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A novel statistically-based approach to regionalize extreme precipitation events using temperature data

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Abstract

Extreme precipitation events have been increasing and intensifying over the past few decades, posing challenges for modeling and prediction, as well as for policy and decision making. While traditional approaches often focus solely on studying the precipitation process, recent studies advocate for considering multiple processes and variables to better understand the drivers and anomalies of precipitation. This is especially underexplored in South America. To address this, we propose a novel approach that combines time series modeling and quantile regression to estimate the extreme quantiles of precipitation based on maximum daily temperatures. This methodology helps in understanding the relationships between these processes and contributes to identifying gauge stations with coherent climatic covariability, offering valuable insights into the regionalization of extreme events.

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References

  • AghaKouchak A, Chiang F, Huning LS, Love CA, Mallakpour I, Mazdiyasni O, Moftakhari H, Papalexiou SM, Ragno E, Sadegh M (2020) Climate extremes and compound hazards in a warming world. Annu Rev Earth Planet Sci 48:519–548

    Article  CAS  Google Scholar 

  • Al-Ghussain L (2019) Global warming: review on driving forces and mitigation. Environ Progr Sustain Energy 38(1):13–21

    Article  CAS  Google Scholar 

  • Allan RP, Barlow M, Byrne MP, Cherchi A, Douville H, Fowler HJ, Gan TY, Pendergrass AG, Rosenfeld D, Swann AL et al (2020) Advances in understanding large-scale responses of the water cycle to climate change. Ann N Y Acad Sci 1472(1):49–75

    Article  Google Scholar 

  • Barreiro M (2017) Interannual variability of extratropical transient wave activity and its influence on rainfall over Uruguay. Int J Climatol 37(12):4261–4274

    Article  Google Scholar 

  • Brockwell PJ, Davis RA (1991) Time series: theory and methods. Springer, New York

    Book  Google Scholar 

  • Camilloni I, Barros V (2000) The Parana river response to El Nino 1982–83 and 1997–98 events. J Hydrometeorol 1(5):412–430

    Article  Google Scholar 

  • Camilloni I, Montroull N, Gulizia C, Saurral RI (2022) La Plata Basin hydroclimate response to solar radiation modification with stratospheric aerosol injection. Front Clim 4:763983

    Article  Google Scholar 

  • Cerón WL, Kayano MT, Andreoli RV, Avila-Diaz A, Ayes I, Freitas ED, Martins JA, Souza RAF (2021) Recent intensification of extreme precipitation events in the La Plata Basin in Southern South America (1981–2018). Atmos Res 249:105299

    Article  Google Scholar 

  • Chatfield C, Xing H (2019) The analysis of time series: an introduction with R. Chapman and hall/CRC, Boca Raton

    Book  Google Scholar 

  • Collazo S, Barrucand M, Rusticucci M (2019) Summer seasonal predictability of warm days in Argentina: statistical model approach. Theoret Appl Climatol 138:1853–1876

    Article  Google Scholar 

  • Donat M, Alexander LV, Yang H, Durre I, Vose R, Dunn RJ, Willett KM, Aguilar E, Brunet M, Caesar J et al (2013) Updated analyses of temperature and precipitation extreme indices since the beginning of the twentieth century: the HadEX2 dataset. J Geophys Res Atmos 118(5):2098–2118

    Article  Google Scholar 

  • Doyle ME, Saurral RI, Barros VR (2012) Trends in the distributions of aggregated monthly precipitation over the La Plata Basin. Int J Climatol 32(14):2149–2162

    Article  Google Scholar 

  • Fagundes FFA, Bastos IRP, Reboita MS, Escobar GCJ (2021) Análise de um episódio de baixa térmica do noroeste da argentina. Rev Bras de Geogr Física 14(01):094–105

    Article  Google Scholar 

  • Ferreira L, Saulo C, Seluchi M (2010) Características de la depresión del noroeste argentino en el período 1997–2003: criterios de selección y análisis estadístico. Meteorologica 35(1):17–28

