On the spatio-temporal coherence of extreme precipitation indices in subtropical Argentina

Autores
Ricetti, Lorenzo; Hurtado, Santiago Ignacio; Agosta Scarel, Eduardo A.
Año de publicación
2025
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
This study evaluates the spatio-temporal coherence of regional extreme precipitation indices in subtropical Argentina (STAr) derived from rain gauge station data from 1991 to 2021. For the regionalization two machine learning clustering algorithms are used—Ward's method and K-means—and a novel stepwise regionalization approach, HAZ. While machine learning algorithms require the apriori definition of the optimal number of clusters, which varies considerably with the used metric and selection criteria, the HAZ method relies on a Pearson's correlation coefficient threshold and avoids this limitation. In most cases machine learning algorithms struggled to produce coherent regions, with fewer clusters prioritizing spatial coherence at the expense of temporal consistency, and vice versa. Conversely, the HAZ method systematically outperformed machine learning approaches, providing regions with adequate spatio-temporal coherence. Notably, HAZ permits some stations to remain unclustered, allowing to reflect the local variability in extreme precipitation. The overall good performance of the HAZ method demonstrates its potential for broader applications in hydro-climatic studies. Moreover, two intensity indices were unsuitable for regionalization due to poor coherence, while the other three were prone to regionalization throughout the year. The Accumulated index, particularly using the 95th percentile as a threshold, emerged as the most representative, effectively synthesizing extreme precipitation characteristics in STAr. Finally, the necessity of validating the spatio-temporal internal coherence of clustering algorithms outputs is emphasized to avoid mischaracterization and ensure robust regionalization results.
EEA Bariloche
Fil: Ricetti, Lorenzo. Universidad Nacional de La Plata. Facultad de Ciencias Astronómicas y Geofísicas. Grupo de investigación en Clima, Variabilidad y Extremos (CLAVE); Argentina
Fil: Ricetti, Lorenzo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Hurtado, Santiago Ignacio. Instituto Nacional de Tecnologia Agropecuaria (INTA). Estación Experimental Agropecuaria Bariloche. Instituto de Investigaciones Forestales y Agropecuarias Bariloche; Argentina
Fil: Hurtado, Santiago Ignacio. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Instituto de Investigaciones Forestales y Agropecuarias Bariloche; Argentina
Fil: Hurtado, Santiago Ignacio. Universidad Nacional de La Plata. Facultad de Ciencias Astronómicas y Geofísicas. Grupo de investigación en Clima, Variabilidad y Extremos (CLAVE); Argentina
Fil: Agosta Scarel, Eduardo A. Carmelite NGO. Climate Change and Sustainability Section; Estados Unidos
Fil: Agosta Scarel, Eduardo A. Spanish Episcopal Conference. Integral Ecology Department; España
Fuente
Atmospheric Research 320 : 108082 (July 2025)
Materia
Evento Meteorológico Extremo
Precipitación Atmosférica
Lluvia Torrencial
Zona Subtropical
Argentina
Extreme Weather Events
Precipitation
Torrential Rains
Subtropical Zones
Nivel de accesibilidad
acceso restringido
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
INTA Digital (INTA)
Institución
Instituto Nacional de Tecnología Agropecuaria
OAI Identificador
oai:localhost:20.500.12123/22031
Acceder
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oai_identifier_str oai:localhost:20.500.12123/22031
network_acronym_str INTADig
repository_id_str l
network_name_str INTA Digital (INTA)
spelling On the spatio-temporal coherence of extreme precipitation indices in subtropical ArgentinaRicetti, LorenzoHurtado, Santiago IgnacioAgosta Scarel, Eduardo A.Evento Meteorológico ExtremoPrecipitación AtmosféricaLluvia TorrencialZona SubtropicalArgentinaExtreme Weather EventsPrecipitationTorrential RainsSubtropical ZonesThis study evaluates the spatio-temporal coherence of regional extreme precipitation indices in subtropical Argentina (STAr) derived from rain gauge station data from 1991 to 2021. For the regionalization two machine learning clustering algorithms are used—Ward's method and K-means—and a novel stepwise regionalization approach, HAZ. While machine learning algorithms require the apriori definition of the optimal number of clusters, which varies considerably with the used metric and selection criteria, the HAZ method relies on a Pearson's correlation coefficient threshold and