Estimation of Monthly Reference Evapotranspiration with Scarce Information Using Machine Learning in Southwestern Colombia
DOI:
https://doi.org/10.24215/1850468Xe024Keywords:
artificial neural network, FAO-56 Penman-Monteith, performance metrics, Southwestern Colombia, evapotranspirationAbstract
This research aimed to identify an alternative method to estimate reference evapotranspiration (ETo) with scarce climatological information in southwestern Colombia between 1983-2017 by evaluating and comparing different machine learning techniques. The FAO Penman-Monteith (FAO-PM56) was used as the reference method and four empirical methods (Hargreaves, Thornthwaite, Cenicafé, and Turc) were assessed with five metrics to evaluate the method of best fit to FAO-PM56, root mean square error (RMSE), mean absolute error (MAE), mean bias error (MBE), Nash-Sutcliffe model efficiency coefficient (NSE), and Pearson correlation coefficient (R). Three models were designed using machine learning techniques to estimate ETo, multiple linear regression (MLR), artificial neural networks (ANN), and autoregressive integrated moving average model (ARIMA). The results showed that the ARIMA-M3 model reported the best performance metrics (RMSE = 4.13 mm month-1, MAE = 3.15 mm month-1, MBE = -0.08 mm month-1, NSE = 0.96 and r = 0.98). However, it restricts in that it can only be used locally and cannot be extrapolated to other climatological stations,because it was calibrated with specific conditions (exogenous variables) and stations,unlike the ANN-M1 model, which only requires training the network for its application. This method will allow estimating ETo in places with scarce information, as vital for water management in places with much uncertainty regarding accessibility and availability.
Downloads
References
Adnan, R. M., Heddam, S., Yaseen, Z. M., Shahid, S., Kisi, O., Li, B., 2021: Prediction of potential evapotranspiration using temperature-based heuristic approaches. Sustainability13(1):1–21. https://doi.org/10.3390/su13010297
Ahmadi, S. H., Javanbakht, Z., 2020: Assessing the physical and empirical reference evapotranspiration (ETo) models and time series analyses of the influencing weather variables on ETo in a semiarid area. Journal of Environmental Management 276:111278. https://doi.org/10.1016/j.jenvman.2020.111278
Allen, R. G., Pereira, L. S., Raes, D., Smith, M., 1998: Crop evapotranspiration – guidelines for computing crop water requirements – FAO Irrigation and drainage 56, pp. 1-322.
Alves, W. B., Rolim, G. D., Aparecido, L. E., 2017: Reference evapotranspiration forecasting by artificial neural networks. Engenharia Agricola 37(6):1116–1125. https://doi.org/10.1590/1809-4430-eng.agric.v37n6p1116-1125/2017
Antonopoulos, V. Z., Antonopoulos, A. V., 2017: Daily reference evapotranspiration estimates by artificial neural networks technique and empirical equations using limited input climate variables. Computers and Electronics in Agriculture 132, 86–96. https://doi.org/10.1016/j.compag.2016.11.011
Ayaz, A., Rajesh, M., Singh, S. K., Rehana, S., 2021: Estimation of reference evapotranspiration using machine learning models with limited data. AIMS Geosciences7(3):268–290. https://doi.org/10.3934/geosci.2021016
Barco, J., Cuartas, A., Mesa, O., Poveda, G., Vélez, J. I., Hoyos, C., Mejía, J. F., Botero, B., 2000: Estimación de la evapotranspiración en Colombia. Avances en recursos hidráulicos, 7:43–51.
Basconcillo, J., Duran, G. A., Fransisco, A., Abastillas, R., Hilario, F., Juanillo, E., Solis, A. L., Lucero, A. J., Maratas, S.-L., 2017:Evaluation of SpatialInterpolationTechniquesforOperationalClimateMonitoring in thePhilippines. SOLA 13:114–119. https://doi.org/10.2151/sola.2017-021
Biggs, T. W., Marshall, M., Messina, A., 2016: Mapping daily and seasonal evapotranspiration from irrigated crops using global climate grids and satellite imagery: Automation and methods comparison. Water Resour. Res.52:7311–7326. https://doi.org/10.1002/2016WR019107
Birara, H., Mishra, S. K. & Pandey, R. P, 2020: Comparison of methods for evapotranspiration computation in the Tana Basin, Ethiopia,Volume 97, In: Hydrological Extremes. Springer, pp. 1-472.
