A blade element model is developed to include unsteady lift and drag coefficients and it is shown that this provides improved prediction of the spectrum of root bending moment of a tidal turbine blade for two definitions of onset turbulence. Computational Fluid Dynamics (CFD) is used to model the lift and drag forces on a 2D aerofoil using the relative onset flow from the different turbulence generation methods. Inclusion of the magnitude of high frequency fluctuations within the blade load spectra, results in an increased number of load cycles and improves prediction of Damage Equivalent Load (DEL). Discrepancy between prediction and existing experimental data is improved from 15% to within 2%. The signal-to-noise ratio (SNR) of relative velocity has been used to characterise the onset flow, and a relationship is established between onset flow conditions and the resultant fatigue loads (DEL). This has been applied to a number of onset conditions, typical of channel shear flows and upstream turbine wakes. For a given SNR, there is a 3.3 factor of variation in the DEL across the range of tip-speed-ratio (TSR) shown and over a single TSR value there is a 6.5 factor of variation across the DEL, over the largest range of SNR.