Global sensitivity analysis of large-scale flood loss models Article Swipe
Francesca Pianosi
,
Georgios Sarailidis
,
Kirsty Styles
,
Philip B. Oldham
,
Stephen Hutchings
,
Rob Lamb
,
Thorsten Wagener
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.5194/egusphere-2025-3310
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.5194/egusphere-2025-3310
Flood loss models are increasingly used in the (re)insurance sector to inform a range of financial decisions. These models simulate the interactions between flood hazard, vulnerability and exposure over large spatial domains, requiring a range of input information and modelling assumptions. Due to this high level of complexity, evaluating the impact of uncertain input data and assumptions on modelling results, and therefore the overall model “acceptability”, remains a very complex process. In this paper, we advocate for the use of global sensitivity analysis (GSA), a generic technique to analyse the propagation of multiple uncertainties through mathematical models, to improve the sensitivity testing of flood loss models and the identification of their key sources of uncertainty. We discuss key challenges in the application of GSA to large-scale flood models, propose pragmatic strategies to overcome these challenges, and showcase the type of insights that can be obtained by GSA through two proof-of-principle applications to a commercial model, JBA Risk Management’s flood loss model, for the transboundary Rhine River basin in Europe, and Queensland in Australia.
Related Topics
Concepts
Flood myth
Scale (ratio)
Vulnerability (computing)
Sensitivity (control systems)
Computer science
Identification (biology)
Process (computing)
Risk analysis (engineering)
Hazard
Key (lock)
Operations research
Environmental resource management
Environmental science
Geography
Engineering
Business
Cartography
Computer security
Biology
Botany
Organic chemistry
Electronic engineering
Archaeology
Chemistry
Operating system
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.5194/egusphere-2025-3310
- OA Status
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- Related Works
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- OpenAlex ID
- https://openalex.org/W4413081817
All OpenAlex metadata
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https://openalex.org/W4413081817Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.5194/egusphere-2025-3310Digital Object Identifier
- Title
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Global sensitivity analysis of large-scale flood loss modelsWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-07-17Full publication date if available
- Authors
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Francesca Pianosi, Georgios Sarailidis, Kirsty Styles, Philip B. Oldham, Stephen Hutchings, Rob Lamb, Thorsten WagenerList of authors in order
- Landing page
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https://doi.org/10.5194/egusphere-2025-3310Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.5194/egusphere-2025-3310Direct OA link when available
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Flood myth, Scale (ratio), Vulnerability (computing), Sensitivity (control systems), Computer science, Identification (biology), Process (computing), Risk analysis (engineering), Hazard, Key (lock), Operations research, Environmental resource management, Environmental science, Geography, Engineering, Business, Cartography, Computer security, Biology, Botany, Organic chemistry, Electronic engineering, Archaeology, Chemistry, Operating systemTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.River | 166 |
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| abstract_inverted_index.basin | 167 |
| abstract_inverted_index.flood | 24, 104, 127, 159 |
| abstract_inverted_index.input | 37, 54 |
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| abstract_inverted_index.model | 65 |
| abstract_inverted_index.range | 14, 35 |
| abstract_inverted_index.their | 111 |
| abstract_inverted_index.these | 134 |
| abstract_inverted_index.(GSA), | 84 |
| abstract_inverted_index.global | 81 |
| abstract_inverted_index.impact | 51 |
| abstract_inverted_index.inform | 12 |
| abstract_inverted_index.model, | 155, 161 |
| abstract_inverted_index.models | 3, 19, 106 |
| abstract_inverted_index.paper, | 74 |
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| abstract_inverted_index.discuss | 117 |
| abstract_inverted_index.generic | 86 |
| abstract_inverted_index.hazard, | 25 |
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| abstract_inverted_index.models, | 97, 128 |
| abstract_inverted_index.overall | 64 |
| abstract_inverted_index.propose | 129 |
| abstract_inverted_index.remains | 67 |
| abstract_inverted_index.sources | 113 |
| abstract_inverted_index.spatial | 31 |
| abstract_inverted_index.testing | 102 |
| abstract_inverted_index.through | 95, 148 |
| abstract_inverted_index.advocate | 76 |
| abstract_inverted_index.analysis | 83 |
| abstract_inverted_index.domains, | 32 |
| abstract_inverted_index.exposure | 28 |
| abstract_inverted_index.insights | 141 |
| abstract_inverted_index.multiple | 93 |
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| abstract_inverted_index.overcome | 133 |
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| abstract_inverted_index.results, | 60 |
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| abstract_inverted_index.Australia. | 173 |
| abstract_inverted_index.Queensland | 171 |
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| abstract_inverted_index.challenges, | 135 |
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| abstract_inverted_index.identification | 109 |
| abstract_inverted_index.proof-of-principle | 150 |
| abstract_inverted_index.“acceptability”, | 66 |
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| institutions_distinct_count | 7 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/13 |
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| sustainable_development_goals[0].display_name | Climate action |
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| citation_normalized_percentile.is_in_top_10_percent | False |