Blaney H., Criddle, W., 1950: Determining water requirements in irrigated areas from climatological and irrigation data.
Bouznad, I. E., Guastaldi, E., Zirulia, A., Brancale, M., Barbagli, A., Bengusmia, D., 2020: Trend analysis and spatiotemporal prediction of precipitation, temperature, and evapotranspiration values using the ARIMA models: case of the Algerian Highlands. Arabian Journal of Geosciences 13(24). https://doi.org/10.1007/s12517-020-06330-6
Box, G. E. P., Jenkins, G. M., Reinsel, G. C., Ljung, G. M., 1994: Time series forecasting: forecasting and control. Fifth edition. Prentice Hall. Englewood Cliff. New Jersey, pp. 1-422.
Canchala, T., Alfonso-Morales, W., Carvajal-Escobar, Y., Cerón, W. L., Caicedo-Bravo, E., 2020: Monthly Rainfall Anomalies Forecasting for Southwestern Colombia Using Artificial Neural. Water 12(9):2628. https://doi.org/10.3390/w12092628
Canchala, T., Ocampo-Marulanda, C., Alfonso-Morales, W., Carvajal-Escobar, Y., Ceron, W. L., Caicedo-Bravo, E. (2022). Techniques for monthly rainfall regionalization in southwestern Colombia. Anais da Academia Brasileira de Ciências, 94.https://doi.org/10.1590/0001-3765202220201000
Canchala-Nastar, T., Carvajal-Escobar, Y., Alfonso-Morales, W., Loaiza, W. C., Caicedo, E., 2019: Estimation of missing data of monthly rainfall in southwestern Colombia using artificial neural networks. Data in Brief 26. https://doi.org/10.1016/j.dib.2019.104517
Cannarozzo, M., Noto, L. V., Viola, F. Spatial distribution of rainfall trends in Sicily (1921-2000)., 2006: Physics and Chemistry of the Earth 31(18):1201–1211. https://doi.org/10.1016/j.pce.2006.03.02
Castañeda, L., Rao, P., 2005: Comparison of methods for estimating reference evapotranspiration in Southern California. Journal of Environmental Hydrology 1–10.http://hydroweb.com/jeh/jeh2005/castaneda.pdf
Cerón, W., Andreoli, R., Kayano, M., Canchala, T., Carvajal-Escobar, Y., Souza, R., 2021: Comparison of spatial interpolation methods for annual and seasonal rainfall in two hotspots of biodiversity in South America. Annals of the Brazilian Academy of Sciences 93(1).https://doi.org/10.1590/0001-3765202120190674
Choi, Y., Jeon, J., 2018: Comparison of Artificial Neural Network and Empirical Models to Determine Daily Reference Evapotranspiration. Journal of the Korean Society of Agricultural Engineers 60(6):43–54. https://doi.org/10.5389/KSAE.2018.60.6.043
Cobaner, M., 2010: Evapotranspiration estimation by two different neuro-fuzzy inference systems. Journal of Hydrology 398(3–4):292–302, https://doi.org/10.1016/j.jhydrol.2010.12.030
De Veaux R. D., Ungar, L. H., 1994:Multicollinearity: A tale of two nonparametric regressions. Lecture Notes in Statistics393-402. https://doi.org/10.1007/978-1-4612-2660-4_40
Diouf, O. C., Weihermüller, L., Ba, K., Faye, S. C., Faye, S., Vereecken, H., 2016: Estimation of Turc reference evapotranspiration with limited data against the Penman-Monteith Formula in Senegal. Journal of Agriculture and Environment for International Development110(1), 117–137. https://doi.org/10.12895/jaeid.20161.417
Feng Y., Peng Y., Cui N., 2017: Modeling reference evapotranspiration using extreme learning machine and generalized regression neural network only with temperature data. Comput Electron Agric 136, 71–78.https://doi.org/10.1016/j.compag.2017.01.027
Ferreira, L. B., da Cunha, F. F., de Oliveira, R. A., FernandesFilho, E. I., 2019: Estimation of reference evapotranspiration in Brazil with limited meteorological data using ANN and SVM – A new approach. Journal of Hydrology 572, 556–570. https://doi.org/10.1016/j.jhydrol.2019.03.028
Ferreira, L. B., França da Cunha, F., Zanetti, S. S., 2021: Selecting models for the estimation of reference evapotranspiration for irrigation scheduling purposes. PloS ONE 16. https://doi.org/10.1371/journal.pone.0245270
Fisher, D., Pringle, H., 2013: Evaluation of alternative methods for estimating reference evapotranspiration. Agricultural Sciences 04(08):51–60. https://doi.org/10.4236/as.2013.48a008
Fonseca-Luengo, D., Lillo-Saavedra, M., Lagos, L. O., García-Pedrero, A., Gonzalo-Martín, C., 2018: Use of machine learning to improve the robustness of spatial estimation of evapotranspiration. Lecture Notes in Computer Science 10657:237–245. https://doi.org/10.1007/978-3-319-75193-1_29
Fox, J., 2016: Applied Regression Analysis and Generalized Linear Models. 3rd edition, SAGE Publications, pp. 1-817.
