Abadi, A.M., Rowe, C.M., Andrade, M. 2019. Climate regionalization in Bolivia: A combination of non-hierarchical and consensus clustering analyses based on precipitation and temperature. International Journal of Climatology, 40(10), 4408-4421. https://doi.org/10.1002/joc.6464
Aliaga, V.S., Ferrelli, F., Piccolo, M.C. 2017. Regionalization of climate over the Argentine Pampas. International Journal of Climatology, 37(S1), 1237-1247. https://doi.org/10.1002/joc.5079
Arthur, D., Vassilvitskii, S. 2007. k-means++: The advantages of careful seeding. In: Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms. Society for Industrial and Applied Mathematics, pp. 1027-1035. https://dl.acm.org/doi/10.5555/1283383.1283494
[+]
Abadi, A.M., Rowe, C.M., Andrade, M. 2019. Climate regionalization in Bolivia: A combination of non-hierarchical and consensus clustering analyses based on precipitation and temperature. International Journal of Climatology, 40(10), 4408-4421. https://doi.org/10.1002/joc.6464
Aliaga, V.S., Ferrelli, F., Piccolo, M.C. 2017. Regionalization of climate over the Argentine Pampas. International Journal of Climatology, 37(S1), 1237-1247. https://doi.org/10.1002/joc.5079
Arthur, D., Vassilvitskii, S. 2007. k-means++: The advantages of careful seeding. In: Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms. Society for Industrial and Applied Mathematics, pp. 1027-1035. https://dl.acm.org/doi/10.5555/1283383.1283494
Badr, H., Zaitchik, B.F., Dezfuli, A.K. 2015. A tool for hierarchical climate regionalization. Earth Science Informatics, 8, 945-958. https://doi.org/10.1007/s12145-015-0221-7
Bradley, J. 1968. Distribution-Free Statistical Tests. Prentice-Hall, Englewood Cliffs, NJ, 283-310.
Coulibaly, P., Evora, N.D. 2005. Comparison of neural network methods for infilling missing daily weather records. Journal of Hydrogy, 341 (1-2), 27-41. https://doi.org/10.1016/j.jhydrol.2007.04.020
De Gooijer, J. 2017. Elements of Nonlinear Time Series Analysis and Forecasting. Springer Series in Statistics. Springer International Publishing. https://doi.org/10.1007/978-3-319-43252-6
De León Pérez, D. 2017. Análisis de sincronización espacio-temporal de señales océano-atmosféricas y variables hidroclimatológicas de Colombia. Tesis de maestría, Facultad de Ingeniería, Pontificia Universidad Javeriana. http://hdl.handle.net/10554/35731.
Dingman, S.L. 2015. Physical Hydrology: Third Edition. Waveland Press. https://www.waveland.com/browse.php?t=382
Domínguez, E., Angarita, H., Rivera, H. 2010. Viabilidad para pronósticos hidrológicos de niveles diarios, semanales y decadales en Colombia. Ingeniería e investigación, 30(2), 178-187. Disponible en http://www.scielo.org.co/pdf/iei/v30n2/v30n2a18.pdf. Último acceso: marzo de 2021.
Dubois, P., Hinsen, K., Hugunin, J. 1996. Numerical python. Computers in Physics, 10(3), 262. https://doi.org/10.1063/1.4822400
Environmental Systems Research Institute (ESRI). 2015. Arcgis for desktop. http://www.esri.com
Fedorov, V. 2015. Spatial and temporal variations in solar climate of earth in the present epoch. Izvestiya, Atmospheric and Oceanic Physics, 51(8), 779-791. https://doi.org/10.1134/S0001433815080034
Gómez Lende, S. 2011. Región y regionalización: Su teoría y su método. El nuevo orden espacial del territorio argentino. Universidad del Bío-Bío, 26, 83-122. http://www.ubiobio.cl/miweb/webfile/media/222/Tiempo/2011/%2327.05.pdf
Govindaraju, R.S., 2000. Artificial neural networks in hydrology. ii: Hydrologic applications. Journal of Hydrologic Engineering, 5(2), 124-137. https://doi.org/10.1061/(ASCE)1084-0699(2000)5:2(124)
Grubbs, F.E. 1950. Sample criteria for testing outlying observations. Annals of Mathematical Statistics, 21(1), 27-58. https://doi.org/10.1214/aoms/1177729885
Haan, C. 1977. Statistical Methods in Hydrology. Iowa State University Press. http://hdl.handle.net/1969.3/24532
Hannachi, A. 2004. A primer for eof analysis of climate data. Department of Meteorology, University of Reading, 1-33.
HIMAT. 1978. Resolución 00337, por el cual se adopta un sistema de codificación para las estaciones hidrometeorológicas. Instituto Colombiano de Hidrología, Meteorología y Adecuación de Tierras, Bogotá, Colombia.
