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dc.contributor.authorPeleg, N
dc.contributor.authorBan, N
dc.contributor.authorGibson, MJ
dc.contributor.authorChen, AS
dc.contributor.authorPaschalis, A
dc.contributor.authorBurlando, P
dc.contributor.authorLeitão, JP
dc.date.accessioned2022-07-01T12:47:35Z
dc.date.issued2022-06-25
dc.date.updated2022-07-01T11:47:06Z
dc.description.abstractSynthetic design storms are often used to plan new drainage systems or assess flood impacts on infrastructure. To simulate extreme rainfall events under climate change, design storms can be modified to match a different return frequency of extreme rainfall events as well as a modified temporal distribution of rainfall intensities. However, the same magnitude of change to the rainfall intensities is often applied in space. Several hydrological applications are limited by this. Climate change impacts on urban pluvial floods, for example, require the use of 2D design storms (rainfall fields) at sub-kilometer and sub-hourly scales. Recent kilometer scale climate models, also known as convection-permitting climate models (CPM), provide rainfall outputs at a high spatial resolution, although rainfall simulations are still restricted to a limited number of climate scenarios and time periods. We nevertheless explored the potential use of rainfall data obtained from these models for hydrological flood impact studies by introducing a method of spatial quantile mapping (SQM). To demonstrate the new methodology, we extracted high-resolution rainfall simulations from a CPM for four domains representing different urban areas in Switzerland. Extreme storms that are plausible under the present climate conditions were simulated with a 2D stochastic rainfall model. Based on the CPM-informed stochastically generated rainfall fields, we modified the design storms to fit the future climate scenario using three different methods: the SQM, a uniform quantile mapping, and a uniform adjustment based on a rainfall–temperature relationship. Throughout all storms, the temporal distribution of rainfall was the same. Using a flood model, we assessed the impact of different rainfall adjustment methods on urban flooding. Significant differences were found in the flood water depths and areas between the three methods. In general, the SQM method results in a higher flood impact than the storms that were modified otherwise. The results suggest that spatial storm profiles may need to be re-adjusted when assessing flood impacts.en_GB
dc.description.sponsorshipSwiss National Science Foundation (SNSF)en_GB
dc.description.sponsorshipAlan Turing Instituteen_GB
dc.description.sponsorshipPRACEen_GB
dc.format.extent104258-
dc.identifier.citationVol. 166, article 104258en_GB
dc.identifier.doihttps://doi.org/10.1016/j.advwatres.2022.104258
dc.identifier.grantnumber194649en_GB
dc.identifier.urihttp://hdl.handle.net/10871/130120
dc.identifierORCID: 0000-0003-3708-3332 (Chen, Albert S)
dc.identifierScopusID: 57193002441 (Chen, Albert S)
dc.identifierResearcherID: E-2735-2010 (Chen, Albert S)
dc.language.isoenen_GB
dc.publisherElsevieren_GB
dc.relation.urlhttps://doi.org/10.5281/zenodo.6563635en_GB
dc.rights© 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).en_GB
dc.subjectSpatial quantile mappingen_GB
dc.subjectExtreme rainfallen_GB
dc.subjectUrban flooden_GB
dc.subjectHigh-resolution rainfallen_GB
dc.subjectClimate changeen_GB
dc.titleMapping storm spatial profiles for flood impact assessmentsen_GB
dc.typeArticleen_GB
dc.date.available2022-07-01T12:47:35Z
dc.identifier.issn0309-1708
exeter.article-number104258
dc.descriptionThis is the final version. Available from Elsevier via the DOI in this record. en_GB
dc.descriptionCode availability: An example of the Spatial Quantile Mapping (SQM) method can be found in the Zenodo archive at https://doi.org/10.5281/zenodo.6563635. This script (Peleg, 2022) reproduces Fig. 3 from the manuscript.en_GB
dc.identifier.journalAdvances in Water Resourcesen_GB
dc.relation.ispartofAdvances in Water Resources, 166
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en_GB
dcterms.dateAccepted2022-06-19
rioxxterms.versionVoRen_GB
rioxxterms.licenseref.startdate2022-06-25
rioxxterms.typeJournal Article/Reviewen_GB
refterms.dateFCD2022-07-01T12:43:19Z
refterms.versionFCDVoR
refterms.dateFOA2022-07-01T12:47:43Z
refterms.panelBen_GB
refterms.dateFirstOnline2022-06-25


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© 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Except where otherwise noted, this item's licence is described as © 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).