Modelización de precipitación extrema en España: un enfoque no estacionario integrando teleconexiones climáticas
Enviado: 13-12-2024
|Aceptado: 14-05-2025
|Publicado: 30-07-2025
Derechos de autor 2025 Ingeniería del Agua
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-CompartirIgual 4.0.
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Palabras clave:
precipitación extrema, variabilidad climática, estacionalidad, modelos paramétricos, máxima verosimilitud, indicadores atmosféricos, ajuste no estacionario, eventos extremos, distribución generalizada de extremos
Agencias de apoyo:
Resumen:
El presente trabajo analiza cómo determinados procesos físicos, como la estacionalidad y la variabilidad climática, influyen en la estructura de los eventos de precipitación extrema mensual en España. El objetivo es evaluar cómo la incorporación de patrones estacionales y variaciones atmosféricas en los modelos mejora la estimación de estos eventos en comparación con enfoques que asumen estacionariedad. La metodología empleada se basa en modelos paramétricos que utilizan funciones sinusoidales para capturar variaciones estacionales, junto con índices de teleconexión, aplicando técnicas de máxima verosimilitud para el ajuste. Los resultados muestran que estos modelos logran un ajuste más preciso en la estimación de los extremos de precipitación, superando a los modelos estacionarios. Se concluye que considerar la variabilidad climática y los patrones estacionales en los modelos contribuye a mejorar la comprensión de los efectos de los procesos climáticos en estos fenómenos extremos.
Citas:
Adlouni, S.E., Ouarda, T.B.M.J., Zhang, X., Roy, R., Bobée, B. 2007. Generalized maximum likelihood estimators for the nonstationary generalized extreme value model. Water Resources Research, 43(3), W03410. https://doi.org/10.1029/2005WR004545
Agencia Estatal de Meteorología (AEMET) & Instituto Português do Mar e da Atmosfera (IPMA). 2011. Atlas climático ibérico: Temperatura del aire y precipitación (1971-2000) = Air temperature and precipitation (1971-2000).
Bracken, C., Holman, K.D., Rajagopalan, B., Moradkhani, H. 2018. A Bayesian hierarchical approach to multivariate nonstationary hydrologic frequency analysis. Water Resources Research, 54(1), 243–255. https://doi.org/10/gdb2pv
Bracken, C., Rajagopalan, B., Cheng, L., Kleiber, W., Gangopadhyay, S. 2016. Spatial Bayesian hierarchical modeling of precipitation extremes over a large domain. Water Resources Research, 52(8), 6643–6655. https://doi.org/10.1002/2016WR018768
Bradley, A.A. 1998. Regional frequency analysis methods for evaluating changes in hydrologic extremes. Water Resources Research, 34(4), 741–750. https://doi.org/10/bmd5xg
Brown, S.J., Caesar, J., Ferro, C.A.T. 2008. Global changes in extreme daily temperature since 1950. Journal of Geophysical Research: Atmospheres, 113(D5), D05115. https://doi.org/10/fvckxv
Chebana, F., Charron, C., Ouarda, T.B.M.J., Martel, B. 2014. Regional frequency analysis at ungauged sites with the generalized additive model. Journal of Hydrometeorology, 15(6), 2418–2428. https://doi.org/10/gm79vh
Coles, S. 2001. An introduction to statistical modeling of extreme values. Springer. https://doi.org/10.1007/978-1-4471-3675-0
De Luis, M., González-Hidalgo, J.C., Brunetti, M., Longares, L.A. 2011. Precipitation concentration changes in Spain 1946–2005. Natural Hazards and Earth System Sciences, 11(5), 1259–1265. https://doi.org/10.5194/nhess-11-1259-2011
Diez-Sierra, J., del Jesus, M. 2019. Subdaily rainfall estimation through daily rainfall downscaling using random forests in Spain. Water, 11(1), Article 1. https://doi.org/10/gg9m6r
Fisher, R.A., Tippett, L.H.C. 1928. Limiting forms of the frequency distribution of the largest or smallest member of a sample. Mathematical Proceedings of the Cambridge Philosophical Society, 24(2), 180–190. https://doi.org/10/bscxvn
Gonzalez, S., Bech, J. 2017. Extreme point rainfall temporal scaling: A long term (1805-2014) regional and seasonal analysis in Spain. International Journal of Climatology, 37(15), 5068–5079. https://doi.org/10/gm235v
Gregersen, I.B., Madsen, H., Rosbjerg, D., Arnbjerg-Nielsen, K. 2013. A spatial and nonstationary model for the frequency of extreme rainfall events. Water Resources Research, 49(1), 127–136. https://doi.org/10.1029/2012WR012570
Gumbel, E.J. 1958. Statistics of extremes. Columbia University Press.
