A simulation-optimization methodology to model urban catchments under non-stationary extreme rainfall events

Urban drainage is being affected by Climate Change, whose effects are likely to alter the intensity of rainfall events and result in variations in peak discharges and runoff volumes which stationary-based designs might not be capable of dealing with. Therefore, there is a need to have an accurate an...

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Autores: Jato Espino, Daniel|||0000-0002-1964-6667, Sillanpää, Nora, Charlesworth, Susanne M., Rodríguez Hernández, Jorge|||0000-0003-1596-4024
Tipo de recurso: artículo
Fecha de publicación:2017
País:España
Institución:Universidad de Cantabria (UC)
Repositorio:UCrea Repositorio Abierto de la Universidad de Cantabria
Idioma:inglés
OAI Identifier:oai:repositorio.unican.es:10902/11548
Acceso en línea:http://hdl.handle.net/10902/11548
Access Level:acceso abierto
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spelling A simulation-optimization methodology to model urban catchments under non-stationary extreme rainfall eventsJato Espino, Daniel|||0000-0002-1964-6667Sillanpää, NoraCharlesworth, Susanne M.Rodríguez Hernández, Jorge|||0000-0003-1596-4024Urban drainage is being affected by Climate Change, whose effects are likely to alter the intensity of rainfall events and result in variations in peak discharges and runoff volumes which stationary-based designs might not be capable of dealing with. Therefore, there is a need to have an accurate and reliable means to model the response of urban catchments under extreme precipitation events produced by Climate Change. This research aimed at optimizing the stormwater modelling of urban catchments using Design of Experiments (DOE), in order to identify the parameters that most influenced their discharge and simulate their response to severe storms events projected for Representative Concentration Pathways (RCPs) using a statistics-based Climate Change methodology. The application of this approach to an urban catchment located in Espoo (southern Finland) demonstrated its capability to optimize the calibration of stormwater simulations and provide robust models for the prediction of extreme precipitation under Climate Change.This paper was possible thanks to the research projects RHIVU (Ref. BIA2012-32463) and SUPRIS-SUReS (Ref. BIA 2015-65240-C2-1-R MINECO/FEDER, UE), financed by the Spanish Ministry of Economy and Competitiveness with funds from the State General Budget (PGE) and the European Regional Development Fund (ERDF). The authors wish to express their gratitude to all the entities that provided the data necessary to develop this research: Helsinki Region Environmental Services Authority HSY, Map Service of Espoo, National Land Survey of Finland, Geological Survey of Finland, EURO-CORDEX and European Climate Assessment & Dataset.Elsevier LtdUniversidad de Cantabria20172017-06-07journal articlehttp://purl.org/coar/resource_type/c_6501NAhttp://purl.org/coar/version/c_be7fb7dd8ff6fe43info:eu-repo/semantics/articlehttp://hdl.handle.net/10902/11548Environmental Modelling and Software, 2019, 122, 103960reponame:UCrea Repositorio Abierto de la Universidad de Cantabriainstname:Universidad de Cantabria (UC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Atribución-NoComercial-SinDerivadas 3.0 Españahttp://creativecommons.org/licenses/by-nc-nd/3.0/es/info:eu-repo/semantics/openAccessoai:repositorio.unican.es:10902/115482026-06-02T12:39:31Z
dc.title.none.fl_str_mv A simulation-optimization methodology to model urban catchments under non-stationary extreme rainfall events
title A simulation-optimization methodology to model urban catchments under non-stationary extreme rainfall events
spellingShingle A simulation-optimization methodology to model urban catchments under non-stationary extreme rainfall events
Jato Espino, Daniel|||0000-0002-1964-6667
title_short A simulation-optimization methodology to model urban catchments under non-stationary extreme rainfall events
title_full A simulation-optimization methodology to model urban catchments under non-stationary extreme rainfall events
title_fullStr A simulation-optimization methodology to model urban catchments under non-stationary extreme rainfall events
title_full_unstemmed A simulation-optimization methodology to model urban catchments under non-stationary extreme rainfall events
title_sort A simulation-optimization methodology to model urban catchments under non-stationary extreme rainfall events
dc.creator.none.fl_str_mv Jato Espino, Daniel|||0000-0002-1964-6667
Sillanpää, Nora
Charlesworth, Susanne M.
Rodríguez Hernández, Jorge|||0000-0003-1596-4024
author Jato Espino, Daniel|||0000-0002-1964-6667
author_facet Jato Espino, Daniel|||0000-0002-1964-6667
Sillanpää, Nora
Charlesworth, Susanne M.
Rodríguez Hernández, Jorge|||0000-0003-1596-4024
author_role author
author2 Sillanpää, Nora
Charlesworth, Susanne M.
Rodríguez Hernández, Jorge|||0000-0003-1596-4024
author2_role author
author
author
dc.contributor.none.fl_str_mv Universidad de Cantabria
description Urban drainage is being affected by Climate Change, whose effects are likely to alter the intensity of rainfall events and result in variations in peak discharges and runoff volumes which stationary-based designs might not be capable of dealing with. Therefore, there is a need to have an accurate and reliable means to model the response of urban catchments under extreme precipitation events produced by Climate Change. This research aimed at optimizing the stormwater modelling of urban catchments using Design of Experiments (DOE), in order to identify the parameters that most influenced their discharge and simulate their response to severe storms events projected for Representative Concentration Pathways (RCPs) using a statistics-based Climate Change methodology. The application of this approach to an urban catchment located in Espoo (southern Finland) demonstrated its capability to optimize the calibration of stormwater simulations and provide robust models for the prediction of extreme precipitation under Climate Change.
publishDate 2017
dc.date.none.fl_str_mv 2017
2017-06-07
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
NA
http://purl.org/coar/version/c_be7fb7dd8ff6fe43
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10902/11548
url http://hdl.handle.net/10902/11548
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Atribución-NoComercial-SinDerivadas 3.0 España
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Atribución-NoComercial-SinDerivadas 3.0 España
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Elsevier Ltd
publisher.none.fl_str_mv Elsevier Ltd
dc.source.none.fl_str_mv Environmental Modelling and Software, 2019, 122, 103960
reponame:UCrea Repositorio Abierto de la Universidad de Cantabria
instname:Universidad de Cantabria (UC)
instname_str Universidad de Cantabria (UC)
reponame_str UCrea Repositorio Abierto de la Universidad de Cantabria
collection UCrea Repositorio Abierto de la Universidad de Cantabria
repository.name.fl_str_mv
repository.mail.fl_str_mv
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