Generación de Caudales Aplicando un Método Simple de Tránsito de Avenidas a Nivel Mensual en La Cuenca La Leche, Perú

##plugins.themes.bootstrap3.article.main##

Máximo Caicedo Yaipén

Luis Villegas Granados

Guillermo Arriola Carrasco

Royser Cayatopa Idrogo

Juan García Chumacero

Noe Marín Bardales


Palabras clave:
Flows, basin, hydrographs, hydrology, flood routing Caudales, cuenca, hidrogramas, hidrología, tránsito de avenidas

Resumen

El tránsito de avenidas por lo general es usado en el análisis y evaluación de inundaciones, sin embargo, ha sido poco estudiado en la extensión y determinación de caudales. Por ello, esta investigación tuvo por objetivo generar caudales aplicando un método simple de tránsito de avenidas conocido como Muskingum a nivel mensual, en la cuenca La Leche de Perú. Se escogió la cuenca en mención, pues en los últimos 40 años ha sufrido grandes inundaciones, viéndose afectada gran parte de la población, terrenos de cultivo e infraestructura local, por lo que se requiere abordar su estudio. La metodología fue del tipo aplicada y de diseño no experimental comparativo. Debido a que el tránsito de avenidas emplea los parámetros de proporcionalidad de volumen y ponderación del tránsito en intervalos de tiempo definido, se creyó conveniente usar indicadores estadísticos para optimizar la comparación de los caudales registrados en las estaciones hidrométricas y los hidrogramas de la simulación de un modelo hidrológico tipo precipitación-escorrentía para diferentes períodos de retorno, obteniéndose significativos resultados en cada caso, según la correlación de Pearson, el criterio de Schultz, el criterio de Nash-Sutcliffe, el error de balance de masas y la prueba t para dos muestras suponiendo varianzas iguales. Finalmente, se concluye que se pueden establecer caudales empleando el tránsito de avenidas con el método Muskingum en la cuenca La Leche, además pueden utilizarse en descargas simuladas donde se disponga de información meteorológica e hidrométrica.

Descargas

Descargas

Los datos de descargas todavía no están disponibles.




Detalles del artículo

Citas

Aboutalebi, M., Haddad, O., & Loáiciga, H. (2016). Application of the SVR-NSGAII to hydrograph routing in open channels. Journal of Irrigation and Drainage Engineering, 142(3). http://doi.org/10.1061/(ASCE)IR.1943-4774.0000969

Aguirre, J., De La Torre Ugarte, D., Bazo, J., Quequezana, P., & Collado, M. (2019). Evaluation of early action mechanisms in Peru regarding preparedness for El Niño. International Journal of Disaster Risk Science, 10(4). 493-510. http://doi.org/10.1007/s13753-019-00245-x

Akbari, R., Hessami-Kermani, M., & Shojaee, S. (2020). Flood Routing: Improving outflow using a new non-linear Muskingum model with four variable parameters coupled with PSO-GA algorithm. Water Resources Management, 34(10), 3291-3316. http://doi.org/10.1007/s11269-020-02613-5

Akbari, R., & Hessami-Kermani, M. (2021). Parameter estimation of Muskingum model using grey wolf optimizer algorithm. MethodsX, 8. http://doi.org/10.1016/j.mex.2021.101589

Alhumoud, J. (2022). Analysis and evaluation of flood routing using Muskingum method. Journal of Applied Engineering Science, 20(4), 1366-1377. http://doi.org/10.5937/jaes0-37455

Alhumoud, J., & Almashan, N. (2019). Muskingum method with variable parameter estimation. Mathematical Modelling of Engineering Problems, 6(3), 355-362. http://doi.org/10.18280/mmep.060306

Arriola, G., Villegas, L., Arbulú, J., & Sotomayor, G (2021). Estimación del tránsito de avenidas empleando el método de Muskingum en la estación El Tambo de la cuenca Chicama, Perú. Revista Ingeniería: Ciencia, Tecnología e Innovación, 8(2), 15-29. https://doi.org/10.26495/icti.v8i2.1901

Arriola, G., Villegas, L., Fernandez, J., Vallejos, J., & Idrogo, C. (2023). Assessment of parameters of the generalized extreme value distribution in rainfall of the Peruvian north. Revista Politecnica, 52(2), 99-112. https://doi.org/10.33333/rp.vol52n2.10