Hatzaki, M., Flocas, H.A., Oikonomou, C., Giannakopoulos, C. 2010. Future changes in the relationship of precipitation intensity in Eastern Mediterranean with large scale circulation. Advances in Geosciences, 23, 31–36. Scopus. https://doi.org/10/fgbg5f
Hosking, J.R.M., Wallis, J.R. 1993. Some statistics useful in regional frequency analysis. Water Resources Research, 29(2), 271–281. https://doi.org/10/df96h6
Intergovernmental Panel on Climate Change. 2013. Climate change 2013: The physical science basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (T.F. Stocker et al., Eds.). Cambridge University Press. https://www.ipcc.ch/report/ar5/wg1/
Jenkinson, A.F. 1955. The frequency distribution of the annual maximum (or minimum) values of meteorological elements. Quarterly Journal of the Royal Meteorological Society, 81(348), 158–171. https://doi.org/10.1002/qj.49708134804
Katz, R.W., Parlange, M.B., Naveau, P. 2002. Statistics of extremes in hydrology. Advances in Water Resources, 25(8–12), 1287–1304. https://doi.org/10.1016/S0309-1708(02)00056-8
Kharin, V.V., Zwiers, F.W. 2000. Changes in the extremes in an ensemble of transient climate simulations with a coupled atmosphere–ocean GCM. Journal of Climate, 13(21), 3760–3788. https://doi.org/10.1175/1520-0442(2000)013<3760:CITEIA>2.0.CO;2
Kharin, V.V., Zwiers, F.W. 2005. Estimating extremes in transient climate change simulations. Journal of Climate, 18(8), 1156–1173. https://doi.org/10.1175/JCLI3320.1
Leadbetter, M.R., Lindgren, G., Rootzén, H. 1983. Extremes and related properties of random sequences and processes. Springer.
Lez-Rouco, J.F.G. 2001. Quality control and homogeneity of precipitation data in the Southwest of Europe. Journal of Climate, 14, 15. https://doi.org/10.1175/1520-0442(2001)014<0964:QCAHOP>2.0.CO;2
Llabrés-Brustenga, A., Rius, A., Rodríguez-Solà, R., Casas-Castillo, M.C., Redaño, A. 2019. Quality control process of the daily rainfall series available in Catalonia from 1855 to the present. Theoretical and Applied Climatology, 137(3–4), 2715–2729. https://doi.org/10/gktkps
López, J., Francés, F. 2013. Non-stationary flood frequency analysis in continental Spanish rivers, using climate and reservoir indices as external covariates. Hydrology and Earth System Sciences, 17(8), 3189–3203. https://doi.org/10.5194/hess-17-3189-2013
Lopez-Bustins, J.A., Martin-Vide, J., Sanchez-Lorenzo, A. 2008. Iberia winter rainfall trends based upon changes in teleconnection and circulation patterns. Global and Planetary Change, 63(2–3), 171–176. https://doi.org/10/bm3wqx
López-Bustins, J.A., Martin-Vide, J., Prohom, M., Cordobilla, M.J. 2016. Variabilidad intraanual de la Oscilación del Mediterráneo Occidental (WeMO) y ocurrencia de episodios torrenciales en Cataluña. In J. Olcina-Cantos, A.M. Rico-Amorós, & E. Moltó-Mantero (Eds.), Clima, sociedad, riesgos y ordenación del territorio (pp. 171–182). Servicio de Publicaciones de la UA. https://doi.org/10.14198/XCongresoAECAlicante2016-16
Martin-Vide, J., Fernández-Belmonte, D. 2001. El índice NAO y la precipitación mensual en la España peninsular. Investigaciones Geográficas, 26, 41–58. https://doi.org/10.14198/INGEO2001.26.07
Martin-Vide, J. 2004. Spatial distribution of a daily precipitation concentration index in peninsular Spain. International Journal of Climatology, 24(8), 959–971. https://doi.org/10.1002/joc.1030
Martin-Vide, J., Lopez-Bustins, J.A. 2006. The Western Mediterranean Oscillation and rainfall in the Iberian Peninsula. International Journal of Climatology, 26(11), 1455–1475. https://doi.org/10/b4n43q
