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Uncertainties in measuring populations potentially impacted by sea level rise and coastal flooding

Uncertainties in measuring populations potentially impacted by sea level rise and coastal flooding
Uncertainties in measuring populations potentially impacted by sea level rise and coastal flooding
A better understanding of the impact of global climate change requires information on the locations and characteristics of populations affected. For instance, with global sea level predicted to rise and coastal flooding set to become more frequent and intense, high-resolution spatial population datasets are increasingly being used to estimate the size of vulnerable coastal populations. Many previous studies have undertaken this by quantifying the size of populations residing in low elevation coastal zones using one of two global spatial population datasets available – LandScan and the Global Rural Urban Mapping Project (GRUMP). This has been undertaken without consideration of the effects of this choice, which are a function of the quality of input datasets and differences in methods used to construct each spatial population dataset. Here we calculate estimated low elevation coastal zone resident population sizes from LandScan and GRUMP using previously adopted approaches, and quantify the absolute and relative differences achieved through switching datasets. Our findings suggest that the choice of one particular dataset over another can translate to a difference of more than 7.5 million vulnerable people for countries with extensive coastal populations, such as Indonesia and Japan. Our findings also show variations in estimates of proportions of national populations at risk range from <0.1% to 45% differences when switching between datasets, with large differences predominantly for countries where coarse and outdated input data were used in the construction of the spatial population datasets. The results highlight the need for the construction of spatial population datasets built on accurate, contemporary and detailed census data for use in climate change impact studies and the importance of acknowledging uncertainties inherent in existing spatial population datasets when estimating the demographic impacts of climate change.

1932-6203
e48191-[7pp]
Mondal, P.
c76cc945-7503-4ea3-a7db-2bc8f8c350b6
Tatem, A.J.
6c6de104-a5f9-46e0-bb93-a1a7c980513e
Mondal, P.
c76cc945-7503-4ea3-a7db-2bc8f8c350b6
Tatem, A.J.
6c6de104-a5f9-46e0-bb93-a1a7c980513e

Mondal, P. and Tatem, A.J. (2012) Uncertainties in measuring populations potentially impacted by sea level rise and coastal flooding. PLoS ONE, 7 (10), e48191-[7pp]. (doi:10.1371/journal.pone.0048191).

Record type: Article

Abstract

A better understanding of the impact of global climate change requires information on the locations and characteristics of populations affected. For instance, with global sea level predicted to rise and coastal flooding set to become more frequent and intense, high-resolution spatial population datasets are increasingly being used to estimate the size of vulnerable coastal populations. Many previous studies have undertaken this by quantifying the size of populations residing in low elevation coastal zones using one of two global spatial population datasets available – LandScan and the Global Rural Urban Mapping Project (GRUMP). This has been undertaken without consideration of the effects of this choice, which are a function of the quality of input datasets and differences in methods used to construct each spatial population dataset. Here we calculate estimated low elevation coastal zone resident population sizes from LandScan and GRUMP using previously adopted approaches, and quantify the absolute and relative differences achieved through switching datasets. Our findings suggest that the choice of one particular dataset over another can translate to a difference of more than 7.5 million vulnerable people for countries with extensive coastal populations, such as Indonesia and Japan. Our findings also show variations in estimates of proportions of national populations at risk range from <0.1% to 45% differences when switching between datasets, with large differences predominantly for countries where coarse and outdated input data were used in the construction of the spatial population datasets. The results highlight the need for the construction of spatial population datasets built on accurate, contemporary and detailed census data for use in climate change impact studies and the importance of acknowledging uncertainties inherent in existing spatial population datasets when estimating the demographic impacts of climate change.

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Published date: 24 October 2012
Organisations: Geography & Environment, PHEW – S (Spatial analysis and modelling)

Identifiers

Local EPrints ID: 344435
URI: http://eprints.soton.ac.uk/id/eprint/344435
ISSN: 1932-6203
PURE UUID: 47a48f01-2bb1-4d09-9069-2e057339681f
ORCID for A.J. Tatem: ORCID iD orcid.org/0000-0002-7270-941X

Catalogue record

Date deposited: 05 Nov 2012 13:40
Last modified: 15 Mar 2024 03:43

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Contributors

Author: P. Mondal
Author: A.J. Tatem ORCID iD

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