Probability distributions in Stiefel manifold such as the von-Mises Fisher and Bingham distributions find diverse applications in signal processing and other applied sciences. Use of these statistical models in practice is complicated by the difficulties in numerical evaluation of their normalization constants. In this letter, we derive asymptotical approximations to the normalization constants via recent results in random matrix theory. The derived approximations take simple forms and are reasonably accurate in regimes of practical interest. As an application, we show that the proposed analytical results lead to a remarkably reduction of the sampling complexity compared to existing simulation based approaches.