Flow estimation in nested basins through hydrologic regionalization: case study of the Agua Caliente and Retes Rivers

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Fernando Watson-Hernández
Valeria Serrano-Nuñez
Isabel Guzmán-Arias

Abstract

Estimating flow rates in regions with a scarcity of hydrometeorological data requires special techniques for this type of case, one of which is hydrological regionalization based on the transfer of parameters between river basins. The present study implemented the HBV Light hydrologic model to estimate flows in the Retes River sub-basin, located in the northern area of Cartago, Costa Rica, by transferring and applying parameters previously calibrated in the Agua Caliente River sub-basin. The statistical analysis of the results demonstrates the model’s satisfactory performance, as evidenced by a coefficient of determination (R²) of 0.865 and a Nash-Sutcliffe efficiency index (NSE) of 0.76 during the dry season (January-April). This substantiates the model’s aptitude in accurately replicating the hydrological dynamics within nested watersheds. Furthermore, it was ascertained that parameters such as soil water storage capacity (FC), maximum storage fraction (LP) and water distribution control (BETA) are congruent with those observed in previous studies conducted on analogous coverages. Despite the observed decline in accuracy during the rainy season, the study demonstrates that meticulous calibration and regionalisation based on physical and climatic characteristics facilitate the acquisition of reliable flow estimates. This methodological approach constitutes a contribution to the optimisation of water resource management in agricultural ecosystems, which are particularly vulnerable to climate variability and change. For the estimation of flow in regions with scarce hydrometeorological data, hydrologic regionalization based on parameter transfer between watersheds is a key methodology. The present study implemented the HBV Light hydrologic model to estimate flows in the Retes River sub-basin, located in the northern area of Cartago, Costa Rica, by transferring and applying parameters previously calibrated in the Agua Caliente River sub-basin.

Article Details

How to Cite
Watson-Hernández, F., Serrano-Nuñez, V., & Guzmán-Arias, I. (2026). Flow estimation in nested basins through hydrologic regionalization: case study of the Agua Caliente and Retes Rivers. Tecnología En Marcha Journal, 39(2), Pág. 3–19. https://doi.org/10.18845/tm.v39i2.7961
Section
Artículo científico

References

[1] N. Addor, G. Nearing, C. Prieto, A. J. Newman, N. Le Vine y M. P. Clark, “A ranking of hydrological signatures based on their predictability in space,” Water Resources Research, vol. 54, no. 11, pp. 8792–8812, Nov. 2018, doi: 10.1029/2018WR022606.

[2] M. Ballestero Vargas y T. López Lee, El nexo entre el agua, la energía y la alimentación en Costa Rica: El caso de la cuenca alta del río Reventazón, Serie Recursos Naturales e Infraestructura, no. 182. Santiago, Chile: CEPAL, 2017. [En línea]. Disponible en: https://repositorio.cepal.org/bitstream/handle/11362/42507/2/S1701032_es.pdf

[3] H. E. Beck, M. Pan, P. Lin, J. Seibert, A. I. J. M. van Dijk y E. F. Wood, “Global fully distributed parameter regionalization based on observed streamflow from 4,229 headwater catchments,” J. Geophys. Res. Atmos., vol. 125, no. 17, Sep. 2020, doi: 10.1029/2019JD031485.

[4] J. Carvajal, “Implementación de una metodología participativa de estrategias de adaptación al cambio climático en recursos hídricos en la parte alta de la cuenca del río Reventado, Cartago, Costa Rica,” Tesis de maestría, CATIE, Turrialba, Costa Rica, 2014. [En línea]. Disponible en: https://repositorio.catie.ac.cr/bitstream/handle/11554/7081/Implementacion_de_una_metodologia_participativa.pdf

[5] Comité Regional de Recursos Hidráulicos (CRRH), El clima, su variabilidad y cambio climático en Costa Rica. Costa Rica, 2008.

[6] W. Dou et al., “Fuzzy kappa for the agreement measure of fuzzy classifications,” Neurocomputing, vol. 70, nos. 4–6, pp. 726–734, Jan. 2007, doi: 10.1016/j.neucom.2006.10.007.

