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Effects of Different Meteorological Variables on Reference Evapotranspiration Modeling: Application of Principal Component Analysis

Authors: Josiah Adeyemo, Akinola Ikudayisi


The correct estimation of reference evapotranspiration (ETₒ) is required for effective irrigation water resources planning and management. However, there are some variables that must be considered while estimating and modeling ETₒ. This study therefore determines the multivariate analysis of correlated variables involved in the estimation and modeling of ETₒ at Vaalharts irrigation scheme (VIS) in South Africa using Principal Component Analysis (PCA) technique. Weather and meteorological data between 1994 and 2014 were obtained both from South African Weather Service (SAWS) and Agricultural Research Council (ARC) in South Africa for this study. Average monthly data of minimum and maximum temperature (°C), rainfall (mm), relative humidity (%), and wind speed (m/s) were the inputs to the PCA-based model, while ETₒ is the output. PCA technique was adopted to extract the most important information from the dataset and also to analyze the relationship between the five variables and ETₒ. This is to determine the most significant variables affecting ETₒ estimation at VIS. From the model performances, two principal components with a variance of 82.7% were retained after the eigenvector extraction. The results of the two principal components were compared and the model output shows that minimum temperature, maximum temperature and windspeed are the most important variables in ETₒ estimation and modeling at VIS. In order words, ETₒ increases with temperature and windspeed. Other variables such as rainfall and relative humidity are less important and cannot be used to provide enough information about ETₒ estimation at VIS. The outcome of this study has helped to reduce input variable dimensionality from five to the three most significant variables in ETₒ modelling at VIS, South Africa.

Keywords: irrigation, Principal Component Analysis, reference evapotranspiration, Vaalharts

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O. Olofintoye, Adeyemo, and F. Otieno, Evolutionary algorithms and water resources optimisation. Berlin: Springer Berlin Heidelberg, 2013.
[2] A. K. Mishra and V. P. Singh, "Drought modeling – A review," Journal of Hydrology, vol. 403, pp. 157-175, 6/6/ 2011.
[3] A. Ramoelo , N. Majozi , R. Mathieu , N. Jovanovic , A. Nickless , and S. Dzikiti "Validation of Global Evapotranspiration Product (MOD16) using Flux Tower Data in the African Savanna, South Africa," Remote Sensing, vol. 6, pp. 7406-7423, 2014.
[4] S. Traore, T. Kerh, and L. A. Gibson, "Modeling Reference Evapotranspiration by Generalized Regression Neural Network in Semiarid Zone of Africa," WSEAS Transactions on Information Science & Applications, vol. 6, pp. 991-1000, 2008.
[5] R. G. Allen, L. S. Pereira, D. Raes, and M. Smith, "Crop Evapotranspiration - Guidelines for Computing Crop Water Requirements," Food and Agriculture Organisation09-01-2010 1998.
[6] R. G. Allen, M. E. Jensen, J. L. Wright, and R. D. Burman, "Operational Estimates of Reference Evapo-transpiration," Agronomy Journal, vol. 81, pp. 650–662, 1989.
[7] D. Lee and P. Vanrolleghem, "Adaptive Consensus Principal Component Analysis for On-Line Batch Process Monitoring," Environmental Monitoring and Assessment, vol. 92, pp. 119-135, 2004.
[8] J. Costa, M. Alves, and E. Ferreira, "Principal component analysis and quantitative image analysis to predict effects of toxics in anaerobic granular sludge," Bioresource Technology, vol. 100, pp. 1180-1185, 2009.
[9] J. Lennox and C. Rosen, "Adaptive multiscale principal components analysis for online monitoring of wastewater treatment," Water Science and Technology, vol. 45, pp. 227-235, 2002.
[10] F. Visconti, J. M. de Paz, and J. L. Rubio, "Principal component analysis of chemical properties of soil saturation extracts from an irrigated Mediterranean area: Implications for calcite equilibrium in soil solutions," Geoderma, vol. 151, pp. 407-416, 7/15/ 2009.
[11] E. S. Köksal, "Hyperspectral reflectance data processing through cluster and principal component analysis for estimating irrigation and yield related indicators," Agricultural Water Management, vol. 98, pp. 1317-1328, 5/30/ 2011.
[12] A. Biglari and J. C. Sutherland, "An a-posteriori evaluation of principal component analysis-based models for turbulent combustion simulations," Combustion and Flame, vol. 162, pp. 4025-4035, 10// 2015.
[13] S. W. Jeong, G.-S. Kim, W. S. Lee, Y.-H. Kim, N. J. Kang, J. S. Jin, et al., "The effects of different night-time temperatures and cultivation durations on the polyphenolic contents of lettuce: Application of principal component analysis," Journal of Advanced Research, vol. 6, pp. 493-499, 5// 2015.
[14] R. G. Ellington, "Quantification of the impact of the impact of irrigtion on the acquifer underlying the Vaalharts irrigation scheme," MSc, Institute of Groundwater Studies, University of Free State, 2003.
[15] O. I. Ojo, "Mapping and Modeling of Irrigation Induced Salinity of Vaal-Harts Irrigation Scheme in South Africa," DTech, Civil Engineering, Tshwane University of Technology, Pretoria, 2013.
[16] VIS, "Vaalharts Irrigation Scheme", ed: Wikimedia Foundation, Inc., 2013.
[17] O. O. Olofintoye, "Real Time Optimal Water Allocation in the Orange River Catchment in South Africa," DTech, Civil Engineering, Durban University of Technology, Durban, 2015.
[18] MATLAB, PCA Toolbox for use with MATLAB. USA, 2012.
[19] R. Tantra, C. Oksel, K. N. Robinson, A. Sikora, X. Z. Wang, and T. A. Wilkins, "A method for assessing nanomaterial dispersion quality based on principal component analysis of particle size distribution data," Particuology, vol. 22, pp. 30-38, 10// 2015.