Arriola, G., Villegas, L., Marín, N., Idrogo, C., Piedra, J., & Arbulú, J. (2022). Assessment of climatic aggressiveness and precipitation concentration in the Chancay-Lambayeque basin, Peru. Revista Politecnica, 50(2), 15-22. https://doi.org/10.33333/rp.vol50n2.02

Ayala, I., Oré, J., Requena, D., Oré, R., Torres, E., & Montes, E. (2018). Flow routing in the natural channel of the Ichu river experimental basin through neural networks. Journal of Environmental Science and Engineering A, (7), 387-403. http://doi.org/10.17265/2162-5298/2018.10.001

Badfar, M., Barati, R., Dogan, E., & Tayfur, G. (2021). Reverse flood routing in rivers using linear and nonlinear Muskingum models. Journal of Environmental Science and Engineering, 26(6). https://doi.org/10.1061/(ASCE)HE.1943-5584.0002088

Balcázar, L., Bâ, K., Díaz-Delgado, C., Quentin, E., & Minga-León, S. (2019). Daily discharges modelling in a basin in southern Ecuador with precipitation and temperature estimated by satellite. Agrociencia, 53(4), 465-486. Retrieved from the SCOPUS database

Bazargan, J., & Norouzi, H. (2018). Investigation the effect of using variable values for the parameters of the linear Muskingum method using the Particle Swarm Algorithm (PSO). Water Resources Management, 32(14), 4763-4777. https://doi.org/10.1007/s11269-018-2082-6

Bozorg-Haddad, O., Abdi-Dehkordi, M., Hamedi, F., Pazoki, M., & Loáiciga, H. (2019). Generalized storage equations for flood routing with nonlinear Muskingum models. Water Resources Management, 33(8), 2677-2691. http://doi.org/10.1007/s11269-019-02247-2

Bozorg-Haddad, O., Mohammad-Azari, S., Hamedi, F., Pazoki, M., & Loáiciga, H. (2020). Application of a new hybrid non-linear Muskingum model to flood routing. Proceedings of the Institution of Civil Engineers: Water Management, 173(3), 109-120. http://doi.org/10.1680/jwama.19.00075

Carrizales, J., Rodas, M., & Castillo, L. (2022). Analysis of human physical vulnerability using static equilibrium techniques of a hazard flood for the determination of unsafe areas in the city of Catacaos - Piura, Peru. IOP Conference Series: Earth and Environmental Science, 958(1). http://doi.org/10.1088/1755-1315/958/1/012024

Colín-García, G., Palacios-Vélez, E., Fernández-Reynoso, D. S., López-Pérez, A., Flores-Magdaleno, H., Ascencio-Hernández, R., & Canales-Islas, E. (2023). Hydrological modeling with the SWAT model using different spatial distributions of soil type in the Mixteco River basin. Terra Latinoamericana, 41. https://doi.org/10.28940/terra.v41i0.1566

Ehteram, M., Othman, F., Yaseen, Z., Afan, H., Allawi, M., Malek, M., Ahmed, A., Shadid, S., Singh, V., & El-Shafie, A. (2018). Improving the Muskingum flood routing method using a hybrid of particle swarm optimization and bat algorithm. Water (Switzerland), 10(6). https://doi.org/10.3390/w10060807

Farzin, S., Singh, V., Karami, H., Farahani, N., Ehteram, M., Kisi, O., Allawi, M., Mohd, N., & El-Shafie, A. (2018). Flood routing in river reaches using a three-parameter Muskingum model coupled with an improved bat algorithm. Water (Switzerland), 10(9). http://doi.org/10.3390/w10091130

Fenton, J. (2019). Flood routing methods. Journal of Hydrology, 570, 251-264. https://doi.org/10.1016/j.jhydrol.2019.01.006

Gasiorowski, D., & Szymkiewicz, R. (2020). Identification of parameters influencing the accuracy of the solution of the nonlinear Muskingum equation. Water Resources Management, 34(10), 3147-3164. https://doi.org/10.1007/s11269-020-02599-0

Guachamín, W., Páez-Bimos, S., & Horna, N. (2019). Evaluación de productos IMERG V03 y TMPA V7 en la detección de crecidas caso de estudio cuenca del río Cañar. Revista Politecnica, 42(2), 37-48. https://doi.org/10.33333/rp.vol42n2.942

