Published Jun 20, 2016



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Lisandro Núñez-Galeano, MSc

Juan Diego Giraldo-Osorio, PhD

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Abstract

The present study has developed a regional frequency analysis for Annual Maximum Temperature (AMT) in the hydrographic basins of Colombia. The L-moments methodology was applied for the regionalization. Five stages were considered to apply the methodology: data analysis; the L-Moments estimation; identification of homogeneous regions; fit of probability density functions (pdf) to observed data and estimation of quantile values; and finally, the developing and drawing of regionalized maps. Overall, fifteen homogeneous regions were identified and selected for the regionalization of AMT, which meet specific criteria of homogeneity and discordance. Several pdf for regional frequency analysis were tested in order to select the best probability function. Finally, regionalized temperature maps were generated for several return periods. Using the L-Moments methodology, the regionalization procedure was done using the average of AMT as the key scale parameter. The regionalization procedure ensures, as far as possible, a coherent-basin approach: the boundaries between homogeneous regions were drawn, complying with the catchment borders.

Keywords

regionalización, metodología de los L-moments, análisis regional de frecuencias, temperatura máxima anualRegionalization, L-moments methodology, regional frequency analysis, annual maximum temperature

References
[1] T. T. Hailegeorgis, S. T. Thorolfsson, and K. Alfredsen, “Regional frequency analysis of extreme precipitation with consideration of uncertainties to update IDF curves for the city of Trondheim,” J. Hydrol., vol. 498, pp. 305–318, Aug. 2013.
[2] A. P. García-Marín, J. Estévez, C. Sangüesa-Pool, R. Pizarro-Tapia, J. L. Ayuso-Muñoz, and F. J. Jimenez-Hornero, “The use of the exponent K(q) function to delimit homogeneous regions in regional frequency analysis of extreme annual daily rainfall,” Hydrol. Process., vol. 29, no. 1, pp. 139–151, Jan. 2015.
[3] H. Wazneh, F. Chebana, and T. B. M. J. Ouarda, “Delineation of homogeneous regions for regional frequency analysis using statistical depth function,” J. Hydrol., vol. 521, pp. 232–244, Feb. 2015.
[4] J. Clavet-Gaumont, L. Sushama, M. N. Khaliq, O. Huziy, and R. Roy, “Canadian RCM projected changes to high flows for Québec watersheds using regional frequency analysis,” Int. J. Climatol, vol. 33, no. 14, pp. 2940–2955, Nov. 2013.
[5] A. Sarhadi and M. Heydarizadeh, “Regional frequency analysis and spatial pattern characterization of Dry Spells in Iran,” Int. J. Climatol., vol. 34, no. 3, pp. 835–848, Mar. 2014.
[6] X. Sun, M. Thyer, B. Renard, and M. Lang, “A general regional frequency análisis framework for quantifying local-scale climate effects: A case study of ENSO effects on Southeast Queensland rainfall,” J. Hydrol., vol. 512, pp. 53–68, May 2014.
[7] J. Weiss and P. Bernardara, “Comparison of local indices for regional frequency analysis with an application to extreme skew surges,” Water Resour. Res., vol. 49, no. 5, pp. 2940–2951, May 2013.
[8] J. Weiss, P. Bernardara, and M. Benoit, “Formation of homogeneous regions for regional frequency analysis of extreme significant wave heights,” J. Geophys. Res. Oceans, vol. 119, no. 5, pp. 2906–2922, May 2014.
[9] J. R. M. Hosking and J. R. Wallis, Regional Frequency Analysis: An Approach Based on LMoments, Ed. Revised. Cambridge: Cambridge University Press, 2005.
[10] J. R. M. Hosking, “L-moments: Analysis and estimation of distributions using linear combinations of order statistics,” J. R. Statist. Soc., vol. 52, no. 1, pp. 105–124, 1990.
[11] S. Gabriele and F. Chiaravalloti, “Using the meteorological information for the regional rainfall frequency analysis: An application to sicily,” Water Resour. Manage., vol. 27, no. 6, pp. 1721–1735, Dec. 2012.
[12] J. R. Wallis, M. G. Schaefer, B. L. Barker, and G. H. Taylor, “Regional precipitationfrequency analysis and spatial mapping for 24-hour and 2-hour durations for Washington State,” Hydrol. Earth Syst. Sci., vol. 11, no. 1, pp. 415–442, Jan. 2007.
[13] Y. D. Chen, G. Huang, Q. Shao, and C.-Y. Xu, “Regional analysis of low flow using Lmoments for Dongjiang basin, South China,” Hydrol. Sci. J., vol. 51, no. 6, pp. 1051–1064, Dec. 2006.
[14] Ó. J. Mesa Sánchez, J. I. Vélez Upegui, J. D. Giraldo Osorio, and D. I. Quevedo Tejada, “Adaptación del método de muliescalamiento para la estimación de caudales máximos en Colombia,” Meteorol. Colomb., vol. 7, pp. 149–156, Mar. 2003.
