Intensificación del ciclo hidrológico en Suramérica tropical: detección y atribución
La evidencia científica sugiere que el cambio climático antropogénico tiene el potencial de cambiar la frecuencia y la magnitud de los eventos de precipitación al intensificar el ciclo hidrológico, en particular las precipitaciones extremas y la ocurrencia de eventos secos en diferentes regiones del...
- Autores:
-
Leandro Arévalo, Manuel Felipe
- Tipo de recurso:
- Fecha de publicación:
- 2025
- Institución:
- Universidad de Antioquia
- Repositorio:
- Repositorio UdeA
- Idioma:
- spa
- OAI Identifier:
- oai:bibliotecadigital.udea.edu.co:10495/46504
- Acceso en línea:
- https://hdl.handle.net/10495/46504
- Palabra clave:
- Climate change
Ciclo hidrológico
Hydrologic cycle
Precipitación atmósferica - medición
Precipitation (Meteorology)- Measurement
Índices de extremos de precipitación
Cambio climático
http://vocabularies.unesco.org/thesaurus/concept4559
ODS 13: Acción por el Clima. Adoptar medidas urgentes para combatir el cambio climático y sus efectos
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- openAccess
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- http://creativecommons.org/licenses/by-nc-sa/4.0/
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| dc.title.spa.fl_str_mv |
Intensificación del ciclo hidrológico en Suramérica tropical: detección y atribución |
| title |
Intensificación del ciclo hidrológico en Suramérica tropical: detección y atribución |
| spellingShingle |
Intensificación del ciclo hidrológico en Suramérica tropical: detección y atribución Climate change Ciclo hidrológico Hydrologic cycle Precipitación atmósferica - medición Precipitation (Meteorology)- Measurement Índices de extremos de precipitación Cambio climático http://vocabularies.unesco.org/thesaurus/concept4559 ODS 13: Acción por el Clima. Adoptar medidas urgentes para combatir el cambio climático y sus efectos |
| title_short |
Intensificación del ciclo hidrológico en Suramérica tropical: detección y atribución |
| title_full |
Intensificación del ciclo hidrológico en Suramérica tropical: detección y atribución |
| title_fullStr |
Intensificación del ciclo hidrológico en Suramérica tropical: detección y atribución |
| title_full_unstemmed |
Intensificación del ciclo hidrológico en Suramérica tropical: detección y atribución |
| title_sort |
Intensificación del ciclo hidrológico en Suramérica tropical: detección y atribución |
| dc.creator.fl_str_mv |
Leandro Arévalo, Manuel Felipe |
| dc.contributor.advisor.none.fl_str_mv |
Arias Gómez, Paola Andrea Salazar Villegas, Juan Fernando |
| dc.contributor.author.none.fl_str_mv |
Leandro Arévalo, Manuel Felipe |
| dc.contributor.researchgroup.none.fl_str_mv |
Grupo de Ingeniería y Gestión Ambiental (GIGA) |
| dc.contributor.jury.none.fl_str_mv |
Carmona Duque, Alejandra María Rojo Hernández, Julián David |
| dc.subject.unesco.none.fl_str_mv |
Climate change |
| topic |
Climate change Ciclo hidrológico Hydrologic cycle Precipitación atmósferica - medición Precipitation (Meteorology)- Measurement Índices de extremos de precipitación Cambio climático http://vocabularies.unesco.org/thesaurus/concept4559 ODS 13: Acción por el Clima. Adoptar medidas urgentes para combatir el cambio climático y sus efectos |
| dc.subject.lemb.none.fl_str_mv |
Ciclo hidrológico Hydrologic cycle Precipitación atmósferica - medición Precipitation (Meteorology)- Measurement |
| dc.subject.proposal.spa.fl_str_mv |
Índices de extremos de precipitación |
| dc.subject.unescouri.none.fl_str_mv |
Cambio climático http://vocabularies.unesco.org/thesaurus/concept4559 |
| dc.subject.ods.none.fl_str_mv |
ODS 13: Acción por el Clima. Adoptar medidas urgentes para combatir el cambio climático y sus efectos |
| description |
La evidencia científica sugiere que el cambio climático antropogénico tiene el potencial de cambiar la frecuencia y la magnitud de los eventos de precipitación al intensificar el ciclo hidrológico, en particular las precipitaciones extremas y la ocurrencia de eventos secos en diferentes regiones del mundo. El objetivo de este Trabajo de Investigación es identificar una posible intensificación del ciclo hidrológico en Suramérica Tropical a partir de diferentes bases de datos de referencia. Además, plantea evaluar el desempeño de los modelos de circulación general contenidos en la Sexta Fase del Proyecto de Comparación de Modelos Acoplados (CMIP6 por sus iniciales en inglés) en la simulación de extremos de precipitación, así como su intensificación y tendencias en la región de estudio. Los datos de referencia se obtuvieron a partir de cinco productos de precipitación de diferentes características (CHIRPS-v2.0, GPM, PERSIANN-CDR, CPC y HadEX3) para las últimas décadas, y las simulaciones se obtuvieron de un total de 12 modelos climáticos globales (ESM) y 6 modelos globales de alta resolución (HighResMIP). Las regiones de interés son el noroeste (NWS), el norte (NSA) y el noreste (NES) de Suramérica, así como la región del Monzón de Suramérica (SAM), las cuales han sido propuestas para el análisis de evidencia científica regional del sexto informe de evaluación del Panel Intergubernamental sobre Cambio Climático (IPCC por sus iniciales en inglés). Las tendencias de las series de tiempo de los índices considerados fueron estimadas mediante las pruebas no paramétricas de Mann-Kendall y Sen Slope. Para la evaluación y comparación de los modelos con las observaciones, se calcularon sus sesgos y tendencias y se realizó un análisis de desempeño mediante diagramas de Taylor, para cada modelo y para el promedio multimodelo. Finalmente, se realizó un estudio de detección y atribución de la tendencia de cambio identificada en las observaciones de CHIRPS, utilizando el método Optimal Fingerprinting y los experimentos de forzamientos externos contenidos en el Proyecto de Comparación de Modelos de Detección y Atribución (DAMIP por sus iniciales en inglés). Estos forzamientos corresponden a un experimento de variabilidad natural, un experimento de gases de efecto invernadero antropogénicos y un experimento de aerosoles antropogénicos, con el objetivo de considerar la contribución de forzamientos naturales y antropogénicos. Nuestros resultados muestran evidencia de una intensificación del ciclo hidrológico durante las últimas tres décadas en las regiones de estudio, según los diferentes datos de referencia. Entre las señales más claras se identifican una intensificación de los eventos húmedos en la zona norte de NWS, particularmente el occidente y la región Andina de Colombia, y una intensificación de los eventos secos en las regiones SAM y NES. En general, los modelos CMIP6 y HighResMIP tienen dificultades para simular la variabilidad espacial y temporal de los extremos de precipitación tanto húmedos como secos, y solamente logran identificar la señal de cambio de los extremos secos en las regiones SAM y NES. Los resultados del estudio de detección y atribución no permitieron encontrar una señal de un forzamiento antropogénico en los cambios observados en las regiones de estudio para los índices de extremos de precipitación. |
| publishDate |
2025 |
| dc.date.accessioned.none.fl_str_mv |
2025-06-24T15:42:17Z |
| dc.date.issued.none.fl_str_mv |
2025 |
| dc.type.none.fl_str_mv |
Trabajo de grado - Maestría |
| dc.type.redcol.none.fl_str_mv |
http://purl.org/redcol/resource_type/TM |
| dc.type.content.none.fl_str_mv |
Text |
| dc.type.coarversion.none.fl_str_mv |
http://purl.org/coar/version/c_b1a7d7d4d402bcce |
| dc.type.driver.none.fl_str_mv |
info:eu-repo/semantics/masterThesis |
| dc.type.version.none.fl_str_mv |
info:eu-repo/semantics/draft |
| status_str |
draft |
| dc.identifier.citation.none.fl_str_mv |
Leandro Arévalo, M. F. (2025). Intensificación del ciclo hidrológico en Suramérica tropical: detección y atribución [Tesis de maestría]. Universidad de Antioquia, Medellín, Colombia. |
| dc.identifier.uri.none.fl_str_mv |
https://hdl.handle.net/10495/46504 |
| identifier_str_mv |
Leandro Arévalo, M. F. (2025). Intensificación del ciclo hidrológico en Suramérica tropical: detección y atribución [Tesis de maestría]. Universidad de Antioquia, Medellín, Colombia. |
| url |
https://hdl.handle.net/10495/46504 |
| dc.language.iso.none.fl_str_mv |
spa |
| language |
spa |
| dc.relation.references.none.fl_str_mv |
