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Bare-earth DEM generation from ArcticDEM and its use in flood simulation

Abstract:
Floods are one of the most prevalent natural disasters, and advancements in geospatial technologies have revolutionized flood management, particularly the use of Digital Elevation Models (DEMs) in hydrological modelling. However, a comprehensive analysis DEMs integration in flood risk management is lacking. This study addresses this gap through a thorough Systematic Literature Review focusing on the combined application of DEMs and hydrological models in flood mitigation and risk management. The SLR scrutinized 21 articles, revealing eight key themes: DEM data sources and characteristics, DEM integration with hydrological models, flood hazard mapping applications, terrain impact assessment, model performance evaluation, machine learning in flood management, ecosystem services and resilience, and policy and governance implications. These findings emphasize the importance of precise DEM selection and correction for successful flood modelling, highlighting Advanced Land Observing Satellite as the most effective freely available DEM for use with the HEC-RAS unsteady flood model. This integration significantly enhances flood mitigation efforts and strengthens management strategies. Finally, this study underscores the pivotal role of DEM integration in crafting effective flood mitigation strategies, especially in addressing climate change challenges and bolstering community and ecosystem resilience
Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.5194/nhess-23-375-2023

Authors

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Institution:
University of Oxford
Role:
Author
ORCID:
0000-0002-5250-0184
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Role:
Author
ORCID:
0000-0001-9192-9963
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Role:
Author
ORCID:
0000-0001-5793-9594


Publisher:
Copernicus Publications
Journal:
Natural Hazards and Earth System Sciences More from this journal
Volume:
23
Issue:
1
Pages:
375-391
Publication date:
2023-02-01
DOI:
EISSN:
1684-9981
ISSN:
1561-8633


Language:
English
Keywords:
Pubs id:
2374450
Local pid:
pubs:2374450
Source identifiers:
W4318833618
Deposit date:
2026-02-16
ARK identifier:
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