Revista de Investigaciones Altoandinas - Journal of High Andean Research

Vol. 26 No. 4 (2024)
Original articles

Effect of meteorological variables on reference evapotranspiration using multivariate statistical methods in the Mosna river basin

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  • Adan Alcides Acevedo Cruz,
  • Esteban Pedro Reyes Roque
Adan Alcides Acevedo Cruz
Universidad Nacional de Trujillo, Trujillo, Perú
Esteban Pedro Reyes Roque
Universidad Nacional Santiago Antúnez de Mayolo, Huaraz, Perú

Published 2024-11-30

Keywords

  • meteorological variables,
  • reference evapotranspiration,
  • Penman-Monteith,
  • principal component analysis and Mosna River

Copyright (c) 2024 Adan Alcides Acevedo Cruz, Esteban Pedro Reyes Roque

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Acevedo Cruz, A. A., & Reyes Roque, E. P. (2024). Effect of meteorological variables on reference evapotranspiration using multivariate statistical methods in the Mosna river basin. Revista De Investigaciones Altoandinas - Journal of High Andean Research, 26(4), 180-185. https://doi.org/10.18271/ria.2024.622

Abstract

Accurate estimation of the reference evapotranspiration (ETo) is essential for adequate planning and management of water resources for irrigation. The FAO56 Penman-Monteith model is the standard method for predicting ETo, however, its application is very restricted in many geographic areas due to the lack of complete meteorological data, therefore, the objective of this research work was to determine the variables that most affect the variation of ETo in the Mosna River basin. Meteorological data were provided by SENAMHI (1964-2023) and NASA POWER (1981-2021). As results of the principal component analysis (PCA) and ascending hierarchical classification (CJA) models, it was found that the most important variables in the estimation of ETo are maximum temperature, solar radiation, relative humidity and wind speed, while the minimum temperature has less importance in the calculation of ETo with a variance of 92.80% in the first two components (PCA1 and PCA2). Therefore, this study allowed us to reduce the dimensionality of the variables from five to four most significant variables in ETo modeling, with the limitation that wind speed must be validated in the field.

pdf (Spanish)