Gautam, R., Sinha, A. K., 2016: Time series analysis of reference crop evapotranspiration for Bokaro District, Jharkhand, India. Journal of Water and Land Development 30(1):51–56. https://doi.org/10.1515/jwld-2016-0021
Gobernación de Nariño, 2019: Plan participativo de desarrollo departamental 2016-2019. https://publicadministration.un.org/unpsa/Portals/0/UNPSA_Submitted_Docs/Plan%20de%20Desarrollo%20Departamental%20Nari%C3%B1o%20Coraz%C3%B3n%20del%20Mundo..pdf?ver=2018-11-29-171310-447, accessedon 01/05/2022.
Goh, E. H., Ng, J. L., Huang, Y. F., Yong, S. L. S., 2021: Performance of potential evapotranspiration models in Peninsular Malaysia. Journal of Water and Climate Change00(0). https://doi.org/10.2166/wcc.2021.018
Goldfeld, S., Quandt, R., 1965: Some Tests for Homoscedasticity. Journal of the American Statistical Association 60:539–547. https://doi.org/10.1080/01621459.1965.10480811
Granata, F., Gargano, R., de Marinis, G., 2020: Artificial intelligence based approaches to evaluate actual evapotranspiration in wetlands. Science of the Total Environment 703:135653. https://doi.org/10.1016/j.scitotenv.2019.135653
Hamdi, M. R., Bdour, A. N., Tarawneh, Z. S., 2008: Developing Reference Crop Evapotranspiration Time Series Simulation Model Using Class a Pan : A Case Study for the Jordan. Jordan Journal of Earth and Environmental Sciences 1(1):33–44.https://eis.hu.edu.jo/deanshipfiles/pub10431145.pdf
Hargreaves, H. G., Samani, Z. A. Reference crop evapotranspiration from temperature., 1985: Applied Eng. In Agric 1(3).
Haykin, S., 1994: Neural Networks: A Comprehensive Foundation, Volume: 3, Prentice Hall PTR, pp. 1-823.https://cdn.preterhuman.net/texts/science_and_technology/artificial_intelligence/Neural%20Networks%20-%20A%20Comprehensive%20Foundation%20-%20Simon%20Haykin.pdf
Huizhi, L., Jianwu, F., 2012: Seasonal and interannual variations of evapotranspiration and energy exchange over different land surfaces in a semiarid area of China. Journal of Applied Meteorology and Climatology51(10): 1875–1888. https://doi.org/10.1175/JAMC-D-11-0229.1
Huo, Z., Feng, S., Kang, S., Dai, X., 2012: Artificial neural network models for reference evapotranspiration in an arid area of northwest China. Journal of Arid Environments 82:81–90. https://doi.org/10.1016/j.jaridenv.2012.01.016
Igbadun, H. E., Mahoo, H. F., Tarimo, A. K. P. R., Salim, B. A., 2006: Crop water productivity of an irrigated maize crop in Mkoji sub-catchment of the Great Ruaha River Basin, Tanzania. AgriculturalWater Management85(1–2):141–150. https://doi.org/10.1016/j.agwat.2006.04.003
Jaramillo, A. Evapotranspiración de referencia en la Región Andina de Colombia. Cenicafé, 2006:57(4):288–298.https://www.cenicafe.org/es/publications/arc057(04)288-298.pdf
Jarque, C., Bera, A., 1980: Efficient tests for normality, homoscedasticity, and serial independence of regression residuals. Economics Letters6:255–259. http://l.academicdirect.org/Horticulture/GAs/Refs/Jarque&Bera_1980.pdf
Jensen, M. E., Burman R. D., Allen, R. G., 1990: Evapotranspiration and irrigation water requirements. ASCE Manual and Report No. 70, pp. 1-744.
