Harnessing long-term gridded rainfall data and microtopographic insights to characterise risk from surface water flooding

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dc.contributor.authorMukherjee, Kriti
dc.contributor.authorRivas Casado, Mónica
dc.contributor.authorRamachandran, Rakhee
dc.contributor.authorLeinster, Paul
dc.contributor.editorSchumann Guy J-P
dc.date.accessioned2024-10-21T13:10:43Z
dc.date.available2024-10-21T13:10:43Z
dc.date.freetoread2024-10-21
dc.date.issued2024-09-24
dc.date.pubOnline2024-09-24
dc.description.abstractClimate projections like UKCP18 predict that the UK will move towards a wetter and warmer climate with a consequent increased risk from surface water flooding (SWF). SWF is typically caused by localized convective rainfall, which is difficult to predict and requires high spatial and temporal resolution observations. The likelihood of SWF is also affected by the microtopographic configuration near buildings and the presence of resilience and resistance measures. To date, most research on SWF has focused on modelling and prediction, but these models have been limited to 2 m resolution for England to avoid excessive computational burdens. The lead time for predicting convective rainfall responsible for SWF can be as little as 30 minutes for a 1 km x 1 km part of the storm. Therefore, it is useful to identify the locations most vulnerable to SWF based on past rainfall data and microtopography to provide better risk management measures for properties. In this study, we present a framework that uses long-term gridded rainfall data to quantify SWF hazard at the 1 km x 1 km pixel level, thereby identifying localized areas vulnerable to SWF. We also use high-resolution photographic (10 cm) and LiDAR (25 cm) DEMs, as well as a property flood resistance and resilience (PFR) database, to quantify SWF exposure at property level. By adopting this methodology, locations and properties vulnerable to SWF can be identified, and appropriate SWF management strategies can be developed, such as installing PFR features for the properties at highest risk from SWF.
dc.description.journalNamePLoS ONE
dc.description.sponsorshipWe acknowledge EPSRC funding EP/ N010329/1 for Building Resilience into Risk Management (BRIM).
dc.format.mediumElectronic-eCollection
dc.identifier.citationMukherjee K, Rivas Casado M, Ramachandran R, Leinster P. (2024) Harnessing long-term gridded rainfall data and microtopographic insights to characterise risk from surface water flooding. PLoS ONE, Volume 19, Issue 9, September 2024, Article number e0310753
dc.identifier.eissn1932-6203
dc.identifier.elementsID554044
dc.identifier.issn1932-6203
dc.identifier.issueNo9
dc.identifier.paperNoe0310753
dc.identifier.urihttps://doi.org/10.1371/journal.pone.0310753
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/23091
dc.identifier.volumeNo19
dc.languageEnglish
dc.language.isoen
dc.publisherPLOS (Public Library of Science)
dc.publisher.urihttps://journals.plos.org/plosone/article?id=10.1371/journal.pone.0310753
dc.relation.isreferencedbyhttps://doi.org/10.57996/cran.ceres-2602
dc.rightsAttribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subject37 Earth Sciences
dc.subject3701 Atmospheric Sciences
dc.subject2.2 Factors relating to the physical environment
dc.subject13 Climate Action
dc.subjectGeneral Science & Technology
dc.subject.meshFloods
dc.subject.meshRain
dc.subject.meshEngland
dc.subject.meshModels, Theoretical
dc.subject.meshRain
dc.subject.meshModels, Theoretical
dc.subject.meshEngland
dc.subject.meshFloods
dc.subject.meshFloods
dc.subject.meshRain
dc.subject.meshEngland
dc.subject.meshModels, Theoretical
dc.titleHarnessing long-term gridded rainfall data and microtopographic insights to characterise risk from surface water flooding
dc.typeArticle
dc.type.subtypeJournal Article
dcterms.dateAccepted2024-09-06

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