And their vertical errors which approach or even exceed the projected SLR heights for this century incline to trigger serious misestimation of PPE [2,21].…”

Section: Introductionmentioning

confidence: 99%

“…Recently, new satellite Lidarbased DEM data were produced for global SLR assessment and have been updated to a second version (GLL_DTM_v2, hereafter: GLL) at 0.01-degree horizontal resolution (∼1 km near the equator) [17,34]. Validation with existing well-described and accurate local DTM revealed that GLL exhibits higher vertical accuracy than other global DEM and is more suitable for global SLR assessment [3]. Therefore, it is now necessary to combine new elevation data and population grids to quantify the PPE of the LECZ and compare it with previous data sources for uncertainty analysis.…”

Section: Introductionmentioning

confidence: 99%

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Uncertainty analysis of potential population exposure within the coastal lowlands of mainland China

Li,

Yao,

et al. 2023

Environ. Res. Lett.

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With accelerating global sea level rise (SLR) driven by climate change, accurate estimates of potential population exposure (PPE) within the low-elevation coastal zones (LECZ) are critical for coastal planning and assessing the benefits of climate mitigation. Multiple digital elevation models (DEM) and population grid datasets have been used for the PPE assessment of coastal lowlands. However, the uncertainty arising from differences in data sources and production methods results in poorly guided estimates. In this study, four global DEM and five population datasets were used to estimate the PPE in the LECZ of China and to assess the uncertainty of PPE estimation. Based on the DEM and population grid with the best accuracy, we found that more than 13.82% of China’s residents lived in the LECZ in 2010. Different DEM-population combinations yielded significantly different PPE estimates, ranging between 3.59-24.61 million and 31.56-112.24 million people in the LECZ below 1 m and 4 m elevation, respectively. The satellite Lidar-based DEM improves the estimates of the LECZ and obtains the PPE within LECZ below 4 m elevation that far exceeds those of other DEM datasets. The usage of WorldPop and LandScan population datasets leads to an underestimation of PPE within the LECZ of China. In contrast, integrating more geospatial big data helps generate better population grids, thus reducing the uncertainty of coastal PPE estimates. There is still a need to improve the availability and accuracy of coastal geospatial data and to deepen the understanding of coastal vulnerability.

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Section: Srtmmentioning

confidence: 99%

Section: Srtmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Uncertainty analysis of potential population exposure within the coastal lowlands of mainland China

Li,

Yao,

et al. 2023

Environ. Res. Lett.

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A multi-risk approach for projecting climate change-associated coastal flood, applied to India

Jennath,

Paul

2024

Nat Hazards

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An integrated approach to the spatial distribution of the coastal infrastructure vulnerability by using coastal vulnerability index and hot spot analysis: a case study of Kusadasi-Selcuk

Kuleli,

Bayazit

2024

Nat Hazards

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In this study, a new methodology integrating spatial coastal vulnerability index (CVI) and hot spot analysis was proposed for a comprehensive and holistic coastal infrastructure (natural, built-up and socioeconomic) vulnerability assessment and determination of its spatial distribution. The Selçuk-Kuşadası coastal region, which is located on the Aegean coast of Turkey, was chosen as the case study area. For calculation spatial CVI, eight physical variables (geomorphology, coastal slope, relief, mean seal level, mean tide range, mean wave high, shoreline erosion and accretion) were used. Spatial CVI analyses were performed using basic methods of geographical information systems. CVI results were classified as 1 to 5 across the study area. The spatial distribution of vulnerabilities of coastal infrastructures has statistically tested with hotspot analysis. In conclusion; over 55% of infrastructure falls within high or very high vulnerability classes. It is seen that 57% of the infrastructure in the build-up category, 59% of the natural environment, and more than 42% of the socio-economic infrastructure are located in areas falling into the high or very high vulnerability classes. Additionally, $1.1 billion worth of land assets and a population of approximately 143.000 people are located in areas that fall into the high or very high vulnerability classes. Statistically significant clusters of high vulnerability for historical sites, tourism, roads, land value, population density, and urban areas were determined by hot spot analysis. According to the results of the developed integrated method, suggestions were presented for the preparation of coastal infrastructure vulnerability adaptation strategies.

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New LiDAR‐Based Elevation Model Shows Greatest Increase in Global Coastal Exposure to Flooding to Be Caused by Early‐Stage Sea‐Level Rise

Cited by 19 publications

References 36 publications

Uncertainty analysis of potential population exposure within the coastal lowlands of mainland China

Uncertainty analysis of potential population exposure within the coastal lowlands of mainland China

A multi-risk approach for projecting climate change-associated coastal flood, applied to India

An integrated approach to the spatial distribution of the coastal infrastructure vulnerability by using coastal vulnerability index and hot spot analysis: a case study of Kusadasi-Selcuk

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