Méndez, F.J., Menéndez, M., Luceño, A., Losada, I.J. 2007. Analyzing monthly extreme sea levels with a time-dependent GEV model. Journal of Atmospheric and Oceanic Technology, 24(5), 894–911. https://doi.org/10.1175/JTECH2009.1
Menéndez, M., Méndez, F.J., Izaguirre, C., Luceño, A., Losada, I.J. 2009. The influence of seasonality on estimating return values of significant wave height. Coastal Engineering, 56(3), 211–219. https://doi.org/10/cc4znz
Meseguer-Ruiz, Ó., López-Bustins, J.A., Arbiol-Roca, L., Martin-Vide, J., Miró-Pérez, J.J., Estrela-Navarro, M.J. 2018. Episodios de precipitación torrencial en el este y sureste ibéricos y su relación con la variabilidad intraanual de la oscilación del Mediterráneo occidental (WeMO) entre 1950 y 2016. Asociación Española de Climatología. https://repositorio.aemet.es/handle/20.500.11765/9882
Mínguez, R., Méndez, F.J., Izaguirre, C., Menéndez, M., Losada, I.J. 2010. Pseudo-optimal parameter selection of non-stationary generalized extreme value models for environmental variables. Environmental Modelling and Software, 25(12), 1592–1607. https://doi.org/10.1016/j.envsoft.2010.05.008
Redolat, D., Monjo, R., Lopez-Bustins, J.A., Martin-Vide, J. 2019. Upper-Level Mediterranean Oscillation index and seasonal variability of rainfall and temperature. Theoretical and Applied Climatology, 135(3), 1059–1077. https://doi.org/10.1007/s00704-018-2424-6
Renard, B. 2011. A Bayesian hierarchical approach to regional frequency analysis. Water Resources Research, 47, W11513. https://doi.org/10/ddswhg
Roderick, T.P., Wasko, C., Sharma, A. 2020. An improved covariate for projecting future rainfall extremes? Water Resources Research, 56(8). https://doi.org/10.1029/2019WR026924
Rodrigo, F.S. 2016. Influencia de la NAO en la covariabilidad de temperaturas y precipitaciones de invierno en España, 1946-2005. In J. Olcina-Cantos, A.M. Rico-Amorós, & E. Moltó-Mantero (Eds.), Clima, sociedad, riesgos y ordenación del territorio (pp. 377–386). Servicio de Publicaciones de la UA. https://doi.org/10.14198/XCongresoAECAlicante2016-35
Salas, J.D., Obeysekera, J. 2014. Revisiting the concepts of return period and risk for nonstationary hydrologic extreme events. Journal of Hydrologic Engineering, 19(3), 554–568. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000820
Scarf, P.A. 1992. Estimation for a four parameter generalized extreme value distribution. Communications in Statistics - Theory and Methods, 21(8), 2185–2201. https://doi.org/10/dgzqwg
Smith, R.L. 1985. Maximum likelihood estimation in a class of nonregular cases. Biometrika, 72(1), 67–90. https://doi.org/10/bdpxgt
Smith, R.L. 1989. Extreme value analysis of environmental time series: An application to trend detection in ground-level ozone. Statistical Science, 4(4), 367–377. https://doi.org/10.1214/ss/1177012400
Solari, S., Losada, M.A. 2011. Non-stationary wave height climate modeling and simulation. Journal of Geophysical Research, 116(C9), C09032. https://doi.org/10.1029/2011JC007101
Stedinger, J.R., Vogel, R.M., Foufoula-Georgiou, E. 1993. Frequency analysis of extreme events. In D.R. Maidment (Ed.), Handbook of hydrology. McGraw-Hill.
University of East Anglia. 2021. Climatic Research Unit—Groups and Centres—UEA. https://www.uea.ac.uk/groups-and-centres/climatic-research-unit
Urrea-Méndez, D., del Jesus, M. 2023. Estimating extreme monthly rainfall for Spain using non-stationary techniques. Hydrological Sciences Journal, 68(7), 903–919. https://doi.org/10.1080/02626667.2023.2193294
Wang, X.L., Zwiers, F.W., Swail, V.R. 2004. North Atlantic Ocean wave climate change scenarios for the twenty-first century. Journal of Climate, 17(12), 2368–2383. https://doi.org/10/dcwvbg