Abdelmoaty, H. M., Papalexiou, S. M., Rajulapati, C. R., & AghaKouchak, A. (2021). Biases beyond the mean in CMIP6 extreme precipitation: A global investigation, Earths Future, 9, e2021EF002196. Akinsanola, A. A., Kooperman, G. J., Pendergrass, A. G., Hannah, W. M., & Reed, K. A. (2020). Seasonal representation of extreme precipitation indices over the United States in CMIP6 present-day simulations. Environmental Research Letters, 15(9), 094003. Akinsanola, A. A., Ongoma, V., & Kooperman, G. J. (2021). Evaluation of CMIP6 models in simulating the statistics of extreme precipitation over Eastern Africa. Atmospheric Research, 254, 105509. Allan, R. P., Barlow, M., Byrne, M. P., Cherchi, A., Douville, H., Fowler, H. J., ... & Zolina, O. (2020). Advances in understanding large‐scale responses of the water cycle to climate change. Annals of the New York Academy of Sciences, 1472(1), 49-75. Allen, M. R., & Tett, S. F. (1999). Checking for model consistency in optimal fingerprinting. Climate Dynamics, 15, 419-434. Allen, M. R., & Stott, P. A. (2003). Estimating signal amplitudes in optimal fingerprinting, Part I: Theory. Climate Dynamics, 21, 477-491. Allen, M. R., Gillett, N. P., Kettleborough, J. A., Hegerl, G., Schnur, R., Stott, P. A., ... & Barnett, T. P. (2006). Quantifying anthropogenic influence on recent near-surface temperature change. Surveys in Geophysics, 27, 491-544. Almazroui, M., Ashfaq, M., Islam, M. N., Rashid, I. U., Kamil, S., Abid, M. A., ... & Sylla, M. B. (2021). Assessment of CMIP6 performance and projected temperature and precipitation changes over South America. Earth Systems and Environment, 5(2), 155-183. Arias, P. A., Martínez, J. A., Mejía, J. D., Pazos, M. J., Espinoza, J. C., & Wongchuig-Correa, S. (2020). Changes in normalized difference vegetation index in the Orinoco and Amazon River basins: Links to tropical Atlantic surface temperatures. Journal of Climate, 33(19), 8537-8559. Arias, P. A., Ortega, G., Villegas, L. D., & Martínez, J. A. (2021). Colombian climatology in CMIP5/CMIP6 models: Persistent biases and improvements. Revista Facultad de Ingeniería Universidad de Antioquia, (100), 75-96. Asadieh, B., & Krakauer, N. Y. (2015). Global trends in extreme precipitation: climate models versus observations. Hydrology and Earth System Sciences, 19(2), 877-891. Ashouri, H., Hsu, K. L., Sorooshian, S., Braithwaite, D. K., Knapp, K. R., Cecil, L. D., ... & Prat, O. P. (2015). PERSIANN-CDR: Daily precipitation climate data record from multisatellite observations for hydrological and climate studies. Bulletin of the American Meteorological Society, 96(1), 69-83. Avila-Diaz, A., Torres, R. R., Zuluaga, C. F., Cerón, W. L., Oliveira, L., Benezoli, V., ... & Medeiros, F. (2023). Current and Future Climate Extremes Over Latin America and Caribbean: Assessing Earth System Models from High Resolution Model Intercomparison Project (HighResMIP). Earth Systems and Environment, 7(1), 99-130. Avila-Diaz, A., Benezoli, V., Justino, F., Torres, R., & Wilson, A. (2020). Assessing current and future trends of climate extremes across Brazil based on reanalyses and earth system model projections. Climate Dynamics, 55(5-6), 1403-1426. Bao, Qing; He, Bian (2019). CAS FGOALS-f3-H model output prepared for CMIP6 HighResMIP highresSST-present. Version. Earth System Grid Federation. Bazzanela, A. C., Dereczynski, C., Luiz-Silva, W., & Regoto, P. (2023). Performance of CMIP6 models over South America. Climate Dynamics, 1-16. Bindoff, N. L., Stott, P. A., AchutaRao, K. M., Allen, M. R., Gillett, N., Gutzler, D., Hansingo, K., Hegerl, G., Hu, Y., Jain, S., Mokhov, I. I., Overland, J., Perlwitz, J., Sebbari, R., & Zhang, X. (2013). Detection and attribution of climate change: From global to regional. En T. F. Stocker, D. Qin, G.-K. Plattner, M. Tignor, S. K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, & P. M. Midgley (Eds.), Climate change 2013: The physical science basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (pp. 867–952). Cambridge University Press. Bône, C., Gastineau, G., Thiria, S., Gallinari, P., & Mejia, C. (2023). Detection and attribution of climate change using a neural network. Journal of Advances in Modeling Earth Systems, 15(10), e2022MS003475. Boucher, O., et al. (2018). IPSL IPSL-CM6A-LR model output prepared for CMIP6 CMIP historical (Version 20180803) [Data set]. Earth System Grid Federation. Caparoci Nogueira, S. M., Moreira, M. A., & Lordelo Volpato, M. M. (2018). Evaluating precipitation estimates from Eta, TRMM and CHRIPS Data in the south-southeast region of Minas Gerais State—Brazil. Remote Sensing, 10(2), 313. Caretta, M. A., Mukherji, A., Arfanuzzaman, M., Betts, R. A., Gelfan, A., Hirabayashi, Y., Lissner, T. K., Liu, J., Lopez Gunn, E., Morgan, R., Mwanga, S., & Supratid, S. (2022). Water. En H.-O. Pörtner, D. C. Roberts, M. Tignor, E. S. Poloczanska, K. Mintenbeck, A. Alegría, M. Craig, S. Langsdorf, S. Löschke, V. Möller, A. Okem, & B. Rama (Eds.), Climate change 2022: Impacts, adaptation, and vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (pp. 551–712). Cambridge University Press. Cavalcante, R. B. L., da Silva Ferreira, D. B., Pontes, P. R. M., Tedeschi, R. G., da Costa, C. P. W., & de Souza, E. B. (2020). Evaluation of extreme rainfall indices from CHIRPS precipitation estimates over the Brazilian Amazonia. Atmospheric Research, 238, 104879. Cepeda Arias, E., & Cañon Barriga, J. (2022). Performance of high-resolution precipitation datasets CHIRPS and TerraClimate in a Colombian high Andean Basin. Geocarto International, 37(27), 17382-17402. Cerón, W. L., Kayano, M. T., Andreoli, R. V., Avila-Diaz, A., Ayes, I., Freitas, E. D., ... & Souza, R. A. (2021). Recent intensification of extreme precipitation events in the La Plata Basin in Southern South America (1981–2018). Atmospheric Research, 249, 105299. Cerón, W. L., Andreoli, R. V., Kayano, M. T., Canchala, T., Ocampo-Marulanda, C., Avila-Diaz, A., & Antunes, J. (2022). Trend pattern of heavy and intense rainfall events in Colombia from 1981–2018: A trend-EOF approach. Atmosphere, 13(2), 156. Chen, M., Shi, W., Xie, P., Silva, V. B., Kousky, V. E., Wayne Higgins, R., & Janowiak, J. E. (2008). Assessing objective techniques for gauge‐based analyses of global daily precipitation. Journal of Geophysical Research: Atmospheres, 113(D4). Chen, H., & Sun, J. (2017). Contribution of human influence to increased daily precipitation extremes over China. Geophysical Research Letters, 44(5), 2436-2444. Chen, D., Rojas, M., Samset, B. H., Cobb, K., Diongue Niang, A., Edwards, P., Emori, S., Faria, S. H., Hawkins, E., Hope, P., Huybrechts, P., Meinshausen, M., Mustafa, S. K., Plattner, G.-K., & Tréguier, A.-M. (2021). Framing, context, and methods. En V. Masson-Delmotte, P. Zhai, A. Pirani, S. L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M. I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J. B. R. Matthews, T. K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, & B. Zhou (Eds.), Climate change 2021: The physical science basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (pp. 147–286). Cambridge University Press. Chen, M., Huang, Y., Li, Z., Larico, A. J. M., Xue, M., Hong, Y., ... & Morales, I. Y. (2022). Cross-Examining Precipitation Products by Rain Gauge, Remote Sensing, and WRF Simulations over a South American Region across the Pacific Coast and Andes. Atmosphere, 13(10), 1666. Chen, H., Chen, S. X., & Mu, M. (2024). A statistical review on the optimal fingerprinting approach in climate change studies. Climate Dynamics, 62(2), 1439-1446. Danabasoglu, G. (2019). NCAR CESM2 model output prepared for CMIP6 CMIP historical (Version 20190401) [Data set]. Earth System Grid Federation. de Medeiros, F. J., de Oliveira, C. P., & Avila-Diaz, A. (2022). Evaluation of extreme precipitation climate indices and their projected changes for Brazil: From CMIP3 to CMIP6. Weather and Climate Extremes, 38, 100511. DelSole, T., Trenary, L., Yan, X., & Tippett, M. K. (2019). Confidence intervals in optimal fingerprinting. Climate Dynamics, 52, 4111-4126. Dereczynski, C., Chou, S. C., Lyra, A., Sondermann, M., Regoto, P., Tavares, P., ... & de los Milagros Skansi, M. (2020). Downscaling of climate extremes over South America–Part I: Model evaluation in the reference climate. Weather and Climate Extremes, 29, 100273. Dix, M., et al. (2019). CSIRO-ARCCSS ACCESS-CM2 model output prepared for CMIP6 CMIP historical [Data set]. Earth System Grid Federation. Donat, M. G., Lowry, A. L., Alexander, L. V., O’Gorman, P. A., & Maher, N. (2016). More extreme precipitation in the world’s dry and wet regions. Nature Climate Change, 6(5), 508-513. Donat, M. G., Delgado‐Torres, C., De Luca, P., Mahmood, R., Ortega, P., & Doblas‐Reyes, F. J. (2023). How credibly do CMIP6 simulations capture historical mean and extreme precipitation changes?. Geophysical Research Letters, 