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A 30 m global flood inundation model for any climate scenario

A 30 m global flood inundation model for any climate scenario
A 30 m global flood inundation model for any climate scenario
Global flood mapping has developed rapidly over the past decade, but previous approaches have limited scope, function, and accuracy. These limitations restrict the applicability and fundamental science questions that can be answered with existing model frameworks. Harnessing recently available data and modeling methods, this paper presents a new global ∼30 m resolution Global Flood Map (GFM) with complete coverage of fluvial, pluvial, and coastal perils, for any return period or climate scenario, including accounting for uncertainty. With an extensive compilation of global benchmark case studies—ranging from locally collected event water levels, to national inventories of engineering flood maps—we execute a comprehensive validation of the new GFM. For flood extent comparisons, we demonstrate that the GFM achieves a critical success index of ∼0.75. In the more discriminatory tests of flood water levels, the GFM deviates from observations by ∼0.6 m on average. Results indicating this level of global model fidelity are unprecedented in the literature. With an optimistic scenario of future warming (SSP1-2.6), we show end-of-century global flood hazard (average annual inundation volume) increases are limited to 9% (likely range -6%–29%); this is within the likely climatological uncertainty of −8%–12% in the current hazard estimate. In contrast, pessimistic scenario (SSP5-8.5) hazard changes emerge from the background noise in the 2040s, rising to a 49% (likely range of 7%–109%) increase by 2100. This work verifies the fitness-for-purpose of this new-generation GFM for impact analyses with a variety of beneficial applications across policymaking, planning, and commercial risk assessment.
0043-1397
Haigh, Ivan
945ff20a-589c-47b7-b06f-61804367eb2d
Haigh, Ivan
945ff20a-589c-47b7-b06f-61804367eb2d

Haigh, Ivan (2024) A 30 m global flood inundation model for any climate scenario. Water Resources Research, 60 (8). (doi:10.1029/2023WR036460).

Record type: Article

Abstract

Global flood mapping has developed rapidly over the past decade, but previous approaches have limited scope, function, and accuracy. These limitations restrict the applicability and fundamental science questions that can be answered with existing model frameworks. Harnessing recently available data and modeling methods, this paper presents a new global ∼30 m resolution Global Flood Map (GFM) with complete coverage of fluvial, pluvial, and coastal perils, for any return period or climate scenario, including accounting for uncertainty. With an extensive compilation of global benchmark case studies—ranging from locally collected event water levels, to national inventories of engineering flood maps—we execute a comprehensive validation of the new GFM. For flood extent comparisons, we demonstrate that the GFM achieves a critical success index of ∼0.75. In the more discriminatory tests of flood water levels, the GFM deviates from observations by ∼0.6 m on average. Results indicating this level of global model fidelity are unprecedented in the literature. With an optimistic scenario of future warming (SSP1-2.6), we show end-of-century global flood hazard (average annual inundation volume) increases are limited to 9% (likely range -6%–29%); this is within the likely climatological uncertainty of −8%–12% in the current hazard estimate. In contrast, pessimistic scenario (SSP5-8.5) hazard changes emerge from the background noise in the 2040s, rising to a 49% (likely range of 7%–109%) increase by 2100. This work verifies the fitness-for-purpose of this new-generation GFM for impact analyses with a variety of beneficial applications across policymaking, planning, and commercial risk assessment.

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Wing-et-al-2023_WRR_revised_clean - Accepted Manuscript
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More information

Accepted/In Press date: 15 July 2024
e-pub ahead of print date: 21 August 2024

Identifiers

Local EPrints ID: 502711
URI: http://eprints.soton.ac.uk/id/eprint/502711
ISSN: 0043-1397
PURE UUID: 58bf01f6-d0d5-4b66-9d69-b666d4b11646
ORCID for Ivan Haigh: ORCID iD orcid.org/0000-0002-9722-3061

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Date deposited: 07 Jul 2025 16:37
Last modified: 22 Aug 2025 01:56

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