Jing, W., Yaseen, Z. M., Shahid, S., Saggi, M. K., Tao, H., Kisi, O., Salih, S. Q., Al-Ansari, N., Chau, K. W.,2019: Implementation of evolutionary computing models for reference evapotranspiration modeling: short review, assessment and possible future research directions. Engineering Applications of Computational Fluid Mechanics 13(1):811–823. https://doi.org/10.1080/19942060.2019.1645045
Jolliffe, I., 2002: Principal Component Analysis. Springer, pp. 1-518.
Knoben, W. J. M., Freer, J. E., Woods, R. A., 2019: Technical note: Inherent benchmark or not? Comparing Nash-Sutcliffe and Kling-Gupta efficiency scores. Hydrology and Earth System Sciences 23(10)4323–4331. https://doi.org/10.5194/hess-23-4323-2019
Kumar, M., Raghuwanshi, N. S., Singh, R., Wallender, W. W., Pruitt, W. O., 2002: Estimating of Evapotranspiration Using Artificial Neural Network. Journal of Irrigation and Drainage Engineering 128:224–233. https://doi.org/10.1061/(ASCE)0733-9437(2002)128:4~224!4
Łabędzki, L., Bąk, B., Smarzyńska, K., 2014:Spatio-temporal variability and trends of Penman-Monteith reference evapotranspiration (FAO-56) in 1971-2010 under climatic conditions of Poland. Polish Journal of Environmental Studies 23(6):2083–2091. https://doi.org/10.15244/pjoes/27816
Laidi, M., Hanini, S., El Hadj, A., 2018: Novel approach for estimating monthly sunshine duration using artificial neural networks: A case study. Journal of Sustainable Development of Energy, Water and Environment Systems 6(3):405–414. https://doi.org/10.13044/j.sdewes.d6.0226
Landeras, G., Ortiz-Barredo, A., López, J. J., 2008: Comparison of artificial neural network models and empirical and semi-empirical equations for daily reference evapotranspiration estimation in the Basque Country (Northern Spain). Agricultural Water Management95(5):553–565. https://doi.org/10.1016/j.agwat.2007.12.011
Laqui, W., Zubieta, R., Rau, P., Mejía, A., Lavado, W., Ingol, E., 2019: Can artificial neural networks estimate potential evapotranspiration in Peruvian highlands?. Modeling Earth Systems and Environment 5(4):1911–1924. https://doi.org/10.1007/s40808-019-00647-2
Lee, B.-Y., Yang, S.-K., Kwon, K.-H., Kim, J.-B., 2012: The Effect of Evapotranspiration by Altitude and Observation of Lysimeter. Journal of Environmental Science International 21(6):749–755. https://doi.org/10.5322/jes.2012.21.6.749
Lima, J. G., Viana, P. C., Sobrinho, J. E., Couto, J. P., 2019:Comparison of ETo estimation methods and sensitivity analysis for different Brazilian climates. Irriga 2019:24(3), 538–551. https://doi.org/10.15809/irriga.2019v24n3p538-551
Liu, Q., Yang, Z., Cui, B., 2008: Spatial and temporal variability of annual precipitation during 1961-2006 in Yellow River Basin, China. Journal of Hydrology 361(3–4):330–338. https://doi.org/10.1016/j.jhydrol.2008.08.002
Maček, U., Bezak, N., Šraj, M., 2018: Reference evapotranspiration changes in Slovenia, Europe. Agricultural and Forest Meteorology 260–261:183–192. https://doi.org/https://doi.org/10.1016/j.agrformet.2018.06.014
Mendoza C., C. J., Peña Q., A. J., 2021: Reference evapotranspiration estimation by different methods for the sucroenergy sector of Colombia. Revista Brasileira de EngenhariaAgricola e Ambiental25(9):583–590. https://doi.org/10.1590/1807-1929/agriambi.v25n9p583-590
Meneses, K. C., Aparecido, L. E., Meneses, K. C., Farias M. F., 2020:Estimatingpotentialevapotranspiration in maranhãostateusing artificial neural networks. Revista Brasileira de Meteorologia 2020:35(4):675–682. https://doi.org/10.1590/0102-77863540072
