On the spatio-temporal coherence of extreme precipitation indices in subtropical Argentina
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 stepw...
| Autores principales: | , , |
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| Formato: | info:ar-repo/semantics/artículo |
| Lenguaje: | Inglés |
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Elsevier
2025
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| Materias: | |
| Acceso en línea: | 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 |
| _version_ | 1855038503596851200 |
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| author | Ricetti, Lorenzo Hurtado, Santiago Ignacio Agosta Scarel, Eduardo A. |
| author_browse | Agosta Scarel, Eduardo A. Hurtado, Santiago Ignacio Ricetti, Lorenzo |
| author_facet | Ricetti, Lorenzo Hurtado, Santiago Ignacio Agosta Scarel, Eduardo A. |
| author_sort | Ricetti, Lorenzo |
| collection | INTA Digital |
| 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. |
| format | info:ar-repo/semantics/artículo |
| id | INTA22031 |
| institution | Instituto Nacional de Tecnología Agropecuaria (INTA -Argentina) |
| language | Inglés |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | Elsevier |
| publisherStr | Elsevier |
| record_format | dspace |
| spelling | INTA220312025-04-24T10:42:31Z On the spatio-temporal coherence of extreme precipitation indices in subtropical Argentina Ricetti, Lorenzo Hurtado, Santiago Ignacio Agosta Scarel, Eduardo A. Evento Meteorológico Extremo Precipitación Atmosférica Lluvia Torrencial Zona Subtropical Argentina Extreme Weather Events Precipitation Torrential Rains Subtropical Zones 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 2025-04-24T10:39:54Z 2025-04-24T10:39:54Z 2025-07 info:ar-repo/semantics/artículo info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion 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 eng 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 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) application/pdf Elsevier Atmospheric Research 320 : 108082 (July 2025) |
| spellingShingle | Evento Meteorológico Extremo Precipitación Atmosférica Lluvia Torrencial Zona Subtropical Argentina Extreme Weather Events Precipitation Torrential Rains Subtropical Zones Ricetti, Lorenzo Hurtado, Santiago Ignacio Agosta Scarel, Eduardo A. 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 |
| 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_short | 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 |
| topic | Evento Meteorológico Extremo Precipitación Atmosférica Lluvia Torrencial Zona Subtropical Argentina Extreme Weather Events Precipitation Torrential Rains Subtropical Zones |
| 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 |
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