50(14), e2022GL102466. Dong, S., Sun, Y., Li, C., Zhang, X., Min, S. K., & Kim, Y. H. (2021). Attribution of extreme precipitation with updated observations and CMIP6 simulations. Journal of Climate, 34(3), 871-881. Dong, T., Zhu, X., Deng, R., Ma, Y., & Dong, W. (2022). Detection and attribution of extreme precipitation events over the Asian monsoon region. Weather and Climate Extremes, 38, 100497. Douville, H., Raghavan, K., Renwick, J., Allan, R. P., Arias, P. A., Barlow, M., Cerezo-Mota, R., Cherchi, A., Gan, T. Y., Gergis, J., Jiang, D., Khan, A., Pokam Mba, W., Rosenfeld, D., Tierney, J., & Zolina, O. (2021). Water cycle changes. En V. Masson-Delmotte, P. Zhai, A. Pirani, S. L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M. I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J. B. R. Matthews, T. K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, & B. Zhou (Eds.), Climate change 2021: The physical science basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (pp. 1055–1210). Cambridge University Press. Li, Y., Chen, K., Yan, J., & Zhang, X. (2021). Uncertainty in optimal fingerprinting is underestimated. Environmental Research Letters, 16(8), 084043. Li, S., Chen, Y., Wei, W., Fang, G., & Duan, W. (2024). The increase in extreme precipitation and its proportion over global land. Journal of Hydrology, 628, 130456. Dunn, R. J., Alexander, L. V., Donat, M. G., Zhang, X., Bador, M., Herold, N., ... & Bin Hj Yussof, M. N. A. (2020). Development of an updated global land in situ‐based data set of temperature and precipitation extremes: HadEX3. Journal of Geophysical Research: Atmospheres, 125(16), e2019JD032263. Espinoza, J. C., Ronchail, J., Marengo, J. A., & Segura, H. (2019). Contrasting North–South changes in Amazon wet-day and dry-day frequency and related atmospheric features (1981–2017). Climate Dynamics, 52(9-10), 5413-5430. Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., & Taylor, K. E. (2016). Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geoscientific Model Development, 9(5), 1937-1958. Feng, T., Zhu, X., & Dong, W. (2023). Historical assessment and future projection of extreme precipitation in CMIP6 models: Global and continental. International Journal of Climatology. Funk, C., Peterson, P., Landsfeld, M., Pedreros, D., Verdin, J., Shukla, S., ... & Michaelsen, J. (2015). The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes. Scientific data, 2(1), 1-21. Gillett, N. P., Shiogama, H., Funke, B., Hegerl, G., Knutti, R., Matthes, K., ... & Tebaldi, C. (2016). The detection and attribution model intercomparison project (DAMIP v1. 0) contribution to CMIP6. Geoscientific Model Development, 9(10), 3685-3697. Giorgi, F., Im, E. S., Coppola, E., Diffenbaugh, N. S., Gao, X. J., Mariotti, L., & Shi, Y. (2011). Higher hydroclimatic intensity with global warming. Journal of Climate, 24(20), 5309-5324. Golaz, J. C., Van Roekel, L. P., Zheng, X., Roberts, A. F., Wolfe, J. D., Lin, W., ... & Bader, D. C. (2022). The DOE E3SM Model Version 2: overview of the physical model and initial model evaluation. Journal of Advances in Modeling Earth Systems, 14(12). Haarsma, R. J., Roberts, M. J., Vidale, P. L., Senior, C. A., Bellucci, A., Bao, Q., ... & von Storch, J. S. (2016). High resolution model intercomparison project (HighResMIP v1. 0) for CMIP6. Geoscientific Model Development, 9(11), 4185-4208. Haghtalab, N., Moore, N., Heerspink, B. P., & Hyndman, D. W. (2020). Evaluating spatial patterns in precipitation trends across the Amazon basin driven by land cover and global scale forcings. Theoretical and Applied Climatology, 140, 411-427. Hasselmann, K. (1979). On the signal-to-noise problem in atmospheric response studies. En T. Shawn (Ed.), Meteorology of tropical oceans (pp. 251–259). Royal Meteorological Society. Hasselmann, K. (1997). Multi-pattern fingerprint method for detection and attribution of climate change. Climate dynamics, 13, 601-611. Hegerl, G. C., von Storch, H., Hasselmann, K., Santer, B. D., Cubasch, U., & Jones, P. D. (1996). Detecting greenhouse-gas-induced climate change with an optimal fingerprint method. Journal of Climate, 9(10), 2281-2306. Hegerl, G. C., & North, G. R. (1997). Comparison of statistically optimal approaches to detecting anthropogenic climate change. Journal of Climate, 10(5), 1125-1133. Hegerl, G. C., et al. (2010). Good practice guidance paper on detection and attribution related to anthropogenic climate change. En T. F. Stocker et al. (Eds.), Meeting report of the Intergovernmental Panel on Climate Change expert meeting on detection and attribution of anthropogenic climate change (pp. 1–8). IPCC Working Group I Technical Support Unit, University of Bern. Hegerl, G., & Zwiers, F. (2011). Use of models in detection and attribution of climate change. WIREs Climate Change, 2(4), 570–591. Heim Jr, R. R. (2015). An overview of weather and climate extremes–Products and trends. Weather and Climate Extremes, 10, 1-9. Herold, N., Behrangi, A., & Alexander, L. V. (2017). Large uncertainties in observed daily precipitation extremes over land. Journal of Geophysical Research: Atmospheres, 122(2), 668-681. Huffman, G. J., Bolvin, D. T., Braithwaite, D., Hsu, K., Joyce, R., Xie, P., & Yoo, S. H. (2015). NASA global precipitation measurement (GPM) integrated multi-satellite retrievals for GPM (IMERG). Algorithm theoretical basis document (ATBD) version, 4(26), 30. Jones, P. W. (1999). First-and second-order conservative remapping schemes for grids in spherical coordinates. Monthly Weather Review, 127(9), 2204-2210. Kendall, M. G. (1975). Rank Correlation Methods. New York, NY: Oxford University Press. Kim, Y. H., Min, S. K., Zhang, X., Sillmann, J., & Sandstad, M. (2020). Evaluation of the CMIP6 multi-model ensemble for climate extreme indices. Weather and Climate Extremes, 29, 100269. Kirchmeier-Young, M. C., & Zhang, X. (2020). Human influence has intensified extreme precipitation in North America. Proceedings of the National Academy of Sciences, 117(24), 13308-13313. Krasting, J. P., et al. (2018). NOAA-GFDL GFDL-ESM4 model output prepared for CMIP6 CMIP historical (Version 20190726) [Data set]. Earth System Grid Federation. Lagos-Zúñiga, M., Balmaceda-Huarte, R., Regoto, P., Torrez, L., Olmo, M., Lyra, A., ... & Bettolli, M. L. (2024). Extreme indices of temperature and precipitation in South America: trends and intercomparison of regional climate models. Climate Dynamics, 1-22. Lawrence, M. G. (2005). The relationship between relative humidity and the dewpoint temperature in moist air: A simple conversion and applications. Bulletin of the American Meteorological Society, 86(2), 225-234. Li, L., et al. (2019). CAS FGOALS-g3 model output prepared for CMIP6 CMIP historical (Version 20190826) [Data set]. Earth System Grid Federation. Li, C., Zwiers, F., Zhang, X., Li, G., Sun, Y., & Wehner, M. (2021). Changes in annual extremes of daily temperature and precipitation in CMIP6 models. Journal of Climate, 34(9), 3441-3460. Li, Y., Chen, K., Yan, J., & Zhang, X. (2021). Uncertainty in optimal fingerprinting is underestimated. Environmental Research Letters, 16(8), 084043. Li, Z., Liu, T., Huang, Y., Peng, J., & Ling, Y. (2022). Evaluation of the CMIP6 precipitation simulations over global land. Earth's Future, 10(8), e2021EF002500. López-Bermeo, C., Montoya, R. D., Caro-Lopera, F. J., & Díaz-García, J. A. (2022). Validation of the accuracy of the CHIRPS precipitation dataset at representing climate variability in a tropical mountainous region of South America. Physics and Chemistry of the Earth, Parts A/B/C, 127, 103184. Luo, N., Guo, Y., Chou, J., & Gao, Z. (2022). Added value of CMIP6 models over CMIP5 models in simulating the climatological precipitation extremes in China. International Journal of Climatology, 42(2), 1148-1164. Ma, S., Wang, T., Yan, J., & Zhang, X. (2023). Optimal Fingerprinting with Estimating Equations. Journal of Climate, 36(20), 7109-7122. Mann, H. B. (1945). Nonparametric tests against trend. Econometrica: Journal of the econometric society, 245-259. Manz, B., Páez-Bimos, S., Horna, N., Buytaert, W., Ochoa-Tocachi, B., Lavado-Casimiro, W., & Willems, B. (2017). Comparative ground validation of IMERG and TMPA at variable spatiotemporal scales in the tropical Andes. Journal of Hydrometeorology, 18(9), 2469-2489. Martinez, J. A., Arias, P. A., Dominguez, F., & Prein, A. (2024). Mesoscale structures in the Orinoco basin during an extreme precipitation event in the tropical Andes. Frontiers in Earth Science, 11, 1307549. Martinez, J.A., Junquas, C., Bozkurt, D., Viale, M., Fita, L., Trachte, K., Campozano, L., Silva, Y., Solman, S., Arias, P.A., Blacutt, L.A., Condom, T., Espinoza, J.C., Sorensson, A. (2024). Recent