SELECCIÓN DE MODELO HIDROLÓGICO INCLUYENDO LA TEMPERATURA AMBIENTAL PARA INFRAESTRUCTURAS DE SIEMBRA Y COSECHA DE AGUA

Carlos Alberto Cabanillas Agreda; Josué Wilder Moisés Hoyos Aguilar

Autores/as

Carlos Alberto Cabanillas Agreda Facultad de Ciencias Agropecuarias, Universidad Nacional de Trujillo, Av. Juan Pablo II s/n – Ciudad Universitaria, Trujillo, Perú https://orcid.org/0000-0003-4269-949X
Josué Wilder Moisés Hoyos Aguilar Facultad de Ciencias Agropecuarias, Universidad Nacional de Trujillo, Av. Juan Pablo II s/n – Ciudad Universitaria, Trujillo, Perú https://orcid.org/0009-0009-1402-5587

Palabras clave:

Escorrentía, precipitación, ciclo hidrológico, cambio climático

Resumen

El propósito de este estudio es investigar acerca de modelos hidrológicos, que permita seleccionar el modelo más adecuado que incluya la temperatura ambiental para obtener un caudal de diseño para el predimensionamiento correcto de infraestructuras de siembra y cosecha de agua. Es fundamental que los modelos reflejen con precisión las condiciones locales; aunque la falta de recursos y datos específicos puede dificultar el desarrollo de modelos propios, estos suelen ser más útiles y efectivos que aquellos creados en contextos distintos. Muchos modelos hidrológicos actuales se desarrollaron en otros países y para condiciones hidrológicas que no siempre coinciden con el área de aplicación. Por ejemplo, algunos modelos simulan la escorrentía superficial a partir del exceso de infiltración, apropiados para suelos de alta infiltración y lluvias de baja intensidad. En otras zonas, sería más adecuado un modelo basado en el exceso de saturación. Esta revisión de literatura, centrada principalmente en estudios publicados entre 2019 y 2023, cubre temas como la modelación de cuencas, sistemas hidrológicos, objetivos de la modelación y criterios de selección del modelo más adecuado. Se presentan fuentes actualizadas y recursos que sirven de base para seleccionar el modelo ideal para infraestructuras de siembra y cosecha de agua.

DOI: http://dx.doi.org/10.17268/rebiol.2024.44.02.03

Citas

Abdullah, A., Wanchang, Z., Muhammad, A., & Ahmed, E. (2022). Application of hydrological model to assess river flow in the transboundary cryosphere and data-scarce watershed, a case study: Chitral-Kabul River Basin (C-KRB) in Pakistan. Water Supply, 22(4), 3842-3862.

Agudelo-Otálora, L., Moscoso-Barrera, W., Paipa-Galeano, L., & Mesa-Sciarrotta, C. (2018). Comparison of physical models and artificial intelligence for prediction of flood levels. (10.24850/j-tyca-2018-04-09, Ed.) TAmTw5doYsPnRMpQfVUNziLW98oBwRqwAL, 9, 79-91.

Alemayehu, T., Gupta, H., Van Griensven, A., & Bauwens, W. (2022). On the Calibration of Spatially Distributed Hydrologic Models for Poorly Gauged Basins: Exploiting Information from Streamflow Signatures and Remote Sensing-Based Evapotranspiration Data. Water (Switzerland), 14(8). doi:https//:10.3390/w14081252

Ali, L., & Valdés, J. (2017). Modelos hidrológicos, tipos e aplicações. FAE, Curitiba, 20(1), 129-137.

Almeida, J., & Valdés, L. (2017). Modelos hidrológicos, tipos e aplicações. FAE,Curitiba, 20(1), 129-137. Obtenido de https://revistafae.fae.edu/revistafae/article/view/113/435

Atta-ur, R., Sagheer, A., Mohammed, G., Rashad, A., Shabib, A., & y. M. (2022). Rainfall Prediction System Using Machine Learning Fusion for Smart Cities. Sensor, 22(3504). doi:https://doi.org/10.3390/s22093504

Avtar Singh, J., Deepika, B., Retinder, K., Suhail, A., & Kuljit, K. (2021). Hydrological modeling to simulate stream flow under changing climate conditions in Jhelum catchment, western Himalaya. Journal of Hydrology(593). doi:doi:https://doi.org/10.1016/j.jhydrol.2020.125887

Baioni, E., Porta, G., Cattaneo, N., & Guadagnini, A. (2022). Assessment of Hydrological Processes in an Ungauged Catchment in Eritrea. Hidrology, 9(68). doi:https://doi.org/10.3390/hydrology9050068

Balcázar, L. B.-D.-L. (2022). Daily discharges modelling in a basin in southern Ecuador with precipitation and temperature estimated by satellite. Agrociencia, 53(4), 465-486.

