as the fiducial model. In this figure, the curve of $\Delta v(z)$ is plotted according to Eq. (\[3\]), with the fiducial values of parameters given by the best fit to the SN+CMB+BAO data; the error bars on $\Delta v$, i.e., $\sigma_{\Delta v}$, for each redshift bin, are plotted according to Eq. (\[4\]) for E-ELT, and according to the detailed prescriptions described in the above section (the part entitled “SKA mock data”) for SKA1 and SKA2. We find that in the E-ELT case the error of $\Delta v$ decreases with the increase of redshift, and vice versa in the SKA1 case or the SKA2 case. In Fig. \[ELTvsSKA\], we plot the two-dimensional posterior contours at $68\%$ and $95\%$ confidence level (CL) in the $\Lambda$CDM model. We clearly see that using the SKA1-only mock data, the $\Lambda$CDM model can only be loosely constrained, while the model can be well constrained using the SKA2-only mock data.

In addition, form Fig. \[ELTvsSKA\], we clearly see that in the $\Lambda$CDM model, from the E-ELT, $\Omega_{\rm m}$ and $h$ are in strong anti-correlation while constraints from SKA1 or SKA2 provide a positive correlation for $\Omega_{\rm m}$ and $h$, and thus the orthogonality of the two degeneracy 