Hernández-Andrade, A., & Martínez-Martínez, S. (2019). Flood routing on a reservoir: Hydrologic or hydraulic?. Tecnologia y Ciencia del Agua, 10(6), 147-177. https://doi.org/10.24850/j-tyca-2019-06-06

Hernández-Romero, P., Patiño-Gómez, C., Corona-Vásquez, B., & Martínez-Austria, P. (2022). Rainfall/runoff hydrological modeling using satellite precipitation information. Water Practice and Technology, 17(5), 1082-1098. https://doi.org/10.2166/wpt.2022.048

Kadhim, M., Al-Bedyry, N., & Omran, I. (2022). Evaluation of flood routing models and their relationship to the hydraulic properties of the Diyala river red. IOP Conference Series: Earth and Environmental Science, 961(1). https://doi.org/10.1088/1755-1315/961/1/012058

Kadim, M., Omran, I., & Al-Taai, A. (2021). Optimization of the nonlinear Muskingum model parameters for the river routing, Tigris river a case study. International Journal of Design and Nature and Ecodynamics, 16(6), 649-656. https://doi.org/10.18280/ijdne.160605

Kang, L., & Zhou, L. (2018). Parameter estimation of variable-parameter nonlinear Muskingum model using excel solver. IOP Conference Series Earth and Environmental Science, 121(5). http://doi.org/10.1088/1755-1315/121/5/052047

Katipoglu, O, & Sarigol, M. (2023. Coupling machine learning with signal process techniques and particle swarm optimization for forecasting flood routing calculations in the Eastern Black Sea Basin, Türkiye. Environmental Science and Pollution Research. https://doi.org/10.1007/s11356-023-25496-6

Khalifeh, S., Esmaili, K., Khodashenas, S., & Khalifeh, V. (2020). Estimation of nonlinear parameters of the type 5 Muskingum model using SOS algorithm. MethodsX, 7. http://doi.org/10.1016/j.mex.2020.101040

Lee, E., Lee, H., & Kim, J. (2018). Development and application of advanced Muskingum flood routing model considering continuous flow. Water (Switzerland), 10(6). http://doi.org/10.3390/w10060760

Lavado, W., Labat, D., & Ronchail, J. (2013). Trends in rainfall and temperature in the Peruvian Amazon–Andes basin over the last 40 years (1965-2007). Hydrological Processes, 27(20), 2944-2957. https://doi.org/10.1002/hyp.9418

Luna-Romero, A., Ramírez, I., Sánchez, C., Conde, J., Agurto, L., & Villaseñor, D. (2018). Distribución espacio-temporal de la precipitación en la cuenca del río Jubones, Ecuador: 1975-2013. Scientia Agropecuaria, 9(1), 63-70. https://dx.doi.org/10.17268/sci.agropecu.2018.01.07

Matovelle, C., Heras, D., & Solano-Peláez, J. (2022). Imputation efficiency of missing rainfall data using computational tools in a river basin, Jubones-Ecuador. Revista Politecnica, 50(2), 23-30. http://doi.org/10.33333/rp.vol50n2.03

Moradi, E., Yaghoubi, B., & Shabanlou, S. (2023). A new technique for flood routing by nonlinear Muskingum model and artificial gorilla troops algorithm. Applied Water Science, 13(2). http://doi.org/10.1007/s13201-022-01844-8

Norouzi, H., & Bazargan, J. (2020). Flood routing by linear Muskingum method using two basic floods data using Particle Swarm Optimization (PSO) algorithm. Water Resources Management, 20(5), 1897-1908. http://doi.org/10.2166/ws.2020.099

Norouzi, H., & Bazargan, J. (2022) Flood routing using the Muskingum-Cunge method and application of different routing parameters. Sadhana - Academy Proceedings in Engineering Sciences, 47(4). https://doi.org/10.1007/s12046-022-02049-0

Okkan, U., & Kirdemir, U. (2020). Locally tuned hybridized particle swarm optimization for the calibration of the nonlinear Muskingum flood routing model. Journal of Water and Climate Change, 11(1S), 343-358. http://doi.org/10.2166/wcc.2020.015

Oñate-Valdivieso, F., Bosque-Sendra, J., Sastre-Merlin, A., & Ponce, V. (2016). Calibration, validation and evaluation of a lumped hydrologic model in a mountain area in Southern Ecuador. Agrociencia, 50(8), 945-963. Retrieved from the SCOPUS database

Pashazadeh, A., & Javan, M. (2020). Comparison of the gene expression programming, Artificial Neural Network (ANN), and equivalent Muskingum inflow models in the flood routing of multiple branched rivers. Theoretical and Applied Climatology, 139(3-4), 1349-1362. http://doi.org/10.1007/s00704-019-03032-2

Pazos, M., & Mayorga, D. (2019). Hidrología agrícola. Babahoyo, Ecuador: Cidepro.

