Location of the Colombian hydroelectric projects with installed capacity greater than 20 MW.

Publicado

2024-11-14

Colombian monthly energy inflows predictability

Predictibilidad de los aportes mensuales de energía en Colombia

DOI:

https://doi.org/10.15446/dyna.v91n234.114287

Palabras clave:

streamflow forecasting, uncertainty, Colombian streamflow’s predictability (en)
pronóstico de caudales, incertidumbre, predictibilidad de los caudales colombianos (es)

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Autores/as

Streamflow forecasting is essential for water resources management in several social and economic strategic sectors, involving space-temporal variability modeling of the hydrological processes and the influence of several climatic phenomena. Furthermore, high water-dependent sectors such as the Colombian electricity market, require not only the expected streamflow values but also the occurrence probability or reliability bands of such forecast inflows necessary in robust risk analyses. We propose a mathematical approach for monthly streamflow forecasting in Colombia and quantify its predictability, incorporating climate model outcomes as a time series of macroclimatic indexes and punctual hydro-climatological stations. The methodology integrates parametric and non-parametric models, exogenous variables analysis, and uncertainty estimation through stochastic modeling. This research will contribute to the Colombian hydrology understanding and provide elements for risk analysis, planning, and decision-making in social and economic sectors involved with water resources management.

El pronóstico de caudales es esencial en la gestión de los recursos hídricos en varios sectores sociales y económicos estratégicos e involucra la modelación de variabilidad espacio temporal de los procesos hidrológicos y la influencia de varios fenómenos climáticos. Además, la alta dependencia del agua en sectores como el mercado eléctrico colombiano requiere (solo los valores de caudal esperados, sino también su probabilidad de ocurrencia o bandas de confianza de tales pronósticos necesaria en análisis robustos de riesgo. Se propone un enfoque matemático para el pronóstico mensual de caudales en Colombia y la cuantificación de su predictibilidad, incorporando resultados de modelos climáticos como series temporales de índices macroclimáticos y estaciones hidroclimatológicas puntuales. La metodología integra la aplicación de modelos paramétricos y (paramétricos, el análisis de variables exógenas y la estimación de la incertidumbre mediante modelos estocásticos. Esta investigación contribuirá en el entendimiento de la hidrología colombiana y brindará elementos para el análisis de riesgo, la planificación y la toma de decisiones en los sectores sociales y económicos involucrados con la gestión de los recursos hídricos.

Referencias

[1] Al-Saati, N.H., Omran, I.I., Salman, A.A., Al-Saati, Z., and Hashim, K.S., Statistical modeling of monthly streamflow using time series and artificial neural network models: Hindiya Barrage as a case study. Water Practice & Technology 16(2), pp. 681–691, 2021. DOI: https://doi.org/10.2166/wpt.2021.012.

[2] Bezerra, B., Veiga, A., Barroso, L.A., and Pereira, M., Stochastic long-term hydrothermal scheduling with parameter uncertainty in autoregressive streamflow models. IEEE Transactions on power systems, 32(2), 2017. DOI: https://doi.org/10.119/TPWRS.2016.2572722.

[3] Beyaztas, U., Arikan, B.B., Beyaztas, B.H., and Kahya, E., Construction of prediction intervals for palmer drought severity index using bootstrap. Journal of Hydrology 559, pp. 461-470, 2018. DOI: https://doi.org/10.1016/j.jhydrol.2018.02.021.

[4] Bjerknes, J., Atmospheric teleconnections from the equatorial pacific. Monthly Weather Review, 97(3), pp. 163-172, 1969. DOI: https://doi.org/10.1175/1520-0493(1969)097<0163:ATFTEP>2.3.CO;2

[5] Box, G.E.P., and Jenkins, G.M., Time series analysis: forecasting and control. Holden-Day, 1976. ISBN: 0816211043, 9780816211043.

[6] Cao, L., Mees, A., and Judd, K., Dynamics from multivariate time series. Physica D: Nonlinear Phenomena, 121(1-2), pp. 75–88, 1998. DOI: https://doi.org/10.1016/S0167-2789(98)00151-1.

[7] Carvajal, L.F., Salazar, J.E., Mesa, O.J., y Poveda, G., Predicción hidrológica en Colombia mediante análisis espectral singular y máxima entropía. Ingeniería Hidráulica en México, XIII, (1), pp. 7–16, 1998.

[8] Chen, D., and Cane, M.A., El Niño prediction and predictability. Journal of Computational Physics, 227(7), pp. 3625-3640, 2008. DOI: https://doi.org/10.1016/j.jcp.2007.05.014.