References

  1. Alam, M. M., Akter, Mst. Y., Islam, A. R. M. T., Mallick, J., Kabir, Z., Chu, R., Arabameri, A., Pal, S. C., Masud, M. A. A., Costache, R. y Senapathi, V. (2024). A review of recent advances and future prospects in calculation of reference evapotranspiration in Bangladesh using soft computing models. Journal of Environmental Management, 351, 119714. https://doi.org/10.1016/j.jenvman.2023.119714
  2. Aldás, J. y Uriel, E. (2017). Análisis multivariante aplicado con R. (2a ed.). Paraninfo.
  3. Allen, R. G., Pereira, L. S., Raes, D. y Smith, M. (1998). Crop evapotranspiration: Guidelines for computing crop water requirements (FAO Irrigation and drainage paper 56.). Food and Agricultural Orgainzation of the United Nations. http://www.fao.org/docrep/x0490e/x0490e00.htm
  4. ANA. (2015). Estudio de balance hídrico de la cuenca del río Mosna. Autoridad Nacional del Agua. https://repositorio.ana.gob.pe/handle/20.500.12543/2493
  5. Chu, R., Li, M., Islam, A. R. Md. T., Fei, D. y Shen, S. (2019). Attribution analysis of actual and potential evapotranspiration changes based on the complementary relationship theory in the Huai River basin of eastern China. International Journal of Climatology, 39(10), 4072-4090. https://doi.org/10.1002/joc.6060
  6. Elbeltagi, A., Deng, J., Wang, K. y Hong, Y. (2020). Crop Water footprint estimation and modeling using an artificial neural network approach in the Nile Delta, Egypt. Agricultural Water Management, 235, 106080. https://doi.org/10.1016/j.agwat.2020.106080
  7. Husson, F., Lê, S. y Pagès, J. (2016). Analyse de données avec R (2eme édition). Presses universitaires de Rennes.
  8. Ikudayisi, A. y Adeyemo, J. (2016). Effects of Different Meteorological Variables on Reference Evapotranspiration Modeling: Application of Principal Component Analysis. International Journal of Environmental, Chemical, Ecological, Geological and Geophysical Engineering, 10, 623-627.
  9. Islam, S. y Alam, A. K. M. R. (2021). Performance evaluation of FAO Penman-Monteith and best alternative models for estimating reference evapotranspiration in Bangladesh. Heliyon, 7(7), e07487. https://doi.org/10.1016/j.heliyon.2021.e07487
  10. Jerin, J. N., Islam, H. M. T., Islam, A. R. Md. T., Shahid, S., Hu, Z., Badhan, M. A., Chu, R. y Elbeltagi, A. (2021). Spatiotemporal trends in reference evapotranspiration and its driving factors in Bangladesh. Theoretical and Applied Climatology, 144(1), 793-808. https://doi.org/10.1007/s00704-021-03566-4
  11. Lei, L. (2014). Assessment of Water Quality Using Multivariate Statistical Techniques in the Ying River Basin, China [Thesis of Master of Science.]. Natural Resource and Environment. In the University of Michigan.
  12. Mandhi, D., Sanaz, J. y Samad, E. (2017). Climate change impacts on spatial-temporal variations of reference evapotranspiration in Iran. Water Harvesting Research, 2(1), 13-23. https://doi.org/10.22077/jwhr.2017.592
  13. Ndiaye, P. M., Bodian, A., Diop, L., Deme, A., Dezetter, A., Djaman, K. y Ogilvie, A. (2020). Trend and Sensitivity Analysis of Reference Evapotranspiration in the Senegal River Basin Using NASA Meteorological Data. Water, 12(7), 1957. https://doi.org/10.3390/w12071957
  14. Núñez-González, G., Velázquez-Pérez, D., Pelayo-Cortés, F. J., Barboza-Jiménez, P., Núñez-González, G., Velázquez-Pérez, D., Pelayo-Cortés, F. J. y Barboza-Jiménez, P. (2019). Análisis del comportamiento de la evapotranspiración de referencia durante el periodo de lluvias en cinco estaciones meteorológicas de la cuenca Lerma-Chapala. Ingeniería agrícola y biosistemas, 11(2), 147-159. https://doi.org/10.5154/r.inagbi.2018.06.014
  15. Olivares, B., Hernández, R., Coelho, R., Molina, J. C. y Pereira, Y. (2018). Analysis of climate types: Main strategies for sustainable decisions in agricultural areas of Carabobo, Venezuela. Scientia Agropecuaria, 9(3), 359-369. https://doi.org/10.17268/sci.agropecu.2018.03.07
  16. Olofintoye, O., Adeyemo, J. y Otieno, F. (2013). Evolutionary Algorithms and Water Resources Optimization. En O. Schütze, C. A. Coello Coello, A.-A. Tantar, E. Tantar, P. Bouvry, P. Del Moral, & P. Legrand (Eds.), EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation II (pp. 491-506). Springer. https://doi.org/10.1007/978-3-642-31519-0_32
  17. Pour, S. H., Wahab, A. K. A., Shahid, S. y Ismail, Z. B. (2020). Changes in reference evapotranspiration and its driving factors in peninsular Malaysia. Atmospheric Research, 246, 105096. https://doi.org/10.1016/j.atmosres.2020.105096
  18. Raza, A., Saber, K., Hu, Y., L. Ray, R., Ziya Kaya, Y., Dehghanisanij, H., Kisi, O. y Elbeltagi, A. (2023). Modelling reference evapotranspiration using principal component analysis and machine learning methods under different climatic environments. Irrigation and Drainage, 72(4), 945-970. https://doi.org/10.1002/ird.2838
  19. Salam, R., Islam, A. R. M. T., Pham, Q. B., Dehghani, M., Al-Ansari, N. y Linh, N. T. T. (2020). The optimal alternative for quantifying reference evapotranspiration in climatic sub-regions of Bangladesh. Scientific Reports, 10(1), 20171. https://doi.org/10.1038/s41598-020-77183-y
  20. Santos, M. S. N. D., Castro, I. A. D., Oro, C. E. D., Zabot, G. L. y Tres, M. V. (2021). Reference crop evapotranspiration in distinct agricultural regions of Southern Brazil: A comparison of improved empirical models. Revista Engenharia na Agricultura - REVENG, 29. https://doi.org/10.13083/reveng.v29i1.12418
  21. Silva, M. y Mendoza, N. (2020). Evaluación comparativa y predicción espacial de la evapotranspiración de referencia mediante métodos geoestadísticos. Bioagro, 32(2), 107-116.
  22. Srivastava, P. K., Singh, P., Mall, R. K., Pradhan, R. K., Bray, M. y Gupta, A. (2020). Performance assessment of evapotranspiration estimated from different data sources over agricultural landscape in Northern India. Theoretical and Applied Climatology, 140(1-2), 145-156. https://doi.org/10.1007/s00704-019-03076-4
  23. Villa, A. O., Ontiveros, R. E., Ruiz, O., González, A., Quevedo, A. y Ordóñez, L. M. (2021). Variación espacio-temporal de la evapotranspiración de referencia a partir de métodos empíricos en Chihuahua, México. Ingeniería Agrícola y Biosistemas (e2007-4026), vol. 13, no. 1. http://repositorio.imta.mx/handle/20.500.12013/2258
  24. Vollset, S. E., Goren, E., Yuan, C.-W., Cao, J., Smith, A. E., Hsiao, T., Bisignano, C., Azhar, G. S., Castro, E., Chalek, J., Dolgert, A. J., Frank, T., Fukutaki, K., Hay, S. I., Lozano, R., Mokdad, A. H., Nandakumar, V., Pierce, M., Pletcher, M. y Murray, C. J. L. (2020). Fertility, mortality, migration, and population scenarios for 195 countries and territories from 2017 to 2100: A forecasting analysis for the Global Burden of Disease Study. The Lancet, 396(10258), 1285-1306. https://doi.org/10.1016/S0140-6736(20)30677-2
  25. Yang, L., Feng, Q., Adamowski, J. F., Yin, Z., Wen, X., Wu, M., Jia, B. y Hao, Q. (2020). Spatio-temporal variation of reference evapotranspiration in northwest China based on CORDEX-EA. Atmospheric Research, 238, 104868. https://doi.org/10.1016/j.atmosres.2020.104868
  26. Zhao, L., Zhao, X., Zhou, H., Wang, X. y Xing, X. (2021). Prediction model for daily reference crop evapotranspiration based on hybrid algorithm and principal components analysis in Southwest China. Computers and Electronics in Agriculture, 190, 106424. https://doi.org/10.1016/j.compag.2021.106424

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