Mohawesh, O. E., 2013: Artificial neural networkforestimatingmonthlyreferenceevapotransirationunderarid and semiaridenvironments. Archives of Agronomy and SoilScience 59(1), 105–117. https://doi.org/10.1080/03650340.2011.603126
Moncayo, C., 2015: Valle del Cauca, Antioquia y Nariño, departamentos líderes en producción.,https://incp.org.co/valle-del-cauca-antioquia-y-narino-departamentos-lideres-en-produccion/#:~:text=Nari%C3%B1o%20tiene%20una%20producci%C3%B3n%20significativa,banano%20(12%2C8%25,accessedon31.03.2022.
Monteiro, A. F., Martins, F. B., Torres, R. R., de Almeida, V. H., Abreu, M. C., Mattos, E. V., 2021:Intercomparison and uncertainty assessment of methods for estimating evapotranspiration using a high-resolution gridded weather dataset over Brazil. Theoretical and Applied Climatology 146(1–2), 583–597. https://doi.org/10.1007/s00704-021-03747-1
Montgomery, D., Peck E., Vining, G., 2002:Introducción al análisis de regresión lineal. Compañía Editorial Continental. México D.F, pp- 1-590.
Mossad, A., Alazba, A. A., 2016: Simulation of temporal variation for reference evapotranspiration under arid climate. Arabian Journal of Geosciences 9(5). https://doi.org/10.1007/s12517-016-2482-y
Murat, A., Serhat, Ö., 2018: Artificial intelligence (AI) Studies in Water Resources. Natural and Engineering Sciences 3(2), 187–195.https://www.researchgate.net/publication/325212839_Artificial_Intelligence_AI_Studies_in_Water_Resources/fulltext/5afe2e41aca272b5d84a9e57/Artificial-Intelligence-AI-Studies-in-Water-Resources.pdf
Nash, J. E., Sutcliffe, J. V., 1980: River flow forecasting through conceptual models. Part I—A discussion of principles. Journal of Hydrology10(3):282–290. https://doi.org/10.1016/0022-1694(70)90255-6
Nema, M. K., Khare, D., Chandniha, S. K., 2017: Application of artificial intelligence to estimate the reference evapotranspiration in sub-humid Doon valley. Applied Water Science 7(7):3903–3910. https://doi.org/10.1007/s13201-017-0543-3
Ocampo-Marulanda, C., Fernández-Álvarez, C., Cerón, W. L., Canchala, T., Carvajal-Escobar Y., Alfonso-Morales W., 2022: A spatiotemporal assessment of the high-resolution CHIRPS rainfall dataset in southwestern Colombia using combined principal component analysis. Ain Shams Eng. J. 13(5):101739. https://doi.org/10.1016/j.asej.2022.101739
Petković, D., Gocic, M., Trajkovic, S., Shamshirband, S., Motamedi, S., Hashim, R., Bonakdari, H., 2015: Determination of the most influential weather parameters on reference evapotranspiration by adaptive neuro-fuzzy methodology. Computers and Electronics in Agriculture 114, 277–284. https://doi.org/10.1016/j.compag.2015.04.012
Pinos, J., Chacón, G., Feyen, J., 2020: Comparative Analysis of Reference Evapotranspiration Models with Application to The Wet Andean Páramo Ecosystem in Southern Ecuador. Meteorologica, 45(1):25–45. http://www.meteorologica.org.ar/wp-content/uploads/2020/03/v45n1a02-1.pdf
Poveda, G., Vélez, J. I., Mesa, O. J., Cuartas, A., Barco, J., Mantilla, R. I., Mejía, J. F., Hoyos, C. D., Ramírez, J. M., Ceballos, L. I., Zuluaga, M. D., Arias, P. A., Botero, B. A., Montoya, M. I., Giraldo, J. D., Quevedo, D. I., 2017: Linking Long-Term Water Balances and Statistical Scaling to Estimate River Flows along the Drainage Network of Colombia. Journal of Hydrologic Engineering 12(1):4–13. https://doi.org/10.1061/(asce)1084-0699(2007)12:1(4)
Priestley C. H. B. Taylor R. J., 1972: On the assessment of surface heat flux and evaporation using large-scale parameters. Mon. Weather Rev 100:2:81–92.