[7] E. V. García Medina, “Análisis comparativo de los modelos hidrológicos semidistribuidos GR4J, SOCONT y HBV aplicados al pronóstico,” Tesis de licenciatura, Univ. Nacional de Cajamarca, Cajamarca, Perú, 2024.

[8] S. Huang, S. Eisner, J. O. Magnusson, C. Lussana, X. Yang y S. Beldring, “Improvements of the spatially distributed hydrological modelling using the HBV model at 1 km resolution for Norway,” J. Hydrol., vol. 577, p. 123585, 2019, doi: 10.1016/j.jhydrol.2019.03.051.

[9] Y. Hundecha y A. Bárdossy, “Modeling of the effect of land use changes on the runoff generation of a river basin through parameter regionalization of a watershed model,” J. Hydrol., vol. 292, nos. 1–4, pp. 281–295, Jun. 2004, doi: 10.1016/j.jhydrol.2004.01.002.

[10] J. Jenness y J. J. Wynne, Cohen’s kappa and classification table metrics 2.0: An ArcView 3.x extension for accuracy assessment of spatially explicit models. Flagstaff, AZ, USA: U.S. Geological Survey, Southwest Biological Science Center, 2005. [En línea]. Disponible en: https://research.fs.usda.gov/treesearch/25707

[11] M. Méndez y L. Calvo-Valverde, “Development of the HBV-TEC hydrological model,” Procedia Eng., vol. 154, pp. 1116–1123, 2016, doi: 10.1016/j.proeng.2016.07.521.

[12] N. Mizukami et al., “Towards seamless large-domain parameter estimation for hydrologic models,” Water Resources Research, vol. 53, no. 9, pp. 8020–8040, Sep. 2017, doi: 10.1002/2017WR020401.

[13] N. Mizukami et al., “On the choice of calibration metrics for ‘high-flow’ estimation using hydrologic models,” Hydrol. Earth Syst. Sci., vol. 23, no. 6, pp. 2601–2614, Jun. 2019, doi: 10.5194/hess-23-2601-2019.

[14] I. Narváez, “Percepción sobre la tendencia de caudales, precipitación, temperatura y cambio de uso del suelo con relación al uso y manejo del agua en la zona norte de Cartago, Costa Rica,” Tesis, Costa Rica, 2013.

[15] H. Ouatiki, A. Boudhar, A. Ouhinou, A. Beljadid, M. Leblanc y A. Chehbouni, “Sensitivity and interdependency analysis of the HBV conceptual model parameters in a semi-arid mountainous watershed,” Water, vol. 12, no. 9, p. 2440, Aug. 2020, doi: 10.3390/w12092440.

[16] S. Pool, D. Viviroli y J. Seibert, “Value of a limited number of discharge observations for improving regionalization: A large-sample study across the United States,” Water Resources Research, vol. 55, no. 1, pp. 363–377, Jan. 2019, doi: 10.1029/2018WR023855.

[17] T. Razavi y P. Coulibaly, “An evaluation of regionalization and watershed classification schemes for continuous daily streamflow prediction in ungauged watersheds,” Can. Water Resour. J., vol. 42, no. 1, pp. 2–20, Jan. 2017, doi: 10.1080/07011784.2016.1184590.

[18] T. Sassolas-Serrayet, R. Cattin y M. Ferry, “The shape of watersheds,” Nat. Commun., vol. 9, no. 1, p. 3791, Sep. 2018, doi: 10.1038/s41467-018-06210-4.

[19] J. Seibert y M. J. P. Vis, “Teaching hydrological modeling with a user-friendly catchment-runoff-model software package,” Hydrol. Earth Syst. Sci., vol. 16, no. 9, pp. 3315–3325, 2012, doi: 10.5194/hess-16-3315-2012.

[20] Z. Song, J. Xia, G. Wang, D. She, C. Hu y S. Hong, “Regionalization of hydrological model parameters using gradient boosting machine,” Hydrol. Earth Syst. Sci., vol. 26, no. 2, pp. 505–524, Jan. 2022, doi: 10.5194/hess-26-505-2022.

[21] A. Trabado, “Centro Agronómico Tropical de Investigación y Enseñanza,” 2023. [En línea]. Disponible en: https://hdl.handle.net/20.500.14075/BCO24017604e