Peña, O., More, M., Nima, R., & Marchan, H. (2023). Artificial neural network model for the prediction of the "El Niño" Phenomenon in the Region of Piura (Peru). TECHNO Review. International Technology, Science and Society Review / Revista Internacional de Tecnología, Ciencia y Sociedad, 13(4). http://doi.org/10.37467/revtechno.v13.4815

Qiang, Z., Qiaoping, F., Xingjun, H., & Jun, L. (2020). Parameter estimation of Muskingum model based on whale optimization algorithm with elite opposition-based learning. IOP Conference Series: Materials Science and Engineering, 780(2). http://doi.org/10.1088/1757-899X/780/2/022013

Rad, S., Junfeng, D., Jingxuan, X., Zitao, L., Linyan, P., Wan, Z., & Lin, L. (2022). Lijiang flood characteristics and implication of karst storage through Muskingum flood routing via HEC-HMS, S. China. Hydrology Research, 53(12), 1480-1493. http://doi.org/10.2166/nh.2022.060

Rollenbeck, R., Orellana-Alvear, J., Rodriguez, R., Macalupu, S., & Nolasco, P. (2021). Calibration of X-band radar for extreme events in a spatially complex precipitation region in north Peru: Machine learning vs. empirical approach. Atmosphere, 12(12). http://doi.org/10.3390/atmos12121561

Sayed, B., Al-Mohair, H., Alkhayyat, A., Ramírez-Coronel, A., & Elsahabi, M. (2023). Comparing machine-learning-based black box techniques and white box models to predict rainfall-runoff in a northern area of Iraq, the Little Khabur river. Water Science and Technology, 87(3), 812-822. http://doi.org/10.2166/wst.2023.014

SENAMHI. (2017). Nota técnica N° 002: Atlas de erosión de suelos por regiones hidrológicas del Perú. Lima, Perú: Dirección de Hidrología del Servicio Nacional de Meteorología e Hidrología del Perú. https://repositorio.senamhi.gob.pe/handle/20.500.12542/261

Tahiri, A., Che, D., Ladeveze, D., Chiron, P., & Archimède, B. (2022). Network flow and flood routing model for water resources optimization. Scientific Reports, 12(1). https://doi.org/10.1038/s41598-022-06075-0

Tan, J., Xie, X., Zuo, J., Xing, X., Liu, B., Xia, Q., & Zhang, Y. (2021). Coupling random forest and inverse distance weighting to generate climate surfaces of precipitation and temperature with Multiple-Covariates. Journal of Hydrology, 598. https://doi.org/10.1016/j.jhydrol.2021.126270

Vargas, Z., Valdez, S., & Paredes-Tavares, J. (2021). Spatio-temporal interpolation of rainfall data in western Mexico. 2021 Mexican International Conference on Computer Science, ENC 2021. https://doi.org/10.1109/ENC53357.2021.9534803

Vatankhah, A. (2021). The lumped Muskingum flood routing model revisited: The storage relationship. Hydrological Sciences Journal, 66(11), 1625-1637. https://doi.org/10.1080/02626667.2021.1957475

Wang, Z., Wang, Z., Feng, P., Dong, Y., Zhang, Z., & Yang, Y. (2022). Study on applicability of remote sensing precipitation products in hilly-plain-wetland complex area of northeast China. Water Supply, 22(3), 3498 – 3507. https://doi.org/10.2166/WS.2021.387

Zang, S., Li, Z., Yao, C., Zhang, K., Sun, M., & Kong, X. (2020). A new runoff routing scheme for Xin’anjiang model and its routing parameters estimation based on geographical information. Water (Switzerland), 11(2), 1-18. http://doi.org/10.3390/W12123429

Zheng, H., Sang, G., & Yan, C. (2018). Study on risk assessment method of mountain torrent disaster of Wendeng District. IOP Conference Series: Earth and Environmental Science, 208(1). http://doi.org/10.1088/1755-1315/208/1/012023