Mapping storm spatial profiles for flood impact assessments
dc.contributor.author | Peleg, N | |
dc.contributor.author | Ban, N | |
dc.contributor.author | Gibson, MJ | |
dc.contributor.author | Chen, AS | |
dc.contributor.author | Paschalis, A | |
dc.contributor.author | Burlando, P | |
dc.contributor.author | Leitão, JP | |
dc.date.accessioned | 2022-07-01T12:47:35Z | |
dc.date.issued | 2022-06-25 | |
dc.date.updated | 2022-07-01T11:47:06Z | |
dc.description.abstract | Synthetic 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.sponsorship | Swiss National Science Foundation (SNSF) | en_GB |
dc.description.sponsorship | Alan Turing Institute | en_GB |
dc.description.sponsorship | PRACE | en_GB |
dc.format.extent | 104258- | |
dc.identifier.citation | Vol. 166, article 104258 | en_GB |
dc.identifier.doi | https://doi.org/10.1016/j.advwatres.2022.104258 | |
dc.identifier.grantnumber | 194649 | en_GB |
dc.identifier.uri | http://hdl.handle.net/10871/130120 | |
dc.identifier | ORCID: 0000-0003-3708-3332 (Chen, Albert S) | |
dc.identifier | ScopusID: 57193002441 (Chen, Albert S) | |
dc.identifier | ResearcherID: E-2735-2010 (Chen, Albert S) | |
dc.language.iso | en | en_GB |
dc.publisher | Elsevier | en_GB |
dc.relation.url | https://doi.org/10.5281/zenodo.6563635 | en_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.subject | Spatial quantile mapping | en_GB |
dc.subject | Extreme rainfall | en_GB |
dc.subject | Urban flood | en_GB |
dc.subject | High-resolution rainfall | en_GB |
dc.subject | Climate change | en_GB |
dc.title | Mapping storm spatial profiles for flood impact assessments | en_GB |
dc.type | Article | en_GB |
dc.date.available | 2022-07-01T12:47:35Z | |
dc.identifier.issn | 0309-1708 | |
exeter.article-number | 104258 | |
dc.description | This is the final version. Available from Elsevier via the DOI in this record. | en_GB |
dc.description | Code 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.journal | Advances in Water Resources | en_GB |
dc.relation.ispartof | Advances in Water Resources, 166 | |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | en_GB |
dcterms.dateAccepted | 2022-06-19 | |
rioxxterms.version | VoR | en_GB |
rioxxterms.licenseref.startdate | 2022-06-25 | |
rioxxterms.type | Journal Article/Review | en_GB |
refterms.dateFCD | 2022-07-01T12:43:19Z | |
refterms.versionFCD | VoR | |
refterms.dateFOA | 2022-07-01T12:47:43Z | |
refterms.panel | B | en_GB |
refterms.dateFirstOnline | 2022-06-25 |
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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/).