avoids this limitation. In most cases machine learning algorithms struggled to produce coherent regions, with fewer clusters prioritizing spatial coherence at the expense of temporal consistency, and vice versa. Conversely, the HAZ method systematically outperformed machine learning approaches, providing regions with adequate spatio-temporal coherence. Notably, HAZ permits some stations to remain unclustered, allowing to reflect the local variability in extreme precipitation. The overall good performance of the HAZ method demonstrates its potential for broader applications in hydro-climatic studies. Moreover, two intensity indices were unsuitable for regionalization due to poor coherence, while the other three were prone to regionalization throughout the year. The Accumulated index, particularly using the 95th percentile as a threshold, emerged as the most representative, effectively synthesizing extreme precipitation characteristics in STAr. Finally, the necessity of validating the spatio-temporal internal coherence of clustering algorithms outputs is emphasized to avoid mischaracterization and ensure robust regionalization results.EEA BarilocheFil: Ricetti, Lorenzo. Universidad Nacional de La Plata. Facultad de Ciencias Astronómicas y Geofísicas. Grupo de investigación en Clima, Variabilidad y Extremos (CLAVE); ArgentinaFil: Ricetti, Lorenzo. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Hurtado, Santiago Ignacio. Instituto Nacional de Tecnologia Agropecuaria (INTA). Estación Experimental Agropecuaria Bariloche. Instituto de Investigaciones Forestales y Agropecuarias Bariloche; ArgentinaFil: Hurtado, Santiago Ignacio. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Instituto de Investigaciones Forestales y Agropecuarias Bariloche; ArgentinaFil: Hurtado, Santiago Ignacio. Universidad Nacional de La Plata. Facultad de Ciencias Astronómicas y Geofísicas. Grupo de investigación en Clima, Variabilidad y Extremos (CLAVE); ArgentinaFil: Agosta Scarel, Eduardo A. Carmelite NGO. Climate Change and Sustainability Section; Estados UnidosFil: Agosta Scarel, Eduardo A. Spanish Episcopal Conference. Integral Ecology Department; EspañaElsevier2025-04-24T10:39:54Z2025-04-24T10:39:54Z2025-07info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttp://hdl.handle.net/20.500.12123/22031https://www.sciencedirect.com/science/article/abs/pii/S01698095250017470169-80951873-2895https://doi.org/10.1016/j.atmosres.2025.108082Atmospheric Research 320 : 108082 (July 2025)reponame:INTA Digital (INTA)instname:Instituto Nacional de Tecnología Agropecuariaenginfo:eu-repograntAgreement/INTA/2023-PD-L02-I091, Adaptación a la variabilidad y al cambio global: herramientas para la gestión de riesgos, la reducción de impactos y el aumento de la resiliencia de socioecosistemasinfo:eu-repo/semantics/restrictedAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)2025-09-11T10:25:41Zoai:localhost:20.500.12123/22031instacron:INTAInstitucionalhttp://repositorio.inta.gob.ar/Organismo científico-tecnológicoNo correspondehttp://repositorio.inta.gob.ar/oai/requesttripaldi.nicolas@inta.gob.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:l2025-09-11 10:25:42.297INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuariafalse
dc.title.none.fl_str_mv On the spatio-temporal coherence of extreme precipitation indices in subtropical Argentina
title On the spatio-temporal coherence of extreme precipitation indices in subtropical Argentina
spellingShingle On the spatio-temporal coherence of extreme precipitation indices in subtropical Argentina
Ricetti, Lorenzo
Evento Meteorológico Extremo
Precipitación Atmosférica
Lluvia Torrencial
Zona Subtropical
Argentina
Extreme Weather Events
Precipitation
Torrential Rains
Subtropical Zones
title_short On the spatio-temporal coherence of extreme precipitation indices in subtropical Argentina
title_full On the spatio-temporal coherence of extreme precipitation indices in subtropical Argentina
title_fullStr On the spatio-temporal coherence of extreme precipitation indices in subtropical Argentina
title_full_unstemmed On the spatio-temporal coherence of extreme precipitation indices in subtropical Argentina
title_sort On the spatio-temporal coherence of extreme precipitation indices in subtropical Argentina
dc.creator.none.fl_str_mv Ricetti, Lorenzo
Hurtado, Santiago Ignacio
Agosta Scarel, Eduardo A.
author Ricetti, Lorenzo
author_facet Ricetti, Lorenzo
Hurtado, Santiago Ignacio
Agosta Scarel, Eduardo A.
author_role author
author2 Hurtado, Santiago Ignacio
Agosta Scarel, Eduardo A.