progress in atmospheric modeling over the Andes: a review of atmospheric processes and research gaps. Reviews of Geophysics, submitted. McKitrick, R. (2022). Checking for model consistency in optimal fingerprinting: a comment. Climate Dynamics, 58(1), 405-411. Mesa, O., Urrea, V., & Ochoa, A. (2021). Trends of hydroclimatic intensity in Colombia. Climate, 9(7), 120. Min, S. K., Zhang, X., Zwiers, F. W., & Hegerl, G. C. (2011). Human contribution to more-intense precipitation extremes. Nature, 470(7334), 378-381. Mizuta, R., Yoshimura, H., Ose, T., Hosaka, M., & Yukimoto, S. (2019). MRI MRI-AGCM3-2-H model output prepared for CMIP6 HighResMIP highresSST-future [Data set]. Earth System Grid Federation. Oliveira, R., Maggioni, V., Vila, D., & Morales, C. (2016). Characteristics and diurnal cycle of GPM rainfall estimates over the central Amazon region. Remote Sensing, 8(7), 544. Ortega, G., Arias, P. A., Villegas, J. C., Marquet, P. A., & Nobre, P. (2021). Present‐day and future climate over central and South America according to CMIP5/CMIP6 models. International Journal of Climatology, 41(15), 6713-6735. Otto, F. E. (2023). Attribution of Extreme Events to Climate Change. Annual Review of Environment and Resources, 48, 813-828. Palharini, R. S. A., Vila, D. A., Rodrigues, D. T., Quispe, D. P., Palharini, R. C., de Siqueira, R. A., & de Sousa Afonso, J. M. (2020). Assessment of the extreme precipitation by satellite estimates over South America. Remote Sensing, 12(13), 2085. Palomino-Ángel, S., Anaya-Acevedo, J. A., & Botero, B. A. (2019). Evaluation of 3B42V7 and IMERG daily-precipitation products for a very high-precipitation region in northwestern South America. Atmospheric Research, 217, 37-48. Paik, S., Min, S. K., Zhang, X., Donat, M. G., King, A. D., & Sun, Q. (2020). Determining the anthropogenic greenhouse gas contribution to the observed intensification of extreme precipitation. Geophysical Research Letters, 47(12), e2019GL086875. Paredes-Trejo, F. J., Barbosa, H. A., & Kumar, T. L. (2017). Validating CHIRPS-based satellite precipitation estimates in Northeast Brazil. Journal of arid environments, 139, 26-40. Pradhan, R. K., Markonis, Y., Godoy, M. R. V., Villalba-Pradas, A., Andreadis, K. M., Nikolopoulos, E. I., ... & Hanel, M. (2022). Review of GPM IMERG performance: A global perspective. Remote Sensing of Environment, 268, 112754. Rajulapati, C. R., Papalexiou, S. M., Clark, M. P., & Pomeroy, J. W. (2021). The perils of regridding: examples using a global precipitation dataset. Journal of Applied Meteorology and Climatology, 60(11), 1561-1573. Ribes, A., Azaïs, J. M., & Planton, S. (2009). Adaptation of the optimal fingerprint method for climate change detection using a well-conditioned covariance matrix estimate. Climate Dynamics, 33, 707-722. Ribes, A., Planton, S., & Terray, L. (2013). Application of regularised optimal fingerprinting to attribution. Part I: Method, properties and idealised analysis. Climate dynamics, 41, 2817-2836. Rivera, J. A., Marianetti, G., & Hinrichs, S. (2018). Validation of CHIRPS precipitation dataset along the Central Andes of Argentina. Atmospheric Research, 213, 437-449. Rivera, J. A., Hinrichs, S., & Marianetti, G. (2019). Using CHIRPS dataset to assess wet and dry conditions along the semiarid central-western Argentina. Advances in Meteorology, 2019. Rivera, J. A., & Arnould, G. (2020). Evaluation of the ability of CMIP6 models to simulate precipitation over Southwestern South America: Climatic features and long-term trends (1901–2014). Atmospheric Research, 241, 104953. Rozante, J. R., Vila, D. A., Barboza Chiquetto, J., Fernandes, A. D. A., & Souza Alvim, D. (2018). Evaluation of TRMM/GPM blended daily products over Brazil. Remote Sensing, 10(6), 882. Schipper, E. L. F., Revi, A., Preston, B. L., Carr, E. R., Eriksen, S. H., Fernandez-Carril, L. R., Glavovic, B., Hilmi, N. J. M., Ley, D., Mukerji, R., Muylaert de Araujo, M. S., Perez, R., Rose, S. K., & Singh, P. K. (2022). Climate resilient development pathways. En H.-O. Pörtner, D. C. Roberts, M. Tignor, E. S. Poloczanska, K. Mintenbeck, A. Alegría, M. Craig, S. Langsdorf, S. Löschke, V. Möller, A. Okem, & B. Rama (Eds.), Climate change 2022: Impacts, adaptation, and vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (pp. 2655–2807). Cambridge University Press. Scoccimarro, E., Bellucci, A., & Peano, D. (2017). CMCC CMCC-CM2-VHR4 model output prepared for CMIP6 HighResMIP [Data set]. Earth System Grid Federation. Seland, Ø., et al. (2019). NCC NorESM2-LM model output prepared for CMIP6 CMIP historical (Version 20190815) [Data set]. Earth System Grid Federation. Sen, P. K. (1968). Estimates of the regression coefficient based on Kendall's tau. Journal of the American statistical association, 63(324), 1379-1389. Seneviratne, S. I., Zhang, X., Adnan, M., Badi, W., Dereczynski, C., Di Luca, A., Ghosh, S., Iskandar, I., Kossin, J., Lewis, S., Otto, F., Pinto, I., Satoh, M., Vicente-Serrano, S. M., Wehner, M., & Zhou, B. (2021). Weather and climate extreme events in a changing climate. En V. Masson-Delmotte, P. Zhai, A. Pirani, S. L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M. I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J. B. R. Matthews, T. K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, & B. Zhou (Eds.), Climate change 2021: The physical science basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (pp. 1513–1766). Cambridge University Press. Shen, Z., Yong, B., Gourley, J. J., Qi, W., Lu, D., Liu, J., ... & Zhang, J. (2020). Recent global performance of the Climate Hazards group Infrared Precipitation (CHIRP) with Stations (CHIRPS). Journal of Hydrology, 591, 125284. Shepherd, T. G., Boyd, E., Calel, R. A., Chapman, S. C., Dessai, S., Dima-West, I. M., ... & Zenghelis, D. A. (2018). Storylines: an alternative approach to representing uncertainty in physical aspects of climate change. Climatic change, 151, 555-571. Skansi, M., Brunet, M., Sigró, J., Aguilar, E., Groening, J. A. A., Bentancur, O. J., ... & Jones, P. D. (2013). Warming and wetting signals emerging from analysis of changes in climate extreme indices over South America. Global and Planetary Change, 100, 295-307. Srivastava, A., Grotjahn, R., & Ullrich, P. A. (2020). Evaluation of historical CMIP6 model simulations of extreme precipitation over contiguous US regions. Weather and Climate Extremes, 29, 100268. Sun, Q., Zhang, X., Zwiers, F., Westra, S., & Alexander, L. V. (2021). A global, continental, and regional analysis of changes in extreme precipitation. Journal of Climate, 34(1), 243-258. Stott, P. A., Allen, M. R., & Jones, G. S. (2003). Estimating signal amplitudes in optimal fingerprinting. Part II: application to general circulation models. Climate dynamics, 21, 493-500. Stott, P. A., Gillett, N. P., Hegerl, G. C., Karoly, D. J., Stone, D. A., Zhang, X., & Zwiers, F. (2010). Detection and attribution of climate change: a regional perspective. Wiley interdisciplinary reviews: climate change, 1(2), 192-211. Swart, N. C., Cole, J. N., Kharin, V. V., Lazare, M., Scinocca, J. F., Gillett, N. P., ... & Winter, B. (2019). The Canadian earth system model version 5 (CanESM5. 0.3). Geoscientific Model Development, 12(11), 4823-4873. Tatebe, H., & Watanabe, M. (2018). MIROC MIROC6 model output prepared for CMIP6 CMIP historical (Version 20191016) [Data set]. Earth System Grid Federation. Taylor, K. E. (2001). Summarizing multiple aspects of model performance in a single diagram. Journal of geophysical research: atmospheres, 106(D7), 7183-7192. Tu, C.Y. (2021). AS-RCEC HiRAM-SIT-HR model output prepared for CMIP6 HighResMIP hist-1950 [Data set]. Earth System Grid Federation. Urrea V., Ochoa A., & Mesa O. (2016). Validación de la base de datos de precipitación CHIRPS para Colombia a escala diaria, mensual y anual en el periodo 1981-2014. XXVII Congreso Latinoamericano de Hidráulica, Lima, Perú, 28–30 septiembre 2016. Urrea, V., Ochoa, A., & Mesa, O. (2019). Seasonality of rainfall in Colombia. Water Resources Research, 55(5), 4149-4162. Valencia, S., Marín, D. E., Gómez, D., Hoyos, N., Salazar, J. F., & Villegas, J. C. (2023). Spatio-temporal assessment of Gridded precipitation products across topographic and climatic gradients in Colombia. Atmospheric Research, 285, 106643. Vallejo‐Bernal, S. M., Urrea, V., Bedoya‐Soto, J. M., Posada, D., Olarte, A., Cárdenas‐Posso, Y., ... & Poveda, G. (2021). Ground validation of TRMM 3B43 V7 precipitation estimates over Colombia. Part I: Monthly and seasonal timescales. International