Barresi Armoa, O. L., Sauvage, S., Houska, T., Bieger, K., Schürz, C., & Sánchez Pérez, J. M. (2023). Representation of hydrological components under a changing climate—A case study of the Uruguay River Basin using the new version of the Soil and Water Assessment Tool Model (SWAT+). Water Resources Management, 37(5), 1123-1140. https://doi.org/10.3390/w15142604

Beven, K. J. (2019). Towards a methodology for testing models as hypotheses in the inexact sciences. Proceedings of The Royal Society A Mathematical Physical and Engineering Sciences, 475(2224). doi:https://doi.org/10.1098/rspa.2018.0862

Brendel, A., Ferrelli, F., Piccolo, M., & Perillo, G. (2021). Effects of rainfall variability on the morphometry of water resources in a hydrographic basin located in pampas, Argentina. Revista Geografica Venezolana, 62(1), 92 - 106.

Casado-Rodríguez, J. y. (2022). Hydrograph separation for tackling equifinality in conceptual hydrological models. Journal of Hydrology, 610. doi:httos//:10.1016/j.jhydrol.2022.127816

Chen, Y., Wang, L., Shi, X., Zeng, C., Wang, Y., Wang, G., Qiangba, C., Yue, C., Sun, Z., Renzeng, O., & Zhang, F. (2023). Impact of climate change on the hydrological regimes of the midstream section of the Yarlung Tsangpo River Basin based on SWAT model. Water Resources Research, 59(3), 234-250. https://doi.org/10.3390/w15040685

Eccles, R., Zhang, H., Hamilton, D., Trancoso, R., & Syktus, J. (2023). Impacts of climate change on nutrient and sediment loads from a subtropical catchment. Water Resources Research, 59(4), 456-470. https://doi.org/10.1016/j.jenvman.2023.118738

Fahimnia, B., Pournader, M., Siemsen, E., Bendoly, E., & Wang, C. (2019). Behavioral operations and supply chain management—A review and literature mapping. Decision Sciences, 50(6), 1127–1183.

Fernadez-Soto, E., & Peña-Cortés, F. (2016). Aplicación de un modelo hidrológico espacialmente distribuido en dos cuencas costeras de la Región de La Araucanía (Chile). Estudios Geograficos(77), 35-56.

Galata, A. W. (2022). Modeling the hydrological characteristics of Hangar Watershed, Ethiopia. Water Supply, 22(4), 3896–3907.

Guo, L., Huang, K., Wang, G., & Lin, S. (2022). Development and evaluation of temperature-induced variable source area runoff generation model. Journal of Hydrology, 610.

He, Y., Xiong, J., Guo, S., Zhong, S., Yu, C., & Ma, S. (2023). Using Multi-Source Data to Assess the Hydrologic Alteration and Extremes under a Changing Environment in the Yalong River Basin. Journal of Hydrology. https://doi.org/10.3390/w15071357

Herrera-Franco G., et. al. (2020). Water Sowing and Harvesting (SyCA), ancestral techniques that solve problems of the XXI century . LACCEI International Multi-Conference for Engineering, Education, and Technology, 27-31.

Hughes, D., Birkinshaw, S., Parkin, G., Bovolo, C. I., Ó Dochartaigh, B., MacDonald, A., Franklin, A. L., Cummings, G., & Pereira, R. (2023). An innovative hydrological model for the sparsely-gauged Essequibo River basin, northern Amazonia. Journal of Hydrology, 123, 45-67. https://doi.org/10.1080/15715124.2023.2278678

Instituto de Hidrología (IDEAM)(s.f.). Protocolo de Modelación hidrológica e hidráulica. Obtenido de http://documentacion.ideam.gov.co/cgi-bin/koha/opac-detail.pl?biblionumber=38314

Kratzert, F., Klotz, D., Brenner, C., Schulz, K., & Herrnegger, M. (2018). Rainfall–runoff modelling using Long Short-Term Memory (LSTM) networks. Eurpean Geocienses Union, 22(11), 6005–6022.