[9] Córdoba, S., Palomino, R., Raquel, Gámiz, S., Castro, Y., and Esteban, M., Influence of tropical Pacific SST on seasonal precipitation in Colombia: prediction using El Niño and El Niño Modoki. Climate Dynamics, 44(5-6), pp. 1293–1310, 2015. DOI: https://doi.org/10.1007/s00382-014-2232-3.

[10] Córdoba, S., Palomino, R., Raquel, Gámiz, S., Castro, Y., and Esteban, M., Seasonal streamflow prediction in Colombia using atmospheric and oceanic patterns. Journal of Hydrology, 538, pp. 1–12, 2016. DOI: https://doi.org/10.1016/j.jhydrol.2016.04.003.

[11] Dracup, J.A., and Gutie, F., An analysis of the feasibility of long-range streamflow forecasting for Colombia using El Niño–Southern Oscillation indicators. Journal of Hydrology, 246, pp. 181–196, 2001. DOI: https://doi.org/10.1016/S0022-1694(01)00373-0.

[12] Favereau, M., Lorca, A., Negrete-Pincetic, M., and Vicuña, S., Robust streamflow forecasting: a student’s t-mixture vector autoregressive model. Stochastic Environmental Research and Risk Assessment 36, pp. 3979–3995, 2022. DOI: https://doi.org/10.1007/s00477-022-02241-y.

[13] Ham, Y.G., Kim, J.H., and Luo, J.J., Deep learning for multi-year ENSO forecasts. Nature, 573(7775), pp. 568–572, 2019. DOI: https://doi.org/10.1038/s41586-019-1559-7.

[14] Holton, J.R., and Hakim, G.J., An introduction to dynamic meteorology. 5th Ed., 2012. DOI: https://doi.org/10.1016/C2009-0-63394-8.

[15] Jaramillo, A., y Chaves, B., Distribución de la precipitación en Colombia analizada mediante conglomeración estadística. Cenicafé, 51(2), pp. 102–113, 2000.

[16] Kelman, J., Vieira, A., and Rodriguez, J.E., El Niño influence on streamflow forecasting. Stochastic Environmental Research and Risk Assessment, 14, pp. 123–138, 2000. DOI: https://doi.org/10.1007/PL00009776.

[17] Laing, A.G., and Fritsch, J.M., Mesoscale convective complexes in Africa. Monthly Weather Review, 121(8), pp. 2254–2263, 1993. DOI: https://doi.org/10.1175/1520-0493(1993)121<2254:MCCIA>2.0.CO;2.

[18] Yan, L., Feng, J., Hang, T., and Zhu, Y., Flow interval prediction based on deep residual network and lower and upper boundary estimation method. Applied Soft Computing Journal, 104, art. 107228, 2021. DOI: https://doi.org/10.1016/j.asoc.2021.107228.

[19] L’Heureux, M.L., Tippett, M.K., Takahashi, K., Barnston, A.G., Becker, E.J., Bell, G.D., Di Liberto, T.E., Gottschalck, J., Halpert, M.S., Hu, Z.Z., Johnson, N.C., Xue, Y., and Wang, W., Strength outlooks for the El Niño-Southern oscillation. Weather and Forecasting, 34(1), pp. 165–175, 2019. DOI: https://doi.org/10.1175/WAF-D-18-0126.1.

[20] McPhaden, M.J., Tropical Pacific Ocean heat content variations and ENSO persistence barriers. Geophysical Research Letters, 30(9), pp. 1995–1998, 2003. DOI: https://doi.org/10.1029/2003GL016872.

[21] Mejía, J.F., Mesa, O.J., Poveda, G., Vélez, J.I., Hoyos, C., Mantilla, R., Barco, J., Cuartas, L.A., Montoya, M., y Botero, B., Distribución espacial y ciclos anual y semianual de la precipitación en Colombia. DYNA, 127, pp. 7-26, 1999.

[22] Meng, J., Fan, J., Ludescher, J., Agarwal, A., Chen, X., Bunde, A., Kurths, J., and Schellnhuber, H.J., Complexity-based approach for El Niño magnitude forecasting before the spring predictability barrier. Proceedings of the National Academy of Sciences of the United States of America, 117(1), pp. 177–183, 2020. DOI: https://doi.org/10.1073/pnas.1917007117.

[23] Mesa, O.J., Poveda, G., y Carvajal, L.F., Introducción al clima de Colombia. Universidad Nacional de Colombia, Medellín, primera edición, 1997. ISBN: 9586281442, 9789586281447.