Journal of Climatology, 41(1), 601-624. Wu, T., Lu, Y., Fang, Y., Xin, X., Li, L., Li, W., ... & Liu, X. (2019). The Beijing climate center climate system model (BCC-CSM): The main progress from CMIP5 to CMIP6. Geoscientific Model Development, 12(4), 1573-1600. Wu, W., Li, Y., Luo, X., Zhang, Y., Ji, X., & Li, X. (2019). Performance evaluation of the CHIRPS precipitation dataset and its utility in drought monitoring over Yunnan Province, China. Geomatics, Natural Hazards and Risk, 10(1), 2145-2162. Xu, H., Chen, H., & Wang, H. (2022). Detectable human influence on changes in precipitation extremes across China. Earth's Future, 10(2), e2021EF002409. Yao, J., Chen, Y., Chen, J., Zhao, Y., Tuoliewubieke, D., Li, J., ... & Mao, W. (2021). Intensification of extreme precipitation in arid Central Asia. Journal of Hydrology, 598, 125760. Yukimoto, S., et al. (2019). MRI MRI-ESM2.0 model output prepared for CMIP6 CMIP historical (Version 20190603) [Data set]. Earth System Grid Federation. Zhang, X., Wan, H., Zwiers, F. W., Hegerl, G. C., & Min, S. K. (2013). Attributing intensification of precipitation extremes to human influence. Geophysical Research Letters, 40(19), 5252-5257. Zeder, J., & Fischer, E. M. (2020). Observed extreme precipitation trends and scaling in Central Europe. Weather and Climate Extremes, 29, 100266. Zhao, Y., Xu, X., Huang, W., Wang, Y., Xu, Y., Chen, H., & Kang, Z. (2019). Trends in observed mean and extreme precipitation within the Yellow River Basin, China. Theoretical and Applied Climatology, 136, 1387-1396. Ziehn, T., et al. (2019). CSIRO ACCESS-ESM1.5 model output prepared for CMIP6 CMIP [Data set]. Earth System Grid Federation. Zittis, G., Bruggeman, A., & Lelieveld, J. (2021). Revisiting future extreme precipitation trends in the Mediterranean. Weather and Climate Extremes, 34, 100380. |
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Arias Gómez, Paola AndreaSalazar Villegas, Juan FernandoLeandro Arévalo, Manuel FelipeGrupo de Ingeniería y Gestión Ambiental (GIGA)Carmona Duque, Alejandra MaríaRojo Hernández, Julián David2025-06-24T15:42:17Z2025Leandro Arévalo, M. F. (2025). Intensificación del ciclo hidrológico en Suramérica tropical: detección y atribución [Tesis de maestría]. Universidad de Antioquia, Medellín, Colombia.https://hdl.handle.net/10495/46504La evidencia científica sugiere que el cambio climático antropogénico tiene el potencial de cambiar la frecuencia y la magnitud de los eventos de precipitación al intensificar el ciclo hidrológico, en particular las precipitaciones extremas y la ocurrencia de eventos secos en diferentes regiones del mundo. El objetivo de este Trabajo de Investigación es identificar una posible intensificación del ciclo hidrológico en Suramérica Tropical a partir de diferentes bases de datos de referencia. Además, plantea evaluar el desempeño de los modelos de circulación general contenidos en la Sexta Fase del Proyecto de Comparación de Modelos Acoplados (CMIP6 por sus iniciales en inglés) en la simulación de extremos de precipitación, así como su intensificación y tendencias en la región de estudio. Los datos de referencia se obtuvieron a partir de cinco productos de precipitación de diferentes características (CHIRPS-v2.0, GPM, PERSIANN-CDR, CPC y HadEX3) para las últimas décadas, y las simulaciones se obtuvieron de un total de 12 modelos climáticos globales (ESM) y 6 modelos globales de alta resolución (HighResMIP). Las regiones de interés son el noroeste (NWS), el norte (NSA) y el noreste (NES) de Suramérica, así como la región del Monzón de Suramérica (SAM), las cuales han sido propuestas para el análisis de evidencia científica regional del sexto informe de evaluación del Panel Intergubernamental sobre Cambio Climático (IPCC por sus iniciales en inglés). Las tendencias de las series de tiempo de los índices considerados fueron estimadas mediante las pruebas no paramétricas de Mann-Kendall y Sen Slope. Para la evaluación y comparación de los modelos con las observaciones, se calcularon sus sesgos y tendencias y se realizó un análisis de desempeño mediante diagramas de Taylor, para cada modelo y para el promedio multimodelo. Finalmente, se realizó un estudio de detección y atribución de la tendencia de cambio identificada en las observaciones de CHIRPS, utilizando el método Optimal Fingerprinting y los experimentos de forzamientos externos contenidos en el Proyecto de Comparación de Modelos de Detección y Atribución (DAMIP por sus iniciales en inglés). Estos forzamientos corresponden a un experimento de variabilidad natural, un experimento de gases de efecto invernadero antropogénicos y un experimento de aerosoles antropogénicos, con el objetivo de considerar la contribución de forzamientos naturales y antropogénicos. Nuestros resultados muestran evidencia de una intensificación del ciclo hidrológico durante las últimas tres décadas en las regiones de estudio, según los diferentes datos de referencia. Entre las señales más claras se identifican una intensificación de los eventos húmedos en la zona norte de NWS, particularmente el occidente y la región Andina de Colombia, y una intensificación de los eventos secos en las regiones SAM y NES. En general, los modelos CMIP6 y HighResMIP tienen dificultades para simular la variabilidad espacial y temporal de los extremos de precipitación tanto húmedos como secos, y solamente logran identificar la señal de cambio de los extremos secos en las regiones SAM y NES. Los resultados del estudio de detección y atribución no permitieron encontrar una señal de un forzamiento antropogénico en los cambios observados en las regiones de estudio para los índices de extremos de precipitación.COL0008619MaestríaMagíster en Ingeniería Ambiental114 páginasapplication/pdfspaUniversidad de AntioquiaMaestría en Ingeniería AmbientalMedellín, ColombiaFacultad de IngenieríaCampus Medellín - Ciudad Universitariahttp://creativecommons.org/licenses/by-nc-sa/4.0/info:eu-repo/semantics/openAccessAttribution-NonCommercial-ShareAlike 4.0 Internationalhttp://purl.org/coar/access_right/c_abf2Intensificación del ciclo hidrológico en Suramérica tropical: detección y atribuciónTrabajo de grado - Maestríahttp://purl.org/redcol/resource_type/TMTexthttp://purl.org/coar/version/c_b1a7d7d4d402bcceinfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/draftAbdelmoaty, H. M., Papalexiou, S. M., Rajulapati, C. R., & AghaKouchak, A. (2021). Biases beyond the mean in CMIP6 extreme precipitation: A global investigation, Earths Future, 9, e2021EF002196.Akinsanola, A. A., Kooperman, G. J., Pendergrass, A. G., Hannah, W. M., & Reed, K. A. (2020). Seasonal representation of extreme precipitation indices over the United States in CMIP6 present-day simulations. Environmental Research Letters, 15(9), 094003.Akinsanola, A. A., Ongoma, V., & Kooperman, G. J. (2021). Evaluation of CMIP6 models in simulating the statistics of extreme precipitation over Eastern Africa. Atmospheric Research, 254, 105509.Allan, R. P., Barlow, M., Byrne, M. P., Cherchi, A., Douville, H., Fowler, H. J., ... & Zolina, O. (2020). Advances in understanding large‐scale responses of the water cycle to climate change. Annals of the New York Academy of Sciences, 1472(1), 49-75.Allen, M. R., & Tett, S. F. (1999). Checking for model consistency in optimal fingerprinting. Climate Dynamics, 15, 419-434.Allen, M. R., & Stott, P. A. (2003). Estimating signal amplitudes in optimal fingerprinting, Part I: Theory. Climate Dynamics, 21, 477-491.Allen, M. R., Gillett, N. P., Kettleborough, J. A., Hegerl, G., Schnur, R., Stott, P. A., ... & Barnett, T. P. (2006). Quantifying anthropogenic influence on recent near-surface temperature change. Surveys in Geophysics, 27, 491-544.Almazroui, M., Ashfaq, M., Islam, M. N., Rashid, I. U., Kamil, S., Abid, M. A., ... & Sylla, M. B. (2021). Assessment of CMIP6 performance and projected temperature and precipitation changes over South America. Earth Systems and Environment, 5(2), 155-183.Arias, P. A., Martínez, J. A., Mejía, J. D., Pazos, M. J., Espinoza, J. C., & Wongchuig-Correa, S. (2020). Changes in normalized difference vegetation index in the Orinoco and Amazon River basins: Links to tropical Atlantic surface temperatures. Journal of Climate, 33(19), 8537-8559.Arias, P. A., Ortega, G., Villegas, L. D., & Martínez, J. A. (2021). Colombian climatology in CMIP5/CMIP6 models: Persistent biases and improvements. Revista Facultad de Ingeniería Universidad de Antioquia, (100), 75-96.Asadieh, B., & Krakauer, N. Y. (2015). Global trends in extreme precipitation: climate models versus observations. Hydrology and Earth System Sciences, 19(2), 