Hotellin, H. 1933. Analysis of a complex of statistical variables into principal components. Journal of Educational Psychology, 24, 417-441. https://doi.org/10.1037/h0071325
Hunter, J.D. 2007. Matplotlib: A 2d graphics environment. Computing In Science & Engineering, 9(3), 90-95. https://doi.org/10.1109/MCSE.2007.55
IDEAM. 2001. El medio ambiente en Colombia, 2nd Edición. Instituto de Hidrología, Meteorología y Estudios Ambientales, IDEAM, Bogotá D.C., Colombia. http://www.ideam.gov.co
IPCC, Stocker, T., Qin, D., Plattner, G.K., Tignor, M., Allen, S., Boschung, J., Nauels, A., Xia, Y., Bex, V., Midgley, P. 2013. Annex III: Glossary. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, book section AIII, p. 1447-1466. https://www.climatechange2013.org
Jones, E., Oliphant, T., Peterson, P., et al. 2001. SciPy: Open source scientific tools for Python. http://www.scipy.org/
Leland, C., Pederson, N., Hessl, A.m Nachin, D, Davi, N., D'Arrigo, R., Jacoby, G., 2013. Climate regionalization in Bolivia: A combination of non-hierarchical and consensus clustering analyses based on precipitation and temperature. Dendrochronologia, 31(3), 205-215. https://doi.org/10.1016/j.dendro.2012.11.003
León, G., Zea, J., Eslava, J. 2000. Circulación general del trópico y la zona de confluencia intertropical en Colombia. Meteorología Colombiana, 1, 31-38. http://168.176.14.11/fileadmin/content/geociencias/revista_meteorologia_colombiana/numero01/01_05.pdf
Leys, C., Ley, C., Klein, O., Bernard, P., Licata, L. 2013. Detecting outliers: Do not use standard deviation around the mean, use absolute deviation around the median. Journal of Experimental Social Psychology, 49(4), 764-766. https://doi.org/10.1016/j.jesp.2013.03.013
Lorenz, E.N. 1970. Climatic change as a mathematical problem. Journal of Applied Meteorology, 9(3), 325-329. https://doi.org/10.1175/1520-0450(1970)009%3C0325:CCAAMP%3E2.0.CO;2
MADS. 2012. Decreto 1640, por medio del cual se reglamenta los instrumentos para la planificación ordenación y manejo de las cuencas hidrográficas y acuíferos, y se dictan otras disposiciones. Ministerio de Ambiente y Desarrollo Sostenible, Bogotá, Colombia. Disponible en https://www.funcionpublica.gov.co/eva/gestornormativo/norma.php?i=49987
Malcolm, G.A., McDonnell, J.J. 2005. Encyclopedia of hydrological science. John Wiley & Sons Ltd. https://doi.org/10.1002/0470848944
McKinney, W. 2010. Pandas: a foundational python library for data analysis and statistics. Python for High Performance and Scientific Computing, 14(9), 1-9.
Mesa, O.J., Poveda, G., Carvajal, L. 1997. Introducción al clima de Colombia. Universidad Nacional de Colombia.
Montgomery, D., Jennings, C., Kulahci, M. 2015. Introduction to Time Series Analysis and Forecasting. Wiley Series in Probability and Statistics. Wiley. https://onlinelibrary.wiley.com/doi/abs/10.1111/jtsa.12203
Montoya Gaviria, G. d. J. 2008. Lecciones de meteorología dinámica y modelamiento atmosférico. Universidad Nacional de Colombia. Facultad de Ciencias. http://ciencias.bogota.unal.edu.co/fileadmin/Facultad_de_Ciencias/Publicaciones/Archivos_Libros/Libros_Geociencias/Lecciones_de_Meteorologia_dinamica/Metereologia.pdf
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duch- esnay, E. 2011. Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research, Microtome Publishing. hal-00650905v1. Disponible en https://hal.inria.fr/hal-00650905v1. Último acceso: marzo de 2021.
Poveda, G. 2004. La Hidroclimatología de Colombia: Una síntesis desde la escala inter-decadal hasta la escala diurna. Revista de la Academia Colombiana de Ciencias, 28(107), 201-222. Disponible en https://www.accefyn.com/revista/Vol_28/107/201-222.pdf.
Último acceso: marzo de 2021.
Poveda, G., Jaramillo, A., Mantilla, R. 2001. Asociación entre el fenómeno el niño y las anomalías de humedad del suelo y del índice "ndvi" en colombia. In: IX Congreso Latinoamericano e Ibérico de Meteorología y VIII Congreso Argentino de Meteorología. p. 8.
http://www.accefyn.com/revista/Vol_28/107/201-222.pdf
Poveda, G., Mesa, O.J. 1997. Feedbacks between hydrological processes in tropical south america and large-scale ocean-atmospheric phenomena. Journal of Climate, 10(10), 2690-2702. https://doi.org/10.1175/1520-0442(1997)010%3C2690:FBHPIT%3E2.0.CO;2
Python Core Team. 2017. Python: A dynamic, open source programming language. https://www.python.org/.
R Core Team. 2017. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/
Rossum, G. 1995. Python reference manual. Tech. rep., CWI (Centre for Mathematics and Computer Science), Amsterdam, The Netherlands, The Netherlands.
Samarasinghe, S. 2016. Neural networks for applied sciences and engineering: from fundamentals to complex pattern recognition. CRC Press. 25, 33
Velasco, A. 2016. Integración del concepto de variabilidad hidroclimática en pronósticos hidrológicos de largo plazo de resolución mensual en Colombia. Tesis de maestría, Facultad de Ingeniería, Pontificia Universidad Javeriana. http://hdl.handle.net/10554/19628
Ward, J.H., Marion, JR. 1963. Application of an Hierarchical Grouping Procedure to a Problem of Grouping Profiles. Educational and Psychological Measurement, 23(1), 69-81. https://doi.org/10.1177/001316446302300107
White, D., Richman, M., Yarnal, B. 1991. Climate regionalization and rotation of principal components. International Journal of Climatology, 11(1), 1-25. http://doi.org/10.1002/joc.3370110102
WMO. 1994. Guía de prácticas hidrológicas: adquisición y proceso de datos, análisis, predicción y otras aplicaciones. Organización Meteorológica Mundial. http://www.whycos.org/hwrp/guide/index_es.php
[-]