Kumar R., H. M. (2022). Rainfall runoff modeling using MIKE 11 NAM of the Jhelum riverin Kashmir Valley, India. Mausam, 73(2), 365-372.

Kumar, M., Elbeltagi, A., Pande, C., Ahmed, A., Chow, M., Pham, Q., . . . Kumar, D. (2022). Applications of Data-driven Models for Daily Discharge Estimation Based on Different Input combinations. Water Resources Management, 2201-2221. doi:https//10.1007/s11269-022-03136-x

Lavado-Casimiro, W. (2020). Estudios Hidrológicos del SENAMHI (Resúmenes ejecutivos-2020). ISBN: 978-612-48315-0-8. Obtenido de https://www.senamhi.gob.pe/load/

Li. J., D. Q., & y Wang, C. (2018). Parameter optimization for carbon and water fluxes in two global land surface models based on surrogate modelling. International Journal of Climatology, 38, 1016-1031. Obtenido de https://rmets.onlinelibrary.wiley.com/doi/abs/10.1002/joc.5428

Mai, J., Craig, J., Tolson, B., & Arsenault, R. (2022). The sensitivity of simulated streamflow to individual hydrologic processes across North America. Nature Communications, 13(455).

Martín-Martín, A., Orduna-Malea, E., Thelwall, M., & Delgado López-Cózar, E. (2018). Google scholar, web of science, and Scopus: A systematic comparison of citations in 252 subject categories. Journal of Informetrics, 12(4), 1160–1177. 123 46 Annals of Operations Research (2023) 327:7–47

Mehlath, S., & Lone, M. (2022). Hydrological modeling to simulate stream flow in the Sindh Valley watershed, northwest Himalayas. Modeling Earth Systems and Environment, 8(2461–2470).

Morgan Uliana, M., Da Silva, D., Castro Moreira, M., Bueno Pereira, S., & Reis Pereira, D. (2020). Modelos Hidrológicos Sac-Sma E Iph Ii: Calibração e Avaliação Do Desempenho na Estimativa de Vazões Na Bacia do Rio Piracicaba (Mg). Irriga, Botucatu, 25(2). doi:http://dx.doi.org/10.15809/irriga.2020v25n2p202-222

Mukundan, R., Gelda, R. K., Moknatian, M., Zhang, X., & Steenhuis, T. S. (2023). Watershed-scale modeling of dissolved organic carbon in variable source areas. Water Resources Research. https://doi.org/10.1016/j.jhydrol.2023.130052

Ocampo, J., & Vélez, Q. (2013). Análisis comparativo de modelos hidrológicos de simulación continua en cuencas de alta montaña: caso del Río Chinchiná. Revista Ingenierías Universidad de Medellín, 13(24).

Ochoa-Tocachi, B., Cuadros Adriazola, E., Arapa Guzmán, E., Aste Cannock, N., Ochoa-Tocachi, E., & Vivien, B. (2022). Guía de Modelación Hidrológica para la Infraestructura Natural.

Pessacg, N., Flaherty, S., Brandizi, L., Rechencq, M., Asorey, M., Castiñeira, L., & Solman, S. (2018). Water yield in the Limay River Basin: Modelling and calibration. Meteorologica, 43(2), 3-23.

Qiu, J., Shen, Z., & Xie, H. (2023). Drought impacts on hydrology and water quality under climate change. Journal of Hydrology, 123, 45-60. https://doi.org/10.1016/j.scitotenv.2022.159854

Rafiei-Sardooi, E., Azareh, A., Joorabian Shooshtari, S., & Parteli, E. J. (2022). Long-term assessment of land-use and climate change on water scarcity in an arid basin in Iran. Ecological Modelling, 467. doi:https//:10.1016/j.ecolmodel.2022.109934

Rafiei-Sardooi, E., Azareh, A., Joorabian, S. S., & Parteli, E. J. (2022). Long-term assessment of land-use and climate change on water scarcity in an arid basin in Iran. Ecological Modelling, 467. doi:https://10.1016/j.ecolmodel.2022.109934

SENAMHI. (2020). Estudios Hidrol´ogicos del SENAMHI. Resúmenes Ejecutivos. Obtenido de https://cdn.www.gob.pe/uploads/document/file/1307757/Estudios-hidrol%C3%B3gicos-SENAMHI-set-2020.pdf

Silveira A., Cavalcanti A., Filho E. y Severino M. (2022). Calibration of SWMM’s hydrological model for the watershed vai e volta stream — Poços de Caldas, Minas Gerais [Calibração do modelo hidrológico SWMM para a bacia do ribeirão vai e volta — Poços de Caldas, Minas Gerais]. Engenharia Sanitaria e Ambiental, 27, 55-65.