877-891.Ashouri, H., Hsu, K. L., Sorooshian, S., Braithwaite, D. K., Knapp, K. R., Cecil, L. D., ... & Prat, O. P. (2015). PERSIANN-CDR: Daily precipitation climate data record from multisatellite observations for hydrological and climate studies. Bulletin of the American Meteorological Society, 96(1), 69-83.Avila-Diaz, A., Torres, R. R., Zuluaga, C. F., Cerón, W. L., Oliveira, L., Benezoli, V., ... & Medeiros, F. (2023). Current and Future Climate Extremes Over Latin America and Caribbean: Assessing Earth System Models from High Resolution Model Intercomparison Project (HighResMIP). Earth Systems and Environment, 7(1), 99-130.Avila-Diaz, A., Benezoli, V., Justino, F., Torres, R., & Wilson, A. (2020). Assessing current and future trends of climate extremes across Brazil based on reanalyses and earth system model projections. Climate Dynamics, 55(5-6), 1403-1426.Bao, Qing; He, Bian (2019). CAS FGOALS-f3-H model output prepared for CMIP6 HighResMIP highresSST-present. Version. Earth System Grid Federation.Bazzanela, A. C., Dereczynski, C., Luiz-Silva, W., & Regoto, P. (2023). Performance of CMIP6 models over South America. Climate Dynamics, 1-16.Bindoff, N. L., Stott, P. A., AchutaRao, K. M., Allen, M. R., Gillett, N., Gutzler, D., Hansingo, K., Hegerl, G., Hu, Y., Jain, S., Mokhov, I. I., Overland, J., Perlwitz, J., Sebbari, R., & Zhang, X. (2013). Detection and attribution of climate change: From global to regional. En T. F. Stocker, D. Qin, G.-K. Plattner, M. Tignor, S. K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, & P. M. Midgley (Eds.), Climate change 2013: The physical science basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (pp. 867–952). Cambridge University Press.Bône, C., Gastineau, G., Thiria, S., Gallinari, P., & Mejia, C. (2023). Detection and attribution of climate change using a neural network. Journal of Advances in Modeling Earth Systems, 15(10), e2022MS003475.Boucher, O., et al. (2018). IPSL IPSL-CM6A-LR model output prepared for CMIP6 CMIP historical (Version 20180803) [Data set]. Earth System Grid Federation.Caparoci Nogueira, S. M., Moreira, M. A., & Lordelo Volpato, M. M. (2018). Evaluating precipitation estimates from Eta, TRMM and CHRIPS Data in the south-southeast region of Minas Gerais State—Brazil. Remote Sensing, 10(2), 313.Caretta, M. A., Mukherji, A., Arfanuzzaman, M., Betts, R. A., Gelfan, A., Hirabayashi, Y., Lissner, T. K., Liu, J., Lopez Gunn, E., Morgan, R., Mwanga, S., & Supratid, S. (2022). Water. En H.-O. Pörtner, D. C. Roberts, M. Tignor, E. S. Poloczanska, K. Mintenbeck, A. Alegría, M. Craig, S. Langsdorf, S. Löschke, V. Möller, A. Okem, & B. Rama (Eds.), Climate change 2022: Impacts, adaptation, and vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (pp. 551–712). Cambridge University Press.Cavalcante, R. B. L., da Silva Ferreira, D. B., Pontes, P. R. M., Tedeschi, R. G., da Costa, C. P. W., & de Souza, E. B. (2020). Evaluation of extreme rainfall indices from CHIRPS precipitation estimates over the Brazilian Amazonia. Atmospheric Research, 238, 104879.Cepeda Arias, E., & Cañon Barriga, J. (2022). Performance of high-resolution precipitation datasets CHIRPS and TerraClimate in a Colombian high Andean Basin. Geocarto International, 37(27), 17382-17402.Cerón, W. L., Kayano, M. T., Andreoli, R. V., Avila-Diaz, A., Ayes, I., Freitas, E. D., ... & Souza, R. A. (2021). Recent intensification of extreme precipitation events in the La Plata Basin in Southern South America (1981–2018). Atmospheric Research, 249, 105299.Cerón, W. L., Andreoli, R. V., Kayano, M. T., Canchala, T., Ocampo-Marulanda, C., Avila-Diaz, A., & Antunes, J. (2022). Trend pattern of heavy and intense rainfall events in Colombia from 1981–2018: A trend-EOF approach. Atmosphere, 13(2), 156.Chen, M., Shi, W., Xie, P., Silva, V. B., Kousky, V. E., Wayne Higgins, R., & Janowiak, J. E. (2008). Assessing objective techniques for gauge‐based analyses of global daily precipitation. Journal of Geophysical Research: Atmospheres, 113(D4).Chen, H., & Sun, J. (2017). Contribution of human influence to increased daily precipitation extremes over China. Geophysical Research Letters, 44(5), 2436-2444.Chen, D., Rojas, M., Samset, B. H., Cobb, K., Diongue Niang, A., Edwards, P., Emori, S., Faria, S. H., Hawkins, E., Hope, P., Huybrechts, P., Meinshausen, M., Mustafa, S. K., Plattner, G.-K., & Tréguier, A.-M. (2021). Framing, context, and methods. En V. Masson-Delmotte, P. Zhai, A. Pirani, S. L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M. I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J. B. R. Matthews, T. K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, & B. Zhou (Eds.), Climate change 2021: The physical science basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (pp. 147–286). Cambridge University Press.Chen, M., Huang, Y., Li, Z., Larico, A. J. M., Xue, M., Hong, Y., ... & Morales, I. Y. (2022). Cross-Examining Precipitation Products by Rain Gauge, Remote Sensing, and WRF Simulations over a South American Region across the Pacific Coast and Andes. Atmosphere, 13(10), 1666.Chen, H., Chen, S. X., & Mu, M. (2024). A statistical review on the optimal fingerprinting approach in climate change studies. Climate Dynamics, 62(2), 1439-1446.Danabasoglu, G. (2019). NCAR CESM2 model output prepared for CMIP6 CMIP historical (Version 20190401) [Data set]. Earth System Grid Federation.de Medeiros, F. J., de Oliveira, C. P., & Avila-Diaz, A. (2022). Evaluation of extreme precipitation climate indices and their projected changes for Brazil: From CMIP3 to CMIP6. Weather and Climate Extremes, 38, 100511.DelSole, T., Trenary, L., Yan, X., & Tippett, M. K. (2019). Confidence intervals in optimal fingerprinting. Climate Dynamics, 52, 4111-4126.Dereczynski, C., Chou, S. C., Lyra, A., Sondermann, M., Regoto, P., Tavares, P., ... & de los Milagros Skansi, M. (2020). Downscaling of climate extremes over South America–Part I: Model evaluation in the reference climate. Weather and Climate Extremes, 29, 100273.Dix, M., et al. (2019). CSIRO-ARCCSS ACCESS-CM2 model output prepared for CMIP6 CMIP historical [Data set]. Earth System Grid Federation.Donat, M. G., Lowry, A. L., Alexander, L. V., O’Gorman, P. A., & Maher, N. (2016). More extreme precipitation in the world’s dry and wet regions. Nature Climate Change, 6(5), 508-513.Donat, M. G., Delgado‐Torres, C., De Luca, P., Mahmood, R., Ortega, P., & Doblas‐Reyes, F. J. (2023). How credibly do CMIP6 simulations capture historical mean and extreme precipitation changes?. Geophysical Research Letters, 50(14), e2022GL102466.Dong, S., Sun, Y., Li, C., Zhang, X., Min, S. K., & Kim, Y. H. (2021). Attribution of extreme precipitation with updated observations and CMIP6 simulations. Journal of Climate, 34(3), 871-881.Dong, T., Zhu, X., Deng, R., Ma, Y., & Dong, W. (2022). Detection and attribution of extreme precipitation events over the Asian monsoon region. Weather and Climate Extremes, 38, 100497.Douville, H., Raghavan, K., Renwick, J., Allan, R. P., Arias, P. A., Barlow, M., Cerezo-Mota, R., Cherchi, A., Gan, T. Y., Gergis, J., Jiang, D., Khan, A., Pokam Mba, W., Rosenfeld, D., Tierney, J., & Zolina, O. (2021). Water cycle changes. En V. Masson-Delmotte, P. Zhai, A. Pirani, S. L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M. I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J. B. R. Matthews, T. K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, & B. Zhou (Eds.), Climate change 2021: The physical science basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (pp. 1055–1210). Cambridge University Press.Li, Y., Chen, K., Yan, J., & Zhang, X. (2021). Uncertainty in optimal fingerprinting is underestimated. Environmental Research Letters, 16(8), 084043.Li, S., Chen, Y., Wei, W., Fang, G., & Duan, W. (2024). The increase in extreme precipitation and its proportion over global land. Journal of Hydrology, 628, 130456.Dunn, R. J., Alexander, L. V., Donat, M. G., Zhang, X., Bador, M., Herold, N., ... & Bin Hj Yussof, M. N. A. (2020). Development of an updated global land in situ‐based data set of temperature and precipitation extremes: HadEX3. Journal of Geophysical Research: Atmospheres, 125(16), e2019JD032263.Espinoza, J. C., Ronchail, J., Marengo, J. A., & Segura, H. (2019). Contrasting North–South changes in Amazon wet-day and dry-day frequency and related atmospheric features (1981–2017). Climate Dynamics, 52(9-10), 5413-5430.Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., & Taylor, K. E. (2016). Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geoscientific Model Development, 9(5), 1937-1958.Feng, T., Zhu, X., & Dong, W. (2023). Historical assessment and future projection of extreme precipitation in CMIP6 models: Global and continental. International Journal of Climatology.Funk, C., Peterson, P., Landsfeld, M., Pedreros, D., Verdin, J., Shukla, S., ... & Michaelsen, J. (2015). The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes. Scientific data, 2(1), 1-21.Gillett, N. P., Shiogama, H., Funke, B., Hegerl, G., Knutti, R., Matthes, K., ... & Tebaldi, C. (2016). The detection and attribution model intercomparison project (DAMIP v1. 0) contribution to CMIP6. Geoscientific Model Development, 9(10), 3685-3697.Giorgi, F., Im, E. S., Coppola, E., Diffenbaugh, N. S., Gao, X. J., Mariotti, L., & Shi, Y. (2011). Higher hydroclimatic intensity with global warming. Journal of Climate, 24(20), 5309-5324.Golaz, J. C., Van Roekel, L. P., Zheng, X., Roberts, A. F., Wolfe, J. D., Lin, W., ... & Bader, D. C. (2022). The DOE E3SM Model Version 2: overview of the physical model and initial model evaluation. Journal of Advances in Modeling Earth Systems, 14(12).Haarsma, R. J., Roberts, M. J., Vidale, P. L., Senior, C. A., Bellucci, A., Bao, Q., ... & von Storch, J. S. (2016). High resolution model intercomparison project (HighResMIP v1. 0) for CMIP6. Geoscientific Model Development, 9(11), 4185-4208.Haghtalab, N., Moore, N., Heerspink, B. P., & Hyndman, D. W. (2020). Evaluating spatial patterns in precipitation trends across the Amazon basin driven by land cover and global scale forcings. Theoretical and Applied Climatology, 140, 411-427.Hasselmann, K. (1979). On the signal-to-noise problem in atmospheric response studies. En T. Shawn (Ed.), Meteorology of tropical oceans (pp. 251–259). Royal Meteorological Society.Hasselmann, K. (1997). Multi-pattern fingerprint method for detection and attribution of climate change. Climate dynamics, 13, 601-611.Hegerl, G. C., von Storch, H., Hasselmann, K., Santer, B. D., Cubasch, U., & Jones, P. D. (1996). Detecting greenhouse-gas-induced climate change with an optimal fingerprint method. Journal of Climate, 9(10), 2281-2306.Hegerl, G. C., & North, G. R. (1997). Comparison of statistically optimal approaches to detecting anthropogenic climate change. Journal of Climate, 10(5), 1125-1133.Hegerl, G. C., et al. (2010). Good practice guidance paper on detection and attribution related to anthropogenic climate change. En T. F. Stocker et al. (Eds.), Meeting report of the Intergovernmental Panel on Climate Change expert meeting on detection and attribution of anthropogenic climate change (pp. 1–8). IPCC Working Group I Technical Support Unit, University of Bern.Hegerl, G., & Zwiers, F. (2011). Use of models in detection and attribution of climate change. WIREs Climate Change, 2(4), 570–591.Heim Jr, R. R. (2015). An overview of weather and climate extremes–Products and trends. Weather and Climate Extremes, 10, 1-9.Herold, N., Behrangi, A., & Alexander, L. V. (2017). Large uncertainties in observed daily precipitation extremes over land. Journal of Geophysical Research: Atmospheres, 122(2), 668-681.Huffman, G. J., Bolvin, D. T., Braithwaite, D., Hsu, K., Joyce, R., Xie, P., & Yoo, S. H. (2015). NASA global precipitation measurement (GPM) integrated multi-satellite retrievals for GPM (IMERG). Algorithm theoretical basis document (ATBD) version, 4(26), 30.Jones, P. W. (1999). First-and second-order conservative remapping schemes for grids in spherical coordinates. Monthly Weather Review, 127(9), 2204-2210.Kendall, M. G. (1975). Rank Correlation Methods. New York, NY: Oxford University Press.Kim, Y. H., Min, S. K., Zhang, X., Sillmann, J., & Sandstad, M. (2020). Evaluation of the CMIP6 multi-model ensemble for climate extreme indices. Weather and Climate Extremes, 29, 100269.Kirchmeier-Young, M. C., & Zhang, X. (2020). Human influence has intensified extreme precipitation in North America. Proceedings of the National Academy of Sciences, 117(24), 13308-13313.Krasting, J. P., et al. (2018). NOAA-GFDL GFDL-ESM4 model output prepared for CMIP6 CMIP historical (Version 20190726) [Data set]. Earth System Grid Federation.Lagos-Zúñiga, M., Balmaceda-Huarte, R., Regoto, P., Torrez, L., Olmo, M., Lyra, A., ... & Bettolli, M. L. (2024). Extreme indices of temperature and precipitation in South America: trends and intercomparison of regional climate models. Climate Dynamics, 1-22.Lawrence, M. G. (2005). The relationship between relative humidity and the dewpoint temperature in moist air: A simple conversion and applications. Bulletin of the American Meteorological Society, 86(2), 225-234.Li, L., et al. (2019). CAS FGOALS-g3 model output prepared for CMIP6 CMIP historical (Version 20190826) [Data set]. Earth System Grid Federation.Li, C., Zwiers, F., Zhang, X., Li, G., Sun, Y., & Wehner, M. (2021). Changes in annual extremes of daily temperature and precipitation in CMIP6 models. Journal of Climate, 34(9), 3441-3460.Li, Y., Chen, K., Yan, J., & Zhang, X. (2021). Uncertainty in optimal fingerprinting is underestimated. Environmental Research Letters, 16(8), 084043.Li, Z., Liu, T., Huang, Y., Peng, J., & Ling, Y. (2022). Evaluation of the CMIP6 precipitation simulations over global land. Earth's Future, 10(8), e2021EF002500.López-Bermeo, C., Montoya, R. D., Caro-Lopera, F. J., & Díaz-García, J. A. (2022). Validation of the accuracy of the CHIRPS precipitation dataset at representing climate variability in a tropical mountainous region of South America. Physics and Chemistry of the Earth, Parts A/B/C, 127, 103184.Luo, N., Guo, Y., Chou, J., & Gao, Z. (2022). Added value of CMIP6 models over CMIP5 models in simulating the climatological precipitation extremes in China. International Journal of Climatology, 42(2), 1148-1164.Ma, S., Wang, T., Yan, J., & Zhang, X. (2023). Optimal Fingerprinting with Estimating Equations. Journal of Climate, 36(20), 7109-7122.Mann, H. B. (1945). Nonparametric tests against trend. Econometrica: Journal of the econometric society, 245-259.Manz, B., Páez-Bimos, S., Horna, N., Buytaert, W., Ochoa-Tocachi, B., Lavado-Casimiro, W., & Willems, B. (2017). Comparative ground validation of IMERG and TMPA at variable spatiotemporal scales in the tropical Andes. Journal of Hydrometeorology, 18(9), 2469-2489.Martinez, J. A., Arias, P. A., Dominguez, F., & Prein, A. (2024). Mesoscale structures in the Orinoco basin during an extreme precipitation event in the tropical Andes. Frontiers in Earth Science, 11, 1307549.Martinez, J.A., Junquas, C., Bozkurt, D., Viale, M., Fita, L., Trachte, K., Campozano, L., Silva, Y., Solman, S., Arias, P.A., Blacutt, L.A., Condom, T., Espinoza, J.C., Sorensson, A. (2024). Recent progress in atmospheric modeling over the Andes: a review of atmospheric processes and research gaps. Reviews of Geophysics, submitted.McKitrick, R. (2022). Checking for model consistency in optimal fingerprinting: a comment. Climate Dynamics, 58(1), 405-411.Mesa, O., Urrea, V., & Ochoa, A. (2021). Trends of hydroclimatic intensity in Colombia. Climate, 9(7), 120.Min, S. K., Zhang, X., Zwiers, F. W., & Hegerl, G. C. (2011). Human contribution to more-intense precipitation extremes. Nature, 470(7334), 378-381.Mizuta, R., Yoshimura, H., Ose, T., Hosaka, M., & Yukimoto, S. (2019). MRI MRI-AGCM3-2-H model output prepared for CMIP6 HighResMIP highresSST-future [Data set]. Earth System Grid Federation.Oliveira, R., Maggioni, V., Vila, D., & Morales, C. (2016). Characteristics and diurnal cycle of GPM rainfall estimates over the central Amazon region. Remote Sensing, 8(7), 544.Ortega, G., Arias, P. A., Villegas, J. C., Marquet, P. A., & Nobre, P. (2021). Present‐day and future climate over central and South America according to CMIP5/CMIP6 models. International Journal of Climatology, 41(15), 6713-6735.Otto, F. E. (2023). Attribution of Extreme Events to Climate Change. Annual Review of Environment and Resources, 48, 813-828.Palharini, R. S. A., Vila, D. A., Rodrigues, D. T., Quispe, D. P., Palharini, R. C., de Siqueira, R. A., & de Sousa Afonso, J. M. (2020). Assessment of the extreme precipitation by satellite estimates over South America. Remote Sensing, 12(13), 2085.Palomino-Ángel, S., Anaya-Acevedo, J. A., & Botero, B. A. (2019). Evaluation of 3B42V7 and IMERG daily-precipitation products for a very high-precipitation region in northwestern South America. Atmospheric Research, 217, 37-48.Paik, S., Min, S. K., Zhang, X., Donat, M. G., King, A. D., & Sun, Q. (2020). Determining the anthropogenic greenhouse gas contribution to the observed intensification of extreme precipitation. Geophysical Research Letters, 47(12), e2019GL086875.Paredes-Trejo, F. J., Barbosa, H. A., & Kumar, T. L. (2017). Validating