[24] Palm, B.G., Bayer, F.M., and Cintra, R.J., Prediction intervals in the beta autoregressive moving model. Communications in statistics – simulation and computation. 52(8), pp. 3635–3656, 2023. DOI: https://doi.org/10.1080/03610918.2021.1943440.

[25] Poveda, G., Retroalimentación dinámica entre El Niño Oscilación del Sur y la hidrología de Colombia. Tesis de Doctorado, Universidad Nacional de Colombia, sede Medellín, 1998.

[26] Poveda, G., La hidroclimatología de Colombia: una síntesis desde la escala interdecadal hasta la escala diurna. Revista Academia Colombiana de Ciencias, 28(10), pp. 201–222, 2004. DOI: https://doi.org/10.18257/raccefyn.28(107).2004.1991.

[27] Poveda, G., Gil, M.M., and Quiceno, N., El ciclo anual de la hidrología de Colombia en relación con el ENSO y la NAO. Bulletin de l’Institut Français d’Études Andines, 27(3), pp. 721–731, 1998.

[28] Poveda, G., Jaramillo, A., Gil, M.M., Quiceno, N., and Mantilla, R., Seasonality in ENSO-related precipitation, river discharges, soil moisture, and vegetation index in Colombia. Water Resources Research, 37(8), pp. 2169–2178, 2001. DOI: https://doi.org/10.1029/2000WR900395.

[29] Poveda, G., and Mesa, O.J., On the existence of Lloró (the rainiest locality on Earth), Enhanced ocean-land-atmosphere interaction by a low-level jet. Geophysical Research Letters 27(11), pp. 1675-1678, 2000. DOI: https://doi.org/10.1029/1999GL006091.

[30] Poveda, G., and Mesa, O.J., Feedbacks between hydrological processes in tropical South America and large-scale ocean-atmospheric phenomena. Journal of Climate, 10(10), pp. 2690–2702, 1997. DOI: https://doi.org/10.1175/1520-0442(1997)010<2690:FBHPIT>2.0.CO;2.

[31] Poveda, G., y Mesa, O.J., La corriente de chorro superficial del oeste (“del Chocó”) y otras dos corrientes de chorro en Colombia climatología y variabilidad durante las fases del ENSO. Ciencias de la Tierra, 23(89), pp. 517–528, 1999. DOI: https://doi.org/10.18257/raccefyn.23(89).1999.2848.

[32] Poveda, G., Mesa, O.J., Salazar, L.F., Arias, P.A., Moreno, H.A., Vieira, S.C., Agudelo, P.A., Toro, V.G., and Álvarez, J.F., The diurnal cycle of precipitation in the tropical Andes of Colombia. Monthly Weather Review, 133(1), pp. 228–240, 2005. DOI: https://doi.org/10.1175/MWR-2853.1.

[33] Poveda, G., Vélez, J.I., and Mesa, O.J., Atlas Hidrológico de Colombia. Universidad Nacional de Colombia, Medellín, 2000.

[34] Rodríguez, N., y Siado, P., Un pronóstico paramétrico de la inflación colombiana. Revista Colombiana de Estadística, 26(2), pp. 89-128, 2003.

[35] Rogers, J.C., The association between the North Atlantic Oscillation and the Southern Oscillation in the Northern Hemisphere. Monthly Weather Review, 112(10), pp. 1999-2015, 1984. DOI: https://doi.org/10.1175/1520-0493(1984)112<1999:TABTNA>2.0.CO;2.

[36] Rojo, J.D., Carvajal, L.F., and Velásquez, J.D., Streamflow prediction using a forecast combining system. IEEE Latin America Transactions, 13(4), pp. 1035–1040, 2015. DOI: https://doi.org/10.1109/TLA.2015.7106354.

[37] Thomas, H., and Fiering, M., Mathematical synthesis of streamflow sequences for the analysis of river basins by simulations. Design of Water Resource Systems, Edited by Mass et al., Harvard University Press, Cambridge, pp. 459-493, 1962. DOI: https://doi.org/10.4159/harvard.9780674421042.c15.

[38] Thombs, L.A., and Schucany, W.R., Bootstrap prediction intervals for autoregression. Journal of the American Statistical Association, 85(410), pp. 486-492, 1990. DOI: https://doi.org/10.2307/2289788.