Quej, V. H., Almorox, J., Arnaldo, J. A., Moratiel, R., 2019: Evaluation of Temperature-Based Methods for the Estimation of Reference Evapotranspiration in the Yucatán Peninsula, Mexico. Journal of Hydrologic Engineering, 24(2):05018029. https://doi.org/10.1061/(asce)he.1943-5584.0001747
Ramírez, V. H., Mejía, A., Marín, E. V., Arango, R., 2011: Evaluation of models for estimating the reference evapotranspiration in Colombian Coffee Zone. Agronomía Colombiana,29(1):107–114.https://repositorio.unal.edu.co/bitstream/handle/unal/39836/28641-102621-2-PB.pdf?sequence=1&isAllowed=y
Rivas, R., &Caselles, V., 2004: A simplified equation to estimate spatial reference evaporation from remote sensing-based surface temperature and local meteorological data. Remote Sensing of Environment 93(1–2):68–76. https://doi.org/10.1016/j.rse.2004.06.021
Rodrigues, G. C., Braga, R. P., 2021:Estimation of reference evapotranspiration during the irrigation season using nine temperature-based methods in a hot-summer mediterranean climate. Agriculture 11(2):1–15. https://doi.org/10.3390/agriculture11020124
Santos, L. da C., Cruz, G. H., Capuchinho, F. F., José, J. V., & dos Reis, E. F., 2019: Assessment of empirical methods for estimation of reference evapotranspiration in the Brazilian Savannah. Australian Journal of Crop Science 13(7), 1094–1104. https://doi.org/10.21475/ajcs.19.13.07.p1569E
Scholz, M., Kaplan, F., Guy, C. L., Kopka J. Selbig, J., 2005: Non-linear PCA: a missing data approach. Bioinformatics21:3887-3895. https://doi.org/10.1093/bioinformatics/bti634
Shiri J., 2017: Evaluation of FAO56-PM, empirical, semi-empirical and gene expression programming approaches for estimating daily reference evapotranspiration in hyper-arid regions of Iran. Agric Water Manag 188, 101–114.https://doi.org/10.1016/j.agwat.2017.04.009
Stellwagen, E., Tashman, L., 2013: ARIMA : The Models of Box and Jenkins. Foresight: The International Journal of Applied Forecasting,30:28–34.
Tabari, H., Talaee, P. H., 2013: Multilayer perceptron for reference evapotranspiration estimation in a semiarid region. Neural Computing and Applications 23(2):341–348. https://doi.org/10.1007/s00521-012-0904-7
Tabari, H., Talaee, P. H., 2013: Multilayer perceptron for reference evapotranspiration estimation in a semiarid region. Neural Computing and Applications 23(2):341–348. https://doi.org/10.1007/s00521-012-0904-7
Thornthwaite, C., Wilm, H., 1948: Report of the Committee on transpiration and evaporation. Transactions American Geophysical Union 25(10).