    Google Scholar 

  • Fischer EM, Knutti R (2015) Anthropogenic contribution to global occurrence of heavy-precipitation and high-temperature extremes. Nat Clim Chang 5(6):560–564

    Article  Google Scholar 

  • Fowler HJ, Lenderink G, Prein AF, Westra S, Allan RP, Ban N, Barbero R, Berg P, Blenkinsop S, Do HX et al (2021) Anthropogenic intensification of short-duration rainfall extremes. Nat Rev Earth Environ 2(2):107–122

    Article  Google Scholar 

  • Gong Z (2016) Estimation of sample size and power for quantile regression. In: PhD Thesis

  • Grimm AM, Barros VR, Doyle ME (2000) Climate variability in Southern South America associated with El Niño and La Niña events. J Clim 13(1):35–58

    Article  Google Scholar 

  • Gulizia CN, Raggio GA, Camilloni IA, Saurral RI (2022) Changes in mean and extreme climate in Southern South America under global warming of 1.5 C, 2 C, and 3 C. Theoret Appl Climatol 150(1):787–803

    Article  Google Scholar 

  • Hannart A, Vera C, Cerne B, Otto FEL (2015) Causal influence of anthropogenic forcings on the Argentinian heat wave of December 2013. Bull Am Meteor Soc 96(12):41–45. https://doi.org/10.1175/BAMS-D-15-00137.1

    Article  Google Scholar 

  • Hyndman RJ, Khandakar Y (2008) Automatic time series forecasting: the forecast package for R. J Stat Softw 26(3):1–22. https://doi.org/10.18637/jss.v027.i03

    Article  Google Scholar 

  • Hyndman R, Athanasopoulos G, Bergmeir C, Caceres G, Chhay L, O’Hara-Wild M, Petropoulos F, Razbash S, Wang E, Yasmeen F (2022) Forecast: forecasting functions for time series and linear models. In: R package version 8.16. https://pkg.robjhyndman.com/forecast/

  • Kayano MT, Andreoli RV, Souza RAFd (2019) El Niño–southern oscillation related teleconnections over South America under distinct Atlantic multidecadal oscillation and pacific interdecadal oscillation backgrounds: La Niña. Int J Climatol 39(3):1359–1372

    Article  Google Scholar 

  • Koenker R et al (2013) quantreg: Quantile regression. r package version 5.05. R Foundation for Statistical Computing: Vienna) Available at: http://CRAN. R-project. org/package= quantreg

  • Koenker R (2005) Quantile regression. Cambridge University Press, New York

    Book  Google Scholar 

  • Martinkova M, Kysely J (2020) Overview of observed Clausius-Clapeyron scaling of extreme precipitation in midlatitudes. Atmosphere 11(8):786

    Article  Google Scholar 

  • Miao C, Ashouri H, Hsu K-L, Sorooshian S, Duan Q (2015) Evaluation of the Persiann-CDR daily rainfall estimates in capturing the behavior of extreme precipitation events over China. J Hydrometeorol 16(3):1387–1396

    Article  Google Scholar 

  • Olmo M, Bettolli ML, Rusticucci M (2020) Atmospheric circulation influence on temperature and precipitation individual and compound daily extreme events: spatial variability and trends over Southern South America. Weather Clim Extrem 29:100267

    Article  Google Scholar 

  • Panthou G, Mailhot A, Laurence E, Talbot G (2014) Relationship between surface temperature and extreme rainfalls: a multi-time-scale and event-based analysis. J Hydrometeorol 15(5):1999–2011

    Article  Google Scholar 

  • R Core Team (2023) R: a language and environment for statistical computing. In: R foundation for statistical computing, Vienna, Austria. https://www.R-project.org/

  • Regression Q (2017) Handbook of quantile regression. CRC Press, Boca Raton

    Google Scholar 

  • Rivera JA, Otta S, Lauro C, Zazulie N (2021) A decade of hydrological drought in central-western Argentina. Front Water. https://doi.org/10.3389/frwa.2021.640544