[15] Ó. J. Mesa Sánchez, J. I. Vélez Upegui, J. D. Giraldo Osorio, and D. I. Quevedo Tejada, “Regionalización de características medias de la cuenca con aplicación en la estimación de caudales máximos,” Meteorol. Colomb., vol. 7, pp. 141–147, Mar. 2003.
[16] G. Poveda Jaramillo, J. I. Vélez Upegui, Ó. J. Mesa Sánchez, L. I. Ceballos Bonilla, M. D. Zuluaga Arias, and C. D. Hoyos Ortiz, “Estimación de caudales mínimos para Colombia mediante regionalización y aplicación de la curva de recesión de caudales,” Meteorol. Colomb., vol. 6, pp. 73–80, Oct. 2002.
[17] M. V. Vélez, W. Quintero, and J. P. Delgado, “Implementación del modelo MG para Antioquia y el Eje Cafetero,” Avances en Recursos Hidráulicos, vol. 14, pp. 87–96, Oct. 2006.
[18] Y. Carvajal Escobar and J. B. Marco Segura, “Aplicación de métodos estadísticos para la regionalización de precipitación mensual en el Valle del Cauca,” Metereol. Colombg., no. 5, pp. 13–21, Mar. 2002.
[19] J. I. Vélez, G. Poveda, O. Mesa, C. D. Hoyos, J. F. Mejía, D. I. Quevedo, L. F. Salazar, and S. C. Vieira, “Aplicación de diferentes metodolgías para la estimación de curvas intensidadfrecuencia-duración en Colombia,” Meteorol. Colomb., vol. 6, pp. 91–100, Oct. 2002.
[20] G. Poveda, J. Vélez, Ó. Mesa, A. Cuartas, J. Barco, R. Mantilla, J. Mejía, C. Hoyos, J. Ramírez, L. Ceballos, M. Zuluaga, P. Arias, B. Botero, M. Montoya, J. Giraldo, and D. Quevedo, “Linking Long-Term Water Balances and Statistical Scaling to Estimate River Flows along the Drainage Network of Colombia,” J. Hydrol. Eng., vol. 12, no. 1, pp. 4–13, 2007.
[21] L. A. Acevedo Aristizábal, “Estimación hidrológica bajo escenarios de cambio climático en Colombia,” MSc Thesis, Universidad Nacional de Colombia, Medellín, Colombia, 2009.
[22] P. M. Acosta Castellanos and L. X. Sierra Aponte, “Evaluación de métodos de construcción de curvas IDF a partir de distribuciones de probabilidad y parámetros de ajuste,” Revista Facultad de Ingeniería Universidad Pedagógica y Tecnológica de Colombia, vol. 22, no. 35, pp. 25–33, Jul. 2013.
[23] E. G. Pulgarín Dávila, “Fórmulas regionales para la estimación de curvas intensidadfrecuencia-duración basadas en las propiedades de escala de la lluvia (región andina colombiana),” MSc Thesis, Universidad Nacional de Colombia, Medellín, Colombia, 2009.
[24] J. A. Torres, J. I. Ordóñez, and R. Duque, “Comparación de los métodos de máxima verosimilitud y L–momentos en el análisis de frecuencias en la cuenca alta del río Magdalena,” in XX Seminario Nacional de Hidráulica e Hidrología, Barranquilla, 2012.
[25] J. Núñez-Cobo, K. Verbist, J. Ramírez-Hernández, and M. Hallack-Alegría, “Guía metodológica para la aplicación del análisis regional de frecuencia de sequías basado en Lmomentos y resultados de aplicación en América Latina,” UNESCO IHP-LAC-CAZALAC, Montevideo, Uruguay, Technical Document 27, 2010.
[26] K. Verbist, F. Santibáñez, D. Gabriels, and G. Soto, “Atlas of arid and semi-arid zones of Latin America and the Caribbean,” UNESCO IHP-LAC - CAZALAC, Montevideo, Uruguay, Technical Document 26, 2010.
[27] D. Kwiatkowski, P. C. B. Phillips, P. Schmidt, and Y. Shin, “Testing the null hypothesis of stationarity against the alternative of a unit root,” J. Econ., vol. 54, no. 1, pp. 159–178, Oct. 1992.
[28] J. A. Greenwood, J. M. Landwehr, N. C. Matalas, and J. R. Wallis, “Probability weighted moments: Definition and relation to parameters of several distributions expressable in inverse form,” Water Resour. Res., vol. 15, no. 5, pp. 1049–1054, Oct. 1979.
[29] J. R. M. Hosking, J. R. Wallis, and E. F. Wood, “Estimation of the generalized extremevalue distribution by the method of probability-weighted moments,” Technometrics, vol. 27, no. 3, pp. 251–261, Aug. 1985.
[30] S. S. Eslamian and H. Feizi, “Maximum monthly rainfall analysis using L-moments for an arid region in Isfahan Province, Iran,” J. Appl. Meteor. Climatol., vol. 46, no. 4, pp. 494–503, Apr. 2007.
[31] J. R. M. Hosking and J. R. Wallis, “Some statistics useful in regional frequency analysis,”Water Resour. Res., vol. 29, no. 2, pp. 271–281, Feb. 1993.
[32] H. Malekinezhad and A. Zare-Garizi, “Regional frequency analysis of daily rainfall extremes using L-moments approach,” Atmósfera, vol. 27, no. 4, pp. 411–427, Oct. 2014.
[33] R. Bivand, R. Krug, M. Neteler, and S. Jeworutzki, rgrass7: Interface Between GRASS 7 Geographical Information System and R., 2016.
How to Cite
Núñez-Galeano, L., & Giraldo-Osorio, J. D. (2016). Adaptation of the L-moments method for the regionalization for maximum annual temperatures in Colombia. Ingenieria Y Universidad, 20(2), 373-390. https://doi.org/10.11144/Javeriana.iyu20-2.almr
Section
Civil and environmental engineering