Literature DB >> 34188026

Global LiDAR land elevation data reveal greatest sea-level rise vulnerability in the tropics.

A Hooijer1,2, R Vernimmen3.   

Abstract

Coastal flood risk assessments require accurate land elevation data. Those to date existed only for limited parts of the world, which has resulted in high uncertainty in projections of land area at risk of sea-level rise (SLR). Here we have applied the first global elevation model derived from satellite LiDAR data. We find that of the worldwide land area less than 2 m above mean sea level, that is most vulnerable to SLR, 649,000 km2 or 62% is in the tropics. Even assuming a low-end relative SLR of 1 m by 2100 and a stable lowland population number and distribution, the 2020 population of 267 million on such land would increase to at least 410 million of which 72% in the tropics and 59% in tropical Asia alone. We conclude that the burden of current coastal flood risk and future SLR falls disproportionally on tropical regions, especially in Asia.

Entities:  

Year:  2021        PMID: 34188026     DOI: 10.1038/s41467-021-23810-9

Source DB:  PubMed          Journal:  Nat Commun        ISSN: 2041-1723            Impact factor:   14.919


  4 in total

1.  Human populations in the world's mountains: Spatio-temporal patterns and potential controls.

Authors:  James M Thornton; Mark A Snethlage; Roger Sayre; Davnah R Urbach; Daniel Viviroli; Daniele Ehrlich; Veruska Muccione; Philippus Wester; Gregory Insarov; Carolina Adler
Journal:  PLoS One       Date:  2022-07-20       Impact factor: 3.752

2.  Assessing population exposure to coastal flooding due to sea level rise.

Authors:  Mathew E Hauer; Dean Hardy; Scott A Kulp; Valerie Mueller; David J Wrathall; Peter U Clark
Journal:  Nat Commun       Date:  2021-11-25       Impact factor: 14.919

3.  High-resolution global maps of tidal flat ecosystems from 1984 to 2019.

Authors:  Nicholas J Murray; Stuart P Phinn; Richard A Fuller; Michael DeWitt; Renata Ferrari; Renee Johnston; Nicholas Clinton; Mitchell B Lyons
Journal:  Sci Data       Date:  2022-09-06       Impact factor: 8.501

4.  A Preliminary Contribution towards a Risk-Based Model for Flood Management Planning Using BIM: A Case Study of Lisbon.

Authors:  Graziella Del Duca; Gustavo Rocha; Marta Orszt; Luis Mateus
Journal:  Sensors (Basel)       Date:  2022-10-01       Impact factor: 3.847

  4 in total

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