Characterising local flood-inducing heavy rainfall through daily weather types and large-scale climatic patterns: Aotearoa New Zealand study case

Flooding is the most frequent natural hazard in Aotearoa New Zealand and the second most costly after earthquakes. It will change in frequency and intensity, becoming more extreme as climate change impacts are realised. The main inundation driver is heavy rainfall. In this study, flood-inducing heav...

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Autores: Pozo, Andrea, Wilson, Matthew, Katurji, Marwan, Cagigal Gil, Laura|||0000-0001-5384-6382, Méndez Incera, Fernando Javier|||0000-0002-5005-1100, Lane, Emily
Tipo de recurso: artículo
Fecha de publicación:2025
País:España
Institución:Universidad de Cantabria (UC)
Repositorio:UCrea Repositorio Abierto de la Universidad de Cantabria
Idioma:inglés
OAI Identifier:oai:repositorio.unican.es:10902/36207
Acceso en línea:https://hdl.handle.net/10902/36207
Access Level:acceso abierto
Palabra clave:Daily weather types
Flooding
Heavy rainfall
Large-scale climatic patterns
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spelling Characterising local flood-inducing heavy rainfall through daily weather types and large-scale climatic patterns: Aotearoa New Zealand study casePozo, AndreaWilson, MatthewKaturji, MarwanCagigal Gil, Laura|||0000-0001-5384-6382Méndez Incera, Fernando Javier|||0000-0002-5005-1100Lane, EmilyDaily weather typesFloodingHeavy rainfallLarge-scale climatic patternsFlooding is the most frequent natural hazard in Aotearoa New Zealand and the second most costly after earthquakes. It will change in frequency and intensity, becoming more extreme as climate change impacts are realised. The main inundation driver is heavy rainfall. In this study, flood-inducing heavy rainfall is characterised locally by applying synoptic climatological techniques, using the study case of Aotearoa New Zealand. Extending on previous work in the field, a new set of 49 daily weather types (DWTs) is proposed for New Zealand, based on mean sea level pressure (MLSP) and 500hPa geopotential height (500GH) (predictor variables). The role of the DWTs, the large-scale climatic patterns (LSCPs) known to influence rainfall variability, and the wind conditions (as an additional explanatory variable since they play an essential role in the development of these events) as heavy rainfall and flooding (predictand variables) drivers is investigated using the Wairewa catchment (Little River, Canterbury) as the study site. Heavy rainfall is represented through its temporal and spatial features, based on two rainfall datasets (a rain gauge and a gridded product obtained by the Weather Research and Forecasting (WRF) numerical model). Useful relationships are found between the predictor and the predictand variables. Also, the predictor variables' temporal variability (interannual and intra-annual variability, seasonality) plays a key role, translating to the temporal variability of heavy rainfall and flooding. The proposed synoptic climatological approach provides qualitative and quantitative value, displaying the range of weather and climatic configurations leading to different types of storms and flooding and helping in their identification and understandingThis work was supported by New Zealand Government via the Ministry for Business, Innovation and Employment (MBIE), contract C01X2014 and the Projects MyFlood (PLEC2022-009362) and HyBay (PID2022-141181OB-I00), from the Spanish Ministry of Science and Innovation.John Wiley and Sons LtdUniversidad de Cantabria20252025-04-01journal articlehttp://purl.org/coar/resource_type/c_6501NAhttp://purl.org/coar/version/c_be7fb7dd8ff6fe43info:eu-repo/semantics/articlehttps://hdl.handle.net/10902/36207International Journal of Climatology, 2025, 45(5), e8762reponame:UCrea Repositorio Abierto de la Universidad de Cantabriainstname:Universidad de Cantabria (UC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Attribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:repositorio.unican.es:10902/362072026-06-02T12:39:31Z
dc.title.none.fl_str_mv Characterising local flood-inducing heavy rainfall through daily weather types and large-scale climatic patterns: Aotearoa New Zealand study case
title Characterising local flood-inducing heavy rainfall through daily weather types and large-scale climatic patterns: Aotearoa New Zealand study case