    Article  Google Scholar 

  • Roderick TP, Wasko C, Sharma A (2019) Atmospheric moisture measurements explain increases in tropical rainfall extremes. Geophys Res Lett 46(3):1375–1382

    Article  Google Scholar 

  • Rodriguez RN, Yao Y (2017) Five things you should know about quantile regression. In: Proceedings of the SAS global forum 2017 conference, Orlando, pp 2–5

  • Rusticucci M, Barrucand M, Collazo S (2017) Temperature extremes in the Argentina central region and their monthly relationship with the mean circulation and ENSO phases. Int J Climatol 37(6):3003–3017

    Article  Google Scholar 

  • Salio P, Nicolini M, Zipser EJ (2007) Mesoscale convective systems over southeastern South America and their relationship with the South American low-level jet. Mon Weather Rev 135(4):1290–1309

    Article  Google Scholar 

  • Sauter C, Catto JL, Fowler HJ, Westra S, White CJ (2023) Compounding heatwave-extreme rainfall events driven by fronts, high moisture, and atmospheric instability. J Geophys Res Atmos 128(21):2023–038761

    Article  Google Scholar 

  • Seluchi ME, Saulo AC (2012) Baixa do noroeste argentino e baixa do chaco: caracterísitcas, diferenças e semelhanças. Rev Bras de Meteorol 27:49–60

    Article  Google Scholar 

  • Seneviratne SI, Donat MG, Pitman AJ, Knutti R, Wilby RL (2016) Allowable CO2 emissions based on regional and impact-related climate targets. Nature 529(7587):477–483

    Article  CAS  Google Scholar 

  • Seneviratne SI, Zhang X, Adnan M, Badi W, Dereczynski C, Di Luca A, Ghosh S, Iskander I, Kossin J, Lewis S et al (2021) Weather and climate extreme events in a changing climate. In: Masson-Delmotte VP, Zhai A, Pirani SL, Connors C (eds.) Climate Change 2021: The Physical science basis: working group I contribution to the sixth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge, UK, pp. 1513–1766

  • Shenoy S, Gorinevsky D, Trenberth KE, Chu S (2022) Trends of extreme us weather events in the changing climate. Proc Natl Acad Sci 119(47):2207536119

    Article  Google Scholar 

  • Shumway RH, Stoffer DS, Stoffer DS (2000) Time series analysis and its applications, vol 3. Springer, New York

    Book  Google Scholar 

  • Singh H, Pirani FJ, Najafi MR (2020) Characterizing the temperature and precipitation covariability over Canada. Theoret Appl Climatol 139(3):1543–1558

    Article  Google Scholar 

  • Solari FI, Blázquez J, Solman SA (2022) Relationship between frontal systems and extreme precipitation over Southern South America. Int J Climatol. https://doi.org/10.1002/joc.7663

    Article  Google Scholar 

  • Soltani S, Boichu D, Simard P, Canu S (2000) The long-term memory prediction by multiscale decomposition. Signal Process 80(10):2195–2205

    Article  Google Scholar 

  • Spinoni J, Naumann G, Vogt JV (2017) Pan-European seasonal trends and recent changes of drought frequency and severity. Glob Planet Chang 148:113–130

    Article  Google Scholar 

  • Sun X, Wang G (2022) Causes for the negative scaling of extreme precipitation at high temperatures. J Clim 35(18):6119–6134

    Article  Google Scholar 

  • Tencer B, Bettolli ML, Rusticucci M (2016) Compound temperature and precipitation extreme events in Southern South America: associated atmospheric circulation, and simulations by a multi-RCM ensemble. Climate Res 68(2–3):183–199

    Article  Google Scholar 

  • Trenberth KE, Shea DJ (2005) Relationships between precipitation and surface temperature. Geophys Res Lett. https://doi.org/10.1029/2005GL022760

