FACULTY OF SCIENCE
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Item Estimation of surface energy fluxes from bare ground in a tropical station using priestleytaylor method(2013) Adeniyi, M.O.; Nymphas, E. F.This investigation was designed to test the performance of Priestley Taylor method in the partitioning of the available energy into sensible and latent heat fluxes in a tropical site. Compared to eddy covariance measured fluxes, the conventional Priestley Taylor constant (aPT) of 1.25 gave low coefficient of determination and high bias error for both sensible and latent heat fluxes. It overestimated latent heat flux in the noon and afternoon but underestimated sensible heat flux. The bias error reduced and the coefficient of determination increased for sensible heat flux when aPT value was reduced to 1.0. The bias error for latent heat also reduced but the coefficient of determination did not change with the reduction in aPT value. The root mean square error reduced with the reduction in the aPT value. Compared to measured fluxes, coefficient of determination of sensible heat flux ranged from 0.82 to 0.90 while that of latent heat flux ranged from 0.78 to 0.9. Priestley Taylor method is recommended for partitioning of available energy into its component sensible and latent heat fluxes.Item Estimation of bare soil surface temperature from air temperature and soil depth temperature in a tropical station(2011) Adeniyi, M.O.; Nymphas,E.F.Soil surface temperature has critical influence on climate, agricultural and hydrological activities since it serves as a good indicator of the energy budget of the earth’s surface. Two empirical models for estimating soil surface temperature from air temperature and soil depth temperature were developed. The coefficient of determination (R2) of soil surface temperature from the air temperature model ranged from 0.92 - 0.99, while the mean absolute error (MAE) and root mean squared error (RMSE) ranged from 0.5 - 2.48 and 0.77 - 2.630C respectively. For the soil depth model, the R2 value ranged from 0.75 - 0.96, MAE ranged between 1.05 and 4.94, while RMSE ranged from 1.28 - 5.25. Both models performed well on days of year (DOYs), under similar prevailing weather conditions during the model training period.