Stehr, A. ,. (2010). Modelación de la respuesta hidrológica al cambio climático. Tecnología y Ciencias del Agua, 1(4), 37-58.

Stehr, A., Debels, P., Arumi, J., Alcayaga, H., & Romero, F. (2010). Tecnologia y Ciencias del Agua, 1(4), 37-58.

Tang, H., Wang, X., Chen, F., Jiang, L., He, C., & Long, A. (2022). Simulation of manas Rives Runoff bases om ERA5-Land Dataset. Earth Science Frontiers, 29(3), 271-283. doi:https//:10.13745/j.esf.sf.2022.1.50

Uliana, E., Almeida, D., F.T., De Souza, A., Da Cruz, I., Lisboa, L., & De Sousa, J. M. (2019). Application of SAC-SMA and IPH II hydrological models in the teles pires river basin, Brazil. Revista Brasileira de Recursos Hidricos, 24.

Uliana E., et. al (2020). Sac-sma and iph ii hydrological models: Calibration and performance assessment for water flow estimate in piracicaba river basin (mg). IRRIGA, 25(2), 202-222.

Vázquez-Ochoa, L., Correa-Sandoval, A., Vargas-Castilleja, R., Vázquez-Sauceda, M., & Rodríguez-Castro, J. (2021). Modelo hidrológico, calidad del agua y cambio climático: soporte para la gestión hídrica de la cuenca del río Soto la Marina. CienciaUAT, 16(1). doi:https://doi.org/10.29059/cienciauat.v16i1.1498

Vinícius Siqueira, O. C., Lívia Alves, A., Conceição de Maria, M. d., Javier Tomasella, A. C., & Pâmela Aparecida, M. (2021). Impact of climate change on monthly streamflow in the Verde. Revista Ambiente y Aga, 16(4).

Wang, Z., Zhong, R., Lai, C., Zeng, Z., Lian, Y., & Bai, X. (2018). Climate Change enhances the severity and variability of drought in the Pearl River Basin in South China in the 21 st century. Agricultural and Forest Meteorology, 249, 149-162. Obtenido de https://www.sciencedirect.com/science/article/pii/S0168192317304872

Weatherl, R., Henao Salgado, M., Ramgraber, M., Moeck, C., & Schirmer, M. (2021). Estimating surface runoff and groundwater recharge in an urban catchment using a water balance approach. Hydrogeology Journal, 29(7), 2411 - 2428. doi:https//: 10.1007/s10040-021-02385-1

Yang, M., Xu, J., Sang, L., & Liu, Q. (2022). Development and application of the distributed water resources allocation and regulation model based on hydrological cycle. Shuili Xuebao/Journal of Hydraulic Engineering,, 53(4), 456-470. doi:https//:10.13243/j.cnki.slxb.20210759

Yang, X., Zhou, J., Fang, W., & Wang, Y. (2022). Applicability Analysis of Hydrological Models in the Middle and Upper Reaches of Yangtze River. Ciencias Ambientales e Ingeniería, 2, 155-162. doi:DOI: 10.1007/978-981-19-1704-2_14

Zhang, X., Zhao, D., Wang, T., Wu, X., & Duan, B. (2022). A novel rainfall prediction model based on CEEMDAN-PSO-ELM coupled model. Water Supply, 22(4), 4531-4543.

Zhao, Y., Nearing, M., & Guertin, D. (2022). Modeling hydrologic responses using multi-site and single-site rainfall generators in a semi-arid watershed. International Soil and Water Conservation Research, 10(2), 177-187.

Zhu, Z., Fu, C., Wu, H., Wu, H., Cao, Y., & Xia, Y. (2023). Decoding the hundred-year water level changes of the largest saline lake in China: A joint lake-basin modeling study based on a revised SWAT+. Journal of Hydrology, 123, 78-90. https://doi.org/10.1016/j.ejrh.2023.101521