Toro-Trujillo, A., Ramírez, R., Peña, A., Castillo, A., 2015: Estimation models for the reference evapotranspiration. Agrociencia 49:821–836.https://www.redalyc.org/pdf/302/30243055001.pdf
Trajkovic, S., Kolakovic, S., 2009a: Wind-adjusted Turc equation for estimating reference evapotranspiration at humid European locations. Hydrology Research 40(1), 45–52. https://doi.org/10.2166/nh.2009.002
Trajkovic, S., Kolakovic, S., 2009b: Evaluation of reference evapotranspiration equations under humid conditions. Water Resources Management 23(14):3057–3067. https://doi.org/10.1007/s11269-009-9423-4
Traore, S., Wang, Y. M., Kerh, T., 2008: Modeling reference evapotranspiration by generalized regression neural network in semiarid zone of Africa. WSEAS Transactions on Information Science and Applications 5(6):991–1000.https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.535.9934&rep=rep1&type=pdf
Tukimat, N. N. A., Harun, S., Shahid, S., 2012: Comparison of different methods in estimating potential évapotranspiration at Muda Irrigation Scheme of Malaysia. Journal of Agriculture and Rural Development in the Tropics and Subtropics 113(1), 77–85.https://www.jarts.info/index.php/jarts/article/view/2012091441739/175
Turc, L., 1961: Evaluation of irrigation water requirements, potential evapotranspiration: A simple climatic formula evolved up to date. Ann. Agron12(36).
United Nations, 2018:The 2030 Agenda and the Sustainable Development Goals: An opportunity for Latin America and the Caribbean. Goals, Targets and Global Indicators. https://repositorio.cepal.org/bitstream/handle/11362/40156/25/S1801140_en.pdf, accessed on 21/02/2022.
Urrea, V., Ochoa, A., Mesa, O., 2016: Validación de La Base de Datos de Precipitación CHIRPS Para Colombia a Escala Diaria, Mensualy Anual En El Periodo 1981–2014. XXVII Congreso Latinoamericano de Hidráulica; IAHS, Lima, Perú, accessedon17.11.2022.
Valipour, M., 2015: Temperature analysis of reference evapotranspiration models. Meteorological Applications 2015:22(3):385–394. https://doi.org/10.1002/met.1465
Wang, K., Dickinson, R. E., 2012: A review of global terrestrial evapotranspiration: observation, modelling, climatology and climatic variability. Reviews of Geophysics 50(RG2005):1–54. https://doi.org/10.1029/2011RG000373
Yang, Z., Liu, Q., Cui, B., 2011: Spatial distribution and temporal variation of reference evapotranspiration during 1961–2006 in the Yellow River Basin, China. Hydrological Sciences Journal 56(6):1015–1026. https://doi.org/10.1080/02626667.2011.590810
Yao, H., Scott, L., Guay, C., Dillon, P., 2009: Hydrological impacts of climate change predicted for an inland lake catchment in Ontario by using monthly water balance analyses Huaxia. Hydrological Processes 23:2368–2382. https://doi.org/DOI: 10.1002/hyp.7347
Yirga, S., 2019: Modelling reference evapotranspiration for Megecha catchment by multiple linear regression. Modeling Earth Systems and Environment 5(2):471–477. https://doi.org/10.1007/s40808-019-00574-2
Yoder, R. E., Odhiambo, L. O., Wright, W. C., 2005: Evaluation of methods for estimating daily reference crop evapotranspiration at a site in the humid southeast United States. AppliedEngineering in Agriculture 21(2):197–202. https://doi.org/10.13031/2013.18153
Zanetti, S. S., Sousa, E. F., Carvalho, D. F. de, Bernardo, S., 2008:Estimação da evapotranspiração de referencia no estado do Rio de Janeiro usando redes neuraisartificiais. Revista Brasileira de Engenharia Agrícola e Ambiental 12(2):174–180. https://doi.org/10.1590/s1415-43662008000200010
Downloads
Additional Files
Published
Issue
Section
License
Copyright (c) 2023 Juan Camilo Triana-Madrid, Camilo Ocampo-Marulanda, Yesid Carvajal-Escobar, Wilmar Alexander Torres-López, Joshua Triana, Teresita Canchala
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
According to these terms, the material may be shared (copied and redistributed in any medium or format) and adapted (remixed, transformed and created from the material another work), provided that a) the authorship and the original source of publication (journal and URL of the work) are cited, b) it is not used for commercial purposes and c) the same terms of the license are maintained.