    Article  Google Scholar 

  • Vera CS, Díaz LB (2015) Anthropogenic influence on summer precipitation trends over South America in CMIP5 models. Int J Climatol 35(10):3172–3177

    Article  Google Scholar 

  • Viale M, Valenzuela R, Garreaud RD, Ralph FM (2018) Impacts of atmospheric rivers on precipitation in Southern South America. J Hydrometeorol 19(10):1671–1687

    Article  Google Scholar 

  • Waldmann E (2018) Quantile regression: a short story on how and why. Stat Model 18(3–4):203–218

    Article  Google Scholar 

  • Wartenburger R, Hirschi M, Donat MG, Greve P, Pitman AJ, Seneviratne SI (2017) Changes in regional climate extremes as a function of global mean temperature: an interactive plotting framework. Geosci Model Dev 10(9):3609–3634

    Article  Google Scholar 

  • Wei William WS (2013) Time series analysis. Chapter 22. The Oxford Handbook of Quantitative Methods, vol. 2, Statistical Analysis

  • Wickham H (2016) Ggplot2: elegant graphics for data analysis, Springer, https://ggplot2.tidyverse.org

  • Wickham H, Averick M, Bryan J, Chang W, McGowan LD, François R, Grolemund G, Hayes A, Henry L, Hester J, Kuhn M, Pedersen TL, Miller E, Bache SM, Müller K, Ooms J, Robinson D, Seidel DP, Spinu V, Takahashi K, Vaughan D, Wilke C, Woo K, Yutani H (2019) Welcome to the Tidyverse. J Open Sour Softw 4(43):1686. https://doi.org/10.21105/joss.01686

    Article  Google Scholar 

  • Young PC, Pedregal DJ, Tych W (1999) Dynamic harmonic regression. J Forecast 18(6):369–394

    Article  Google Scholar 

  • Yu R, Li J (2012) Hourly rainfall changes in response to surface air temperature over eastern contiguous China. J Clim 25(19):6851–6861

    Article  Google Scholar 

  • Zaninelli PG, Menéndez CG, Falco M, López-Franca N, Carril AF (2019) Future hydroclimatological changes in South America based on an ensemble of regional climate models. Clim Dyn 52:819–830

    Article  Google Scholar 

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Acknowledgements

We thank Dr. Daniela Rodriguez for her valuable comments and the anonymous reviewers for their comments and constructive suggestions.

Funding

Agencia Nacional de Promoción Científica y Tecnológica, Grant Number PICT-2020-SerieA-03172; Funder two PICT-2021-SerieA-4914; UBA No20020220400093BA de la Programación Científica 2023.

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Authors and Affiliations

  1. Facultad de Ciencias Exactas y Naturales, Departamento de Ciencias de la Atmósfera y los Océanos, Universidad de Buenos Aires, Buenos Aires, Argentina

    Melanie Meis, Mariela Sued & Ramiro I. Saurral

  2. Centro de Investigaciones del Mar y la Atmósfera (CIMA), CONICET – Universidad de Buenos Aires, Buenos Aires, Argentina

    Melanie Meis & Ramiro I. Saurral

  3. Instituto Franco-Argentino para el Estudio del Clima y sus Impactos (IRL 3351 IFAECI), CNRS – IRD – CONICET – UBA, Buenos Aires, Argentina

    Melanie Meis & Ramiro I. Saurral

  4. Departamento de Matemática, Facultad de Ciencias Exactas y Naturales, Instituto del Cálculo, CONICET, Universidad de Buenos Aires, Buenos Aires, Argentina

    Mariela Sued

  5. Barcelona Supercomputing Center (BSC), Barcelona, Spain

    Ramiro I. Saurral

  6. School of Mathematics and Statistics, The University of Melbourne, Melbourne, Australia

    Patricia Menéndez

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Meis, M., Sued, M., Saurral, R.I. et al. A novel statistically-based approach to regionalize extreme precipitation events using temperature data. Nat Hazards (2024). https://doi.org/10.1007/s11069-024-06805-9

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