CHIRPS-based satellite precipitation estimates in Northeast Brazil. Journal of arid environments, 139, 26-40.Pradhan, R. K., Markonis, Y., Godoy, M. R. V., Villalba-Pradas, A., Andreadis, K. M., Nikolopoulos, E. I., ... & Hanel, M. (2022). Review of GPM IMERG performance: A global perspective. Remote Sensing of Environment, 268, 112754.Rajulapati, C. R., Papalexiou, S. M., Clark, M. P., & Pomeroy, J. W. (2021). The perils of regridding: examples using a global precipitation dataset. Journal of Applied Meteorology and Climatology, 60(11), 1561-1573.Ribes, A., Azaïs, J. M., & Planton, S. (2009). Adaptation of the optimal fingerprint method for climate change detection using a well-conditioned covariance matrix estimate. Climate Dynamics, 33, 707-722.Ribes, A., Planton, S., & Terray, L. (2013). Application of regularised optimal fingerprinting to attribution. Part I: Method, properties and idealised analysis. Climate dynamics, 41, 2817-2836.Rivera, J. A., Marianetti, G., & Hinrichs, S. (2018). Validation of CHIRPS precipitation dataset along the Central Andes of Argentina. Atmospheric Research, 213, 437-449.Rivera, J. A., Hinrichs, S., & Marianetti, G. (2019). Using CHIRPS dataset to assess wet and dry conditions along the semiarid central-western Argentina. Advances in Meteorology, 2019.Rivera, J. A., & Arnould, G. (2020). Evaluation of the ability of CMIP6 models to simulate precipitation over Southwestern South America: Climatic features and long-term trends (1901–2014). Atmospheric Research, 241, 104953.Rozante, J. R., Vila, D. A., Barboza Chiquetto, J., Fernandes, A. D. A., & Souza Alvim, D. (2018). Evaluation of TRMM/GPM blended daily products over Brazil. Remote Sensing, 10(6), 882.Schipper, E. L. F., Revi, A., Preston, B. L., Carr, E. R., Eriksen, S. H., Fernandez-Carril, L. R., Glavovic, B., Hilmi, N. J. M., Ley, D., Mukerji, R., Muylaert de Araujo, M. S., Perez, R., Rose, S. K., & Singh, P. K. (2022). Climate resilient development pathways. En H.-O. Pörtner, D. C. Roberts, M. Tignor, E. S. Poloczanska, K. Mintenbeck, A. Alegría, M. Craig, S. Langsdorf, S. Löschke, V. Möller, A. Okem, & B. Rama (Eds.), Climate change 2022: Impacts, adaptation, and vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (pp. 2655–2807). Cambridge University Press.Scoccimarro, E., Bellucci, A., & Peano, D. (2017). CMCC CMCC-CM2-VHR4 model output prepared for CMIP6 HighResMIP [Data set]. Earth System Grid Federation.Seland, Ø., et al. (2019). NCC NorESM2-LM model output prepared for CMIP6 CMIP historical (Version 20190815) [Data set]. Earth System Grid Federation.Sen, P. K. (1968). Estimates of the regression coefficient based on Kendall's tau. Journal of the American statistical association, 63(324), 1379-1389.Seneviratne, S. I., Zhang, X., Adnan, M., Badi, W., Dereczynski, C., Di Luca, A., Ghosh, S., Iskandar, I., Kossin, J., Lewis, S., Otto, F., Pinto, I., Satoh, M., Vicente-Serrano, S. M., Wehner, M., & Zhou, B. (2021). Weather and climate extreme events in a changing climate. En V. Masson-Delmotte, P. Zhai, A. Pirani, S. L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M. I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J. B. R. Matthews, T. K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, & B. Zhou (Eds.), Climate change 2021: The physical science basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (pp. 1513–1766). Cambridge University Press.Shen, Z., Yong, B., Gourley, J. J., Qi, W., Lu, D., Liu, J., ... & Zhang, J. (2020). Recent global performance of the Climate Hazards group Infrared Precipitation (CHIRP) with Stations (CHIRPS). Journal of Hydrology, 591, 125284.Shepherd, T. G., Boyd, E., Calel, R. A., Chapman, S. C., Dessai, S., Dima-West, I. M., ... & Zenghelis, D. A. (2018). Storylines: an alternative approach to representing uncertainty in physical aspects of climate change. Climatic change, 151, 555-571.Skansi, M., Brunet, M., Sigró, J., Aguilar, E., Groening, J. A. A., Bentancur, O. J., ... & Jones, P. D. (2013). Warming and wetting signals emerging from analysis of changes in climate extreme indices over South America. Global and Planetary Change, 100, 295-307.Srivastava, A., Grotjahn, R., & Ullrich, P. A. (2020). Evaluation of historical CMIP6 model simulations of extreme precipitation over contiguous US regions. Weather and Climate Extremes, 29, 100268.Sun, Q., Zhang, X., Zwiers, F., Westra, S., & Alexander, L. V. (2021). A global, continental, and regional analysis of changes in extreme precipitation. Journal of Climate, 34(1), 243-258.Stott, P. A., Allen, M. R., & Jones, G. S. (2003). Estimating signal amplitudes in optimal fingerprinting. Part II: application to general circulation models. Climate dynamics, 21, 493-500.Stott, P. A., Gillett, N. P., Hegerl, G. C., Karoly, D. J., Stone, D. A., Zhang, X., & Zwiers, F. (2010). Detection and attribution of climate change: a regional perspective. Wiley interdisciplinary reviews: climate change, 1(2), 192-211.Swart, N. C., Cole, J. N., Kharin, V. V., Lazare, M., Scinocca, J. F., Gillett, N. P., ... & Winter, B. (2019). The Canadian earth system model version 5 (CanESM5. 0.3). Geoscientific Model Development, 12(11), 4823-4873.Tatebe, H., & Watanabe, M. (2018). MIROC MIROC6 model output prepared for CMIP6 CMIP historical (Version 20191016) [Data set]. Earth System Grid Federation.Taylor, K. E. (2001). Summarizing multiple aspects of model performance in a single diagram. Journal of geophysical research: atmospheres, 106(D7), 7183-7192.Tu, C.Y. (2021). AS-RCEC HiRAM-SIT-HR model output prepared for CMIP6 HighResMIP hist-1950 [Data set]. Earth System Grid Federation.Urrea V., Ochoa A., & Mesa O. (2016). Validación de la base de datos de precipitación CHIRPS para Colombia a escala diaria, mensual y anual en el periodo 1981-2014. XXVII Congreso Latinoamericano de Hidráulica, Lima, Perú, 28–30 septiembre 2016.Urrea, V., Ochoa, A., & Mesa, O. (2019). Seasonality of rainfall in Colombia. Water Resources Research, 55(5), 4149-4162.Valencia, S., Marín, D. E., Gómez, D., Hoyos, N., Salazar, J. F., & Villegas, J. C. (2023). Spatio-temporal assessment of Gridded precipitation products across topographic and climatic gradients in Colombia. Atmospheric Research, 285, 106643.Vallejo‐Bernal, S. M., Urrea, V., Bedoya‐Soto, J. M., Posada, D., Olarte, A., Cárdenas‐Posso, Y., ... & Poveda, G. (2021). Ground validation of TRMM 3B43 V7 precipitation estimates over Colombia. Part I: Monthly and seasonal timescales. International Journal of Climatology, 41(1), 601-624.Wu, T., Lu, Y., Fang, Y., Xin, X., Li, L., Li, W., ... & Liu, X. (2019). The Beijing climate center climate system model (BCC-CSM): The main progress from CMIP5 to CMIP6. Geoscientific Model Development, 12(4), 1573-1600.Wu, W., Li, Y., Luo, X., Zhang, Y., Ji, X., & Li, X. (2019). Performance evaluation of the CHIRPS precipitation dataset and its utility in drought monitoring over Yunnan Province, China. Geomatics, Natural Hazards and Risk, 10(1), 2145-2162.Xu, H., Chen, H., & Wang, H. (2022). Detectable human influence on changes in precipitation extremes across China. Earth's Future, 10(2), e2021EF002409.Yao, J., Chen, Y., Chen, J., Zhao, Y., Tuoliewubieke, D., Li, J., ... & Mao, W. (2021). Intensification of extreme precipitation in arid Central Asia. Journal of Hydrology, 598, 125760.Yukimoto, S., et al. (2019). MRI MRI-ESM2.0 model output prepared for CMIP6 CMIP historical (Version 20190603) [Data set]. Earth System Grid Federation.Zhang, X., Wan, H., Zwiers, F. W., Hegerl, G. C., & Min, S. K. (2013). Attributing intensification of precipitation extremes to human influence. Geophysical Research Letters, 40(19), 5252-5257.Zeder, J., & Fischer, E. M. (2020). Observed extreme precipitation trends and scaling in Central Europe. Weather and Climate Extremes, 29, 100266.Zhao, Y., Xu, X., Huang, W., Wang, Y., Xu, Y., Chen, H., & Kang, Z. (2019). Trends in observed mean and extreme precipitation within the Yellow River Basin, China. Theoretical and Applied Climatology, 136, 1387-1396.Ziehn, T., et al. (2019). CSIRO ACCESS-ESM1.5 model output prepared for CMIP6 CMIP [Data set]. Earth System Grid Federation.Zittis, G., Bruggeman, A., & Lelieveld, J. (2021). Revisiting future extreme precipitation trends in the Mediterranean. Weather and Climate Extremes, 34, 100380.Climate changeCiclo hidrológicoHydrologic cyclePrecipitación atmósferica - mediciónPrecipitation (Meteorology)- MeasurementÍndices de extremos de precipitaciónCambio climáticohttp://vocabularies.unesco.org/thesaurus/concept4559ODS 13: Acción por el Clima. 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