spellingShingle Characterising local flood-inducing heavy rainfall through daily weather types and large-scale climatic patterns: Aotearoa New Zealand study case
Pozo, Andrea
Daily weather types
Flooding
Heavy rainfall
Large-scale climatic patterns
title_short Characterising local flood-inducing heavy rainfall through daily weather types and large-scale climatic patterns: Aotearoa New Zealand study case
title_full Characterising local flood-inducing heavy rainfall through daily weather types and large-scale climatic patterns: Aotearoa New Zealand study case
title_fullStr Characterising local flood-inducing heavy rainfall through daily weather types and large-scale climatic patterns: Aotearoa New Zealand study case
title_full_unstemmed Characterising local flood-inducing heavy rainfall through daily weather types and large-scale climatic patterns: Aotearoa New Zealand study case
title_sort Characterising local flood-inducing heavy rainfall through daily weather types and large-scale climatic patterns: Aotearoa New Zealand study case
dc.creator.none.fl_str_mv Pozo, Andrea
Wilson, Matthew
Katurji, Marwan
Cagigal Gil, Laura|||0000-0001-5384-6382
Méndez Incera, Fernando Javier|||0000-0002-5005-1100
Lane, Emily
author Pozo, Andrea
author_facet Pozo, Andrea
Wilson, Matthew
Katurji, Marwan
Cagigal Gil, Laura|||0000-0001-5384-6382
Méndez Incera, Fernando Javier|||0000-0002-5005-1100
Lane, Emily
author_role author
author2 Wilson, Matthew
Katurji, Marwan
Cagigal Gil, Laura|||0000-0001-5384-6382
Méndez Incera, Fernando Javier|||0000-0002-5005-1100
Lane, Emily
author2_role author
author
author
author
author
dc.contributor.none.fl_str_mv Universidad de Cantabria
dc.subject.none.fl_str_mv Daily weather types
Flooding
Heavy rainfall
Large-scale climatic patterns
topic Daily weather types
Flooding
Heavy rainfall
Large-scale climatic patterns
description Flooding is the most frequent natural hazard in Aotearoa New Zealand and the second most costly after earthquakes. It will change in frequency and intensity, becoming more extreme as climate change impacts are realised. The main inundation driver is heavy rainfall. In this study, flood-inducing heavy rainfall is characterised locally by applying synoptic climatological techniques, using the study case of Aotearoa New Zealand. Extending on previous work in the field, a new set of 49 daily weather types (DWTs) is proposed for New Zealand, based on mean sea level pressure (MLSP) and 500hPa geopotential height (500GH) (predictor variables). The role of the DWTs, the large-scale climatic patterns (LSCPs) known to influence rainfall variability, and the wind conditions (as an additional explanatory variable since they play an essential role in the development of these events) as heavy rainfall and flooding (predictand variables) drivers is investigated using the Wairewa catchment (Little River, Canterbury) as the study site. Heavy rainfall is represented through its temporal and spatial features, based on two rainfall datasets (a rain gauge and a gridded product obtained by the Weather Research and Forecasting (WRF) numerical model). Useful relationships are found between the predictor and the predictand variables. Also, the predictor variables' temporal variability (interannual and intra-annual variability, seasonality) plays a key role, translating to the temporal variability of heavy rainfall and flooding. The proposed synoptic climatological approach provides qualitative and quantitative value, displaying the range of weather and climatic configurations leading to different types of storms and flooding and helping in their identification and understanding
publishDate 2025
dc.date.none.fl_str_mv 2025
2025-04-01
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 https://hdl.handle.net/10902/36207
url https://hdl.handle.net/10902/36207
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
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
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
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv John Wiley and Sons Ltd
publisher.none.fl_str_mv John Wiley and Sons Ltd
dc.source.none.fl_str_mv International Journal of Climatology, 2025, 45(5), e8762
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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score 15,812455

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