author2_role author
author
dc.subject.none.fl_str_mv Evento Meteorológico Extremo
Precipitación Atmosférica
Lluvia Torrencial
Zona Subtropical
Argentina
Extreme Weather Events
Precipitation
Torrential Rains
Subtropical Zones
topic Evento Meteorológico Extremo
Precipitación Atmosférica
Lluvia Torrencial
Zona Subtropical
Argentina
Extreme Weather Events
Precipitation
Torrential Rains
Subtropical Zones
dc.description.none.fl_txt_mv This study evaluates the spatio-temporal coherence of regional extreme precipitation indices in subtropical Argentina (STAr) derived from rain gauge station data from 1991 to 2021. For the regionalization two machine learning clustering algorithms are used—Ward's method and K-means—and a novel stepwise regionalization approach, HAZ. While machine learning algorithms require the apriori definition of the optimal number of clusters, which varies considerably with the used metric and selection criteria, the HAZ method relies on a Pearson's correlation coefficient threshold and avoids this limitation. In most cases machine learning algorithms struggled to produce coherent regions, with fewer clusters prioritizing spatial coherence at the expense of temporal consistency, and vice versa. Conversely, the HAZ method systematically outperformed machine learning approaches, providing regions with adequate spatio-temporal coherence. Notably, HAZ permits some stations to remain unclustered, allowing to reflect the local variability in extreme precipitation. The overall good performance of the HAZ method demonstrates its potential for broader applications in hydro-climatic studies. Moreover, two intensity indices were unsuitable for regionalization due to poor coherence, while the other three were prone to regionalization throughout the year. The Accumulated index, particularly using the 95th percentile as a threshold, emerged as the most representative, effectively synthesizing extreme precipitation characteristics in STAr. Finally, the necessity of validating the spatio-temporal internal coherence of clustering algorithms outputs is emphasized to avoid mischaracterization and ensure robust regionalization results.
EEA Bariloche
Fil: Ricetti, Lorenzo. Universidad Nacional de La Plata. Facultad de Ciencias Astronómicas y Geofísicas. Grupo de investigación en Clima, Variabilidad y Extremos (CLAVE); Argentina
Fil: Ricetti, Lorenzo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Hurtado, Santiago Ignacio. Instituto Nacional de Tecnologia Agropecuaria (INTA). Estación Experimental Agropecuaria Bariloche. Instituto de Investigaciones Forestales y Agropecuarias Bariloche; Argentina
Fil: Hurtado, Santiago Ignacio. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Instituto de Investigaciones Forestales y Agropecuarias Bariloche; Argentina
Fil: Hurtado, Santiago Ignacio. Universidad Nacional de La Plata. Facultad de Ciencias Astronómicas y Geofísicas. Grupo de investigación en Clima, Variabilidad y Extremos (CLAVE); Argentina
Fil: Agosta Scarel, Eduardo A. Carmelite NGO. Climate Change and Sustainability Section; Estados Unidos
Fil: Agosta Scarel, Eduardo A. Spanish Episcopal Conference. Integral Ecology Department; España
description This study evaluates the spatio-temporal coherence of regional extreme precipitation indices in subtropical Argentina (STAr) derived from rain gauge station data from 1991 to 2021. For the regionalization two machine learning clustering algorithms are used—Ward's method and K-means—and a novel stepwise regionalization approach, HAZ. While machine learning algorithms require the apriori definition of the optimal number of clusters, which varies considerably with the used metric and selection criteria, the HAZ method relies on a Pearson's correlation coefficient threshold and avoids this limitation. In most cases machine learning algorithms struggled to produce coherent regions, with fewer clusters prioritizing spatial coherence at the expense of temporal consistency, and vice versa. Conversely, the HAZ method systematically outperformed machine learning approaches, providing regions with adequate spatio-temporal coherence. Notably, HAZ permits some stations to remain unclustered, allowing to reflect the local variability in extreme precipitation. The overall good performance of the HAZ method demonstrates its potential for broader applications in hydro-climatic studies. Moreover, two intensity indices were unsuitable for regionalization due to poor coherence, while the other three were prone to regionalization throughout the year. The Accumulated index, particularly using the 95th percentile as a threshold, emerged as the most representative, effectively synthesizing extreme precipitation characteristics in STAr. Finally, the necessity of validating the spatio-temporal internal coherence of clustering algorithms outputs is emphasized to avoid mischaracterization and ensure robust regionalization results.
publishDate 2025
dc.date.none.fl_str_mv 2025-04-24T10:39:54Z
2025-04-24T10:39:54Z
2025-07
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/20.500.12123/22031
https://www.sciencedirect.com/science/article/abs/pii/S0169809525001747
0169-8095
1873-2895
https://doi.org/10.1016/j.atmosres.2025.108082
url http://hdl.handle.net/20.500.12123/22031
https://www.sciencedirect.com/science/article/abs/pii/S0169809525001747
https://doi.org/10.1016/j.atmosres.2025.108082
identifier_str_mv 0169-8095
1873-2895
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repograntAgreement/INTA/2023-PD-L02-I091, Adaptación a la variabilidad y al cambio global: herramientas para la gestión de riesgos, la reducción de impactos y el aumento de la resiliencia de socioecosistemas
dc.rights.none.fl_str_mv info:eu-repo/semantics/restrictedAccess
http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
eu_rights_str_mv restrictedAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv Atmospheric Research 320 : 108082 (July 2025)
reponame:INTA Digital (INTA)
instname:Instituto Nacional de Tecnología Agropecuaria
reponame_str INTA Digital (INTA)
collection INTA Digital (INTA)
instname_str Instituto Nacional de Tecnología Agropecuaria
repository.name.fl_str_mv INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuaria
repository.mail.fl_str_mv tripaldi.nicolas@inta.gob.ar
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score 12.993085

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