[39] Torrence, C., and Webster, P.J., The annual cycle of persistence in the El Niño/Southern Oscillation. Quarterly Journal of the Royal Meteorological Society 124(550), pp. 1985–2004, 1998. DOI: https://doi.org/10.1002/qj.49712455010.

[40] Trenberth, K.E., General characteristics of El Niño-Southern Oscillation. Teleconnection Linking Worldwide Climate Anomalies. Cambridge University Press, New York, 1991.

[41] Van Den Dool, H.M., Searching for analogues, how long must we wait? Tellus A: Dynamic Meteorology and Oceanography, 46(3), pp. 314-324, 1994. DOI: https://doi.org/10.1034/j.1600-0870.1994.t01-2-00006.x.

[42] Waylen, P., and Poveda, G., El Nino-Southern Oscillation and aspects of western South American hydro-climatology. Hydrological Processes, 16(6), pp. 1247–1260, 2002. DOI: https://doi.org/10.1002/hyp.1060.

[43] Webster, P.J., The annual cycle and the predictability of the tropical coupled Ocean-Atmosphere system. Meteorology and Atmospheric Physics, 56, pp. 33–55, 1995. DOI: https://doi.org/10.1007/BF01022520.

[44] Westra, S., Sharma, A., Brown, C., and Lall, U., Multivariate streamflow forecasting using independent component analysis. Water Resources Research, 44(2), pp. 1–11, 2008. DOI: https://doi.org/10.1029/2007WR006104.

[45] Yang, T., Asanjan, A.A., Welles, E., Gao, X., Sorooshian, S., and Liu, X. Developing reservoir monthly inflow forecasts using artificial intelligence and climate phenomenon information. Water Resources Research. Journal of the American Water Resources Association, (53), pp. 2786–2812, 2017. DOI: https://doi.org/10.1002/2017WR020482.

Cómo citar

IEEE

[1]
A. F. Hurtado-Montoya y N. A. Moreno-Reyes, «Colombian monthly energy inflows predictability», DYNA, vol. 91, n.º 234, pp. 24–33, oct. 2024.

ACM

[1]
Hurtado-Montoya, A.F. y Moreno-Reyes, N.A. 2024. Colombian monthly energy inflows predictability. DYNA. 91, 234 (oct. 2024), 24–33. DOI:https://doi.org/10.15446/dyna.v91n234.114287.

ACS

(1)
Hurtado-Montoya, A. F.; Moreno-Reyes, N. A. Colombian monthly energy inflows predictability. DYNA 2024, 91, 24-33.

APA

Hurtado-Montoya, A. F. y Moreno-Reyes, N. A. (2024). Colombian monthly energy inflows predictability. DYNA, 91(234), 24–33. https://doi.org/10.15446/dyna.v91n234.114287

ABNT

HURTADO-MONTOYA, A. F.; MORENO-REYES, N. A. Colombian monthly energy inflows predictability. DYNA, [S. l.], v. 91, n. 234, p. 24–33, 2024. DOI: 10.15446/dyna.v91n234.114287. Disponível em: https://revistas.unal.edu.co/index.php/dyna/article/view/114287. Acesso em: 11 ene. 2025.

Chicago

Hurtado-Montoya, Andrés Felipe, y Nicolás Alberto Moreno-Reyes. 2024. «Colombian monthly energy inflows predictability». DYNA 91 (234):24-33. https://doi.org/10.15446/dyna.v91n234.114287.

Harvard

Hurtado-Montoya, A. F. y Moreno-Reyes, N. A. (2024) «Colombian monthly energy inflows predictability», DYNA, 91(234), pp. 24–33. doi: 10.15446/dyna.v91n234.114287.

MLA

Hurtado-Montoya, A. F., y N. A. Moreno-Reyes. «Colombian monthly energy inflows predictability». DYNA, vol. 91, n.º 234, octubre de 2024, pp. 24-33, doi:10.15446/dyna.v91n234.114287.

Turabian

Hurtado-Montoya, Andrés Felipe, y Nicolás Alberto Moreno-Reyes. «Colombian monthly energy inflows predictability». DYNA 91, no. 234 (octubre 22, 2024): 24–33. Accedido enero 11, 2025. https://revistas.unal.edu.co/index.php/dyna/article/view/114287.

Vancouver

1.
Hurtado-Montoya AF, Moreno-Reyes NA. Colombian monthly energy inflows predictability. DYNA [Internet]. 22 de octubre de 2024 [citado 11 de enero de 2025];91(234):24-33. Disponible en: https://revistas.unal.edu.co/index.php/dyna/article/view/114287

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