Global Sensitivity Analysis (GSA) plays a significant role in quantifying the tangible impact of model inputs on the uncertainty of response variable. As GSA results are strongly affected by correlated inputs, several studies considered this issue, but most of them are quite time-consuming, labor-intensive, and difficult to implement. Accordingly, this paper puts forward a novel strategy based on the Supervised Principal Component analysis (Supervised PCA), benefiting from the Reproducing Kernel Hilbert Space (RKHS). Indeed, by conducting one kind of variance-based sensitivity analysis (SA), a renowned method exclusively customized for models with orthogonal inputs, on Supervised PCA (SPCA) regression, the impact of correlation structure of input variables is effectively taken into account. The ability of the suggested technique is evaluated with five test cases as well as two hydrologic and hydraulic models, and the results are compared and contrasted with those obtained from the correlation ratio method taken as a robust benchmark solution. It is found that the proposed method satisfactorily identifies the sensitivity ordering of model inputs. Furthermore, it is proved in this study that the performance of the proposed approach is also supported by the total contribution index in the derived covariance decomposition equation. Moreover, the proposed method compared to the correlation ratio method, is found to be time efficient and easy to implement. Overall, the proposed scheme is appropriate for high dimensional, relatively nonlinear or expensive models with correlated inputs whose coefficient of determination is larger than 0.5.