The objective of this study was to introduce and demonstrate a computationally efficient, multistep uncertaintyquantification approach for high-fidelity, hypersonic reentry flow simulations, which may include large numbers ofaleatory and epistemic uncertainties. The multistep uncertainty quantification approach included several keycomponents,includingasensitivity-baseddimensionreductionprocessthatusedalocalsensitivityanalysisatselectedsample locations to approximate global sensitivities. Other components included a method to update existingdeterministic samples after dimension reduction and a modified point-collocation nonintrusive polynomial chaosmethod that incorporates existing local sensitivity information. The multistep uncertainty quantification approachwas demonstrated on two model problems. The first was a model for stagnation point convective heat transfer inhypersonic flow. Mixed uncertainty quantification analysis results in reduced dimensions compared well withMonteCarlosimulations.Thesecondproblemwasahigh-fidelity,computationalfluiddynamicsmodelforstagnationpoint radiative heat flux on a Hypersonic Inflatable Aerodynamic Decelerator during a Mars entry. The modelconsisted of 93 uncertain parameters, coming from both flowfield and radiation modeling. The model was reduced toten and five uncertain variables, accounting for 95 and 90% of the total output variance, respectively. Pure aleatory,epistemic, and mixed uncertainty quantification analyses were in agreement with previous work, proving thepotential and applicability of the multistep uncertainty quantification process for complex hypersonic reentry flowmodels.